pachacamac gis project: a practical application of

PACHACAMAC GIS PROJECT: A PRACTICAL APPLICATION OF GEOGRAPHIC INFORMATION SYSTEMS AND REMOTE SENSING TECHNIQUES IN ANDEAN ARCHAEOLOGY

By Go Matsumoto B. A., Kanda University of International Studies, Japan, 1995

A Thesis Submitted in Partial Fulfillment of the Requirements for The Master of Arts Degree Department of Anthropology in the Graduate School Southern Illinois University at Carbondale July 2005

Copyright by Go Matsumoto, 2005 All Rights Reserved

AN ABSTRACT OF THE THESIS OF

Go Matsumoto, for the Master of Arts degree in Anthropology, presented on July 1st, 2005, at Southern Illinois University Carbondale. TITLE: Pachacamac GIS Project: A Practical Application of Geographic Information Systems and Remote Sensing Techniques in Andean Archaeology MAJOR PROFESSOR: Dr. Izumi Shimada, Southern Illinois University at Carbondale

Geographic Information Systems (GIS) have been intensively developed since their origin in the early 1960s and employed for a variety of purposes both in academic and commercial fields thereafter. Following in the steps of precocious applications in disciplines such as forestry and hydrology, a handful of archaeologists began to employ this useful tool for their analyses of spatial phenomena in the early 1980s. In Andean archaeology, GIS together with related peripheral techniques (e.g., remote sensing and GPS) have become increasingly popular, particularly among younger archaeologists who recognize their ability to cope with a wide range of spatial scales and integrate multiple types of data. In accordance with the conceptual transitions of “space” and “landscape” and the expansion of study area over time, GIS have been successfully integrated into the archaeological methodology and even theoretical discussions. In the early 1980s when GIS were first introduced into archaeology, there were two contrasting conceptions: the

i

processualistic spatiality (space as non-problematic abstract backdrop and landscape as a palimpsest of material traces) on one hand, and the postprocessualistic backlash against it, on the other. Correspondingly, GIS applications were also split broadly into two separate directions: processualistic regional modeling studies and postprocessualistic phenomenological reconstructions of past landscape. Backed up by theoretical and methodological advancements in both geography and archaeology and active interactions among archaeologists in professional meetings and on the web, each school of thought is anticipated to go a long way in meeting their respective aims. It is obvious that GIS and related peripheral techniques hold the promise for future archaeological research. However, as the history of archaeological applications of research tools borrowed from other fields foretells, their appropriateness and efficacy need to be carefully assessed as their applications pose major conceptual and practical challenges, not to mention a substantial amount of time, money, and technical expertise. In this context, my case study to create GIS-based digital site maps of Pachacamac, which was a part of the on-going long-term archaeological project on the central coast of Peru (Pachacamac Archaeological Project) was aimed at scrutinizing the potential and limitations of GIS and remote sensing techniques for archaeology and offering guidelines for the most efficient way to use them given resource limitations that commonly confront archaeologists. Although some geographers tend to overdramatize the potentials of GIS,

ii

contemplation on the nature of archaeological research and associated limitations exposes the complexity of archaeological applications of GIS and will bring archaeologists back to stark reality. Archaeologists usually have to select most cost-efficient techniques depending on their research objectives and available resources. The first step to apply GIS in archaeology in general needs to be taken considering the gap between the theories and our reality before us. Using the preparation of the GIS-based site map of Pachacamac as a case study, this thesis illustrates how we can bridge the gap between the theoretical potential of GIS on one hand, and constraints of archaeological reality, on the other. It shows how multiple layers of data as well as both analog and digital spatial data can be effectively integrated in the first digital map of Pachacamac to be produced.

iii

ACKNOWLEDGEMENTS

First, I would like to thank Dr. Izumi Shimada who have provided me with unstinting supports over the past few years and offered me a valuable opportunity to participate in his new project at the site of Pachacamac. Without his supports, my thesis would not have been possible. My committee members, Dr. Susan Ford and Dr. Andrew Balkansky, also gave me a series of invaluable advices and insights. I received generous assistance from many people during the fieldwork in Peru as well. Señora Hide Higa de Calderon and Maria del Pilar Garcia Caycho served PAP project members with daily culinary delights, and Señor Hugo Tsuda and his family tickled my heart with amusing conversations and some Japanese food. Lic. Rafael Segura, the co-director of PAP and my roommate during the fieldwork, and other project members welcomed me with open arms. I thank all of their generosity and hospitality. I also thank Dr. Hartmut Tschauner (Seoul National University, Korea) who loaned me his own RTK Differential GPS. He devoted himself not only to give me an on-site instruction as to how to operate the equipment, but also helped me with GCP measurements and took charge of attendant data post-processing. During the measurement processes, Dr. Ursel Wagner (Technische Universität München, Germany) assisted me as well. I appreciate her valuable advices.

iv

Many procedures of map production described in Chapter 5 could not have been accomplished without the understanding and cooperation of the Department of Geography, Earth Resources Project (ERP) program, and Library Affairs, SIUC. Topography maps were scanned utilizing the facilities in the ERP laboratory, and aerial photographs were processed using ERDAS IMAGINE 8.6 and ArcGIS 8.3 (ArcInfo License) in the Spatial Environmental Analysis Laboratory (SEAL). Plotting the prototype base map prior to the fieldwork in the summer of 2004 was completed using the plotter in the Graduate Assistant Laboratory of the Department of Geography. I thank for their generous support Dr. Tony Oyana, Dr. Xu Gang, Dr. Wanxiao Sun, Girmay Misgna, and Daniel K. Davie. I have also received generous support of many of my friends and former mentors. Especially while I was on a leave of absence from the SIUC, they have given me spurs and, at other times, tranquility to my mind. My special thanks go to Yasuyuki Kosuge, Hitoshi Kurazono, Tomohiro Nagai, Kazuyuki Kurihata, Kengo Kaji, Steve Juzwik, Junko Eccles, and Dr. Misato Tokunaga. Last but not least, I thank my dear son Shinnosuke and beloved helpmate Akiyo. They have always been cheerful and extended me moral supports. Without their encouragement, understanding, and support, not only my thesis but also the reinstatement of my graduate study at the SIUC would not have been possible.

v

TABLE OF CONTENTS

ABSTRACT ................................................................................................................... i ACKNOWLEDGEMENTS ............................................................................................. iv LIST OF TABLES ......................................................................................................... ix LIST OF FIGURES ........................................................................................................ x CHAPTER 1: INTRODUCTION ...................................................................................... 1 CHAPTER 2: SPATIO-TEMPORAL THINKING IN ARCHAEOLOGY .................................. 7 2.1. Germination of Spatial Thinking ..………………………………….…….................. 7 2.2. Environmental Determinism, Chronology, and Diffusionism …….................. 10 2.3. Pursuits of Context, Function, and Process .................................................. 14 2.4. Quantification of Spatiality ………................................................................. 18 2.5. “Meaningful Space” and Postprocessual Reasoning ...................................... 22 CHAPTER 3: ARCHAEOLOGICAL APPLICATIONS OF GIS ……………..…...................... 26 3.1. What are GIS? .…………………………………………………………….……............. 27 3.1.1. Data Structure Models ……............................................................... 29 3.1.2. Merits of GIS for Archaeologists ..…................................................... 35 3.1.3. GIS as an “Analytical Toolbox” …....................................................... 38 3.2. Major Analytical Methods of GIS Applications …........................................... 40 3.2.1. Predictive Modeling ………………………………..................................... 41 3.2.2. Postprocessual Orientation ……………..…………................................. 42 3.3. Future Prospects of GIS Applications ……….…….......................................... 44 3.3.1. Post-Postprocessual Approach to Landscape ..................................... 44 3.3.2. Macro-Regional Paradigm ……..………………...................................... 45 CHAPTER 4: PACHACAMAC DIGITAL MAPPING ......................................................... 49 4.1. Settings ...................................................................................................... 51 4.2. Objectives of My Digital Site Mapping ........................................................... 56 4.2.1. Short- and Long-Term Objectives ...................................................... 57 4.2.2. Struggles against Tight Budgets ..…..……………….............................. 61

vi

CHAPTER 5: PROCEDURES ....................................................................................... 65 5.1. Phase I: Prototype Map Creation .................................................................. 65 5.1.1. Preparation of Topography Map ........................................................ 73 5.1.2. Orthorectification of Aerial Photographs .......………........................... 80 5.1.3. On-screen Digitizing ………………………………………........................ 100 5.2. Phase II: Ground-Truth Checking and GPS Measurements ………...…….….. 107 5.3. Phase III: Data Post-Processing and Consummation of the Final Maps …….. 113 5.3.1. Data Post-Processing of GPS Readings ............................................ 115 5.3.2. Creation of a New DEM ................................................................... 123 5.3.3. Deuter-Orthorectification of Aerial Photographs .............................. 127 5.3.4. Spatial Adjustment of Vector Datasets ............................................ 133 5.3.5. Clean Copies of Field Drawings ....................................................... 136 CHAPTER 6: DISCUSSIONS ..................................................................................... 139 6.1. Two Broad Categories of Mapping Techniques ............................................ 140 6.2. Concomitant Use of Old and New Data Sources .......................................... 142 6.3. Tight Budget and “GIS-Phobia” .................................................................. 145 6.4. Limited Availability of Appropriate Training ...……...................................... 146 6.5. Bridging the Contrasting Approaches ………………..………........................... 148 CHAPTER 7: CONCLUSION ...................................................................................... 152 7.1. Broader Significance to Andean Archaeology …………................................. 152 7.2. In the Near Future ......………………………………………................................ 155 REFERENCES ......................................................................................................... 158 APPENDICES APPENDIX A: HARDWARES AND SOFTWARES ........................................................ 187 APPENDIX B: PROCEDURES MANUAL …………........................................................ 188 B.1. How to georeference a scanned image ........................................................ 188 B.2. How to clip and combine together parts of raster images ............................. 196 B.3. How to define projection and plane coordinate system ……………………....... 201 B.4. How to reproject a raster image ………………………….….............................. 203 B.5. How to clip a subset from raster image ………………………........................... 207 B.6. How to perform geometric corrections of aerial photographs (Phase I) ..……. 208 B.7. How to digitize ground features in orthophotos and topography map ..…….. 225 B.8. How to change projection and coordinate system ….................................... 231 B.9. How to create a new DEM out of multipoint and line features ...................... 236

vii

B.10. How to perform geometric corrections of aerial photographs through an automated DTM extraction (Phase III) .......................................................... 239 APPENDIX C: FIELD DRAWINGS .............................................................................. 251 APPENDIX D: DIGITIZED MAPS ............................................................................... 289 APPENDIX E: SUPPORT DOCUMENTATION ............................................................. 309 E.1. Triangulation Report (Phase I) ……........................................................... 309 E.2. Triangulation Report (Phase III) ………........................................................ 313 VITA ........................................................................................................................ 322

viii

LIST OF TABLES

Table 3-1. Relative advantages of raster and vector representations (Taken from Longley et al. 2001:75) ……………………………………………………….……………………… 32 Table 5-1. Ground Control Points (GCPs) for geometric error correction ……………....… 99 Table 5-2. The dBASE table of attributes for GPS readings ..……………………………… 117 Table A-1. List of hardware and software utilized ……………………………………………. 187 Table B-1. The 49 control points for georeferencing of 30-K .……………………………… 192 Table B-2. The 49 control points for georeferencing of 30-L ..……………………………… 193 Table B-3. The 49 control points for georeferencing of 31-K ....……………..…………….. 194 Table B-4. The 49 control points for georeferencing of 31-L ...….…………………………. 195 Table B-5. The extent of the scanned map images …………………. ………………………. 197 Table B-6. The four hypothetical fiducials ................................................................ 213 Table B-7. The 10 Ground Control Points for triangulation (Phase I) .......................... 219 Table B-8. The 21 Ground Control Points for triangulation (Phase III) ........................ 240

ix

LIST OF FIGURES

Figure 2-1. Squier’s plan of the Palace of the Virgins of the Sun, Island of Coati, Lake Titicaca, 1877 (Taken from Squier 1877:361) ……………………..…………................ 9 Figure 2-2. Uhle’s section view of the cemetery at the foot and under the base of the Painted Temple, 1903 (Taken from Uhle 1903:19, Figure 3-5) …………………………… 9 Figure 3-1. The logical subsystem model of GIS originally put forward by Marble (1990) (Taken from Wheatley and Gillings 2002: 11, Figure 1.2 and partially modified) ..……… 28 Figure 3-2. A GIS overlay consisting of GPS measurements, archaeological structures, contour lines, aerial photograph, and Digital Elevation Model (from top to bottom) …… 32 Figure 3-3. Vector representations of geographic features ……..…………………………… 34 Figure 3-4. Raster representation of a geographic feature …………..……………………… 34 Figure 4-1. The vicinity of the archaeological site of Pachacamac (Scale = 1:39,000) …. 52 Figure 4-2. The archaeological site of Pachacamac (Scale = 1:10,000) …………………... 55 Figure 4-3. Max Uhle’s site map of Pachacamac, 1903 (Taken from Uhle 1903) …..…… 58 Figure 4-4. Adolph Bandelier’s ground plan of Pachacamac, 1892 (Taken from Shimada 1991:XV, XIX, plate 1 and deliberately inverted for comparison with Figure 4-3 above) ....................................................................................................... 58 Figure 4-5. A site map sold at the site museum ……………………………………………….. 60 Figure 5-1. 1:5000 scale topography map created in 1992 by Instituto Geográfico Nacional, Peru (30-k, A.H. JULIO C. TELLO) ……………………………………………………. 66 Figure 5-2. 1:5000 scale topography map created in 1992 by Instituto Geográfico Nacional, Peru (30-l, A.H. PAMPA GRANDE) ………………………………………………….… 67

x

Figure 5-3. 1:5000 scale topography map created in 1992 by Instituto Geográfico Nacional, Peru (31-k, RUINAS DE PACHACAMAC) ...................................................... 68 Figure 5-4. 1:5000 scale topography map created in 1992 by Instituto Geográfico Nacional, Peru (31-l, LURÍN) ....................................................................................... 69 Figure 5-5. Aerial photograph taken on March 12th 1957 by Servicio Aerofotográfico Nacional, Peru (Proyecto 6512-57-5, 626) …………………………………………………….… 70 Figure 5-6. Aerial photograph taken on March 12th 1957 by Servicio Aerofotográfico Nacional, Peru (Proyecto 6512-57-5, 649) …………………………………………………….… 71 Figure 5-7. The work flow of prototype map creation ……………………………………….… 72 Figure 5-8. Examples of the points where GCPs were located ……………………………… 76 Figure 5-9. Methods of resampling (Taken from Lo and Yeung 2002:146) .……………… 78 Figure 5-10. Aerial photographs 626 and 649 seem to have been taken at different times and thus do not compose a stereopair .……………………………………. 82 Figure 5-11. Internal geometry of aerial photographs: (a) single photographs 626 and 649; (b) a stereopair with 60percent overlap; (c) a fiducial mark; and (d) a 3D representation (Taken from Jensen 2000:142-143, Figure 6-5 and 6-6, and partially modified) …………………………………………………………………………………..… 83 Figure 5-12. Comparative geometry of (a) a map and (b) a vertical aerial photograph (Taken from Lillesand et al. 2004:142, Figure 3-8, and partially modified) ………………. 84 Figure 5-13. File and image coordinate systems (Taken from Leica Geosystems GIS & Mapping Division 2002b:29, Figure 3-13, and partially modified) .……………………. 84 Figure 5-14. (a) Collinearity condition and (b) space intersection (Taken from Lillesand et al. 2004:180, Figure 3-.32 and 3.33, and partially modified) ………………… 87 Figure 5-15. The WRS Path/Row scene boundaries of DEM mosaic at the periphery of Pachacamac ..……………………………………………………………………....................... 90

xi

Figure 5-16. The only document available for the orthorectification process in lieu of regular camera calibration reports (INFORME TECNICO FOTOGRAFICO) …………………… 93 Figure 5-17. Data Strip of photograph 649 that consists of level bubble, clock, altimeter, and focal length …………………………….………………………………………….… 96 Figure 5-18. Coordinate information of the datum located allegedly on the top of the Temple of the Sun, provided by Instituto Geográfico Nacional, Peru …………………. 96 Figure 5-19. The “Identify Results” dialog window …………………..……………………..… 98 Figure 5-20. Ten Ground Control Points collected from topography map (X, Y coordinates) and SRTM DEM (Z coordinate) and plotted over the orthorectified aerial photographs 626 and 649 …………………………………………………………………… 98 Figure 5-21. Superposition of topography map over the orthophoto (649) ..…..………. 101 Figure 5-22. A comparison of two images of Templo Viejo in the original, scanned aerial photograph 649 (a) and in a processed photograph by means of a simple image adjustment (b) ……………………………………………………………………. 105 Figure 5-23. Prototype base map layers of Pachacamac .………………………………….. 106 Figure 5-24. Prototype site map of Pachacamac .……………………………………………. 108 Figure 5-25. Leica Geosystems, GS20 Professional Data Mappers, Reference Station (near) and Rover Receiver (far), by courtesy of Hartmut Tschauner (Seoul National University, Korea) …………………………………..... 110 Figure 5-26. A reference point and 23 GCPs measured by an RTK Differential GPS …. 114 Figure 5-27. The work flow of post-fieldwork data post-processing (Phase III) ...……… 116 Figure 5-28. The relationships between earth’s irregular surface, ellipsoid, and geoid (Taken from Lo and Yeung 2002:35 and modified) .…………………………………... 120 Figure 5-29. Displacements between DGPS-measured points and the corresponding points in the orthophotos resampled with SRTM-arc3 DEM ………………………………. 121

xii

Figure 5-30. DGPS-measured points, elevation points, and contour lines ..…………... 124 Figure 5-31. Comparison of three newly created DEMs .…………………………………… 126 Figure 5-32. 3D representations of the site area: (a) aerial photograph 649 orthorectified using DGPS readings and Digital Terrain Model (DTM); (b) DTM extracted from the stereopair of aerial photographs 626 and 649; and (c) quality of DTM. Contour lines are superimposed over the images for reference ………………… 130 Figure 5-33. Displacements between Ground Control Points (DGPS measurements) and the corresponding points in the orthophotos resampled with 10-by-10 m DEM ... 131 Figure 5-34. Displacements between Ground Control Points (DGPS measurements) and the corresponding points in the orthophotos resampled with extracted DTM ....... 132 Figure 5-35. An example of twisted walls (The Convent of Mamacona) ….……………… 135 Figure 5-36. Field drawings were drawn fairly on transparent graph papers superimposed over printed shapefiles for reference .………………………………………… 137 Figure B-1. A warning message ………………...…………..…………………………………… 188 Figure B-2. The “Georeferencing” toolbar of ArcMap ……………..…………………………188 Figure B-3. Locating the control point …………………………………………………………. 189 Figure B-4. The 49 defined control points marked by red crosshairs ....………………… 190 Figure B-5. The table list of defined control points ……..…………………………………… 191 Figure B-6. The “Spatial Analyst” toolbar of ArcMap ..……………………………………… 196 Figure B-7. The Raster Calculator ..…………………………..………………………………… 197 Figure B-8. The resultant GRID image after calculation process ...………………………. 198 Figure B-9. All of the resultant GRID images displayed in the same map display window ………………………………………………………………………………………. 199

xiii

Figure B-10. The Raster Calculator merging all of the clipped image datasets into a large GRID image .………………………………………………………………………………… 200 Figure B-11. A new image dataset consisting of the four clipped GRID images ……..… 200 Figure B-12. The Define Projection Wizard (coverages, grids, TINs) .…………………….. 201 Figure B-13. A dialog message prompting to build pyramids ...…………………………… 202 Figure B-14. The resultant GRID dataset of topography map scanned, georeferenced, and projected ..………………………………………………………………………………………. 203 Figure B-15. The ERDAS IMAGINE 8.6 main menu bar .…………………………………… 204 Figure B-16. The “Reproject Images” dialog window ………………………………………... 204 Figure B-17. The “Projection Chooser” dialog window ..………………………………….… 205 Figure B-18. The “Reproject Images” dialog window ………………………………………... 206 Figure B-19. The input DEM displayed in a Viewer and the “Inquire Box” dialog window .……………………………………………………………………………………….. 207 Figure B-20. The “Subset” dialog window ................................................................. 208 Figure B-21. The “Model Setup” dialog window ......................................................... 209 Figure B-22. The “Projection Chooser” dialog window ............................................... 210 Figure B-23. The “Set Frame-Specific Information” dialog window ............................ 211 Figure B-24. The OrthoBASE Pro main window ........................................................ 211 Figure B-25. The OrthoBASE Pro main window (Pyramids created) ........................... 212 Figure B-26. The “Camera Information” dialog window ............................................. 213 Figure B-27. The Viewer Fiducial Locator ................................................................. 214

xiv

Figure B-28. Fiducial Orientations ........................................................................... 214 Figure B-29. The Fiducial Locator ............................................................................ 215 Figure B-30. Measured fiducials and RMSE ............................................................. 216 Figure B-31. Exterior Information ............................................................................ 217 Figure B-32. The “Point Measurement (Left view: 626.tif Right view: 649.tif)” window ......…………………………………………………………………………………….......... 218 Figure B-33. The 10 Ground Control Points for triangulation (Phase I) ...................... 219 Figure B-34. Tie Points to be automatically computed .…………................................. 220 Figure B-35. The “Triangulation Summary” dialog window ....................................... 221 Figure B-36. Triangulation Report ........................................................................... 222 Figure B-37. The OrthoBASE Pro main window (Triangulation completed) ................ 222 Figure B-38. The “Ortho Resampling” dialog window (Phase I) ................................... 223 Figure B-39. The “Add Single Output” dialog window ................................................ 224 Figure B-40. The status dialog window ..................................................................... 224 Figure B-41. The resultant orthoimages 626 and 649 (Phase I) ................................. 225 Figure B-42. The ArcCatalog main window ............................................................... 226 Figure B-43. The “Create New Shapefile” dialog window ............................................ 226 Figure B-44. The “Spatial Reference Properties” dialog window ................................. 227 Figure B-45. The two orthoimages and topography map ........................................... 229 Figure B-46. The “Editor” toolbar of ArcMap ............................................................. 229

xv

Figure B-47. On-screen digitizing ............................................................................ 230 Figure B-48. Project Wizard (shapefiles, geodatabase) .............................................. 231 Figure B-49. Selection of the input shapefile ............................................................ 232 Figure B-50. The “Spatial Reference Properties” dialog window ................................. 233 Figure B-51. The “Geographic Coordinate System Transformations” dialog window ... 233 Figure B-52. Selection of the geographic transformation(s) ....................................... 234 Figure B-53. Coordinate extents for the output dataset ............................................ 235 Figure B-54. DGPS points superimposed over the orthophoto 649 ……………………… 235 Figure B-55. The “3D Surfacing” dialog window ........................................................ 236 Figure B-56. The “Input Data” dialog window (Point Data to be read out) ................... 237 Figure B-57. The 3D coordinate information of DGPS points ..................................... 237 Figure B-58. The “Input Data” dialog window (Breakline Data to be read out) ............ 238 Figure B-59. The “Surfacing” dialog window ............................................................. 239 Figure B-60. The 21 Ground Control Points for triangulation (Phase III) .................... 241 Figure B-61. The “DTM Extraction” dialog window .................................................... 242 Figure B-62. The “DTM Extraction Properties” dialog window (“General” tab) ............. 243 Figure B-63. The “DTM Extraction Properties” dialog window (“Image Pair” tab) ......... 244 Figure B-64. The “DTM Extraction Properties” dialog window (“Area Selection” tab) ... 245 Figure B-65. The region to be excluded .................................................................... 246

xvi

Figure B-66. The “DTM Extraction Properties” dialog window (“Accuracy” tab) .......... 247 Figure B-67. OrthoBASE Pro main window (DTM extraction completed) .................... 248 Figure B-68. The “Ortho Resampling” dialog window (Phase III) ................................ 249 Figure B-69. The resultant orthoimages 626 and 649 (Phase III) ............................... 250 Figure C-1. The 36 quadrangles of field drawings (Scale = 1:8,500) ............................ 252 Figure C-2. The Painted Temple (Scale = 1:700) ........................................................ 253 Figure C-3. The Old Temple of Pachacamac (Scale = 1:800) ....................................... 254 Figure C-4. Miscellaneous Structure A (Scale = 1:550) …………………........................ 255 Figure C-5. Cemetery A (Scale = 1:500) .………………………………………..................... 256 Figure C-6. South Entrance to Sector I (Scale = 1:600) .......………............................. 257 Figure C-7. Miscellaneous Structure B (Scale = 1:500) ..…………………...................... 258 Figure C-8. Cemetery B (Scale = 1:700) ...……………………………………...................... 259 Figure C-9. Miscellaneous Structure C (Scale = 1:800) .…………………....................... 260 Figure C-10. Miscellaneous Structure D (Scale = 1:500) …………………..................... 261 Figure C-11. Miscellaneous Structure E or Eeckhout’s “B14” (Scale = 1:650) .…........ 262 Figure C-12. Miscellaneous Structure F or Eeckhout’s “B13” (Scale = 1:650) .……..… 263 Figure C-13. Miscellaneous Structure G or Eeckhout’s “B12” (Scale = 1:600) ............ 264 Figure C-14. Miscellaneous Structure H or Eeckhout’s “B11” (Scale = 1:500) ............ 265 Figure C-15. Miscellaneous Structure I or Eeckhout’s “B10” (Scale = 1:500) .............. 266

xvii

Figure C-16. Pukio A and Miscellaneous Structure J (Scale = 1:700) .…..................... 267 Figure C-17. Miscellaneous Structure K (Scale = 1:700) ..…..……………..................... 268 Figure C-18. South Entrance to the Pilgrims’ Plaza (Scale = 1:400) ............................ 269 Figure C-19. “Ushnu” (Scale = 1:600) ..………………………………………...................... 270 Figure C-20. The Pilgrims’ Plaza (Scale = 1: 950) ..…………………………..................... 271 Figure C-21. Miscellaneous Structure L (West) (Scale = 1:700) ..…………................... 272 Figure C-22. Miscellaneous Structure L (East) (Scale = 1:700) ..………....................... 273 Figure C-23. The Pyramid With Ramp XIII (Scale = 1:550) .………………..................... 274 Figure C-24. The Pyramid With Ramp XII (Scale = 1:600) ………………….................... 275 Figure C-25. Miscellaneous Structure M (Scale = 1:550) .......…………....................... 276 Figure C-26. Miscellaneous Structure N or Eeckhout’s “B3” (Scale = 1:800) …........... 277 Figure C-27. Miscellaneous Structure O (Scale = 1:700) ....………………..................... 278 Figure C-28. The House of the Quipus (Scale = 1:600) ...…………………...................... 279 Figure C-29. The Palace of Tauri Chumpi (Scale = 1:800) ...……………....................... 280 Figure C-30. Miscellaneous Structure P (Scale = 1:600) ......……………...................... 281 Figure C-31. Miscellaneous Structure Q (Scale = 1:500) ...…..…………....................... 282 Figure C-32. Miscellaneous Structure R (Scale = 1:500) .......…………........................ 283 Figure C-33. Miscellaneous Structure S (Scale = 1:600) ....………………..................... 284 Figure C-34. Pukio B (Scale = 1:600) .....................…..……………………..................... 285

xviii

Figure C-35. Miscellaneous Structure T (Scale = 1:600) .....……………....................... 286 Figure C-36. Miscellaneous Structure U (Scale = 1:500) ......……………...................... 287 Figure C-37. Pukio C (Scale = 1:500) ..………………………………………....................... 288 Figure D-1. The 18 quadrangles of digitized maps (Scale = 1:8,500) ........................... 290 Figure D-2. The Painted Temple (Scale = 1:750) ........................................................ 291 Figure D-3. The Old Temple of Pachacamac (Scale = 1:1,200) .................................... 292 Figure D-4. The Temple of the Sun (Scale = 1:1,500) ................................................. 293 Figure D-5. The Pilgrims’ Plaza and Pukio A (Scale = 1:2,200) ................................... 294 Figure D-6. The Convent of Mamacona (Scale = 1:900) .............................................. 295 Figure D-7. The Palace of Tauri Chumpi (Scale = 1:800) …......................................... 296 Figure D-8. The Temple of the Monkey (Scale = 1:800) .............................................. 297 Figure D-9. The Pyramid With Ramp I (Scale = 1:800) ............................................... 298 Figure D-10. The Pyramid With Ramp II (Scale = 1:800) ............................................ 299 Figure D-11. The Pyramid With Ramp III (Scale = 1:900) ........................................... 300 Figure D-12. The Pyramid With Ramp IV and Pukio C (Scale = 1:800) ........................ 301 Figure D-13. The Pyramid With Ramp V and VIII (Scale = 1:800) ............................... 302 Figure D-14. The Pyramid With Ramp VI (Scale = 1:900) ........................................... 303 Figure D-15. The Pyramid With Ramp VII (Scale = 1:1,000) ....................................... 304 Figure D-16. The Pyramid With Ramp IX (Scale = 1:600) ........................................... 305

xix

Figure D-17. The Pyramid With Ramp XI, XIV, and the House of the Quipus (Scale = 1:900) ..……………………………………………………………….…………………….. 306 Figure D-18. The Pyramid With Ramp XII (Scale = 1:800) .......................................... 307 Figure D-19. The Pyramid With Ramp XIII (Scale = 1:650) ......................................... 308

xx

1

CHAPTER 1: INTRODUCTION

In Andean archaeology, GIS and related peripheral technologies have been used by only a handful of archaeologists such as Mark Aldenderfer (Aldenderfer 1996, 2001), Nathan Craig (Craig 2000; Craig and Aldenderfer 2003), and Patrick Ryan Williams (Williams 2002, 2003; Williams et al. 2003) until recently. However, we see signs that it is becoming increasingly popular, particularly among younger archaeologists who recognize its ability to cope with a wide range of spatial scales and to integrate or layer multiple types of data. For example, at the 33rd Annual Midwest Conference on Andean and Amazonian Archaeology and Ethnohistory at the University of Missouri, Columbia on February 26 and 27, 2005, two out of 29 papers presented were based on archaeological applications of Geographic Information Systems (GIS) and remote sensing techniques. Billman et al. (2005) discussed the results of their 2004 Field Season at Cerro León, an inferred Early Intermediate Period Mochica colony in the upper reaches of the Moche Valley. They use GIS-based digital maps for plotting the sites distributed within the Valley. Billman and his graduate students are now transferring into GIS geodatabase the conventional site distribution data accumulated through their settlement pattern studies and basically based on hardcopy maps (Billman 2005, personal communication). Ruiz et al. (2005), on the other hand, focused their attention to more methodological aspect of

2

GIS and demonstrated its capability of combining data collected at different scales. They presented isometric three-dimensional maps of 13 Late Archaic sites in the Norte Chico region which were created from a combination of data acquired with Total Station, Global Positioning System (GPS), and aerial photographs. The isometric maps were used for a simple spatial analysis to calculate the volumes of the mounds. The ongoing integration of GIS and many different data sources such as remote sensing techniques and the interactions among archaeologists in professional meetings and on the web (e.g., the gisarch listserve and Archaeology Discussion Conference at http://forums.esri.com/forums.asp?c=87) have accelerated this exciting trend. Although it is obvious that these relatively new inventions hold the promise for future archaeological research, the history of archaeological applications of research tools borrowed from other fields implies that it would take a substantial amount of time and money for us to fully assess their appropriateness and efficacy. In fact this thesis scrutinizes their potential and limitations and offers guidelines for the most efficient way to use them in the context of resource limitations. GIS have been highly developed since their origin in the early 1960s and employed for a variety of purposes thereafter. Many precocious disciplines such as forestry and hydrology have adopted this technology by tailoring it to their specific requirements. Following in the steps of these disciplines, a handful of archaeologists began to use this useful tool for their analyses of spatial phenomena in the early 1980s. In the first half of this thesis (Chapter 2 and 3), I will critically review the history of spatio-temporal analysis

3

in archaeology and archaeological applications of GIS and related techniques to discuss how these new inventions have been integrated into the archaeological methodology and even theoretical discussions. In the early stages of GIS application in American archaeology, predictive modeling of site location was most common, particularly in Cultural Resource Management (CRM). In the 1990s, the modeling studies became more sophisticated and began to consider the spatial distribution of archaeological remains in terms of variables other than environmental features. Maschner (1996:5-13) sketches out several spatial analyses available for GIS-based archaeology. They involve cost surface analysis, viewshed analysis, optimum path analysis, site catchment analysis, boundary definition analysis, and so forth. The most attractive to archaeologists may be viewshed or line-of-sight (LOS) analysis. This “3D-GIS-based” approach helps archaeologists examine the actual view of prehistoric people and explore their perception of landscapes, putting a greater focus on social and cognitive aspects of prehistoric human behavior. Like the theoretical trajectory that American archaeology itself has followed, one of the current trends of GIS application in archaeology is also heading for a postprocessual orientation. Further, GIS are readily anticipated to proceed to a more advanced stage such as Forte’s (2003) attempt for post-postprocessualistic reconstruction and interpretation of ancient landscape through Virtual Reality (VR). With their ability to deal with a wide range of scales, GIS are fully compatible with different levels of data management and spatial analyses: within-structure, within-site,

4

and between-site levels (Clarke 1977). Craig and Aldenderfer (2003) argue, for instance, that real-time data recording at within-structure level is an important agenda for GIS applications in archaeology. There has been no agreement among archaeologists so far as to how within-site and within-structure objects should be registered and stored in GIS data layers for future analytical purpose. However, the level at which GIS can be most effective would be a macro level beyond the inter-site level (e.g., inter-valley and coast-versus-highland levels in Andean regions). While archaeologists have taken advantage of bird’s-eye views by means of aerial photography since Kosok’s first large scale use in the 1940s, the integration of satellite imagery and regional site databases will offer much larger, macro-regional perspectives. In this regard, as Billman and Feinman (1999) acknowledge the feasibility of GIS application for their studies in the Americas, settlement pattern study based on regional full-coverage surveys may be one of the major potential fields of application. This new trend is in synchrony with the macro-regional paradigm in Mesoamerican archaeology proposed by Balkansky (in press). I envisage that GIS application will be a critical component for any future macro-regional studies. This is another direction for future development of GIS-based archaeology. Before the revolutionary introduction of GIS to archaeology in the early 1980s, the most widely used tool for the presentation, analysis, and interpretation of archaeological data was distribution maps on which the spatial dimension was plotted by hand. It is generally said that one of the most important benefits from GIS application in archaeology is that GIS alleviate a huge burden on making distribution maps and avoid a series of

5

human errors by hand (Wheatley and Gillings 2002:18). GIS allow for major data addition, modification, and deletion in the manner that has not been possible with traditional manual mapping methods. Supported by vital capabilities of GIS, as a result, the role of distribution maps dramatically changed from a stand-alone data layer upon which interpretations were based to a foundational data summary or stepping-stone for further detailed analyses. Thus, the timesaving technology provides archaeologists with much more time to spend for analyses and interpretations. The time efficiency and succinctness of GIS-based digital mapping and data management are theoretically true, but in reality, making maps is not an easy task. Previous major studies (Allen et al. [eds.] 1990; Aldenderfer and Maschner [eds.] 1996; Forte and Williams [eds.] 2003; Gaffney and Stančič 1991; Maschner [ed.] 1996; Westcott and Brandon [eds.] 2000; Wheatley and Gillings 2002) put their primary foci upon the analytical capabilities of GIS, which are thought to be the true worth of the technology, and attempted to brush up on the analytical methods of conventional spatial archaeology originally introduced by pioneering works such as Hodder and Orton (1976) and Clarke (1977). None of them, however, discuss mapping procedures in details. As with the cases in other regions of the world, in the Andes there is no ready-to-use digital map for archaeological use. Although archaeologists are required to produce their own maps, constraints such as data scarcity and limited resources will complicate the issues. In the second half of my thesis (Chapter 4 and 5), consequently, I will refer to my GIS-based digital site mapping, which is a part of the on-going long-term archaeological

6

project on the central coast of Peru, Pachacamac Archaeological Project (PAP), directed by Dr. Izumi Shimada (Southern Illinois University at Carbondale). One significance of my map production is that it demonstrates the extent to which we can rely on GIS and related remote sensing techniques to efficiently create high-quality maps given resource limitations. The first step to apply new technological inventions in archaeology needs to be taken considering the gap between the theories and our reality before us. My GIS-based site mapping consists of three phases: (1) prototype map preparation based on the resources available prior to the fieldwork in the summer of 2004; (2) ground-truth checking of the archaeological architectures and Ground Control Point (GCP) measurements by means of a leading-edge Real Time Kinematic Differential GPS (RTK DGPS) of the highest accuracy; and (3) data post-processing and consummation of the final maps. The procedures of each phase will be summarized in Appendix (B). Finally, I will conclude my thesis by discussing implications and insights that I obtained from GIS application to the PAP and broader significance of my mapping to Andean archaeology (Chapter 6 and 7).

7

CHAPTER 2: SPATIO-TEMPORAL THINKING IN ARCHAEOLOGY

Archaeological artifacts, features, structures, and sites are found somewhere in space with no exceptions and thus there are spatial relationships among them. Once they are uncovered by archaeologists with appropriate field methods, they are given spatial and temporal attributes, more specifically, X, Y, and Z coordinates. In so doing, since the time of A. H. L. F. Pitt-Rivers in the late 19th century (Pitt-Rivers 1887, 1888, 1892, 1898), archaeologists have employed plan and section views to help understand relative positioning and temporal sequence of those findings. Tracing back to the historical origin of archaeological interest in spatio-temporal components, in this and following chapters, I review its development over time and discuss how GIS came to be integrated into the methodological and theoretical developments of archaeology.

2.1.

Germination of Spatial Thinking

Until the late 19th century, archaeology had not secured its place as a full-fledged discipline. Many of those who chose to specialize in “archaeology” had been trained in the physical and biological sciences. These 19th-century “archaeologists” (e.g., Sven Nilsson [1868] and Gabriel de Mortillet [1897]) made efforts to bring in their own expertise and to

8

borrow many other theories and methods from a wide variety of disciplines in order to strengthen the claim of archaeology as a systematic scientific discipline: Jean Lamarck’s evolutionary theory, Charles Lyell’s principles of geology, Franz Boas’ anthropo-geography, and so forth (McGee and Warms 1996:6-7, 128-129; Trigger 1989:16-17, 92-93; Willey and Sabloff 1993:38-39). As a result, methodologically speaking, these internal and external enlightenments finally took the form of basic methods of archaeological research such as General Pitt-Rivers’ invention of new field methods and Sir Flinders Petrie’s seriation based on meticulous stratigraphic excavations and artifact analysis (Petrie 1899; Pitt-Rivers 1887, 1888, 1892, 1898). In terms of the methodological inventions of description and classification of archaeological materials, Willey and Sabloff (1993:38-92) labels this period as “Classificatory-Descriptive Period” (1840-1914). With these methodological foundations, many “archaeologists” conducted their fieldworks in different parts of the world, Old and New, and accumulated archaeological materials and produced factual reports rather than travelers’ accounts (tradition of Stephens [1837, 1838, 1841, 1843] and Catherwood [1844]). In the Andes, for instance, they include Johann Tschudi (1869), Francis de Castelnau (1854), Ernest W. Middendorf (1893-1895), Ephraim G. Squier (1877), Sir Clements R. Markham (1856, 1871, 1892, 1910), Wilhelm Reiss and Alphons Stüdel (1880-1887), and Max Uhle (1903). As growing amounts of archaeological materials were recovered and recorded, gridded plan and section views became a standard of archaeological field methods by the end of the

9

Figure 2-1. Squier’s plan of the Palace of the Virgins of the Sun, Island of Coati, Lake Titicaca, 1877 (Taken from Squier 1877:361).

Figure 2-2. Uhle’s section view of the cemetery at the foot and under the base of the Painted Temple, 1903 (Taken from Uhle 1903:19, Figure 3-5).

10

century (Figure 2-1 and 2-2). This indicates that spatial data on distributions of artifacts came to be recognized as essential by archaeologists working in different regions of the world. During this period political authorities in Central Europe in particular abused archaeology for political purposes such as the instigation of nationalism. Since archaeology affirmed its ties to the study of national histories, archaeology directly reflected ulterior motives of and political relationships between emerging states in Europe. For instance, the development of local chronologies was retarded by a reluctance to adopt the Scandinavian Three-Age system, which was opposed largely for nationalistic reasons by a number of prominent German archaeologists (reviewed in Trigger 1989:149-150). At the same time, these political interventions had some unexpected benefits. As Trigger (1989:150) points out, “a concern with historical and ethnic problems led archaeologists to pay increasing attention to the geographical distribution of distinctive types of artifacts and artifact assemblages in an effort to relate them to historical groups.” Here we can see a germination of spatial thinking in archaeology. The first archaeological spatial analysis based on distribution maps was born out of incited nationalism and multidisciplinary atmospheres at an early developmental stage of archaeology.

2.2.

Environmental Determinism, Chronology, and Diffusionism

This trend in Europe was driven forward by the Austro-German school of “anthropo-geographers” typified by Friedrich Ratzel and Franz Boas1 (Clarke 1977:2). As

11

their professional identification suggests, they stressed the roles of landscape and geography, by which the patterns of archaeological distributions were thought to have been conditioned. One of the headstreams of this school of thought can be found in Scandinavian archaeology. As early as the 1840s Jens J. A. Worsaae and his colleagues, searching for the correlations between changing paleoenvironmental settings of Scandinavia and distribution patterns of prehistoric populations of the region, focused their attentions on various environmental variables (e.g., flora, fauna, climatic changes, and soil types) under multidisciplinary collaborations (Trigger 1989:247-248). Their progressive approach also inspired many British and other European archaeologists and resulted in a large-scale campaign of environmental determinism in Europe during the early 20th century. They involve Robert Gradmann (1906), H. J. Mackinder (1904, 1909), O. G. S. Crawford (1921), and Cyril Fox (1923, 1932) (cf. Hellmich 1923; Schliz 1906; Wahle 1915). Fox in particular, in his “The Personality of Britain” (1932), combined the ecological-distributional approach of Gradmann and Crawford with the positional geography of Mackinder to produce some major generalizations about the relationship between landscapes and culture history (Trigger 1989:248-249). It was in this context that Cyril Fox later elaborated a technique that combines a series of archaeological and environmental distribution maps to cover a region or a country changing over several millennia (Clarke 1977:2). It is important to note that distribution maps in the early 20th century had been extended horizontally and had acquired temporal depth. This corresponds to the first half of the period that Willey

12

and Sabloff (1993:96-148) labeled as “Classificatory-Historical Period” (1914-40/ 1940-60) where American archaeologists concerned themselves with stratigraphic, seriational, and classificatory methods and culture-historical syntheses of New World regions and areas. Both in Europe and the United States, artifact classifications now functioned as devices to aid the plotting of culture forms in time and space (Willey and Sabloff 1993:96; Aldenderfer 1996:6). Fox’s approach, in addition, had been widely adopted since the 1930’s by Gordon V. Childe (1934), W. F. Grimes (1945), A. H. Hogg (1943), and Woolridge and Linton (1933) (Clarke 1977:2). At the base of the interpretations of archaeological distributions during the late 19th century through the early 20th century were earlier evolutionary preoccupation and subsequent diffusionism, both of which were profoundly based on a static but widely held view of humanity as “phlegmatic actors” (Trigger 1989:150-155). This somewhat pessimistic worldview led archaeologists to consider cultural traits to be once-and-for-all phenomena insofar as they were originated somewhere as anomalies or mutations and spread to other regions. The origin of those adventitious innovations was traced back to ancient Egypt by Petrie (1939) and to Mesopotamia by Childe (1929, 1939). Through the use of distribution maps and typological sequences of material remains, diffusionists attempted to plot the regions of similar material cultures as “culture areas” at a regional and even continental scale and devoted themselves to reconstruct the culture histories in terms of diffusion, direct contact, and acculturation (Wheatley and Gillings 2002:5). Definitions of culture areas are characterized by two basic premises. First, material

13

culture was thought to be a direct and non-problematic reflection of a distinct people. Second, cultural traits were assumed to spread concentrically or isotopically at the same rate with no constraints of physical space (Aldenderfer 1996:5; Wheatley and Gillings 2002:6). The definitions are arbitrary in nature and vary in scale depending on the problem of interest. As Aldenderfer (1996:5) notes, a good example can be seen in a comparison of Gifford and Kroeber (1937) and Kroeber (1939). Whereas the latter defines a large area as “California” alongside of “the Eastern Woodlands” and “the Great Plains,” the former further divides the same area into smaller areas such as “Pomo culture area.” In any case, the definitions of culture areas became quite subjective because diffusionistic explanations were based upon simple visual examination of distribution maps (Wheatley and Gillings 2002:5). Archaeologists uniformly employed distribution maps, but the features to be plotted on those maps were slightly different in the United States and Europe. While European archaeologists put their emphasis largely on artifactual distributions with close ties to environmental settings, American counterparts gradually included social institutions and settlement pattern as well (Clarke 1977:3). As Boas rejected environmental determinism and turned his attention to a closer examination of social organization of the Eskimo and other groups (Boas 1888), American archaeologists similarly shied away from European environmentalism, resulting in a closer alliance with anthropology.

14

2.3.

Pursuits of Context, Function, and Process

As the United States expanded westward and as Euro-Americans penetrated into different parts of the Americas, greater amounts of archaeological research and publication of its results were undertaken with governmental funding. This steady increase in the description of antiquities, seen in a series of handbooks (Hodge [ed.] 1907-1910; Steward [ed.] 1946-1950), highlighted a significant divergence between new information and the artifactual inventories of culture areas defined by then. The emergent distribution patterns of these new findings could no longer be explained solely from the viewpoint of diffusionism. Thus, diffusionistic explanations gradually disappeared from archaeology. In concert with the growing corpus of antiquities, dissatisfaction among American archaeologists with the limited goals of chronological orderings of the artifacts and events gradually mounted to the critical threshold of tolerance in the late 1930s (Willey and Sabloff 1993:154-155). Archaeologists began to argue over the ultimate objectives and future directions of their discipline (cf. Kluckhohn 1939, 1940). Some considered archaeological objectives should be contextual-functional reconstruction and explanatory exploration of culture change rather than mere chronological ordering and plotting of material cultures. This vision received a boost from the invention of Libby’s radiocarbon dating method that greatly helped free archaeologists from their preoccupation with chronology. Their pursuits of context and function resulted in the reemergence of cultural evolutionism and emphasis on integrative historical and evolutionistic concepts such as

15

“horizon style,” “cultural tradition,” and “culture stage” as well as concern for the underlying causes of culture change. Julian H. Steward’s initial attempt towards the studies in the realm of what he calls “cultural ecology” and “multilinear evolution” is one of the major consequences of this theoretical shift (Steward 1947, 1949, 1955). As William Duncan Strong (1936) and Paul Martin (Martin et al. 1938; Martin and Rinaldo 1939) argued for the need of theoretical collaboration between archaeology and ethnology, the interest of archaeologists in artifacts gradually extended to cover those who created and used those artifacts. In this context Steward, together with F. M. Setzler, argued that archaeologists should make efforts to explain human behaviors and adaptations to environmental settings through examinations of such subject matters as subsistence potentials and settlement patterns (Steward and Setzler 1938). He called for archaeologists to compare specific cultural sequences in specific environmental settings aiming at the discovery of developmental regularities (Willey and Sabloff 1993:155, 178-179). Although it overemphasized the role of environment in the technological aspects of cultural development, Steward’s work on prehistoric regional and community patterns in the American Southwest “certainly stimulated a series of major field researches concerned with locating and mapping archaeological sites on a regional scale with the express purpose of studying the adaptation of social and settlement patterns within an environmental context” (Clarke 1977:3). They involve the survey carried out by Phillips et al. (1951) in the lower Mississippi Valley between 1940-1947, the more influential

16

settlement pattern study by Willey (1953) in the Virú Valley,

2

and the Mesoamerican

ecological study by Palerm and Wolf (1957). This indicates that many American archaeologists finally began to regard environment in a more active or causative sense as a possible explanatory factor in the development of culture rather than a conventional passive role (Willey and Sabloff 1993:176-177). It can be thought of as an antithesis of Boasian antienvironmentalism. In the 1950s and 1960s, settlement pattern studies became one of the basic field tasks of archaeological projects. These studies transformed the concept of study area in terms of spatial scope and patterns. They helped to introduce the regional approach in archaeology and set the stage for nested or multi-stage spatial analysis of different scale areas ranging from activity area to intra- and inter-site scales: (1) building or structure; (2) the arrangement of structures within individual communities; and (3) the distribution of communities across the landscape. In preparation for the major theoretical and methodological shifts during the 1960s to 1970s, distribution maps became a more elaborate tool of sizable scale and temporal sequence with a close tie to this new concept of study area. Underlying the development of the regional approach was another important technological introduction. While the growing popularization of the radiocarbon dating method contributed to rethinking of archaeological time scale, introduction of aerial photographs made possible the extension and new conception of study areas. In the Andes, Robert Shippee and Lieutenant George Johnson conducted an eight-month aerial

17

exploration of Peru for the Peruvian Navy in 1931 (Deuel 1969:235-236). They took a large number of excellent oblique aerial photographs that involved various important archaeological sites and geographical landmarks. Though their aerial campaign had no far-reaching archaeological consequences at the time3, their work laid the base of the present-day Servicio Aerofotográfico Nacional (SAN), Lima, and opened up doors to forementioned regional approaches actively pursued in the next few decades. Since World War II it has become almost a matter of routine for Andean archaeologists to check the available aerial coverage of their study areas. Aerial photographs enabled them to gain a bird’s-eye view overlooking their study area as a whole and to locate archaeological sites and their interrelations within the surrounding topographical settings. Gordon R. Willey and Richard P. Schaedel adopted aerial photographs for their regional studies (Schaedel 1951; Willey 1959); however, no one extracted as much information from the photographic archives as Paul Kosok. Kosok first utilized aerial photographs for his systematic study of coastal irrigation systems and settlements in the 1940s (Kosok 1965). In his posthumous volume Kosok eloquently expresses his delight at the usability of aerial photographs as follow:

There was always tremendous satisfaction in discovering new pyramids, settlements, fortifications, walls, roads and canals on a photograph. Indeed, it was even exciting to find such ruins on photographs after we had seen them in the field. For here they looked quite different! We would often exclaim: Why

18

didn’t we see that there was another ruin right nearby when we were in the field? Why didn’t we see that this wall extended all the way up the hill? Why didn’t we follow the ‘end’ of this canal for another half mile and find its continuation? (Kosok 1965:44)

On the other hand, the understanding of environment as a possible explanatory factor in the development of culture had also taken root in archaeological inferences. These developments established a firm theoretical and methodological basis for the coming period when archaeologists adopted a holistic view of culture and environment in an interacting, systemic relationship and GIS-based spatio-temporal speculations. Nonetheless, it was noteworthy that the scale of an individual study area was still limited to the site level regardless of flowering regional syntheses in the Americas.

2.4.

Quantification of Spatiality

Unduly subjective and thus hazardous interpretations of archaeological distributions in the preceding periods were forcefully challenged by a quantitative “revolution” inaugurated by a handful of archaeologists in the late 1960s and early 1970s. During this reform movement known as “New Archaeology,” American archaeologists began actively to borrow new conceptual models and analytical methods from other disciplines, notably “new geography” that emphasized quantitative locational analysis and modeling (Chorley and Haggett [eds.] 1967; Haggett 1966). They first attempted to

19

move beyond their casual visual examination of distribution maps to obtain a more objective and verifiable approach and explore in more detail the shape, form, and nature of the spatial patterns recognizable in the archaeological record.

From this point on it was no longer sufficient to say that the locations of a number of features … appeared to be grouped together or that some looked to be aligned upon a particular landscape feature. Was there really a grouping or preferred alignment, and if so why? Rather than mere description what was needed was explanation (Wheatley and Gillings 2002:6).

Looking at a distribution map on which site features are plotted, for example, archaeologists of former periods would have intuitively looked for likely clusters of features on the grounds and convergence of similar types in similar environmental settings. However, the advocates of New Archaeology instead tested the validity of the hypothesized groupings empirically or statistically. This hypothetico-deductive approach characterized the New Archaeology and cleared the way for the quantification of spatiality. The most influential of the new concepts introduced during this period and closely related to spatial thinking was system theory, in which culture was considered to be a system in a dynamic equilibrium of the feedbacks between distinctive sub-systems (Wheatley and Gillings 2002:6-7). Within this conception of culture, spatio-temporal

20

distributions of material remains were seen as manifestations of specific functional roles of the culture system to maintain its internal stability facing off against external changes. In this regard, environment was seen as the primary external factor that exerts fundamental influences on human behavior and distribution. Underlying these propositions is a premise that people have led a functional and utilitarian way of life where they attempt to minimize energy costs and maximize efficiencies in order to adapt themselves to external changes. The patterns of their adaptations to environmental settings are eventually inscribed into space as a canvas and thus to be measured as archaeological records on distribution maps. Distribution maps still functioned as a useful device, but with a slight change in their role. They now became a fundamental data summary or stepping-stone for further detailed analyses (Wheatley and Gillings 2002:7). As seen in Hodder and Orton (1976) and Clarke (1977), a whole suite of spatial analytical methods were borrowed wholesale largely from geography, and they were quickly integrated into archaeological field research. They include modern variants of the von Thünen model of agricultural land use (von Thünen 1826), Weber’s model of industrial location (Weber 1909), Christaller’s central place model (Christaller 1933), Hägerstrand’s model of innovation and its diffusion (Hägerstrand 1952), and gravity models of all kinds (cf. Bogue 1949; Duncan 1959; Launhardt 1882; Schäffle 1878). Later on, however, Hodder (1984) demonstrated that analyzing spatial distributions of material cultures by the use of these imported positivistic models is not sufficient to

21

reveal social contexts within which raw materials were transported. A fundamental problem here is that archaeologists attempted to explain sociocultural contexts and processes without the notion of human behavior. As Trigger (1986:7-8) postulated, linking those imperfect records of cultural behavior to the original human activities is the only way in which archaeology can be related to the theories and methods of other social and natural sciences. In this regard, Binford (1977, 1981) searched for absolute or statistical generalizations that link specific types of material culture to specific aspects of human behavior. The greater concern for cultural adaptations to environmental settings and processes noted above meant that archaeologists became receptive to any innovative techniques that serve as effective means of collecting environmental data. In this regard, it is important to note that the first launch of Landsat (Landsat-1; formerly called ERTS-1) in July 1972 also exerted a fundamental influence upon spatial thinking in archaeology. Landsat-1 is an unmanned satellite designed by NASA, with the cooperation of the U.S. Department of the Interior, to acquire data about earth resources on a systematic, repetitive, medium resolution, multispectral basis (Lillesand et al. 2004:404-438). A single scene sensed at a nominal altitude of 900 km (ranging from 880 to 940 km) covered the ground area of 185 by 185 km with ca. 80 m of spatial resolution. Although the spatial resolution was coarser than that of aerial photographs, much larger spatial data now became accessible. This eventually led to broaden the extent of study areas of archaeology and infused new passion into previously meager regional-scale studies. Satellite imagery

22

was excellent particularly with forementioned ecological concerns, and its use became widespread throughout anthropology until the 1980s when GIS was put to work for archaeology.

2.5.

“Meaningful Space” and Postprocessual Reasoning

Until the time of New Archaeology, space had been thought of as a canvas upon which cultural activities left traces or a neutral container for human actions. It was a universal, clearly measurable, and fundamentally external backdrop to cultural activities. Since it was a “taken-for-granted category that, in itself, was non-problematic” (Wheatley and Gillings 2002:8), space remained unchanged all through the ages. The space of the prehistoric time should consequently be identical to that of the modern archaeologists. In the 1980s, however, a group of archaeologists called “postprocessualists” raised questions about the concept of space as a non-problematic abstract backdrop and the image of landscape as a palimpsest of material traces (Thomas 2001:165). According to the postprocessualists, the end product of traditional approaches had been nothing but a history of physical activities that have been done to the land, which often seems to give a wide berth to the past human lives that were lived there (Barrett 1999:26). Instead, they purported that space should be viewed as inherently embedded and implicated in social actions and thus ”cannot exist apart from the events and activities within which it is implicated” (Tilley 1994:10). They also criticized the idea of processualists who considered environmental changes as external stimulation (Wheatley and Gillings 2002:8). The

23

interests of postprocessual archaeologists are re-directed to internal factors such as perception, experience, and movement to pursue the space that had been individually constructed through social actions (e.g., dwelling and routine work) reflecting inequalities and conflicts in the society (Thomas 2001:166). Now that space was seen as no longer something that archaeologists can readily share with their colleagues, distribution maps had no future for postprocessualists. The landscape perceived and experienced through social actions is a fundamentally separate conception from the physical distributions of archaeological materials: “a territory which can be apprehended visually and … a set of relationships between people and places which provide the context for everyday conduct” (Thomas 2001:181). This bipolarity, or Thomas’ (2001) “duplicity,” of the concepts of space and landscape formed a challenging agenda to be tackled in GIS-based spatial speculations, which will be discussed in the following chapters. The phenomenological vision of space that Tilley and some other postprocessualists espouse is essentially a subjective vision that may have no correspondence with the objective reality. Further, these scholars seem not to be concerned with explanation as much as description of the significance of space. An alternative approach to be pursued, as Thomas (2001:181) argues, “will still require that we identify and plot the traces of past activity in the countryside. But the uses to which these traces will be put will have to go beyond the reconstruction of economic regimes and speculations as to how the land may have been perceived by past people.” Even though we cannot get at the crude experience

24

and perception of past people through an act of empathy, we may be able to enter into the same set of material relationships in which the people found themselves in the past and to reanimate the past world using our bodies as analogs for those of the past (Thomas 2001:180-181). The history of spatio-temporal thinking in archaeology, as we have reviewed above, concerns primarily: (1) how archaeologists have assembled and ordered material culture; (2) how they have viewed archaeological distributions and space on which those distributions are plotted; and (3) how the field of vision of archaeologists has become larger in step with the development of field methods and remote sensing technology. Aldenderfer (1996:9) argues, “Despite the introduction of powerful new methods of spatial behavior, new methods for the acquisition of spatial data at very large scales, and useful theoretical constructs that directed inquiry, there remained a significant gap between the desire to work at larger spatial scales and the ability to do it in a practical manner.” Keeping track of the three major concerns mentioned above, GIS fill in this gap and allow us to go further into more advanced spatio-temporal speculations. I will move on to discuss the anatomy and archaeological applications of GIS in the next chapter. Additionally, I will also discuss the challenges and future prospects of GIS-based spatio-temporal analyses in archaeology.

25 Notes

1 Franz Boas, known as the father of American anthropology, was born in Germany and first studied physics. Developing his interest in geography, he finally received his doctorate in geography in 1881 (McGee and Warms 1996:128). 2 In the Andes, many archaeologists modeled their fieldworks after Willey’s settlement pattern study (e.g., Bankes [1972], Billman [1996, 1999a, 1999b], Dillehay [1976], Donnnan [1973], Earle [1972], Eling [1988], Moseley [1975], Nolan [1980], Patterson [1971], Proulx [1985], Schreiber [1999], Silverman [2002], Stanish [1999, 2003], Tschauner [2001], and Wilson [1983, 1988]). 3 This is probably because the bulk of the aerial photographs remained unpublished and were in the possession of the Wenner-Gren Foundation for Anthropological Research, Inc., New York (Kosok 1965:19).

26

CHAPTER 3: ARCHAEOLOGICAL APPLICATIONS OF GIS

In 1963, Roger Tomlinson and colleagues developed the Canada Geographic Information System (CGIS) for the federal and provincial governments to identify the nation’s land resources and their existing and potential uses (Longley et al. 2001:10, 12). This is widely known to be the origin of GIS. Through the major developments of subsequent systems such as DIME-GBF (Dual Independent Map Encoding-Geographic Database Files) by the U.S. Bureau of Census in the late 1960s and the first contemporary vector GIS called ODDESY by the Harvard Laboratory for Computer Graphics in the late 1970s, and the release of ArcInfo in 1981 by Environmental Systems Research Institute, Inc. (ESRI), GIS gradually evolved to the form that we know today (Lo and Yeung 2002:5-7; Longley et al. 2001:11-12). It was in 1984 that the first accessible source of information about GIS in book form, “Basic Readings in Geographic Information Systems” (Marble et al. [eds.] 1984), was published. In this sense, GIS are relatively new technology. For over 20 years thereafter, fueled by various underlying factors such as improving performance of and concurrently decreasing price of computing hardware, GIS have been rapidly developed and employed for various purposes in a huge variety of contexts ranging from governmental to commercial and academic domains. Some archaeologists followed the steps of many precocious disciplines such as

27

forestry and hydrology and began to use this useful tool for their analyses of spatial phenomena in the early 1980s. By this time, archaeologists had been fully aware of the importance of spatial data in their research efforts; however, there remained a significant gap between their desire to work at larger spatial scales and their ability to do so in a practical manner. Here was a pivotal point at which GIS came into play (Aldenderfer 1996:9). GIS provided archaeologists with sophisticated means of scalable data management and manipulation based on various data sources that are different in scale for the purpose of interpreting and explaining spatial and temporal distributions of material culture. This indicates that GIS successfully integrated the three major concerns of conventional spatio-temporal thinking in archaeology discussed in the previous chapter.

3.1.

What are GIS?

GIS are defined by an archaeologist as follows:

Geographic information systems are essentially spatially referenced databases that allow one to control for the distribution of form over space and through time. They are more than computerized cartography because they provide for the storage, mathematical manipulation, quick retrieval and flexible display of spatially referenced data (Green 1990:3).

28

Figure 3-1. The logical subsystem model of GIS originally put forward by Marble (1990) (Taken from Wheatley and Gillings 2002: 11, Figure 1.2 and partially modified).

29

Green’s definition corresponds exactly to Marble’s (1999:12-13) structural model of GIS consisting of four major sub-systems: (1) Data entry subsystem, (2) Data storage and retrieval subsystem, (3) Data manipulation and analysis subsystem, and (4) Data visualization and reporting subsystem. In addition to these four subsystems, Wheatley and Gillings (2002:11) add User Interface (Figure 3-1). From commercial ones such as ESRI’s ArcGIS to noncommercial GRASS1 (Geographical Resources Analysis Support System), recent GIS softwares have basically the same logical structure of those five subsystems. These subsystems are designed to be interrelated to each other to accomplish intricate procedures for storage, analysis, and display of spatially referenced data in response to a user’s commands. As you can see in Figure 3-1, furthermore, we now have a good selection of data input and output devices compatible with GIS. Flexible data input and output in cooperation with those peripheral devices is one of the major features of GIS as well.

3.1.1. Data Structure Models. Geographic data that we deal with in GIS basically link three different types of data: place, time, and attributes. Place is an essential element in geographic information, which is used to plot the objects of interest precisely on a map, whereas time is optional. Attributes are explanatory information assigned to particular places and are subdivided into five different scales: nominal, ordinal, interval, ratio, and cyclic (or directional) (Longley et al. 2001:64). Taking site data as an example, place is the information of site location usually represented by Cartesian coordinates. Attributes are

30

arbitrarily configurable and basically expandable data such as site name, site size, site cluster and associated canal system, linkage information to artifact inventories in external database, and so forth. As of now, there is no way to treat time as a separate data component in GIS. Date datum in conventional DBMS, for instance, takes one of the formats separated by slashes such as “YYYY/MM/DD,” but the short time scale of this ordinary format does not fit an archaeological time scale. The temporal resolution of archaeological time scale is much coarser and takes forms such as “Late Archaic,” “Classic Maya,” “2,500 B.P.,” “1,150 B.C.,” and “Late Intermediate Period.” Further, the radiocarbon dating method that is very widely used by archaeologists does not provide a very precise date, as each assay is accompanied by margins of error: “1215 ± 40 B.P.” and “924-938 A.D. (2 sigma: 0.020).” Consequently, for the time being, time should be integrated into attributes (e.g., “period of occupation” or “C14 dating”), or combined with place and represented as a single component for each period of occupation. The treatment of time still remains to be refined and is one of the major agendas for GIS users including archaeologists to establish truly four-dimensional systems. Archaeologists have to solve this fundamental problem by working out a comprehensive method to integrate all the variants above. In any case, this data structure consisting of three fundamental data types operates quite similarly to that of traditional recording system of site location on maps, linked to card references of attribute information (Maschner 1996; Wheatley and Gillings 2002). For the purpose of display and analysis, both location and attribute data are

31

organized into thematic layers, accumulated one over another (Figure 3-2), and manipulated for further analyses (Maschner 1996:2; Wheatley and Gillings 2002:25-28). These thematic layers, for example, may involve such natural and cultural features as topography, soils, lithology, microclimate, hydrology, roads, vegetation types, and archaeological site distribution. The ability to construct new data layers from those already associated with maps is one of the most important features of GIS. Such sidebar layers may include aspects, slope or grade, view, and so on (Maschner 1996:2). The data layers are stored in one of two formats as vector or raster data. Because of the finite resources (e.g., disk space), infinite information of the real world must be reduced or arbitrarily selected. In other words, it would be impossible to represent in digital format the real world as it is. Each of the two data models of representing the infinite information of the world, consequently, has its own advantages and disadvantages summarized in Table 3-1 (Longley et al. 2001:72-75). The selection of data model must be done depending on the nature of the geographical phenomena of interest and the goals of the research. The vector data model is based upon the “discrete object view” where geographers see the world as an empty space occupied by objects with well-defined boundaries, which are distinguished by their dimensions and represented by points (vertices), lines (sets of vertices connected by precisely straight lines2), and polygons (areas enclosed by a series of straight lines connecting vertices) (Figure 3-3). This concept of space is quite similar to that of archaeologists before postprocessual interventions.

32

Figure 3-2. A GIS overlay consisting of GPS measurements, archaeological structures, contour lines, aerial photograph, and Digital Elevation Model (from top to bottom).

Table 3-1. Relative advantages of raster and vector representations (Taken from Longley et al. 2001:75). Issues

Raster

Vector

Volume of data

Depends on cell size

Depends on density of vertices

Sources of data

Remote sensing, imagery

Social and environmental data

Applications

Resources, environmental

Social, economic, administrative

Software

Raster GIS, image processing

Vector GIS, automated cartography

Resolution

Fixed

Variable

33

Since you have to trace geographical features on the screen placing the vertex or vertices to represent them as either one of the feature types above, vector datasets are time-consuming to make. Although accuracy and volume of this data mode theoretically depend on the density of vertices, in fact, the volume is relatively smaller than that of raster data mode. For example, when I traced contour lines on a topography map of 13 MB, which is a raster dataset, the resultant vector dataset of the traced lines is only ca. 1.5 MB. The vector data mode is more advantageous in the management of objects with well-defined boundaries; therefore, it is more applicable to analyze the spatial relationships between the constituent geographical objects and to ask questions such as “[I]s area 1 adjacent to area 2?” or “[H]ow many square km is area 3?” (Wheatley and Gillings 2002:48-49). The data used for conventional spatial statistics such as point-pattern analysis fall in this type of data model. Additionally, standard GIS are equipped with several useful tools for spatial interpolation that create a continuous surface from a limited number of points distributed and collected on the study area (e.g., trend surface, semivariogram, and kriging). Digital Elevation Model (DEM) can also be created from point and/or line data using interpolation algorithms. The raster data model, on the other hand, is based upon the “field view” where geographers consider the world as a continuous surface, which is divided into a fine mesh of gridded cells with a series of properties or attributes assigned (Figure 3-4). Consequently, this data model copes better with data that change continuously across

34

Figure 3-3. Vector representations of geographic features.

Figure 3-4. Raster representation of a geographic feature.

35

the survey area (e.g., soil data) that has no clear-cut edge or boundary (Wheatley and Gillings 2002:50-57). Aerial photographs and satellite images are utilized to generate independent raster data layers, such as structural geology, vegetation, and soil moisture (Maschner 1996:2). Although the model is less precise than the vector data model, it is more honest to the inherent quality of the geographic data. Since the resolution is fixed as opposed to vector, each cell needs to be small for accuracy (Longley et al. 2001:72-73). The spatial analysis based on this type of continuous data was first invented in geology but has made little impact on the spatial analysis in archaeology until very recently (Orton 2005:154).

3.1.2. Merits of GIS for Archaeologists. GIS have various capabilities that are useful to archaeology. One of the most important benefits from an archaeological application of GIS is that GIS alleviate a huge burden on making distribution maps and avoid a series of human errors in hand-made maps (Wheatley and Gillings 2002:18). Traditional manual mapping methods have never allowed efficient data addition, modification, and deletion. Quite unfortunately, there was no way but to sweep the slate clean. The introduction of GIS revolutionized this cumbersome and labor-intensive situation of data creation and management. Archaeologists need no longer modify their maps themselves. All they have to do is directly modify the data in thematic layers. The modification will automatically be reflected on the map of interest. In addition, the combination of data overlay can be changed very easily according to need. This was not allowed by the conventional manual

36

mapping either. Thus, the timesaving technology provides archaeologists with much more time to spend for analyses and interpretations. Further, the engagement in GIS-based mapping and data collection will help archaeologists heighten their spatial awareness during research efforts. Since data layers and archaeological information in them require being properly georeferenced and aligned with one another in the same coordinate system, GIS overlays will never allow for obscure descriptions of location for data layers. Archaeologists have to, manually or automatically, assign precise coordinates to each object or phenomenon of interest, which is enclosed by vector vertices or represented by a set of raster pixels. Obscure descriptions will simply result in the deterioration in quality of plotting and subsequent analysis. Thus, through the handling of properly georeferenced overlays composed of various data sources, archaeologists accordingly need to improve their conceptions and descriptions of locational information. Secondly, GIS virtually have no limit on scale and allow us to freely zoom in and out. This means that you can move back and forth to display, analyze, and print the maps that are different in scale from continental level down to single grid level of excavation unit. This quasi-scale-free data management structure is completely compatible with the multi-stage spatial conception of conventional settlement pattern study. However, since the spatial resolution of each map layer depends upon the scale at which the features were observed and plotted, archaeologists have to choose the most appropriate scale for the objects and phenomena of interest.

37

Thirdly, GIS are compatible with various data sources and external analytical software. Major data sources involve remotely sensed data (e.g., aerial photography and multispectral satellite imagery) and digital survey equipment (e.g., Total Station and GPS). You can also import traditional hardcopy maps by digitalizing them through the use of scanner and digitizer. In the past several years, the role of the internet has become very important. Various types of geographical and attribute data can be purchased or downloaded free from digital archives on the World Wide Web. Imported data are organized and processed for subsequent spatial analysis and decision making. Although GIS come equipped with standard analytical tools, those data can be transferred to external analytical software for more vital capabilities. The flexible compatibility of data integration in GIS further helps to cultivate sustained interests of archaeologists in multidisciplinary collaborations. As the role of GIS as a comprehensive archaeological database is further developed, much wider variety of data sources will be integrated into the spatio-temporal overlays of GIS. Aside from conventional base map components such as topography maps and remotely sensed data, they involve geophysical and geochemical data (e.g., magnetometry and Gas Chromatograph/Mass Spectrometry) and subsurface information sensed by Ground Penetrating Radar (GPR) (Campana and Francovich 2003; Kvamme 1999; Shimada et al. 2003). Cattani (2005:233) argues that “GIS can be considered as [a] revolutionary tool for archaeological research, not only because it can evaluate or multiply data, but because it press[es] archaeologist to look for more contextual data.”

38

3.1.3. GIS as an “Analytical Toolbox.” Many preceding studies have stressed the role of GIS as an “analytical toolbox,” not a mere cartographic tool for fancy maps or a data management system, considering this as one of the major benefits of archaeological application of GIS. Kvamme (1993) and Bailey and Gatrell (1995) further discuss that spatial data analysis represents much more than query and map overlay. This is certainly true in terms of the basic design of GIS, but at the same time somewhat misleading in a practical sense. A quick glance at the list of analytical functions available in GIS software will make one aware of some limitations. As Orton (2005:148) points out, GIS “frequently lack analytical capability, or are designed to answer the sorts of questions that archaeologists do not commonly ask.” Thus, we are strongly urged to enhance the analytical capability with assistance of external applications and/or to transform “plain vanilla” GIS packages into something very useful and powerful especially for archaeological analysis (Aldenderfer 2001). Many archaeologists will probably take the former approach unless they are very familiar with statistics, mathematics, and computer programming. In either case, as long as archaeologists dare to state that the true worth of GIS is in their spatial analytical capabilities, they have to make themselves familiar with the potentials and limitations of spatial statistics, visual exploration of spatial data (e.g., buffering, Thiessen polygons, and cost surface analysis), and locational modeling in the light of recent developments in each subfield. Orton (2005) discusses the implications of recent developments in spatial statistics, using as an example his point-pattern (vector) and continuous (raster) data analyses of

39

an early Maglemosian site in South Zealand, Denmark respectively by means of ADE-43 and Variowin4. Point-pattern analysis is a conventional approach that deals with the distributions of archaeological remains (e.g., artifacts and sites) and searches for patterns within the sampled distributions (Hodder 1977; Hodder and Orton 1976). Traditional techniques for this type of analysis could not deal well with the three important questions of (1) scale, (2) edge effects, and (3) quality and consistency of archaeological data (Orton 2005:148-149). These techniques examined the patterns over a wide range of scales, but a spatial pattern can demonstrate completely different characteristics at different scales. Therefore, the most reliable result can be gained when the pattern is examined at scale that it was observed. Traditional point-pattern analysis was also based on the assumption of a theoretically infinite study area, while archaeological data are intrinsically finite and have boundaries or edges that confine the study area. Further, the quality and internal consistency will not only affect the choice of technique, but will determine the suitability of the dataset for spatial analysis at all. Strictly speaking, spatial analytical techniques favor intra-site spatial data over inter-site or regional scale data, preferably collected by a single individual or organization over a relatively short period of time. The recent developments in spatial statistics helped to solve the first two of these three problems and have already been reflected in recently developed software such as ADE-4; however, the third is still with us. Archaeologists need to keep their eyes on this challenging issue when conducting any kind of point-pattern analysis. GIS-based graphical explorations of spatial data can solve some long lasting

40

problems in archaeological spatial analyses (Maschner 1996:9-10). As for site catchment analysis, there has been a strong criticism arguing that the concentric rings drawn around sites do little to represent the natural environment or actual human behavior. Thiessen polygons have also been criticized for the lack of control for natural features and measures of agricultural productivity, population size, political power, and time. This is primarily because they have been generated on the assumption that all points are contemporaneous and are of equal weight. These are idealized theoretical models that are intended to be modified using real-world data. Archaeologists treated them, however, as if they can be readily applied to the real world. These problems can be solved with the aid of cost surfaces generated from landscape data and other weighted measures. Thus, multiple techniques can be cooperatively assembled to complement the theoretical and methodological deficiencies of archaeology and to examine and solve more complicated problems.

3.2.

Major Analytical Methods of GIS Applications

As we have seen in the previous chapter, the concepts of space and landscape have gradually changed all along the history of archaeology. In the early 1980s when GIS were first introduced into archaeology, there were two opposing conceptions: the processualistic spatiality (space as non-problematic abstract backdrop and the image of landscape as a palimpsest of material traces), on one hand, and the postprocessualistic backlash against it, on the other. Similarly, GIS applications were also split broadly into

41

two separate directions. Some archaeologists who kept going on their old track began to pursue locational modeling on the basis of conventional settlement pattern study and regional-scale site databases (Westcott and Brandon [eds.] 2000). Those who were oriented to more postprocessualistic approaches, on the other hand, sought to reconstruct past environment for the ultimate purpose of reconstructing past landscape.

3.2.1. Predictive Modeling. Due to its substantive use in many research projects, predictive modeling has become a defining feature of GIS in archaeology, since it emerged in the 1980s. The purpose of this type of modeling is to predict the probability of the occurrence of an archaeological event in a given locus through statistical techniques such as discriminant analysis or logistic regression. For its cost-effective nature, predictive modeling was favored particularly by CRM research and land development programs in the United States. Through the modeling studies, researchers could address potential locations of archaeological sites without field surveys and thus more probably keep themselves away from a threat of destroying them. In other words, the modeling study assumed a vast unsurveyed area. Earlier predictive models came to be criticized because they relied heavily or exclusively on environmental variables; however, in the 1990s the modeling studies went beyond mere locational prediction based on topographic features and began to attribute the spatial distribution of archaeological remains to non-environmental variables. Allen (1996) argued that variability in the productivity of maize agriculture, which largely

42

depends on the duration of the growing season, was critical in the distribution of prehistoric Iroquoian political groups in New York State. In contrast, Hasenstab (1996), focusing on the same political groups but from a different perspective, argued that political conditions were often more important for the settlement distribution than the purely environmental constraints. These recent studies show that predictive modeling and GIS are compatible or complementary. It is likely that predictive modeling will become a more reliable tool for spatial analysis in GIS through some refinements, such as a thorough consideration of both environmental and cultural factors and their interaction. As opposed to its popularization in the United States, earlier modeling study was not appreciated in Europe. There was no huge unsurveyed area left in Europe, which was a primary requisite for conventional predictive modeling as a site location predictor. Consequently, European archaeologists could not find any value in GIS either. Nevertheless, since V. Gaffney and Z. Stančič demonstrated in the early 1990s the value of GIS in the interpretation of regional survey data (Gaffney and Stančič 1991, 1992; Lock and Stančič [eds.] 1995), a significant number of European archaeologists have begun to take serious interest in GIS as well (Wheatley and Gillings 2002:20).

3.2.2. Postprocessual Orientation. Maschner (1996:5-13) sketches out several spatial analyses available for GIS-based archaeology. Those involve cost surface analysis, viewshed analysis, optimum path analysis, site catchment analysis, boundary definition

43

analysis, and so on. The most attractive of these for archaeologists may be viewshed or line-of-sight (LOS) analysis. This 3D-GIS-based approach helps archaeologists examine the actual view of prehistoric people and explore their perception of landscapes, putting larger foci on social and cognitive characteristics of prehistoric human behavior. Wheatley (1996), for example, is concerned with how prehistoric people in Wessex, England perceived their landscapes and with the spatial scale of that perception. By adopting multiple viewsheds from specific points, Ruggles and Medyckyj-Scott (1996) attempt to examine the relationship between stone monuments and astronomical phenomena on the Isle of Mull, Scotland. Furthermore, Madry and Rakos (1996) employ optimum path analysis as well as viewshed analysis to examine the relationship between Celtic hillforts and roads in the Burgundy region of France. They argue a strong correlation of these roads with visibility from the hilltop defenses. Trivial problems of viewshed analysis include tree problem and height-of-viewer problem. These are essentially due to the lack of necessary data such as reconstruction of prehistoric environment and bioarchaeological inference of then-average stature. For their basic concept of space, GIS are inherently more suited for processualistic research issues. Thus, priority is now being placed on the search for more postprocessual applications beyond for viewshed analysis. In order for archaeologists to pursue the experiential phenomena of past people such as perception and experience, it is essential to make efforts to reconstruct the same set of material relationships in which the people found themselves in the past. Postprocessualistic approaches should not be thought of as

44

an antithesis to the preceding processualistic counterparts, but considered as logical outgrowth of the processualism. As a consequence, many multidisciplinary researches are now aimed at reconstructing paleoenvironment for the ultimate purpose of reconstructing past landscape.

3.3.

Future Prospects of GIS Applications

3.3.1. Post-Postprocessual Approach to Landscape. In response to the postprocessual duplicity of the concepts of space and landscape discussed in the previous chapter, some archaeologists who use GIS also began to ask questions such as: “How do we perceive our landscape through our minds?,” “How did the ancient people perceive their landscape through their ancient minds?,” and “How can we perceive the ancient landscape?” All these questions fall under the realm of perception, experience, and epistemology and thus cannot be solved solely by mean of algorithms or theoretical geography (e.g., conceptual models and spatial analysis). From the viewpoint of Batesonian cybernetic approach5 and related ecological schools of thought, Forte (2003) challenges these questions. He puts forward a new approach for reconstructing and interpreting the ancient landscape through Virtual Reality (VR). With their ultimate objective to create integrated cybernetic systems implemented in relation to the rules of neural networks and theories of artificial intelligence (AI), Forte and his colleagues are now developing specific software using C++6 and OpenGL7. In order to contextualize past human activities in the digital environment, they will not only

45

reconstruct ancient physiography in four dimensions with simulated meteorological conditions, but also integrate animal and human characters (or agents) performing specific activities, natural and sociocultural events such as disasters, land degradations, husbandry, farming, herbage, and rituals. Sensory properties (e.g., sound, smell, etc.) will also be “spatialized” and linked to specific spaces or dynamic events. They are expecting the systems to provide two kinds of learning: self-learning of the virtual agents in the digital ecosystem and self-virtual-learning of the user through the interactions with the agents and digital environment. If the creation of such gargantuan systems is indeed feasible, as Thomas (2001) argues, we may be able to enter into the reconstructed set of material relationships in which ancient people found themselves in their mindscape and to surrogate the behaviors of those people through the self-virtual-learning within the systems.

3.3.2. Macro-Regional Paradigm. With its full scalability, GIS are fully compatible with different levels of conventional spatial analyses: intra-structure, intra-site, and inter-site level. As Billman and Feinman (1999) acknowledge the feasibility of GIS application for their studies in the Americas, regional data management, analysis, and modeling based on regional full-coverage surveys will be a potential field of application. Billman (2005, personal communication) and other advocates of conventional settlement pattern surveys have actually begun to transfer into GIS geodatabases their paper-based regional databases accumulated in the past few decades. However, the level at which GIS

46

can be most effective would be a macro level beyond the inter-site level (e.g., inter-valley and coast-versus-highland levels in Andean regions). While archaeologists have already taken advantage of bird’s-eye view by means of aerial photography since Kosok’s first use for his systematic study of coastal irrigation systems and settlements in the 1940s (Kosok 1965), the integration of satellite imagery and existent regional site databases will offer much larger, macro-regional perspective. I suspect that macro-regional synthesis would be another future direction of GIS applications in archaeology. This future direction is in synchrony with the macro-regional paradigm in Mesoamerican archaeology proposed by Balkansky (in press). Balkansky critically points out that the region-centered views of current Mesoamerican archaeology are now confronted by extreme variations among interacting regions that defy their regional-scale explanations. He strongly urges his colleagues to consider the implications of their distribution maps stretching across multiple regions and covering a 3,000-year period from the earliest villages to the Spanish conquest (A.D. 1521). What is now needed is a new comprehensive model that explains the long-term evolutionary trajectories of the interacting regions. Before the introduction of GIS into archaeology, there was no useful technique that enabled us to deal with such a large amount of data at once. I envisage that GIS application will be a critical component for any future macro-regional studies. The volume of spatial data, that is remotely sensed as well as collected in the field, has been growing rapidly. In these days, it has been getting easier to gain access to regional-scale satellite imagery (e.g., Landsat, SPOT8, IKONOS9, etc.) and DEM (e.g.,

47

SRTM10) that can be valuable data sources for the construction of macro-regional site database. As a result, irrespective of different theoretical orientations noted above, new methods for the management and exploration of this swelling database have also been seen as not merely desirable, but essential. Farley and Gisiger (1996) stress the need for a nationwide database and analysis system both for the management of archaeological resources and for macro-regional analyses.

48 Notes

1 GRASS (Geographic Resources Analysis Support System) is a free open source GIS originally developed by the U.S. Army - Construction Engineering Research Laboratory (USA-CERL, 1982-1995), a branch of the US Army Corp of Engineers in Champaign, Illinois. See http://grass.itc.it/ for more information. 2 Some GIS software allows for representation of arcs as points connected by curves rather than straight lines (Longley et al. 2001:73). 3 ADE-4 is a statistical package for multivariate analysis and graphical display developed by Jean Thioulouse, Daniel Chessel, and Sylvain Dolédec in the University of Lyon, France (Thioulouse et al. 1997). Its ADS (Spatial Data Analysis) module composed of three specific programs provides GIS with an additional analytical power: Ripley (for univariate analysis), Intertype (for bivariate analysis), and ADSutil (for data manipulation). For more information, see http://pbil.univ-lyon1.fr/ADE-4/. 4 Variowin is another statistical package developed at the Institute of Mineralogy, University of Lausanne, Switzerland and downloadable free from http://www-sst. unil.ch/research/variowin/. This package enables users to build, in an easy way, a reliable model of spatial continuity which may then be used for geostatistical estimation or simulation. 5 Gregory Bateson developed one of the most powerful concepts to describe and explain the mechanism of learning from experience and organization of knowledge in our brain (Bateson 1972, 1979). He actively employed various ideas, across the disciplinary boundaries, from anthropology, psychotherapy, the socioogy of small group interacton, communication studies, education, general systems theory, developmental biology, and ecology. 6 C++ is an extended version of the previous C language, invented for object-oriented programming. As compared to C language, C++ is more suitable for the development of large-scale systems and easier to compile and debug. 7 OpenGL is a program library for generating two- and three-dimensional graphics, originally developed by Silicon Graphics Inc. It allows users to construct very complicated models and scenes by combining prepared functions. In this regard, OpenGL has been adopted in a wide variety of applications such as Computer Aided Design (CAD), simulation analysis, and the construction of Virtual Reality (VR). 8 SPOT (Système Pour l’Observation de la Terre) is a program to launch a series of earth observation satellites, originally inaugurated by French government and later backed up by Sweden and Belgium (Lillesand et al. 2004:439). 9 IKONOS is one of the high-resolution remote sensing systems launched and operative since 1999. The satellite collects data in four multispectral bands at a nominal ground resolution of 4 m, as well as 1-m-resolution panchromatic band (Lillesand et al. 2004:458-463). 10 SRTM is a joint project of the National Imagery and Mapping Agency (NIMA) and NASA to map the world in three dimensions. During a single Space Shuttle mission on February 11 to 22, 2000, SRTM collected single-pass radar interferometry data covering 119.51 million square km of the earth’s surface, including over 99.9 percent of the land area between 60°N and 56°S latitude. This represents approximately 80 percent of the total land surface worldwide and is home to nearly 95 percent of the world’s population (Lillesand et al. 2004:712).

49

CHAPTER 4: PACHACAMAC DIGITAL MAPPING

Before the introduction of GIS in the early 1980s, the only reliable tool for the presentation, analysis, and interpretation of archaeological spatial data was distribution maps on which the spatial dimension was plotted by hand. As I noted in the previous chapter, one of the most important benefits to be gained from GIS applications in archaeology is its ability to save time and eliminate human errors. GIS allow for major data addition, modification, and deletion in a manner that has never been possible with traditional manual mapping methods, and thus provide archaeologists with much more time to spend for analyses and interpretations. The time efficiency and succinctness of GIS-based digital mapping and data management are theoretically true, but in reality, making maps is a challenging task. Earlier studies (Allen et al. [eds.] 1990; Aldenderfer and Maschner [eds.] 1996; Forte and Williams [eds.] 2003; Gaffney and Stančič 1991; Maschner [ed.] 1996; Westcott and Brandon [eds.] 2000; Wheatley and Gillings 2002) highlighted the analytical capabilities of GIS, which is the true worth of this technology, and attempted to brush up on the analytical methods of conventional spatial archaeology introduced by pioneering works such as Hodder and Orton (1972) and Clarke (1977). None of them, however, discuss mapping procedures in details. As with the cases in other regions of the world, there are

50

no ready-to-use digital maps available for archaeological use in the Andes. Furthermore, constraints such as data scarcity and limited resources are widespread and complicate the issues. This and following chapters are concerned with digital map making of the archaeological site of Pachacamac on the central coast of Peru. The site description in this chapter will be followed by discussions about the potential of GIS-based mapping accompanied by the detailed procedural descriptions. It is important to keep in mind that Andean archaeology as a whole is constrained by limited funds, which in turn lead to a relative data scarcity and limited human and material resources. A significance of my site mapping is that it demonstrates the extent to which we can rely on GIS to create high-quality maps within the bounds of the existing resource limitations. The gap between the theoretical potential and constraints in reality needs to be fully appreciated before taking the first step in applying GIS in archaeology. The next chapter will discuss these constraints in detail. Creating a site map of Pachacamac and its surroundings is a part of the long-term Pachacamac Archaeological Project (hereafter PAP), directed by Izumi Shimada, Southern Illinois University at Carbondale. The first two seasons (2003-2004) aimed to elucidate the social foundations and natural context of the site on the basis of collaborative research by specialists representing different disciplines and nations.1 For this project, Shimada has a clear vision for data creation and storage in both digital and analog formats. Following this vision, I worked as his research assistant during the

51

spring of 2004 to create a digital map of the site and took part in his excavation in the subsequent summer to collect field data for the corrections and further refinements to the map. Rather than conventional location surveys, we employed GIS and remote sensing techniques in accordance with available resources. PAP purchased a set of aerial photographs that were taken in 1957 with an idea to digitize all the archaeological structures recognizable on the photographs displayed on the computer monitor. Due to the fact that the site was huge and some of the procedures of map production were very complicated, it substantively took me almost a year and half on a part-time basis to complete the map.

4.1. Settings

The archaeological site of Pachacamac is located ca. 30km southeast of the modern capital of Peru, Lima.2 The site is thought to have been one of the most powerful religious centers in pre-Hispanic Peru for over 1,000 years. The name of Pachacamac is a Quechuan compound word of pacha (‘Earth’) and camac (‘Maker’) given by the Inca to the central coast Pre-Incaic deity, Ychsma, on the Inca conquest of the region around A.D. 1,460. Based on ethnohistorical literature, the deity is known to have been dualistic with both creative and destructive powers (Rostworowski 1992:23). The site sits on a plateau on the north bank of the Lurín River and ca. 1 km inland from its mouth (Figure 4-1). The plateau looks onto the river mouth, the Pacific Ocean,

52

Figure 4-1. The vicinity of the archaeological site of Pachacamac (Scale = 1:39,000).

53

and a cluster of small islands offshore. As Shimada (2004:4) argues, this excellent view must have been critical in the selection of this location in relation to the religious beliefs among the ancient indigenous populations on the coast that “the Pacific was the source of all water of the entire world and that the offshore islands were the resting places of ancestral spirits and deities.” According to Límite de Zona Arqueológica on the topography maps created by Instituto Geográfico Nacional (IGN), Peru, the archaeological zone of Pachacamac occupies an area of 5.219 km2 (ca. 2.015 square miles), while the full extent of the site remains unknown and the zone is partially occupied by modern-day residential population of A. H. Julio C. Tello (Figure 4-1). Three massive roughly concentric walls segment the zone into four major sectors, I through IV extending from southeast to northwest (Shimada 2004). Sector I (0.168 km2) is an area in the shape of an irregular trapezoid enclosed by Uhle’s (1903) “Temple Enclosure” and is thought to have been the most sacred precinct of the site (Figure 4-2). The enclosure embraces three major monumental structures of different time periods surrounded by cemeteries. The Old Temple of Pachacamac or Templo Viejo, the oddly shaped mound with rounded protrusions, was built and maintained by the Lima or Pachacamac I culture during the late Early Intermediate Period through the Middle Horizon, ca. A.D. 500-850. It measures ca. 270 by 170 m. The Painted Temple or Templo Pintado (Uhle’s “Pachacamaj Temple”) is a polychrome platform mound with nine levels of terraces. It was built by the Pachacamac II culture of

54

the late Middle Horizon and early Late Intermediate Period, ca. A.D. 850-1000 and measures ca. 130 by 60 m. The Temple of the Sun or Templo del Sol is a five-tiered platform mound built by the Inca during the Late Horizon, A.D. 1460-1533. It measures ca. 220 by 160 m (Ray 1991; Shimada 1991:XXVIII). Additionally, it is argued that earlier Lima temples lay underneath the latter two temples, although the dates and nature of the earlier constructions remain uncertain (Paredes and Franco 1985; Patterson 1966:114; Shimada 2004:4-7). The second sector (II; 0.855 km2) is an area that surrounds the sacred precinct and is delimited on the north side by a major NE-SW wall, Uhle’s (1903:62) “Old City Wall” or “Inner City Wall” (Figure 4-2). Along with the first sector, Sector II composes the core area of the site. It is in this sector where most of the Pyramids with Ramps (except for V and VIII), the Pilgrims’ Plaza, and other miscellaneous structures of various sizes are located. Like the ancient city of Cuzco (Uhle 1903: 11), the sector is subdivided into four quarters by two major streets running SW-NE and SE-NW and crossing each other near the center of the site. The multi-level platforms known as the Pyramids with Ramps date to the Ychsma, or Pachacamac III culture (Late Intermediate Period, ca. A.D. 1100-1460), and many of them were built along these axes. As Shimada (2004:3) observes, “[t]oday we have a partial cultural history of the site that has been defined by ordering major, discrete events of temple construction and arrival of outside influences at the site.” Outside the Old City Wall stretches ca. 4.2 km2 area that is covered by sand and sloping up to the northwest. Aside from another massive wall to the northwest, there is

55 Sector IV

O

u

te

r

C

it

y

W

al

l

A. H. Julio C. Tello

Sector III

Ant

i gu

a a P

e nam

ri ca

na

Sur

In

n

er

C

it

y

W

al

l

Sector II

Laguna Urpi Wachak

Templo Pintado Templo Viejo 0

50

100

200 Meters

Sector I

rí

ra

u

te

P

an

L

re

am

io

ar

er

R

C

n

Templo del Sol

ic

an

a

S

u

r

Figure 4-2. The archaeological site of Pachacamac (Scale = 1:10,000).

56

very little construction exposed on the surface of the ground. This area is partitioned by what Uhle (1903) calls the “Outer City Wall” running SW-NE into Sectors III to the southeast and IV to the northwest. Sector III (ca. 1.979 km2) contains a barrio that is inferred to have been a provisional residential area for those who carried out purification of flesh and spirit prior to their entrance to the sacred precincts during the Ychsma and Inca periods. The date and function of the outermost sector (IV; ca. 2.217 km2), on the other hand, is yet to be defined (Shimada 2004; Paredes 1991:88). In my site mapping, I attempted to cover all these sectors above, but the primary focus was upon the first and second sectors given limited time and resources. Structures in Sector III, if any, are not exposed on the surface, and the aerial photographs that I used cover none of Sector IV. Furthermore, some of the structures were not recorded largely due to poor preservation, sand cover, and/or time restriction. They include: (1) the Pyramid with the Ramp XV (Eeckhout 2003:142) that is presumably located underneath the modern-day streets of Av. Santa Rosa and Antigua Carretera Panamericana Sur; (2) the sand-covered Lima temple of Urpay Wachak ca. 240 m southeast of the Urpay Wachak Lagoon; and (3) small “Adobitos Group” structures north of the site museum.

4.2. Objectives of My Digital Site Mapping

PAP has both short- and long-term objectives in creating a detailed, GIS-based digitized map of Pachacamac: creation of a better site map than Uhle’s (1903) and a GIS-based comprehensive site database that can integrate various corpi of data. During

57

the course of operations, I further attempted to demonstrate the extent to which we can rely on GIS and other peripheral techniques (especially remote sensing techniques) to create a high-quality map irrespective of data scarcity and resource limitations.

4.2.1. Short- and Long-Term Objectives. Max Uhle, known as the father of scientific archaeology in the Andes (Rowe 1954), created a high-quality, 1:2,000-scale site map of Pachacamac and attached it to his pioneering volume published in 1903 (Figure 4-3). His site map is still highly admired for its: (1) detailed, informative delineation of architectures of the “inner city” (Sectors I and II), (2) expression of vertical intervals of the buildings by the extent of shading (the higher shaded darker), (3) representation of irregular surface of buildings with uncertain structures by means of thin lines, and so forth. Since Uhle (1903) published his map, no new high-quality map of the entire site has been made for over 100 years in spite of numerous changes to the site ranging from the encroachment of modern settlements to backfilling of looted areas. Though various new plans and reconstruction drawings of excavated temples have been published individually since the late 19th century (Ravines n.d.:53)3, we do not have an accurate, updated site map. Before Uhle, Andrés Baleato illustrated the site in 1793 (Ravines n.d.:67) and Adolph Bandelier prepared a ground plan of the site in 1892; however, it is obvious that these earlier maps could not match that of Uhle in regard to the accuracy and the amount of details when these maps are compared (Figure 4-4; Shimada 1991:XV,

58

Figure 4-3. Max Uhle’s site map of Pachacamac, 1903.

Figure 4-4. Adolph Bandelier’s ground plan of Pachacamac, 1892 (Taken from Shimada 1991:XV, XIX, plate 1 and deliberately inverted for comparison with Figure 4-3 above).

59

XIX, plate 1). Maps found in recent publications (e.g., Figs. 3 and 6 in Franco 1998) consist of tracings of airphoto enlargements imposed over the existing topography maps and lack details. A site map produced probably in February 20024 and sold at the site museum is very informative with rough outlines of architectures, Cartesian coordinates, and contour lines (Figure 4-5). Nonetheless, because of its coarseness and small scale, the map is not fit for the professional use of archaeology. Thus, Uhle’s site map has been the only one that covers a sizable area involving main sectors (I and II) of the site and of an adequate precision and accuracy for research use. Uhle’s map, however, is not high quality enough to be integrated into properly georeferenced GIS map overlays. My site mapping was aimed at creating a better map than Uhle’s in terms of its accuracy and information density. In the long run, our site mapping also aims to build a GIS-based comprehensive site database. GIS overlay and associated geodatabase enable us to accumulate various corpi of data ranging from environmental variation to changes in site layout over time. Much of the pertinent information will be generated by the ongoing multidisciplinary research of the PAP. On a parallel with intensive excavations and my digital site mapping, PAP is conducting diatom, pollen, lithological, and macrobotanical analyses of sediment core taken from the Urpay Wachak Lagoon and the inferred pukio at the east base of the Urpay Wachak temple in 2003 and 2004 (Winsborough et al. 2005). These studies have begun to produce for the first time local paleoenvironmental data. Subsurface information was also collected by GPR. The extensive GPR survey that covered as much as ca. 106,000 m2 was

60

Figure 4-5. A site map sold at the site museum produced probably in February 2002.

61

aimed at defining the location and extent of residential areas of different time periods in combination with intensive surface survey of the entire site (Shimada et al. 2003). Our site database will allow not only efficient integration but also dissemination of the results of all previous and future fieldwork. The latest version of our site map is already open to the general public and always downloadable in PDF format from the official website of PAP.5 Although traditional paper maps are, so to speak, a “stand-alone” data source and cannot be readily shared with those who are interested, the data dissemination in digital format on the WWW may offer a solution to this inconvenient situation.

4.2.2. Struggles against Tight Budgets. The discussions that will follow in the next chapter are based on the current situation of Andean archaeology that is typified by data scarcity and limited resources due to tight budget. These two limitations always go hand-in-hand. Given ample funds, for instance, we could have surveyed the whole area of the site using an auto tracking pulse non-prism total station, or adopted the most accurate digital aerial photographs taken at a favorable altitude by chartering most-advanced airborne camera systems with LIDAR system.6 However, in reality, this is not the case. It is always essential for archaeologists to choose the most efficient methodologies to satisfy their demands within the bounds of available resources. With an optimistic assumption that we could blend productively the conventional and cutting-edge technologies, we chose to begin with a combination of topography maps and

62

old film aerial photographs. At the launching of the project, we could not obtain high-resolution remote sensing and other geographical data easily or for free in Peru as we can do in the United States. Original resources in our hands, furthermore, were not as complete as we hoped. For instance, camera calibration reports usually attached to aerial photographs at the time of purchase were not available. A replacement report does not contain essential information required when removing geometric distortion inherent in photographs. Since there is basically no ready-to-use procedure manual to solve this type of problem, resource limitations in particular required me to be inventive to complete my final map. Therefore, another important objective of my site mapping was to illustrate the extent and manner in which we could rely on GIS and other peripheral techniques to create high-quality map in spite of data scarcity and resource limitations. Without in-depth consideration of the above limitations and other methodological issues of mapping procedures at the time of research design, particularly when dealing with such huge sites as Pachacamac, a mapping project is likely to fail. As a matter of fact, there have been a few abortive mapping projects, digital or analog, that seem to have been inaugurated with aims similar to ours. The difference in feasibility between these abortive projects and PAP will be clear in the discussions in the following chapter. In order for Andean or any archaeologists not to waste time and funds, but to learn from others’ trial and error, it is worth focusing on the methodological aspects of GIS-based map making and presenting the whole procedures in details for future benefit.

63

In the process of map production, I utilized a series of hardware and software. Although many of them are quite expensive and thus unaffordable for personal use, I have kept it in mind that I use products that have larger market share so that interested users may be able to gain easier access to them and to replicate or consult my work more easily. The hardwares and softwares that I used are listed in Appendix A (“HARDWARES AND SOFTWARES”). Needless to say, you may want to substitute other products for those I listed, which will be feasible as well.

64 Notes For more information about the project, see its official website at http://www. pachacamac.net. The research issues and aims are clearly stated there. 2 30 kilometers is an approximate distance from the Plaza de Armas or the principal plaza of Lima. This figure will decrease as the capital is rapidly expanding and encroaching on the site. 3 These include Squier’s (1877) plans of the Temple of the Sun and the Convent of Mamacona, Ray’s (1991) reconstructed model of the Temple of the Sun, Eeckhout’s (2003) plans of the Pyramids with Ramps, and so on. 4 The only manufacturing information that we can identify on the map is “LAG/PPB – FEB.2002.” It is likely to stand for the producer and production date. 5 See http://www.pachacamac.net/archive.html 6 A LIDAR (Light Detection and Ranging) system is a technique to collect elevation information of the earth’s surface by means of a powerful laser sensor, a GPS receiver, and an INS (Inertial Navigation System) unit (Leica Geosystems GIS & Mapping Division 2002b:68). 1

65

CHAPTER 5: PROCEDURES

My digital mapping consists of three broad phases: (1) prototype map preparation based on the resources available prior to the fieldwork in the summer of 2004; (2) ground-truth checking of the archaeological structures and ground control point (GCP) measurements by means of RTK Differential GPS of the highest accuracy and; and (3) data post-processing and consummation of the map. I will discuss these three phases in order in the following sections and provide step-by-step guides for some specific procedures in Appendix B (“PROCEDURES MANUAL”).

5.1. Phase I: Prototype Map Creation

The resources available in my hands as of the spring of 2004 were four sheets of topography maps published in 1992 by Instituto Geográfico Nacional (hereafter IGN), Peru (1:5,000 scale map with 500-by-500 m quadrangles; 30-K, 30-L, 31-K, and 31-L; shown in Figures 5-1, 5-2, 5-3, and 5-4 respectively) and scanned images of two aerial photographs (51.295% overlap in coverage) taken in 1957 by Servicio Aerofotográfico Nacional (hereafter SAN), Peru (PROYECTO 6512-57-5, 262 and 649; shown in Figures 5-5 and 5-6 respectively). These topography maps were the most detailed and recent ones

66

Figure 5-1. 1:5000 scale topography map created in 1992 by Instituto Geográfico Nacional, Peru (30-k, A.H. JULIO C. TELLO).

67

Figure 5-2. 1:5000 scale topography map created in 1992 by Instituto Geográfico Nacional, Peru (30-l, A.H. PAMPA GRANDE).

68

Figure 5-3. 1:5000 scale topography map created in 1992 by Instituto Geográfico Nacional, Peru (31-k, RUINAS DE PACHACAMAC).

69

Figure 5-4. 1:5000 scale topography map created in 1992 by Instituto Geográfico Nacional, Peru (31-l, LURÍN).

70

Figure 5-5. Aerial photograph taken on March 12th 1957 by Servicio Aerofotográfico Nacional, Peru (Proyecto 6512-57-5, 626).

71

Figure 5-6. Aerial photograph taken on March 12th 1957 by Servicio Aerofotográfico Nacional, Peru (Proyecto 6512-57-5, 649).

72

Topography Maps

INTERNET

Hardcopy

Topography Maps

Scanning

Hardcopy Resource Digital Elevation Model

Scanned

Topography Maps

Downloading

Assigning Coordinates (UTM18S)

DEM (WGS84)

Georeferenced Topography Maps

Redefining Projection

Clipping and Merging

Reprojected DEM

Single

Collecting Z Coordinate

Topography Map

Defining Projection

Projected Topography Map (PSAD56/UTM18S)

(PSAD56/UTM18S)

GCPs

Collecting X-Y Coordinates

Contour Lines Shapefile

Softcopy Products Process

Aerial Photographs

camera calibration certificate (incomplete)

Aerial Photographs

Camera Report

Scanning

Scanned

Aerial Photographs

Orthorectifying

Orthorectified Aerial Photographs (PSAD56/UTM18S)

Digitizing

Elevation Points Shapefile

Figure 5-7. The work flow of prototype map creation.

Archaeological Structures Shapefile

73

for the study area available to the public. Since they were the only resource for which I could assume both vertical and horizontal accuracy, I adopted them as the basis of accuracy check. The very first step of Phase I, prototype map production, was to scan the topography maps and to plug them into ESRI’s GIS software, ArcGIS 8.3 as one of the base map layers. The work flow of Phase I is summarized in Figure 5-7. Here care should be taken in using the term “base map.” As already mentioned in the previous chapter, GIS store various types of datasets in different layers and enable us to change the combination of superposition according to need. The term “base map” that I use here is not equivalent to a conventional paper-based map but stands for an ensemble consisting of multiple layers of the same spatial reference information and functioning as a starting point for further data manipulation and analysis. These layers would involve primary datasets such as topography maps, DEM, aerial photographs, satellite imagery, and other secondary layers made out of the primary layers. Likewise, for my mapping, with the topography map layer set up as a standard, other data layers will be assigned the same spatial reference information and superimposed upon one another. Secondary layers such as archaeological structures and contour lines are also created through the digitizing process. Thus, the final maps attached at the back of the thesis are the printed version of certain combinations of different layers of the base map.

5.1.1. Preparation of Topography Map. When digitalizing the hardcopy topography maps, I utilized a GRAPHTEC CS2000 large scanner and saved the scanned images as

74

discrete TIFF files (TIFF GROUP IV compression technology; 15,744 x 12,598 pixels).1 At this point, the four quadrangles are not maps in a real-world coordinate system, but merely graphic images in image space. In other words, in order to be used as maps, they need to be given locational information and transformed from a non-real-world coordinate system (image space) to a real-world coordinate system by means of geometric transformation methods. The process of converting a dataset from one grid system to another by assigning locational information is referred to as “georeferencing.” Georeferencing is usually understood to be a part of a rectification process by which raw data representing the irregular surface of the earth are projected onto a flat mapping space and made to conform to a map projection system (Leica Geosystems GIS & Mapping Division 2002a: 26, 326). In this case, however, since our scanned topography maps had already been projected onto a plane using a specific ellipsoid model or datum and georeferenced by a specific plane coordinate system, simple processes of (1) assigning the Cartesian X-Y coordinates and (2) defining projection will turn them into maps. The former can be performed by the resampling program (labeled as “rectify”) launched from the Georeferencing toolbar of ArcMap, one of the ArcGIS applications for map production and analysis. Using some reference points with known X-Y coordinates called “control points,” the program transforms the input image (a topography map) into a new raster dataset by calculating the pixel values to fill in the output image matrix from the original image matrix (Lillesand et al. 2004:497). Since the transformation is based on the coordinates given by the control points, it is to be understood that the same plane

75

coordinate system are retained in the output image. For each of our topography maps, a total of 49 sets of X-Y coordinates were assigned to the points at which the vertical and horizontal grid lines cross each other so that the exact coordinates for each point is determinable (Figure 5-8). A step-by-step guide for georeferencing is given in Appendix B.1 (“How to georeference a scanned image”). When you perform geometric transformation for georeferencing using ArcMap Georeferencing toolbar, you will be prompted to choose one of the three resampling methods: (1) nearest neighbor assignment, (2) bilinear interpolation, and (3) cubic convolution. Each method has both merits and demerits. The selection of the method depends primarily on the data type of the input image, preference for smooth appearance in the output image, and acknowledgement of alteration of the original input pixel values (ArcGIS Desktop Help; Lillesand et al. 2004:497-499; Lo and Yeung 2002:145-146). Nearest neighbor assignment offers the advantage of computational simplicity and avoids changing the original pixel values of the input image. In this regard, this method is best suitable for categorical data (nominal- or ordinal-scale attributes), where each pixel value represents a class, member, or classification (e.g., land-use and soil maps). However, it concurrently presents the disadvantage that the edges of features in the output image may have a jagged, stepped appearance. On the other hand, bilinear interpolation and cubic convolution provide a smoother finish in the output image. This is because these more sophisticated methods determine an output pixel value by evaluating the values of several pixels surrounding its corresponding pixel in the input image. The new output

76

Figure 5-8. Examples of the points where GCPs were located. Coordinates were assigned to the points at which the vertical and horizontal grids are intersected.

77

pixel value is a weighted average of the values of those surrounding pixels, adjusted to account for their distance from the center of the output pixel (Figure 5-9). The difference between the two methods is that the former determines the four closest pixels whereas the latter considers the block of 16 pixels so that the latter provides a slightly sharper image than the former. Although both of them inevitably alter the original pixel values of the input image, it will not be any problem unless you use them for categorical data. All three methods can be applied to continuous data, with nearest neighbor producing a blocky output, bilinear interpolation producing smoother finish, and cubic convolution the sharpest. I adopted the nearest neighbor method primarily because of its computational simplicity. The jagged edges of the output image are hardly recognizable and do not make any difference. Georeferencing by means of ArcGIS is quite simple and convenient in terms of its intuitive GUI navigation; however, the resampling program has nothing to do with reference information. It only assigns X-Y coordinate values to the pixels of a simple graphic image regardless of the coordinate system to which the assigned values belong. For instance, when you add the image georeferenced above to the Data Frame of ArcMap, you will encounter a warning message: ”One or more layers is missing spatial reference information. Data from those layers cannot be projected.” Therefore, in order to use it as a map, there is one more process to be completed, that is, definition of spatial reference information. Before doing it, however, another supplementary process needs to be implemented here.

78

Figure 5-9. Methods of resampling (Taken from Lo and Yeung 2002:146).

79

Now that each of the four raster images of topography maps is appropriately georeferenced, it is time to combine them together into one piece. Since the images have some margins on every side of the gridded map portion (Figure 5-8), only the quadrangles have to be clipped before combined. In so doing, the Raster Calculator of the Spatial Analyst extension program of ArcGIS is very helpful. You can perform mathematical calculations using operators and functions, execute selection queries, and type in Map Algebra syntax.2 The work flow goes as follows: (1) clipping out the quadrangles from the raster images by defining “analysis extent” within the images in the “Options”; (2) combining the clipped quadrangles into one piece by Map Algebra’s “merge” function; and (3) outputting the merged image into a new GRID (raster) dataset. Appendix B.2 (“How to clip and combine together parts of raster images”) describes in details each of these steps. In order to assign the spatial reference information to the combined topography map layer, I utilized ArcToolbox, another ArcGIS application for data conversion and map projection. Although I do not go further into the details about map projection here, the process of defining projection is very straightforward as long as you know the ellipsoid model or datum and plane coordinate system by which your data source is projected. The plane coordinate system used for our topography maps is explicitly stated on the maps. IGN, the producer of the original topography maps, used the UTM or the Universal Transverse Mercator (“SISTEMA DE CUADRILLADO: UTM CADA 500 METROS”). A UTM zone map at NASA website indicates that the central coast of Peru is located in Zone 18 South (UTM18S) (Jet Propulsion Laboratory 2000). However, these IGN maps do not

80

provide information on the datum and ellipsoid model used for their production. We asked the IGN for the information with a partial success. IGN seems to have used the Provisional South American Datum 1956 (PSAD56). The problem here is that even the technical staffs at the IGN were not necessarily familiar with the spatial reference information of their own maps. Nonetheless, the definition of reference system is feasible with the information that I obtained. The procedures are described in detail in Appendix B.3 (“How to define projection and plane coordinate system”). Once georeferencing and assignment of spatial reference information are successfully undertaken, our topography map raster dataset can be eventually used as a map. From this point on, every single layer to be superimposed over this map layer also needs to be projected by the combination of PSAD56 and UTM18S by which the original topography maps were projected.

5.1.2. Orthorectification of Aerial Photographs. The aerial photographs 626 and 649 based on which we planned to digitize the archaeological structures were taken in March 12, 1957 by SAN (PROYECTO 6512-57-5). Each of them covers the area of ca. 5.52 km² (ca. 2.35 x 2.35 km). Overlapping each other on the west side of the site of Pachacamac, photographs 626 and 649 show great details of archaeological structures (Figures 5-5 and 5-6). Although SAN confidently claims that they should be a stereopair, they are actually not. The orientation of the images and states of exposure (e.g., length and orientation of shadows) led me to conclude that these two photographs were most likely

81

taken from different sides (626 from the west and 649 from the east)3 and at different times (Figure 5-10). A stereoscopic parallax requires the photographs to be acquired at exposure stations from one or two flight lines at the same altitude on the same side of the terrain feature usually with 60 percent overlap between them (Lillesand et al. 2004:658; Jensen 2000:152; Figure 5-11a and b). The overlap between 626 and 649 is apparently less than 60 percent.4 In order to be plugged into an ArcMap data layer along with other maps (e.g., topography map that I prepared above), the photographs are required to be transformed into planimetrically true orthoimages. Whereas any points on a map are located in their true planimetric positions, points on a photograph taken over varying terrain are displaced from their true map positions. This is attributed simply to the fact that the former is a scaled orthographic projection, while the latter yields a perspective projection (Lillesand et al. 2004:141). Figure 5-12 intelligibly illustrates an example of the displacement in a photograph caused by the perspective projection. Note the differences of two images in size, shape, and location of the two trees. In addition to the terrain relief, aerial photographs of perspective projection suffer from various systematic and nonsystematic errors such as camera orientations, earth curvature, film distortion, and measurement errors (Leica Geosystems GIS & Mapping Division 2002b:3). Furthermore, during the manual scanning processes of the original hardcopy photographs 626 and 649, the scanned images were slightly but erroneously rotated. The transformation from a perspective projection (photo) to a scaled orthographic

82

Figure 5-10. Aerial photographs 626 and 649 seem to have been taken at different times and thus do not compose a stereopair. Note the differences in length and orientation of shadows.

83

Figure 5-11. Internal geometry of aerial photographs: (a) single photographs 626 and 649; (b) a stereopair with 60percent overlap; (c) a fiducial mark; and (d) a 3D representation (Taken from Jensen 2000:142-143, Figure 6-5 and 6-6, and partially modified).

84

Figure 5-12. Comparative geometry of (a) a map and (b) a vertical aerial photograph. Note the differences in size, shape, and location of the two trees (Taken from Lillesand et al. 2004:142, Figure 3-8, and partially modified).

Figure 5-13. File and image coordinate systems (Taken from Leica Geosystems GIS & Mapping Division 2002b:29, Figure 3-13, and partially modified).

85

projection (map) in combination with the correction processes of these geometric errors was performed using the softcopy (versus hardcopy) photogrammetry technique called “triangulation” or “orthorectification.” The software that I used for this purpose is IMAGINE OrthoBASE Pro, one of the vital add-on modules contained in Leica Geosystems’ ERDAS IMAGINE 8.6. It is a “comprehensive digital photogrammetry package that allows for the fast and accurate triangulation and orthorectification of images collected from various types of cameras and satellite sensors” (Leica Geosystems GIS & Mapping Division 2002b:3). As opposed to the conventional geometric correction techniques such as polynomial transformation, which are based on general functions not directly related to the specific distortion or error sources, photogrammetry techniques eliminate geometric errors more efficiently and consequently provide more reliable orthoimages. The techniques are unique in that they (1) consider the image-forming geometry to determine the interior misalignment of and exterior angular tilt of photograph at the time of exposure, (2) deal with multiple photographs bundling them as a block and take into account the relationships between overlapping images to minimize and distribute errors within the entire block of images, and (3) explicitly cope with elevation information to eliminate the spatial displacement due to terrain relief (Leica Geosystems GIS & Mapping Division 2002b:18-19). IMAGINE OrthoBASE Pro employs two different types of error correction techniques: single frame orthorectification and block triangulation.5 In either case, you will be first prompted to input several parameters, two groups of which represent respectively interior

86

and exterior orientations of photograph(s). Interior orientation parameters, for instance, involve the locations (X-Y coordinates) of fiducial marks and principal point. Fiducial marks are the eight points located in the four corners and in the centers of the four sides of the focal plane6, or sometimes four points only in the four corners (Figure 5-11). Principal point is mathematically defined as the point at which the lines between cater-cornered fiducials cross each other or as the intersection of the perpendicular line through the perspective center (optical axis7) and the focal plane (Jensen 2000:140). These two sets of parameters are used: (1) to establish an alignment between image and file coordinate systems, (2) to straighten the photo image(s) rotated during the scanning procedure (Figure 5-13), and (3) to approximate and correct radial (or symmetric) lens distortion (Leica Geosystems GIS & Mapping Division 2002b:29-31). Aerial photographs are divided broadly into two types: vertical and oblique. When the former, to which our photographs belong, is taken from a high vantage point in the air, its optical axis is supposed to be nearly vertical, but the focal plane will be tilted to some extent because of the exterior orientation of exposure station. As Figure 5-14a illustrates, exterior orientation of a photograph comes into sight as an angular tilt of the focal plane. This tilt is defined by six parameters that consist of the camera position ( X L , YL , Z L ) and the three rotation angles ( ω ,φ , κ ), based on a mathematical principal of analytical photogrammetry, “collinearity condition” (Leica Geosystems GIS & Mapping Division 2002b:38-40; Lillesand et al. 2004:181-182). Collinearity is a condition in which the camera lens ( L ), any object point in the ground coordinate system ( P ; X P , YP , Z P ), and

87

Figure 5-14. (a) Collinearity condition and (b) space intersection (Taken from Lillesand et al. 2004:180, Figure 3-.32 and 3.33, and partially modified).

88

its photographic image (

p ; xP , yP ) all lie on a straight line ( LP ). Collinearity condition

holds irrespective of the angular tilt of focal plane and thus enables you to quantify the relationships among image coordinates, ground coordinates, camera position, and angular orientation of photograph by means of the two equations called “collinearity equations” (Lillesand et al. 2004:180-181):

⎡ m ( X − X L ) + m12 (YP − YL ) + m13 (Z P − Z L ) ⎤ x p = − f ⎢ 11 P ⎥ ⎣ m31 ( X P − X L ) + m32 (YP − YL ) + m33 (Z P − Z L ) ⎦

(5.1)

⎡ m ( X − X L ) + m22 (YP − YL ) + m23 (Z P − Z L ) ⎤ y p = − f ⎢ 21 P ⎥ ⎣ m31 ( X P − X L ) + m32 (YP − YL ) + m33 (Z P − Z L ) ⎦

(5.2)

where

xP , yP = f =

image coordinates of any point

p

focal length

X P , YP , Z P = ground coordinates of point P X L , YL , Z L = ground coordinates of exposure station L (perspective center) m11 ,..., m33 =

coefficients of a 3 × 3 rotation matrix defined by the angles

ω, φ , and κ that transforms the ground coordinate system to the image coordinate system

It seems relatively rare for these six parameters of exterior orientation to be highly accurate or even to be known for each photograph unless they are measured by an airborne camera system equipped with Differential GPS and IMU (Inertial Measurement Unit) system. Even though the parameters are unknown, they can be calculated from the known X, Y, Z coordinates of a minimum of three GCPs based upon the collinearity condition, where the light rays originating from those GCPs ( P ; X P , YP , Z P ) intersect

89

through the image positions of the GCPs (

p ; xP , yP ) and resect at the perspective center

of the camera ( L ). A statistic technique called least squares adjustment will locate the most probable positions of the image points ( xP , yP ) along with the GCPs ( X P , YP , Z P ) on the same straight lines. This process is one of the major applications of the forementioned equations and is called “space resection.” The block triangulation technique distinguishes itself from single frame orthorectification in that it is capable of simultaneously processing multiple images bundled as a block8 and makes use of another important application of the collinearity equations, “space intersection” or “space forward intersection.” Space intersection, by definition, is “a procedure by which the X, Y, and Z coordinates of any point in the overlap of a stereopair of tilted photographs can be determined” (Lillesand et al. 2004:183) Figure 5-14b illustrates the collinearity conditions associated with a stereopair of photographs. As opposed to single frame orthorectification based solely on space resection, the block triangulation technique implements a simultaneous application of space resection and space intersection of all photographs in a block at once. This method, called “bundle block adjustment,” demonstrates the error minimization and distribution for the entire block and thus maintains a good alignment between adjacent images throughout the assembled mosaic (Leica Geosystems GIS & Mapping Division 2002b:20; Lillesand et al. 2004:183). Furthermore, a substantive quality and quantity of GCPs allows for the creation of a Digital Terrain Model (DTM), a three-dimensional digital representation of the earth’s surface or topography, for the overlap of a stereopair of aerial photographs.

90

Figure 5-15. The WRS Path/Row scene boundaries of DEM mosaic at the periphery of Pachacamac. Each orbit of the space shuttle within a cycle is designated as a path. Along this path, the individual sensor frame center is designated as a row (Lillesand et al. 2004:417).

91

In its most robust form, geometric correction simply resamples the image(s) after the application of collinearity equations and does not eliminate the spatial discrepancies caused by terrain relief. In order to properly remove those discrepancies, elevation information such as DTM, DEM, and TIN (Triangulated Irregular Networks) is required to be integrated during the correction process. Once this is done, the block triangulation technique fulfills the three forementioned characteristics of softcopy photogrammetry technique. The elevation dataset that I used for my mapping is a DEM of 90-by-90 m spatial resolution derived from SRTM (Shuttle Radar Topography Mission) interferometric radar data available online for free at the website of Global Land Cover Facility, Institute for Advanced Computer Studies, University of Maryland, College Park (Global Land Cover Facility 2004; Figure 5-15). Each scene is located according to the Worldwide Reference System (WRS). The archaeological site of Pachacamac is located on the overlap between P008R68, P007R068, and P007R069. A small portion near the upper edge of P007R069 was clipped. Data preparation processes of the downloaded DEM are described in Appendix B.4 (“How to reproject a rater image”) and B.5 (“How to clip a subset from raster image”). Compared to simpler single frame orthorectification, it is somewhat hard to adopt block triangulation because it assumes that the exterior orientation parameters are known and a pair of photographs composes a stereopair. However, space resection can calculate the angular tilt of photographs from the known GCP coordinates, and you can actually use as a stereopair a set of two overlapping photographs acquired at

92

approximately the same altitude, even though they do not fulfill all the conditions of a stereopair. Hence, as much as possible, it would be reasonable to choose the block triangulation technique for its higher accuracy. Based on the discussions above, the work flow of block triangulation is summarized as: (1) input of camera orientation parameters, (2) GCP measurements, (3) calculation of angular tilts of photographs from the know GCP coordinates by means of the collinearity equations, (4) update of exterior orientation parameters, (4) triangulation based on bundle block adjustment, and (5) resampling and correction of terrain relief with reference to elevation information. The step-by-step procedures of geometric corrections of aerial photographs are given in Appendix B.6 (“How to perform geometric corrections of aerial photographs (Phase I)”). Our use of aerial photographs 626 and 649, a pair of pseudo-stereographic images proved to be very problematic for a couple of reasons. First, regular camera calibration certificate was not available. A document in lieu of calibration report, Informe Técnico Fotográfico (Figure 5-16), which was provided by SAN, contained only: (1) project number (PROYECTO; 6512-57-5), (2) picture numbers (FOTOS; 626-649), (3) date of shooting (FECHA DE TOMA; March 12, 1957), (4) scale (ESCALA DE TOMA; 1:10,000 ft), (5) airplane altitude (ESCALA DE VUELO; ca. 50 m ASL), and (6) focal length (DISTANCIA FOCAL; 152.67 mm). It was lacking both interior and exterior orientation parameters such as principal point, fiducial marks, and rotation angles (Figure 5-11a and 5-14a). Interior orientation parameters cannot be calculated from the known coordinates of GCPs as the rotation angles are worked out. Without the film coordinates of the principal point

93

Figure 5-16. The only document available for the orthorectification process in lieu of regular camera calibration reports (INFORME TECNICO FOTOGRAFICO). It contains only: (1) project number (PROYECTO; 6512-57-5), (2) picture numbers (FOTOS; 626-649), (3) date of shooting (FECHA DE TOMA; March 12, 1957), (4) scale (ESCALA DE TOMA; 1:10,000), (5) airplane altitude (ESCALA DE VUELO; ca. 50 m ASL), and (6) focal length (DISTANCIA FOCAL; 152.67 mm). It lacks critical information for geometric corrections of photographs such as principal points, fiducial points, and interior and exterior orientations.

94

of each photograph, I had no choice but to define them as (0, 0) assuming that the optical axis was perfectly perpendicular to the focal plane and thus the principal point was placed completely in the center of the photo image with absolutely no displacement. The film coordinates of fiducial marks were also required to be replaced with such theoretical values. SAN’s website (n.d.) states that they have been using large-format (9-inch or 228-mm) Wild-type film cameras (as opposed to Zeiss-type) such as Leica RC30, 20, 10a, and 10. According to FAQ of ESRI Japan’s website (ESRI Japan 2002), moreover, approximate values of 106 and -106 can be used for the film coordinates of fiducial marks in the photograph taken by 228-mm Wild-type camera. Although it goes without saying that I must be prepared to face substantial margins of error as long as I use these theoretical values for interior orientation parameters during the error correction processes, I had no choice. Furthermore, a closer look at its content led me to conclude that the document from SAN is not only incomplete but also is quite dubious in regard to accuracy. The altitude of 50 m, for instance, is apparently hard to accept if you take into account that each photograph covers the ground area of ca. 2,350 x 2,350 m. Supposed that the scale (1:10,000 ft) and focal length (152.67 mm) stated in the document are true, the altitude should be:

H'=

f 152.67 mm 0.15267 m = 465.3382m = = S ⎛ ⎞ ⎛ 1 ⎞ 1 ⎟ ⎟⎟ ⎜ ⎜⎜ ⎝ 10,000 ft ⎠ ⎝ 3,048m ⎠

(5.3)

95

The content of the document is internally inconsistent. This problem may be solved by consulting the data strip shown on the margin of the photograph 649 (Figure 5-17). The data strip confirms that the focal length is 152.67 mm as stated in the document from SAN but concurrently indicates that the altitude at the time of exposure was 1,600 m rather than either 50 or 465 m. This value sounds quite acceptable for the forementioned ground area. According to the information of the data strip, the scale will be as follows:

S=

f 0.15267 m 1 or 1 : 10,480 = = H' 1,600 m 10,480

(5.4)

It would be safe to say that I should trust the data strip rather than the dubious document provided by SAN. Secondly, prior to the fieldwork, I had no coordinate information (points with X, Y, and Z values) such as GPS readings that I could use as GCPs. The only available document obtained from IGN (PUNTO ESTABLECIDO CONTROL SUPLEMENTARIO DE TERCER ORDEN, Figure 5-18) indicates that somewhere NW of the inferred superstructure on the SE portion of the highest rung of the Temple of the Sun is a firmly established datum point. The datum cannot be recognized on either one of our aerial photographs; therefore, it is useless for the purpose of GCP measurement.9 One of the alternative ways to obtain GCP coordinates is to utilize the georeferenced topography map and DEM. On the topography map layer displayed in ArcMap, using the Identify tool, you can collect X-Y coordinates by clicking the spots at which you would like to locate your

96

Figure 5-17. Data Strip of photograph 649 that consists of level bubble, clock, altimeter, and focal length. Note that the bubble is way beyond the central circle and the altitude is 1.6 kilometers.

Figure 5-18. Coordinate information of the datum located allegedly on the top of the Temple of the Sun, provided by Instituto Geográfico Nacional, Peru.

97

GCPs (Figure 5-19). I took down several pairs of the coordinates shown in the Identify Results and used them later to define the same spots on the photographs as GCPs. Here, the points to be defined on the topography map should correspond to the same spots recognizable on either of our two aerial photographs and concurrently to be evenly scattered within the site area of the photographs (Figure 5-20). The GCPs that I defined are listed in Table 5-1. In the same manner in which X-Y coordinates were collected, Z coordinate (elevation) was also collected from DEM. GCP #1 and 2, for instance, were collected respectively from one of the intersecting points at which two streets of Antigua Panamericana Sur and Av. Lima cross each other, and from Puente Lurín (the bridge of the Lurín River). Since these modern-day structures showed me a discrete and solid appearance, it was relatively easy to define the spots. Other GCPs, on the other hand, were collected from the archaeological site area that is poorly depicted in the topography map. Therefore, as opposed to GCP # 1 and 2, it was much more difficult to find the spots on the aerial photographs that are evenly distributed and readily corresponded to the same spots on the topography map. IGN basically drew nothing but rough outlines of the archaeological structures in their map. Besides, compared to the aerial photographs, the depiction of those structures seems to be partially wrong in size, proportion, and orientation. Realistically, the seven GCPs for 626 and nine for 626 were the maximum number of the points that I could define. The resultant orthoimages were saved as an ERDAS IMAGINE Image file with *.img extension. Superposition of topography map over the orthoimages shows that the best fit

98

Figure 5-19. The “Identify Results” dialog window. The X-Y coordinates are being displayed right above the property table in the right portion of the dialog, “Location: (293053.030594 8644971.451042).”

Figure 5-20. 10 Ground Control Points collected from topography map (X, Y coordinates) and SRTM DEM (Z coordinate) and plotted over the orthorectified aerial photographs 626 and 649. Note that the 10 GCPs are evenly distributed within the area of interest.

Table 5-1. Ground Control Points (GCPs) for geometric error correction. Note that the original Z coordinate for GCP#2, 53, is replaced by 20 (b). The original value of 53 collected from 90-by-90 m DEM was apparently unacceptable because the GCP#2 on Puente Lurín is located below the 20-m contour line.

(a) Photograph 626 GCP# 1 3 4 6 7 8 10

Description Panamericana E Piramide V Mamacuna Cementerio Templo del Sol Coliseo Panamericana W

Type Full Full Full Full Full Full Full

Usage Control Control Control Control Control Control Control

X 293053.030594 293395.079902 293010.554617 293554.620823 293289.876711 292886.856044 292049.206456

Y 8644971.451042 8645003.121138 8644706.332327 8644341.724585 8644015.938500 8643849.684808 8644873.060726

Z

Type Full Full Full Full Full Full Full Full Full

Usage Control Control Control Control Control Control Control Control Control

X 293053.030594 294124.830563 293395.079902 293010.554617 293757.006375 293554.620823 293289.876711 292886.856044 294524.620987

Y 8644971.451042 8645145.555089 8645003.121138 8644706.332327 8644685.831222 8644341.724585 8644015.93850 8643849.684808 8643777.783216

Z

32 28 18 30 74 8 9

(b) Photograph 649 GCP# 1 2 3 4 5 6 7 8 9

Description Panamericana E Puente Lurín Piramide V Mamacuna Piramide XIV Cementerio Templo del Sol Coliseo Camacho Primero

32 53-->20 28 18 40 30 74 8 12

100

(less than 2.5 m) can be seen in the sacred precinct of Sector I (Figure 5-21a). Larger misalignments are likely to pertain to the areas of higher elevation with no GCP measurement. In fact, the worst fit (ca. 9 m) is found on one of the highest hills within the site area where the Pyramid with Ramp III is located and concurrently no Z coordinate was measured (Figure 5-21b). Considering our data source limitations, however, the largest misalignment is still within the limits of what is allowed. What I would like to emphasize here is that the depiction of archaeological architecture in the topography map may be too poor to use as the criterion for the determination of accuracy. Although I used this topography map as the basis of accuracy, in order to perform more accurate orthorectification, it is necessary to obtain more reliable GCPs of higher accuracy such as Differential GPS readings, which are to be taken in the subsequent fieldwork.

5.1.3. On-screen Digitizing. The final process of prototype map creation is to digitize the archaeological structures on the orthorectified aerial photographs, and contour lines, elevation points, and some other modern-day features on the topography map. The process requires you to place all vertices, to trace the outlines of features, and to locate the points simply by clicking the mouse button and thus is essentially time-consuming and tedious. This was performed using ArcCatalog and ArcMap. Each of the digitized points, lines, and polygons was saved as a shapefile, ESRI vector data format. A step-by-step procedure for this process is given in Appendix B.7 (“How to digitize ground features in orthophotos and topography map”).

101 (a) Best Fit (less than 2.5 m) in the sacred precinct of Sector I

(b) Worst Fit (ca. 9 m) near the Pyramid with Ramp III

Figure 5-21. Superposition of topography map over the orthophoto (649).

102

The most important thing to pay attention to here is the decision about which feature type to select for the new shapefiles: Point, Polyline, Polygon, or MultiPoint10. In regular GIS projects, transportation networks (e.g., roads and railways), rivers, and utility pipe lines are represented by line features, while buildings and land parcels are recorded as polygon features. During these digitizing processes, simplifications of real-world objects accompanied by varying levels of information loss take place. For example, there is hardly any road that has a constant width in any part of it. It would be impractical to faithfully depict a road with non-constant width as an irregular polygon. Instead, one just places all vertices along the center line of the road and, if necessary, register a central value for its width in attribute table. The basic assumption for the simplification here is that roads retain constant widths. Such simplification is true of other feature types such as buildings recorded as polygons. The first priority is generally given to the record of accurate locations of and relative positioning among ground features rather than the depiction of accurate and precise shapes of them. In archaeological mapping, plan views created by Total Station depict archaeological structures by sets of contour lines. The structures to be recorded do not necessarily retain their original sharp edges, and rather most of them have been badly eroded. In essence, archaeological structures show varied appearances from discrete constructions to piles of soil depending on their state of preservation. Those that are badly eroded may be recorded more efficiently by contour lines, whereas those remaining intact or reconstructed may be depicted by their solid outlines. Thus, feature types and, in turn,

103

mapping techniques may ultimately need to be selected in accordance with the state of preservation of the structures. For my map making task, however, since we adopted a combination of aerial photographs and topography maps rather than location survey, archaeological structures were recorded by precisely tracing their outlines. Furthermore, I had to make a decision as to whether to depict them by lines or polygons. Starting and end points of some of the wall structures, for instance, were not readily recognizable. Sometimes the original extents and forms could be inferred from their current state of preservation; however, this was not the case with every structure. For this reason, I depicted the structures as sets of lines for the time being, partially reconstructing the portions missing due to erosion and/or destruction, although polygon features were more useful for many occasions in terms of their applicability to spatial analysis such as three-dimensional representation of the structures. Craig (2000) and Craig and Aldenderfer (2003) put forth a new real-time digital recording system of excavation data by means of digital camera and pen-top tablet PC. They take pictures for each gird of excavation unit, rectify them in GIS software, and digitize the objects of interest (e.g., artifacts, structural features, different soil types) as polygon features. Traditional artifact inventories and other field observations are stored in attribute tables associated with their corresponding polygon features. Polygon would be the most appropriate feature type for within-structure, large-scale mapping as far as the objects to be recorded retain discrete boundaries. Once we invent optimum technique

104

for real-time digital recording of continuous surface such as gradual changes in soil type, a major shift in scale (from excavation to survey scale) and timing (from post-fieldwork to real-time recording) of GIS-linked database construction may take place. However, there has been no agreement among archaeologists as to how within-site and within-structure objects should be registered and stored in GIS overlay for future analytical purpose. This may indicate the fact that many of the previous archaeological applications of GIS have not narrowed down from the regional or inter-site level at which sites are represented merely by points. In addition, I subsidiarily utilized image processing software, Adobe Photoshop Creative Suite, during the digitizing process. Decreasing brightness and increasing contrast will sharpen the edges of and enhance the vertical intervals between ground features. This may help you more easily digitize them or even find something that you can not recognize on the ground. Figure 5-22 compares two images of Templo Viejo in the original scanned photograph 649 (a) and in a processed one (b). Note that the shadows are more clearly highlighted so that the vertical intervals can be readily recognized in (b). Without intensive excavation or field reconnaissance it would be quite difficult to make out details of architectural constructions; however, this technique is very useful in a preliminary study. Once the digitizing process was completed, I superimposed all the products created above one over another: topography map, orthoimages, archaeological structures, contour lines, and elevation points from bottom to top (Figure 5-23). Formatting this set

105

(a)

(b)

Figure 5-22. A comparison of two images of Templo Viejo in the original, scanned aerial photograph 649 (a) and in a processed photograph by means of a simple image adjustment (b). Decreasing brightness and increasing contrast will sharpen the edges of ground features.

106

Figure 5-23. Prototype base map layers of Pachacamac. It is quite difficult to digitize all the details of archaeological structures.

107

of data layers as a printable map, I printed out the overlay using a large plotter in preparation for the usage in the fieldwork during the summer of 2004. As you see in Figure 5-24, I could go no further than rough drawings in some parts of the site area. Although the aerial photographs that I used this time show a great deal of architectural features, without examining them in field, it seems rather difficult to digitize all the details of archaeological structures and in turn to create a very precise map. For the purpose of making up for this shortage, I spent seven weeks in Peru for ground-truth checking.

5.2. Phase II: Ground-Truth Checking and GPS Measurements

The main objective of the 7-week fieldwork from mid-June to the end of July 2004 was to carry out a ground-truth checking of archaeological structures in order to refine my prototype base map taking into account the temporal changes that have taken place since the time our aerial photographs were taken almost 50 years ago. Verifying each structure digitized and searching for unknown structures that I could not recognize on the aerial photographs, I measured in and hand-drew the detailed plan views of over 30 architectural structures. Since they were carefully drawn with reference to the structural outlines digitized on the orthoimages and thinly printed on letter-size papers, they retain a good deal of locational accuracy. During this drawing process, I attempted to differentiate the preservation state of structures that could be categorized into three types: (1) reconstructed to varying degree,

108

Figure 5-24. Prototype site map of Pachacamac.

109

(2) well preserved enough to delimit the form, and (3) collapsed and obscure. Uhle’s (1903) technique of depicting buildings of uncertain forms by means of thin lines helped me represent the third category. My primary concern was focused on depicting the large structures and a dozen of small miscellaneous structures scattered here and there that have not drawn the attention of archaeologists to date. Although I spent almost all of the seven weeks of my stay in Peru on the field checking and refinement task, I had a chance to study some of the major temples that have already been mapped and published (Eeckhout 2003; Franco 1998; Michczyński et al. 2003; Ravines n.d.; Ray 1991; and Shimada 1991); nonetheless, as I noted earlier in this chapter, some structures could not be properly examined primarily due to time constraint. Toward the end of my fieldwork, I had the good fortune to take a series of GPS measurements in Sectors I and II. This was performed during the three days from July 25 to 27, 2004 in association with Dr. Hartmut Tschauner (Seoul National University, Korea) and Dr. Ursel Wagner (Technische Universität München, Germany) for the purpose of patching up some displacements of my prototype base map derived seemingly from the use of IGN topography maps as the only reference for the basis of accuracy. Thanks to the courtesy of Tschauner, I could use his Real Time Kinematic Differential GPS (RTK DGPS; Leica Geosystems, GS20 Professional Data Mapper; Figure 5-25). Conventional single GPS receivers can usually provide positions with accuracy of no less than ca. 10 to 15 m because of numerous sources of errors.11 This may be sufficient depending on your need. However, if you require positioning accuracy of better than 10 m

110

Figure 5-25. Leica Geosystems, GS20 Professional Data Mappers, Reference Station (near) and Rover Receiver (far), by courtesy of Dr. Hartmut Tschauner (Seoul National University, Korea). Differential GPS technique requires one GPS receiver (Reference Station) to be located at a known reference point and a second or more receivers (Rover[s]) at the location to be measured.

111

and wish to eliminate errors, you will inevitably need to adopt a more accurate technique such as “Differential GPS,” which requires another GPS receiver as a “Reference Station” or “Control.” As opposed to the single receiver GPS, this technique provides significantly higher accuracy of 1 m, 0.5 m, or even 10 cm margins of error. DGPS requires one GPS receiver (Reference Station) to be located at a reference point and a second or more receivers called “Rover[s]” at the location to be measured. The information from the GPS receivers (both Reference Station and Rover) is combined to determine the position of the Rover. The difference between the known coordinates and the GPS-calculated coordinates is the error that needs to be corrected for the accuracy of survey or mapping grade GPS applications. The correction of this error can be done either by bringing the data from the Reference Station and Rover together in an asynchronous, post-processing mode after the field measurements are completed (Post Processed Kinematic or PPK), or by instantaneously broadcasting the error correction information produced by the Reference Station to the Rover for real-time corrections (Real Time Kinematic or RTK) (Lillesand et al. 2004:34). There are two broad categories of RTK correction messages transferred through different protocols: (1) code differential messages for meter level positioning accuracy, and (2) carrier phase differential messages for centimeter level accuracy. The media for communication between the Reference Station and the Rover include direct cable connection, radio modem, WiFi (Wireless Fidelity based on IEEE 802.11b specification) device, and so forth. Radio modem provides the greatest flexibility

112

and allows for a Reference Station to communicate with multiple Rovers at a time. The GS20 Professional Data Mapper that I used employs the carrier phase differential messages of centimeter level accuracy to be sent by a radio modem for RTK corrections. The only serious problem prior to the measurements was that we did not have any known reference point within the site area. Strictly speaking, as I mentioned above, we could not find the datum point that is said to be on the top of the Temple of the Sun (Figure 5-18). Instead, we found a concrete datum around the east corner of the same rung of the temple. Since these two datum points are obviously separate from each other, it can be hardly said that they are the same point and the gap between them should be taken as margin of error. It was later known as a result of GPS measurements that the two datum points are separated by over 40 meters horizontally and ca. 16 meters vertically. This disconcerting gap will be discussed later. As a last resort, consequently, I decided to arbitrarily set up a new reference point and put the Reference Station at this spot. The location of the point was considered from the aspect of security management of the equipment and signal strength from satellites. It was finally placed at an open space on the NW side of the Pilgrims’ Plaza, where other project members were carrying out excavations. Because the coordinates of this new reference are not properly verified yet, we should acknowledge a fudge factor of at most ca. 15 m for the exact locations of all GPS points for the time being, whereas the relative positioning between them can be thought of as quite accurate with a margin of error of less than 0.5 m. This means that we could not take full advantage of RTK Differential GPS.

113

Measurements without known coordinates, strictly speaking, are nothing but a set of control points floating in a standalone mapping space which does not tie in with any legitimate reference network. Nonetheless, in case the coordinates of our new reference are properly verified or the IGN datum point on the Temple of the Sun is found in the future, the locations of all GPS points will be systematically moved as a single unit to the right positions with a significantly high accuracy. The points to be measured, or the locations of the Rover receiver, were placed on noticeable spots that can be precisely identified on the aerial photographs as well (e.g., an intersecting point of well-preserved walls). They were also evenly scattered within the site area (Figure 5-26). A total of 23 points were set up and measured. They include the concrete datum points on the Temple of the Sun and a small platform mound called “Ushnu” along the south edge of the Pilgrims’ Plaza, both of which were measured for future reference. The measuring process was completed within approximately 20 minutes per point; however, we frequently suffered from abnormal terminations of the Reference Station. Every time it occurred, we had to return to the receiver and restart it. Therefore, it took us three days to measure 23 points.

5.3. Phase III: Data Post-Processing and Consummation of the Final Maps

After the fieldwork, eight months were spent for data post-processing and consummation of the final maps. These processes include: (1) raw data import and conversion of DGPS measurements into a shapefile, (2) creation of a new 10-by-10 m

114

0

50

100

200 Meters

Figure 5-26. A reference point and 23 GCPs measured by an RTK Differential GPS. Note that the points are evenly distributed within the site area.

115

DEM out of the new DGPS readings and other existing elevation data extracted from topography map, (3) deuter-triangulation of the aerial photographs based upon the new DEM and DTM, (4) spatial adjustments of vector datasets of architectures in reference to the resultant orthoimages and the small detail plans drawn in the field, and (5) reproduction and import of clean copies of the field drawings. Figure 5-27 illustrates the work flow of these post-fieldwork data processing procedures.

5.3.1. Data Post-Processing of GPS Readings. Differential GPS measurements taken during the fieldwork were post-processed by Hartmut Tschauner. He used Leica Geosystems’ GIS DataPRO to import and convert raw GPS data into a multipoint shapefile (Figure 5-26). Attributes such as “Point ID,” “Latitude,” “Longitude,” and “Orthoheight” were stored in a dBASE table associated with the shapefile (Table 5-2). Since the resultant multipoint shapefile that I received from Tschauner was already projected by WGS84, I re-projected it using PSAD56 and UTM18S (Appendix B.8, “How to change projection and coordinate system”). Even though the shapefile was still a “floating” map layer, when it was superimposed over the topography map layer, no displacement between them was apparent at a glance. According to Tschauner, however, post-processing was somewhat problematic. First, since we did not have a known reference point as I noted above, Tschauner had no choice but to perform a single-point processing of the first reference measurement (REF00001, REF) and set the coordinates of subsequent reference measurements (REF00002 and

116

Prototype Map Creation

Contour Lines Shapefile

Digitizing

Archaeological Structures Shapefile 1

Elevation Points Shapefile

Aerial Photographs

Creating Surface

GCP Shapefile

Digital Elevation Model

Converting

DeuterOrthorectifying

DGPS Raw Data

Scanned Aerial Photographs

camera calibration certificate (incomplete) Camera Report

Orthorectified Printing

Aerial Photographs 2 (PSAD56/UTM18S)

Adjusting & Modifying

Printing

Archaeological Structures

Field Drawings 1

Fieldwork

Archaeological Structures Shapefile 2

Making Fair Copies

Field Drawings 2 (PSAD56/UTM18S)

Scanning & Rectifying

Archaeoligcal Structures

(PSAD56/UTM18S)

Figure 5-27. The work flow of post-fieldwork data processing (Phase III). Phase III processes include (1) conversion of DGPS measurements into a shapefile, (2) creation of a new 10-by-10 m DEM, (3) deuter-orthorectification of the aerial photographs, (4) spatial adjustments of vector datasets of architectures, and (5) production and import of clean copies of the field drawings.

Table 5-2. The dBASE table of attributes for GPS readings. POINTID

LATITUDE

ORTHHEIGHT

GEOIDSEP

POINT00001 POINT00002

12 15 34.249484 S 12 15 31.732302 S

76 54 76 54

LONGITUDE 6.293248 W 3.436298 W

34.28727 38.34640

23.77 23.79

COORDCLASS MEAS MEAS

USHNU PWR13

ATTRIBUTES

MEASTIME 2004/7/25 2004/7/25

POINT00003

12 15 31.435728 S

76 54

6.552905 W

34.94863

23.78

MEAS

ROOM

2004/7/25

POINT00004

12 15 35.527706 S

76 54

8.656139 W

34.65171

23.76

MEAS

POST

2004/7/25

POINT00005

12 15 34.443152 S

76 54 11.670949 W

33.35287

23.75

MEAS

WALLCRNR

2004/7/25

POINT00006

12 15 29.767245 S

76 54 11.336116 W

30.42555

23.76

MEAS

EWSTREET

2004/7/25

POINT00007

12 15 28.651432 S

76 54

0.768424 W

25.99706

23.80

MEAS

PWR12

2004/7/25

POINT00008

12 15 28.453808 S

76 53 56.085817 W

28.43733

23.82

MEAS

WALLCRNR2

2004/7/25

POINT00009

12 15 32.429485 S

76 53 53.996429 W

31.45113

23.82

MEAS

PWR15N1

2004/7/25

POINT00010

12 15 32.429639 S

76 53 53.996296 W

31.45656

23.82

MEAS

PWR15N2

2004/7/25

POINT00011

12 15 37.247979 S

76 54

2.650071 W

51.38184

23.78

MEAS

PINTADO

2004/7/26

POINT00012

12 15 46.521210 S

76 54

5.014542 W

96.06675

23.76

MEAS

DATUMTMPLSOL

2004/7/26

POINT00013

12 15 47.418441 S

76 54

6.092734 W

93.69171

23.75

MEAS

TMPLSOL

2004/7/26

POINT00014

12 15 26.236323 S

76 54 19.216759 W

16.40138

23.74

MEAS

MAMACUNA

2004/7/26

POINT00015

12 15 12.473550 S

76 53 56.424005 W

36.65530

23.85

MEAS

STORAGE

2004/7/26

POINT00016

12 15 15.514442 S

76 53 44.185453 W

51.75549

23.89

MEAS

TAURICHUMPI

2004/7/26

POINT00017

12 15 23.285258 S

76 53 48.424981 W

54.14340

23.86

MEAS

QUIPUHOUSE

2004/7/26

POINT00018

12 15 34.823621 S

76 54

6.243773 W

32.49834

23.77

MEAS

DATUMUSHNU

2004/7/26

POINT00019

12 15 23.214698 S

76 53 57.944574 W

51.62682

23.82

MEAS

PWR2

2004/7/26

POINT00020

12 15 26.098364 S

76 54

6.614236 W

40.56282

23.79

MEAS

PWR1

2004/7/27

POINT00021

12 15 23.846480 S

76 54 11.573867 W

31.22612

23.77

MEAS

STORAGE2

2004/7/27

POINT00022

12 15 31.367090 S

76 53 44.840153 W

42.91762

23.86

MEAS

EASTEND

2004/7/27

POINT00023

12 15 21.342952 S

76 53 54.325657 W

68.24042

23.84

MEAS

PWR3

2004/7/27

POINT00024

12 15 16.371963 S

76 54

3.928943 W

34.93833

23.81

MEAS

PWR6

2004/7/27

REF00001

12 15 32.493755 S

76 54

5.680532 W

33.83989

23.78

REF

PLAZAREF1

2004/7/25

REF00002

12 15 32.493760 S

76 54

5.680530 W

33.83990

23.78

CTRL

PLAZAREF2

2004/7/26

REF00003

12 15 32.493760 S

76 54

5.680530 W

33.83990

23.78

CTRL

PLAZAREF3

2004/7/27

118

REF00003, CTRLs) to the ones produced by the initial single-point processing. This may result in substantial positioning error of 10 to 15 m. Second, the two reference point measurements from the second and third days could not be properly saved probably because the Reference Station stopped logging somehow. As a result, the GPS raw data were not associated with point IDs and lacked attribute information, including antenna heights. Point IDs randomly switched for the tracks without IDs and the software abended12 when he attempted to re-assign tracks to their proper point IDs. After several runs, he finally succeeded to process all points with apparently excellent accuracy. Nonetheless, two measurements collected on the same points by chance (POINT00009 and POINT00010 in Table 5-2) showed us a significant accuracy of the relative positioning between measured points. Remarkably, these two measurements have only 6.2-mm difference. Third, given the lack of antenna heights for the Reference Station on the second and third days, some points measured by the Rover had wrong elevations. These wrong elevations in the attribute table (ORTHOHEIGHT) had to be corrected manually with the recorded antenna height of 1.187 m for the second day (July 26) and 1.195 m for the third day (July 27). Fourth, elevations may not be very accurate with no locally fitting geoid model for Peru. Elevations measured by GPS are ellipsoidal heights above the reference ellipsoid. Since the ellipsoid is the reference surface for horizontal positions, they need to be converted to orthometric heights that are above a geoid and related to mean sea level.

119

Geoid is a mathematical model for vertical reference that “would be formed if the oceans were allowed to flow freely under the continents to create a single undisrupted global sea level covering the entire planet Earth and adjusted to gravity” (Lo and Yeung 2002:36). Figure 5-28 illustrates the relationships between earth’s irregular surface, ellipsoid, and geoid. The conversion of ellipsoidal heights to orthometric heights can be carried out by interpolation using a GPS tailored geoid model. However, the only geoid model available for Peru is EGM96, a worldwide model that may not possibly be as good as a local model specifically for Peru would be. Locally fitting geoid model for Peru does not exist at this point (Tschauner 2005, personal communication). Let us examine the accuracy of this DGPS-measured GCP shapefile, superimposing it over the orthophoto layers 626 and 649.13 Figure 5-29 illustrates the displacements between DGPS-measured points (starting points of the arrows) and the corresponding points in the orthophotos resampled with SRTM-arc3 DEM (endpoints of the arrows). Sector I, which showed the best fit between topography map and orthophotos (less than 2.5 m), now shows much larger displacements of 5.12 m at the minimum (Templo Pintado) and 8.41 m at the maximum (Templo del Sol) and, in turn, indicates that DGPS measurements do not necessarily fit the topography map that I have utilized considering as the basis of accuracy. Out of the four problems Tschauner pointed out, the first is clearly recognized here. How should we consider this horizontal gap? Which is more accurate, topography map based on traditional location survey or GCPs collected by RTK Differential GPS?

Figure 5-28. The relationships between earth’s irregular surface, ellipsoid, and geoid (Taken from Lo and Yeung 2002:35 and modified).

121

Figure 5-29. Displacements between DGPS-measured points and the corresponding points in the orthophotos resampled with SRTM-arc3 DEM.

122

As you can see in Figure 5-29, the two-dimensional displacements between the DGPS-measured points and their corresponding points in orthophotos show relatively constant magnitude (mean=4.91, 5.31; standard deviation=1.81, 1.50) and directionality (east-to-west). The systematic displacements may be attributed to the positioning error of the reference point due to the limitation of single-point processing technique without known coordinates. It is inferred that the right place of the reference point (and in turn other points collected by the Rover) would be located ca. 5 m east of its current position. In addition, the fluctuations in magnitude of the displacements may be because of the persistent displacements caused by terrain relief. The DEM that I used for geometric error corrections is too coarse to completely eliminate the displacements. There are not only horizontal gaps but also vertical gaps between the topography map and DGPS readings. The topography map produced by IGN plots an elevation point on top of the Temple of the Sun that is 80.9 m above mean sea level. The document obtained from IGN provides a close value of 78.26 m for the datum on the highest rung of the temple (Figure 5-18). On the other hand, our two DGPS measurements on the same rung of the temple as the two points above have heights respectively of 93.69 and 96.07 m. Since there is no large undulation on top of the temple mound, these vertical discrepancies of over 15 m baffled me. As Tschauner points out, they may be attributed to the use of globally fitting geoid model (EGM96) for interpolation of orthometric heights. Considering the difference in the level of technology, another possibility is that there may have been measurement errors during IGN’s location survey and thus we should take our

123

measurements as more accurate. It may not be until we verify the coordinates of our reference point, obtain more precise and accurate DEM for triangulation, and gain a locally fitting geoid model for Peru that we can replace IGN’s topography maps as the basis of accuracy by our new DGPS readings. Nonetheless, I adopted the DGPS readings in order to take advantage of the accuracy of its relative positioning and used them for GCPs during the new DEM and DTM creation and the subsequent triangulation processes. Because I used the GCPs, the reference point that has horizontal discrepancy of at least 5 m, resulting orthophotos and archaeological structures to be digitized on those images must be also out of alignment. However, the systematic displacements of the digitized shapefiles, as I noted above, can theoretically be corrected to be self-consistent by moving all of the objects in the shapefile as a single unit to the legitimate position of reference point when its coordinates are verified.

5.3.2. Creation of New DEMs. The multipoint shapefile of DGPS-measured points was used to create more precise DEMs of finer spatial resolution in combination with contour lines and elevation points shapefiles digitized on the topography map layer (Figure 5-30). This was performed primarily for the purpose of deuter-orthorectification of aerial photographs, which is to be discussed in the next section. The lack of legitimate reference point and locally fitting geoid model seems to have given rise to unexpected displacements in both horizontal and vertical directions between the datasets from two different sources

124

Figure 5-30. DGPS-measured points, elevation points, and contour lines.

125

(RTK DGPS measurements and topography map). However, the horizontal gap between them turned out to be no more than 10 m. Since the ground resolution of the resulting DEM, which is automatically determined by the software, is 10 m, the same points in the different elevation datasets may fall in the same pixel of the output DEM or different pixels next to each other. This indicates that the horizontal displacement will be within one pixel at maximum and may be accepted as a margin of error. Vertical gaps ranging from ca. 0.5 to 15 m, on the other hand, seem to be way beyond acceptable range if taken at face value. As Figure 5-30 shows, however, in that many of the DGPS-measured points are concentrated in the center of the site area where elevation points are scarce and contour lines are divided, they are complementary. Only a few points measured on top of the multi-level platforms are exhibiting a great amount of vertical gaps from contour lines and elevation points. Thus, the integration of the three elevation datasets to create new DEMs is thought to be feasible and rather favorable irrespective of displacements. Merely for the purpose of comparing the results, I created three DEMs in three different combinations of the three datasets: (1) only GDPS readings, (2) combination of contour lines and elevation points, and (3) all of three elevation datasets (Figure 5-31). Needless to say, the third is most accurate. The output range of the first is quite narrow and useless. Because of the small number of input points (23 points), precision is not satisfactory, either. The second and third both look seemingly good, but they differ greatly in the site area. In the second that depended only on contour lines and elevation points, elevation information is originally very scarce in the center of the site area and

126

Figure 5-31. Comparison of three newly created DEMs.

127

interpolated from the points around it. Therefore, it does not represent the heights of large monumental structures (e.g., the Pyramids with Ramps) tightly-packed in the central zone of Sector II and thus is rather sort of a reconstruction of original terrain surface before the site was constructed upon it. The third, in contrast, represents not sufficiently but more in detail actual landscape of the site area. This is most suitable for orthorectification of aerial photographs. The procedure of DEM creation is described in detail in Appendix B.9 (“How to create a new DEM out of multipoint and line features”). I employed the Terrain Surface Interpolation technique of ERDAS IMAGINE 8.6. For the type of output file, I selected Float Single (32-bit values from 0 to 1) that retains precision and concurrently keeps file size relatively small.

5.3.3. Deuter-Orthorectification of Aerial Photographs. The third step of my post-fieldwork data processing is to perform block triangulation of aerial photographs again in two ways to achieve a higher accuracy. First, I followed exactly the same procedure as the previous one but with the new GCPs collected using RTK DGPS (Table 5-2) and resampled the photo images with the new DEM created above instead of SRTM-arc3 DEM of 90-by-90 m spatial resolution. Secondly, more importantly, prior to the resampling process I performed automated extraction of a DTM (Digital Terrain Model) from the overlap of the two aerial photographs using ERDAS IMAGINE OrthoBASE Pro and used it for DEM during another block triangulation process. The latter provides

128

orthoimages based on a very precise DTM of 1-m spatial resolution but covers only the approximately 50%-overlap of the aerial photographs, which corresponds to a western half of the “inner city,” whereas the former covers the entire areas of Sector I and II. The detailed procedures of DTM extraction and subsequent block triangulation are discussed in Appendix B.10 (“How to perform geometric corrections of aerial photographs through an automated DTM extraction (Phase III)”). DTM is a three-dimensional digital representation of the earth’s surface. It is important to note that it does not necessarily represent the man-made (e.g., buildings) and natural (e.g., trees) features located on the earth’s surface. In this sense, DTM needs to be distinguished from DSM (Digital Surface Model) that represents the elevation associated with the earth’s surface including all man-made and natural features (Leica Geosystems GIS & Mapping Division 2002b:65). Major applications of DTM include (1) determining the extent of a watershed, (2) extracting a drainage network for a watershed, (3) determining the slope and aspect associated with a geographic region, (4) modeling and planning for telecommunications, (5) orthorectifying, (6) preparing three-dimensional simulations, (7) analyzing Volumetric Change, (8) estimating River Channel Change, and (9) creating contour maps (Leica Geosystems GIS & Mapping Division 2002b:68-69). This indicates that DTM is used primarily for regional-scale or larger scale analyses, which do not necessarily require very high precision and accuracy. In addition to the input stereopair of images (e.g., aerial photographs and satellite images) as the primary data source, various other techniques and approaches can be

129

used to collect elevation information from the earth’s surface for the automatic extraction of DTM. They involve ground surveying (e.g., Total Station and GPS), traditional photogrammetry, digital stereo plotters, digitized topography maps, radar, Light Detection and Ranging (LIDAR), and so forth (Leica Geosystems GIS & Mapping Division 2002b:66-68). I employed ground surveying and digitized topography map. Generally, the former is highly accurate, but very time-consuming. The latter, in contrast, is not as accurate, but a feasible option when appropriate elevation data are not available. Figure 5-32 shows three-dimensional representations of the site area: (a) aerial photograph 649 orthorectified using DGPS readings and DTM; (b) DTM extracted from our pseudo-stereopair of aerial photographs 626 and 649; and (c) quality of DTM. Contour lines shapefile, which was created during the process of DTM extraction, is superimposed over these three datasets for reference. As Figure 5-33 and 5-34 illustrates, the use of DGPS points for GCP and the new 10-by-10 m DEM and DTM for resampling greatly reduced the displacements between DGPS points and the corresponding points in the orthophotos compared to the previous trial in the pre-fieldwork data processing. As with the case of DTM, block triangulation and other orthorectification techniques are most likely to be suitable essentially for terrain surface creation and spatial analysis at regional or inter-site level rather than for within-site mapping purpose. Unless you can obtain a sizable number of accurate GCPs, DEMs with highly fine resolution, and other supplementary elevation information (e.g., dimensions and orientations of buildings), it would be impossible to precisely depict the landscape of archaeological structures in the

130

Figure 5-32. 3D representations of the site area: (a) aerial photograph 649 orthorectified using DGPS readings and Digital Terrain Model (DTM); (b) DTM extracted from the stereopair of aerial photographs 626 and 649; and (c) quality of DTM. Contour lines are superimposed over the images for reference.

131

Figure 5-33. Displacements between Ground Control Points (DGPS measurements) and the corresponding points in the orthophotos resampled with 10-by-10 m DEM.

132

Figure 5-34. Displacements between Ground Control Points (DGPS measurements) and the corresponding points in the orthophotos resampled with extracted DTM.

133

central area of the site both in two- and three-dimensions. There seem to be further correction techniques for this purpose, but they are apparently out of scope of this thesis. Therefore, in order to refine our map to be more precise and accurate in terms of architectural details, follow-up surveys by means of Total Station will be absolutely imperative. Digital recording system put forward by Craig and Aldenderfer (2003) and Craig (2000) may also be helpful.

5.3.4. Spatial Adjustment of Vector Datasets. The on-screen digitizing during the pre-fieldwork prototype map preparation was performed on a pseudo-stereopair of aerial photographs orthorectified without critical information such as reliable GCPs and appropriate camera information. The lack of that critical information did not allow me to raise the accuracy of geometric corrections up to the desired level (Figure 5-21). As I noted above, the structures digitized on the resultant orthophotos inevitably inherited unignorable spatial discrepancies from their original positions. Consequently, spatial adjustment of those vector datasets was essential. The discrepancies between the DGPS points and their corresponding points in the orthophotos were greatly reduced through the deuter-orthorectification process. This indicated that not only the locations but also the sizes, shapes, and orientations of the archaeological structures were properly corrected. Thus, simple ambulation of the digitized structures in a horizontal direction in accordance with the DGPS readings was not sufficient. Most of the structures required to be digitized again.

134

The orthophotos resampled with the extracted DTM showed best fits with the DGPS readings (Figure 5-34), but the area covered was limited to the west half of the site. Moreover, some of the structures with tall walls or at high altitude (e.g., The Convent of Mamacona, Pyramids with Ramps I, II, and III) in these orthophotos got their walls erroneously twisted (Figure 5-35). In such cases, I consulted my field drawings and measurements for the correct shapes and orientations of structures. As for the major ceremonial mounds that were not included in my field drawings and measurements (e.g., the Temple of the Sun and the Pyramids with Ramps), there were a few of published plan views that could be used for reference (Eeckhout 2003, 2004; Ravines n.d.; Ray 1991; Shimada 1991). What you have to be concerned about when using traditional hardcopy maps is that those maps accompanied by north arrows and scale bars may look planimetrically true, but their sizes and orientations are significantly different from those measured in the field and our orthophotos. For the area out of range of the DTM-resampled orthophotos, I digitized on the orthophoto 649 resampled with the 10-by-10 m DEM that were less accurate but covered the whole area of the site. The larger displacements between the DGPS points and the corresponding points in the digitized structures (Figure 5-33) were corrected by moving groups of the vector features as a unit to the nearest DGPS points. It is important to note that the locations of these digitized structures may be corrected again when the coordinates of our new reference point in the Pilgrims’ Plaza is properly verified or the IGN datum point on the Temple of the Sun is found in the future.

135

Figure 5-35. An example of twisted walls (The Convent of Mamacona).

136

5.3.5. Clean Copies of Field Drawings. All of my field drawings inserted in Appendix C (“FIELD DRAWINGS”) were originally sketched in the field (see 5.2. “Phase II: Ground-truth Checking and GPS Measurements”) and later redrawn carefully on transparent graph papers superimposed over a printed version of archaeological architectures shapefile for reference (Figure 5-36). The scale was fixed to 1:500 for all drawings and the intersections of X- and Y-coordinate grid lines were marked by crosshairs every 500 m so that the drawings could be rectified more accurately. Each of the completed drawings were xeroxed to eliminate the light blue grids on the graph papers and to make complete black-and-white plates. Those plates were then scanned and rectified in accordance with the same datum and plane coordinate system as other data layers (PSAD56 and UTM18S) so that they can be displayed and manipulated in GIS overlays. I put coordinate grids on each drawing, by means of which one can integrate those hand-drawn maps in his or her own GIS database.

137

Figure 5-36. Field drawings were drawn fairly on transparent graph papers superimposed over printed shapefiles for reference.

138 Notes TIFF (Tagged Image File Format) is an image file format designed and developed by Aldus and Microsoft in the 1980s to gain a universal translator of graphics across various computing platforms; however, it requires nonstandard, often redundant, extensions to enjoy most useful functions (e.g., lossless 24-bit color). It is ironical that the incompatibility of extensions has led some to explain TIFF as “Thousands of Incompatible File Formats.” 2 Map Algebra is a computer language for developers with which ArcGIS is written. This is different from a cartographic concept with the same name originally proposed by Tomlin (1990). 3 The aerial photograph 649 (Figure 3.8) was inverted as a matter of convenience; however it was originally taken from the east. 4 The overlap between photographs 626 and 649 turned out to be 51.295% during the procedures of DTM (Digital Terrain Model) production, which are discussed in section 5.3.3. 5 The comprehensive term “block triangulation” is usually referred to as “aerial triangulation” or “aerotriangulation” when processing frame camera, digital camera, videography, and nonmetric camera imagery, while it is referred to simply as “triangulation” when processing imagery collected with a pushbroom sensor (Leica Geosystems GIS & Mapping Division 2002b:40; Lillesand et al. 2004:166). 6 Focal plane is the contact positive print (or transparency), not the negative print. (Jensen 2000:142) 7 Optical axis is the straight line that is perpendicular to the focal plane of aerial film camera and extends through the center of curvature of the lens to the surface of the earth to be shot (Lillesand et al. 2004:102). 8 When multiple images are to be processed, a minimum of three GCPs are required on each image. 9 After my field reconnaissance, I found out that there is an additional concrete datum around the east corner of the same highest rung of the temple. Since these two datum points are apparently separate from each other, it can be hardly said that they are the same point and the gap between them should be taken as margin of error. The gap is over 40 m in horizontal and nearly 16 m in vertical directions. I suspect that there were substantial measurement errors on the occasion of the location survey. 10 In a MultiPoint feature layer, multiple points share the same set of attributes, while a Point feature composes 1-to-1 relationships between features and attributes. 11 GPS measurements are potentially subject to numerous sources of error. They include clock bias, uncertainties in the satellite orbits (“satellite ephemeris errors”), atmospheric conditions, influences of electrical noise, multipath reflection of transmitted signal, and so forth (Lillesand et al. 2004:34). 12 Abend is a coined term in computer science that derived from “abnormal end.” 13 The GCP shapefile is supposed to be compared with topography map layer. However, since the map does not show the details of archaeological structures, orthophotos were used instead. 1

139

CHAPTER 6: DISCUSSIONS

Paralleling the ongoing transformation of archaeological database from analog to digital, archaeological mapping is in a state of transition. We need to strike a balance between traditional location survey techniques and more recent state-of-the-art technologies (e.g., auto tracking pulse non-prism Total Station, RTK Differential GPS, and high-resolution multispectral remote sensing imagery). Mapping techniques and resultant map data that differ in level of precision and accuracy complicate data integration, resulting in substantial spatial discrepancies between different data layers as with the case of my site mapping. Unless you are blessed with ample funds and time, you will face the problem of bridging two different technologies and associated issues. Although some geographers tend to overdramatize the potentials of GIS, contemplation on the nature of archaeological research and associated limitations exposes the complexity of archaeological applications of GIS and will bring archaeologists back to stark reality. Archaeologists usually have to select the most cost-efficient techniques and data sources for their mapping depending on their research objectives and available resources.

140

6.1.

Two Broad Categories of Mapping Techniques

What became very clear from my site mapping of Pachacamac was that the use of remote sensing imagery readily available to the general public does not adequately fulfill archaeologists’ needs for intra-site and intra-structure mapping and related data collections. In order to minimize the spatial discrepancies between data layers and trace the outlines of structures and features very precisely and accurately, ground-truth checking with GPS was essential. To say nothing of satellite imagery, aerial photography of relatively low spatial resolution and attendant photogrammetry techniques allow archaeologists to cover a large area but do not offer very high precision. Currently available techniques of GIS-based site mapping can be broadly divided into two types: (1) small-scale mapping methods relying primarily on remote sensing data and techniques, and (2) large-scale mapping methods based on location surveys in the field.1 Both require their own hard- and software, and the capability of the equipments and/or the reliability of data sources one selects will directly reflect the quality of final products. The selection of the most appropriate method should depend largely upon required precision and available resources. If one needs to cover a large area even at the expense of precision, the former approach would be recommended. Its relatively light workload does not cost too much to execute. The methods that we employed for our site mapping were relatively handy and thus may be more appropriate for preliminary survey or reconnaissance prior to the fieldwork. In our case, since we aimed to digitize on the orthophotos many of the exposed

141

archaeological structures and to improve the final product up to the level of professional maps, I had to spend a vast amount of time for more precise digitizing. For preliminary survey, however, digitizing process is of course unnecessary. All you have to do is perform geometric corrections of the imagery with reference to known GCPs.2 This task would take only a few days at most. For the latter approach, on the other hand, there is no choice but to slowly build up the map by taking measurements in the field. Although the use of high-end Total Station will shorten the required amount of time, the creation of a high-resolution map for any large and/or complex sites cannot be achieved overnight. You should choose this approach only in cases where you need a very precise map and are prepared to conduct location surveys with perseverance. In Pachacamac, there is another on-going archaeological project, Ychsma Project3, directed by Peter Eeckhout (Université Libre de Bruxelles). This project is composed of thematic subprojects, including digital site mapping. Representing a notable contrast to our small-scale mapping conducted in the same site, their large-scale mapping is based on meticulous location surveys by the use of a Laser Total Station (Ychsma Project 2005a). As of December 2004, it has allowed for three-dimensional representations of the several monumental architectures and the topographies of their vicinities4 (Ychsma Project 2005b, 2005c). Their heavy workload would be fathomable from the fact that their mapping project inaugurated in 2002 is not expected to be completed until 2007 (Ychsma Project 2005a).

142

6.2.

Concomitant Use of Old and New Data Sources

Usually due to limited resources, it is not the case for most archaeologists to prepare their site maps by employing only the large-scale mapping methods. The most likely option for them would be to rely on small-scale mapping methods or a combination of the two. For example, a preliminary map may be made relatively rapidly using the small-scale approach for survey and test-excavation phases. Meanwhile, a more time consuming large-scale mapping could begin. In either case, it is essential to know well various issues pertaining to data acquisition and integration. Over the last few years, GIS users have obtained a growing number of external data sources inherently compatible with GIS; however, many old and new data available in their hands have a lot of limitations in regard to precision, accuracy, and information density. Traditional hardcopy topography maps, for example, are likely to be deficient in information sought by archaeologists because they were not created for archaeological use in the first place. Rather, surveyors consciously or unconsciously leave out archaeological information, which is of no interest for them, from their maps. In the site area of our topography maps, contour lines are segmented and elevation points are scarce. This is why I could not extract enough reliable elevation information from the maps. Without GCP measurement by means of GPS, we would have had to accept the results of low-quality orthorectification based solely on this poor elevation information available prior to the fieldwork. On the other hand, more recent, remotely sensed data in digital format such as

143

SRTM-arc3 DEM and Landsat-7 ETM+ imagery are now accessible for free and enable us to deal with a much larger area in GIS than conventional data resources. Nevertheless, most of these free data are hardly at satisfactory level in terms of spatial resolution for intra-site and/or intra-structure mapping and analysis. The former is of 90 m-resolution, and the latter is of 14 m-resolution. As of now, finer DEM needs to be created by location survey, and when it comes to satellite imagery, very expensive IKONOS imagery of 1 m-resolution (panchromatic) would be most favorable. Either way, scenario is premised on sufficient funds and thus may not be always realistic. It will surely take a while for most of us to get easy access to higher-resolution DEM and satellite imagery. Furthermore, some of the new data may suffer from other problems at the time of data conversion and integration into GIS overlay. As I discussed in the previous chapter, our DGPS measurements underwent a substantial degree of vertical displacement from other data layers probably due to the lack of locally fitting geoid model. Thus, as far as we have to use problematic data sources, both old and new, it would be virtually impossible for us to conduct site mapping and related data collections that are precise and accurate enough to undertake truly scalable analyses ranging from intra-structure (or intra-feature) to macro-regional levels. This implies that a full-scale application of GIS in archaeology is as yet not practical or feasible. Furthermore, even though one can obtain very precise and accurate data by means of the state-of-the-art equipment and techniques, they will not fit well into the conventional site data collected by old, planimetrically less accurate methods. Since we inevitably face and have to accept

144

substantial margins of error that stem out of variability in the selection of points to be measured and other practical details, it may not be worth pursuing the highest precision and accuracy at the expense of limited resources. Not only the selection of the most appropriate mapping techniques, but also the required level of precision and accuracy needs to be carefully considered according to our research interest, field conditions resulting from varied natural and cultural formation processes, expertise of field crew, and available data. Given that all issues in relation to data acquisition, conversion, integration, are still in the experimental stage, it is the time to thoroughly examine from various viewpoints the potentials of each technique in hands such as supervised and unsupervised classifications of ground features, micro-digital terrain modeling, 3D-architectural modeling, oblique aerial photography, and high-resolution satellite imagery. Although it will surely take a substantial amount of time, funds, and labor of experts from different fields to fully assess those techniques, once it is done, we can pick up techniques most appropriate for our research objectives thereafter, based on the understandings of both their merits and demerits. Under no circumstances should we adopt them without deliberate consideration. Inefficient applications will not only waste precious resources, but also unnecessarily detach us away from our own duties such as explanatory explorations of material remains and, if temporarily, lead us to become absorbed merely in technology. We should keep in mind that GIS and other related techniques are nothing but research tools.

145

6.3.

Tight Budget and “GIS-Phobia”

Tight budget may not allow most archaeological projects to consider adopting certain new techniques. In the United States, archaeologists tend to be compelled to individually and annually or biannually seek their research funds, whereas their colleagues in Europe and other regions of the world have relatively easier access to multi-year funding. This is borne out by the fact that many of the multidisciplinary research projects that implement digital site mapping and related technical examinations are based in European institutions with greater long-term stability and personnel support (cf. Bard et al. 2003; Campana and Francovich 2003; Cavalli et al. 2003; Johnson 2005; Lambers 2004). Given the above difference, important future developments in the archaeological application of GIS are more likely to come out of major European projects. Limited resources not only preclude technical examination, but also affect the feasibility of site mapping itself. Although my mapping task in Pachacamac was blessed with excellent equipment and software both in the lab and field, many archaeologists usually have to reconcile themselves to use, at best, moderate-performance equipment and inexpensive software for their works. Thus, for those who are constantly plagued by financial constraints, the idea of “tailor-made Archaeological Information Systems,” which has been advocated by a group of forward-thinking archaeologists (Aldenderfer 2001; Orton 2005), would be nothing more than theoretical ideals. More importantly, impossible to overlook is the fact that such financial distress leads many archaeologists

146

to suffer from “GIS-phobia” – a persistent and irrational fear of the new technology that compels them to avoid it, despite the awareness and reassurance that it is not harmful. In order for GIS and related techniques to achieve further developments with greater assistance from archaeologists, it is critical to create and improve an environment where everyone can have easier and equal access to GIS and data sources. In this regard, the development of free software such as GRASS and KASHMIR 3D are quite encouraging (GRASS Development Team 2005; Sugimoto 2002). With its ultimate goals of storing every piece of existing spatial information within a single knowledge system and making it available free to the general public, GLOBALBASE also sets out architecture of great promise (Mori 2005, n.d.). This system enables us to share map information linked to each other through the WWW and to go freely back and forth between them, irrespective of the differences in coordinate system and whereabouts of map information. It no longer requires any resources except for a computer connected to the internet. The only fear is that the system relies exclusively on the spirit of international volunteerism as with the case of WWW and open-source software. Although the basic philosophy of the system is excellent, its feasibility and practicality are highly questionable. As of May 2005, there seems to be no map data of Andean regions usable for archaeological purposes.

6.4.

Limited Availability of Appropriate Training

In addition to the issues about data quality and tight budget, there is one more issue

147

to be considered in archaeological applications of GIS: the scarcity of intensive training program in GIS and related techniques specifically designed for archaeologists. There seem to have been a growing number of Anthropology departments that accept those techniques as one of the required research tools and encourage their students to train themselves. However, the introductory courses taught in many departments do not necessarily cover the theories and methods required for archaeological site mapping and subsequent data manipulation and analysis. The procedures that I used in my site mapping (see Appendix B) actually went far beyond the scope of introductory courses. Softcopy phogrammetry techniques in particular are not involved even in the advanced courses on remote sensing and image processing and thus need to be studied on your own. Very few universities in the United States, Britain, and Australia provide comprehensive training in GIS and remote sensing techniques specifically designed for archaeologists. Notable exceptions include the University of Arkansas, the University of California at Santa Barbara, Boston University, Rutgers University, the University of York, University College at London, and the University of Sydney (Aldenderfer 2001). It is obvious that there will be a steady demand among archaeologists for intensive GIS training over the next decade. Introducing a regular program of GIS from Geography and tailoring it to the specific needs of archaeologists and anthropologists in general is urgently needed.

148

6.5.

Bridging the Contrasting Approaches

When the issues raised above are actively debated and hopefully resolved, we should be able to focus our attention to the theoretical aspects of GIS applications in archaeology. One of the apparent theoretical issues is the role that GIS may play in reconciling the widening gap between the processual and postprocessual approaches and interest in spatial phenomena. As noted in Chapter 3, early GIS applications in archaeology (e.g., Allen et al. 1990 and Gaffney and Stančič 1991) were limited largely to “regional landscape-based studies” (Wheatley and Gillings 2002:235), although there has been an increasing recognition in recent years of the need for macro-regional modeling of human settlements. In contrast to this expansion of the geographical coverage of processual studies, many archaeologists in the postprocessual camp have insisted on fine-grained contextual analysis of both material and symbolic dimensions of human existence, which tend to force them to focus on small social arenas such as houses and communities. In order to fill in the gap between the two approaches, first of all, we should build a common framework for the accumulation and management of archaeological data that are different in type so that both sides can share the same data. The data to be stored in this framework are required to retain the same level of fine quality across the whole area from domestic to macro-regional levels so as to be able to be applied to the fine-grained contextual analysis at any given locus. The full scalability of GIS is critical to implementing this idea. However, the problems of data scarcity and tight budgets, which

149

I repeatedly noted above, will come into play here again. The quality and density of information across different scales would be a huge challenge that can be dealt only through a careful long-term plan and many years of multi-disciplinary investigation and collaboration. Another way in which the gap between the two approaches may be bridged is to find some key concepts that both sides share. “Distance” may be one of them. Distance is not a non-problematic universal measurement of the physical hiatus between any two points in cultural perception and conception. It varies depending on, for instance, the age, sex, and physical conditions of the person who travels and perceives it. Commonly, adults walk longer and faster than children, and a caravan of men and animals travels faster across a wider range of area when they do so without a heavy burden. Furthermore, the perceived distance may not necessarily be commensurate with the amount of time that they actually spend. It may also vary depending on certain factors such as the type of activity (e.g., trade, pilgrimage, expedition, messaging, farming, fishing, hunting, and so on). This concept of perceived distance would not only re-directs the interests of processualists to internal and non-material factors such as perception, experience, and movement to pursue the space that had been individually constructed through social actions, on one hand, but also helps postprocessualists to quantify the perceived landscape, on the other. It will allow for a refinement of the conventional ideal models such as central place theory and Thiessen polygons. By sorting the perceived distance

150

into different categories, you can generate a series of cost surfaces different in range and apply them to create the sub-models that are more faithful to the past landscape. Thus, critical to better integration of GIS in archaeology, I see the need for both processual and postprocessual camps to seek shared concepts and areas of shared concerns and actively adopt the strengths of each. These two schools are historically linked to each other and should not only be viewed but act in a complementary manner.

151

Notes

1 It should be recalled that a small-scale map covers a large area, while a large-scale map covers a small area. 2 Known GCPs are frequently unavailable prior to the research. They may be collected from topography maps as with the case of my mapping, although the method relies heavily on the experiences of surveyor and thus is very error-prone. 3 For more information about Ychsma Project, see their website at http://www.ulb.ac.be/philo/ychsma/. 4 They include Pyramid Complexes 1, 3, 4, 5, 8, 11, 12, 13, 15, the Temple of the Monkey, the Central Plaza, and the Pilgrims’ Plaza (Ychsma Project 2005b).

152

CHAPTER 7: CONCLUSION

7.1.

Broader Significance to Andean Archaeology

In conclusion, I discuss contributions of our site mapping of Pachacamac to Andean archaeology. First of all, our map represents the first major revision of the site map since Uhle (1903) published his over a century ago. It also is the first digital map, which integrates different data layers of properly georeferenced topographic and archaeological information: (1) contour lines, (2) elevation points, (2) interpolated DEM and DTM, (3) orthophotos, (4) digitized archaeological structures, (5) intensively looted areas, (6) areas where past excavations have been conducted. In order to contextualize the site in a broader perspective, I also obtained and integrated into the overlay a regional mosaic of SRTM-arc3 DEMs and georeferenced multispectral Landsat-7 ETM+ scenes, both of which cover the central Andean regions of Peru (See Figure 5-15). As a result, I am confident that I could achieve the short-term aim of our digital site mapping which was to create a better map than Uhle’s in terms of accuracy and information density. Furthermore, by completing this map within the confines of our limited resources by maximizing the potentials of GIS and related techniques, I feel that I also accomplished another objective; that is, to demonstrate the extent to which we can rely on GIS and

153

related technologies to create high-quality archaeological map given resource limitations. The last few years have seen a rapid increase in archaeological applications of GIS and related techniques among Andean archaeologists (Billman et al. 2005; Craig 2000; Craig and Aldenderfer 2003; Lambers 2004; Ruiz et al. 2005; Williams 2002, 2003; Williams et al. 2003). However, most of their site maps seem not to have been designed with long-term perspective and have not gone beyond a stand-alone data source for a single purpose. In this regard, our long-term objective to construct a comprehensive site database is very important. This aim will also be gradually achieved by integrating the results from on-going excavations and paleoenvironmental reconstruction analyses. Since data collection of paleoenvironmental variables is readily anticipated to increase with close ties to the GIS-based archaeology, it would be truly valuable for us to deliberate in advance as to how we should deal with the resulting datasets and as to what we can do with them. It is particularly important among others to examine how to plot those paleoenvironmental properties onto the three- and four-dimensional mapping space. Concurrently, this type of research will require unstinted on-site cooperation of specialists in physical and biological sciences. It will also be critical to solicit their input as to how best represent their perspectives, interests and findings in the GIS database. Secondly, our site map that includes both architectural and topographic features of the entire site of Pachacamac (including the sacred Urpay Wachak Lagoon) except for the northernmost margin (e.g., Sector V) will allow us to gain a holistic vision of the site. As I noted in Chapter 4, many preceding studies in Pachacamac have tended to focus their

154

primary attentions on the larger monumental architecture such as the temples in the sacred precinct and the walled multilevel platforms built during the Ychsma period. Finally, map making in general not only helps us enhance our spatial reasoning capacity but also can be considered as one of the valuable means by which we can record the current state of the site and assess impacts of both cultural and natural formation processes affecting the site. Many archaeological sites are being destroyed due to both human and natural activities. Actually, as of the summer of 2004, I recognized some changes in the state of preservation between our 48-year-old aerial photographs and the current state of the site. For instance, the east half of the pukio that is located immediately north of the Pyramid with Ramp 1 has been buried, and the modern settlements of A. H. Julio C. Tello have been encroaching from the east on one third of the Sector III. Unfortunately, the deliberation on nomination of Pachacamac as a World Heritage site was postponed in 1999 for the reason that a new management plan needs to be prepared and implemented (World Heritage Committee, UNESCO 1999:49). To this day, no long-term plans exist for the preservation and management of the site. Thus, it is incumbent upon us to document to the best of our ability the site as it exists today and its transformation in recent decades. Data generation and storage in digital format, furthermore, enable us to file those data with no degradation of original quality. It is hoped that this thesis and accompanying digital maps of the site of Pachacamac serve not only as a useful case study of GIS application in archaeology, but also as an integrated database for future investigations at Pachacamac.

155

7.2.

In the Near Future

In a new archaeology textbook that has just been published by Brian M. Fagan, “A brief history of archaeology: Classical times to the twenty-first century”, GIS together with remote sensing techniques are introduced as one of the most promising methods for archaeological research in the next decades (Fagan 2005:239). A few significant technological developments that have recently been taking place in both academic and commercial domains of Geographic Information Science will allow for further advancements in archaeological applications of GIS in the near future. The onset of two new approaches, object-oriented GIS (OO-GIS) and multi-dimensional GIS (3D GIS), is critically important (Wheatley and Gillings 2002:238-243). The combination of these new approaches will go beyond the potentials of the traditionally known two-dimensional systems based on vector/raster data models and may even remove the necessity to tailor a “plain vanilla” GIS package for archaeological use. OO-GIS seek to model the world as a series of discrete objects and categorize them into classes in which they share common features. Each class will have a nested hierarchical structure with “superclass” at the top, “subclass(es)” in the middle, “instance(s)” at the bottom, and the lower classes and instances inherit all of the traits of parent classes above them. Compared to the overly simplified abstractions by means of conventional data models, the nested hierarchical structure of class in OO-GIS seems to be much closer to the manner in which we routinely describe and understand the real world. 3D GIS, on the other hand, provide a new method to plot two points with exactly

156

the same X and Y coordinates using “voxel”, which is a rectangular cube bounded by eight grid nodes and is best thought of as the three-dimensional equivalent of a two-dimensional pixel (Harris and Lock 1996:309; Raper 1989; Worboys 1995:317). In order to raise the level of our understandings of GIS and take full advantage of the achievements of leading-edge initiatives noted above, more archaeologists should certainly be encouraged to train themselves in appropriate technology specially prepared in line with archaeological theories and methods and to explore the potentials of those new technological advancements. In this context, I believe, the discussions in this thesis and accompanying procedures manual (Appendix B) will help to lower the threshold of archaeological applications of GIS and work as an introductory lesson for potential users. Moreover, it would be ideal if we could more actively participate in the world-wide fora of GIS and expound our views from the standpoint of archaeology. Especially in terms of temporality (e.g., temporal GIS), as Wheatley and Gillings (2002:242) point out, there will be some issues through which archaeologists can make a significant contribution to the discipline of GIS as a whole. Though the persistent problem of tight budget seems not to be resolved very easily for the time being, through the improvements of technologies and infrastructures, the datasets available at hand will become more accurate and the cost will inversely decrease. Our financial constraints and attendant limitations of material resources will gradually be mitigated. It is simply a matter of time. In incremental steps, the accumulated knowledge and experience in the actual use of GIS will lead to vigorous discussions

157

among archaeologists on important issues such as a pursuit for archaeological standard to record, store, and manipulate archaeological data. These productive discussions will, in turn, set the stage for the developments of analytical methods which are the true worth of GIS application and our original purpose. Finally, I would like to emphasize again that my digital site mapping of Pachacamac is also just a tip of the iceberg, that is, our long-term aim to establish a digital/analog archive of interdisciplinary site database for subsequent explanatory endeavors to reconstruct past human life.

158

REFERENCES

Aldenderfer, Mark 1996 Introduction. In Anthropology, Space, and Geographic Information Systems, edited by Mark Aldenderfer and Herbert D. G. Maschner, pp.3-18. Oxford University Press, New York. 2001

Interview with Prof. Mark Aldenderfer: “Archaeologists have begun to adapt both

GIS and RS technologies to the investigation of individual archaeological sites, and it is here that both show great promise for the future.” Retrieved February 4th, 2005 from http://www.gisdevelopment.net/interview/previous/ev028.htm Aldenderfer, Mark and Herbert D. G. Maschner (eds.) 1996 Anthropology, Space, and Geographic Information Systems. Oxford University Press, New York. Allen, Kathleen M. Sydoriak 1996 Iroquoian Kandscapes: People, Environments, and the GIS Context. In New Methods, Old Problems: Geographic Information Systems in Modern Archaeological Research, Occasional Paper No.23, edited by Herbert D. G. Maschner, pp.198-222. Center for Archaeological Investigations, Southern Illinois University at Carbondale. Allen, Kathleen M. S., Stanton W. Green, and Ezra B. W. Zubrow (eds.) 1990

Interpreting Space: GIS and Archaeology. Taylor & Francis, New York.

159

Bailey, Trevor C. and Anthony C. Gatrell 1995

Interactive Spatial Data Analysis. Longmans Scientific & Technical, Harlow

Essex. Balkansky, Andrew K. In press Surveys and Mesoamerican Archaeology: The Emerging Macroregional Paradigm. Journal of Archaeological Research, in press. Bankes, G. H. A. 1972 Settlement Patterns in the Lower Moche Valley, North Peru, with Special Reference to the Early Horizon and Early Intermediate Periods. In Man, Settlement, and Urbanism, edited by P. J. Ucko, R. Tringham, and G. W. Dimbleby, pp.903-908. Schenkman Publishing Company, Cambridge. Bard, Kathryn A., Michael C. DiBlasi, Magaly Koch, Livio Crescenzi, A. Catherine D’Andrea, Rodolfo Fattovich, Andrea Manzo, Cinzia Perlingieri, Maurizio Forte, M. Scott Harris, Gerald H. Johnson, Stefano Tilia, and Bartolomeo Trabassi 2003 The Joint Archaeological Project at Bieta Giyorgis (Aksum, Ethiopia) of the Istituto Universitario Orientale, Naples (Italy), and Boston University, Boston (USA): Results, Research Procedures and Preliminary Computer Applications. In The Reconstruction of Archaeological Landscapes through Digital Technologies, edited by M. Forte and P. R. Williams, pp.1-13. BAR International Series 1151, 2003, Oxford.

160

Barrette, J. C. 1999 Chronologies of Landscape. In The Archaeology and Anthropology of Landscape, edited by P. Ucko and R. Layton, pp.21-30. Routledge, London. Bateson, Gregory 1972 Steps to an ecology of mind. Ballantine, New York. 1979 Mind and nature: A necessary unity. Bantam, New York. Billman, Brian R. 1996 The evolution of prehistoric political organizations in the Moche Valley, Peru. Ph.D. dissertation, Department of Anthropology, University of California at Santa Barbara. 1999a Settlement Pattern Research in the Americas. In Settlement Pattern Studies in the Americas: Fifty Years since Virú, edited by Brian R. Billman and Gary M. Feinman, pp.1-5. Smithsonian Institution Press, Washington, D.C. 1999b Reconstructing Prehistoric Political Economies and Cycles of Political Power in the Moche Valley, Peru. In Settlement Pattern Studies in the Americas: Fifty Years since Virú, edited by Brian R. Billman and Gary M. Feinman, pp.131-159. Smithsonian Institution Press, Washington, D.C. Billman, Brian R. and Gary M. Feinman (eds.) 1999 Settlement Pattern Studies in the Americas: Fifty Years since Virú. Smithsonian Institution Press, Washington, D.C.

161

Billman, Brian R., James Kenworthy, and Jennifer Ringberg 2005 Results of the 2004 Field Season at Cerro Leon, A Possible Early Intermediate Period Highland Colony in the Moche Valley, Peru. Paper presented at the 33rd Annual Midwest Conference on Andean and Amazonian Archaeology and Ethnohistory, University of Missouri, Columbia, MO. February 26-27, 2005. Binford, Lewis R. (ed.) 1977 For Theory Building in Archaeology. Academic Press, New York. Binford, Lewis R. 1981 Bones: Ancient Men and Modern Myths. Academic Press, New York. Boas, Franz 1888 The Central Eskimo. Sixth Annual Report of the Bureau of American Ethnology for the Years 1884-1885. Smithsonian Institution, Washington, D.C. Bogue, D. J. 1949 The Structure of the Metropolitan Community: A Study of Dominance and Subdominance. Horace M. Rackman School of Graduate Studies, University of Michigan, Ann Arbor. Campana, Stefano and Riccardo Francovich 2003 Landscape Archaeology in Tuscany: Cultural resource management, remotely sensed techniques, GIS based data integration and interpretation. In The Reconstruction of Archaeological Landscapes through Digital Technologies, edited by M. Forte and P. R. Williams, pp.15-27. BAR International Series 1151, 2003,

162

Oxford. Castelnau, Francis de 1854 Exédition dans les parties centrales de l’Amérique du Sud, troisieme partie: Antiquités des Incas et autres peuples anciens. Bertrand, Paris. Catherwood, Frederick 1844 View of Ancient Monuments in Central America, Chiapas, and Yucatan. Vizetally, London. Cattani, Maurizio 2005 Expectations, assessments and capabilities in managing archaeology through GIS. In Reading Historical Spatial Information from around the World: Studies of Culture and Civilization Based on Geographic Information Systems Data, The 24th International Research Symposium, pp.232-240. International Research Center for Japanese Studies, Japan. Cavalli, R.M., C. M. Marino, and S. Pignatti 2003 Hyperspectral airborne remote sensing as an aid to a better understandingand characterization of buried elements in different archaeological sites. In The Reconstruction of Archaeological Landscapes through Digital Technologies, edited by M. Forte and P. R. Williams, pp.29-32. BAR International Series 1151, 2003, Oxford. Childe, Gordon V. 1929 The Danube in Prehistory. Oxford University Press, Oxford.

163

1934 Neolithic Settlement in the West Scotland. Scottish Geographical Magazine, 50, pp.18-25. 1939 The Dawn of European Civilization, third edition. Kegan Paul, London. Chorley, Richard J. and Peter Haggett (eds.) 1967 Models in Geography. Methuen & Co, Ltd., London. Christaller, Walter 1933 Die zentralen Orte in Süddeutschland: Eine ökonomisch-geographische Untersuchung über die Gesetzmässigkeit der Verbreitung und Entwicklung der Siedlungen mit städtischen Funktionen. Fischer, Jena. Clarke, David. L. (ed.) 1977 Spatial Archaeology. Academic Press, London. Craig, Nathan 2000 Real-Time GIS Construction and Digital Data Recording of the Jiskairumoko Excavation, Peru. SAA Bulletin 18(1). Retrieved February 7th, 2005 from http://www.saa.org/publications/saabulletin/18-1/saa18.html Craig, Nathan and Mark Aldenderfer 2003 Preliminary Stages in the Development of a Real-Time Digital Data Recording System for Archareological Excavation Using ArcView GIS 3.1. Journal of GIS in Archaeology 1:11-22. ESRI. Retrieved February 7th, 2005 from http://www.esri.com/library/journals/archaeology/volume_1/realtime_recording .pdf

164

Crawford, O. G. S. 1921 Man and his Past. Oxford University Press, Oxford. Deuel, Leo. 1969 Flights into Yesterday: The story of aerial archaeology. St. Martin’s Press, New York. Dillehay, T. 1976 Competition and Cooperation in a Prehispanic Multi-Ethnic System in the Central Andes. Ph.D. dissertation, Department of Anthropology, University of Texas at Austin. University Microfilms, Ann Arbor. Donnan, Christopher B. 1973 Moche Occupation of the Santa Valley, Peru. University of California Publications in Anthropology, Vol. 8. University of California Press, Los Angeles. Duncan, B. 1959 Population Distribution and Manufacturing Activity: The Non-Metropolitan United States in 1950. Papers and Proceedings, Regional Science Association 5:95-103. Earle, T. K. 1972 Lurin Valley, Peru: Early Intermediate Period Settlement Development. American Antiquity 37:467-477. Eeckhout, Peter 2003 Ancient Monuments and Patterns of Power at Pachacamac, Central Coast of

165

Peru. In Beiträge zur Allgemeinen und Vergleichenden Archäologie, Band 23, pp.139-182. Verlag Philipp von Zabern GmbH, Mainz. Eling, H. H., Jr. 1988 The role of irrigation networks in emerging societal complexity during late pre-Hispanic times, Jequetepeque Valley, North Coast, Peru. Ph. D. dissertation, University of Texas at Austin. ESRI Japan March 5th, 2002 Orthorectification of Aerial Photography. Retrieved March 24th, 2004 from http://www.esrij.com/support/erdas/faq/camera_model/ cameramodel.html Fagan, Brian M. 2005 A brief history of archaeology: Classical times to the twenty-first century. Prentice Hall, New Jersey. Farley, James A. and Anne Gisiger 1996 Managing the Infrastructure: The Use of a Corporate Metadata for Archaeology. In New Methods, Old Problems: Geographic Information Systems in Modern Archaeological Research, Occasional Paper No.23, edited by Herbert D. G. Maschner, pp.275-299. Center for Archaeological Investigations, Southern Illinois University at Carbondale. Forte, Maurizio 2003 Mindscape: Ecological Thinking, Cyber-Anthropology and Virtual

166

Archaeological Landscapes. In The Reconstruction of Archaeological Landscapes through Digital Technologies, pp.95-108. BAR International Series 1151, 2003, Oxford. Forte, Maurizio and Patrick Ryan Williams (eds.) 2003 The Reconstruction of Archaeological Landscapes through Digital Technologies. BAR International Series 1151, 2003, Oxford. Fox, Cyril 1923 The Archaeology of the Cambridge Region. Cambridge University Press, Cambridge. 1932 The Personality of Britain. National Museum of Wales, Cardiff. Franco Jordan, Regulo G. 1998 La Pirámide con Rampa No. 2 de Pachacamac: Excavaciones y Nuevas Interpretaciones. Trujillo, Peru. Gaffney, V. and Z. Stančič 1991 GIS Approaches to Regional Analysis: A Case Study of the Island of Hvar. Filozofska Fakulteta, Ljubljana. 1992 Didorus Siculus and the Island of Hvar, Dalmatia: Testing the Text with GIS. In Computer Applications and Quantitative Methods in Archaelogy 1991, edited by G. R. Lock and J. Moffet, pp.113-125. BAR International Series S577. Tempus Reparatum, Oxford.

167

Gifford, E. and Alfred Kroeber 1937 Culture Element Distributions: EV, Pomo. University of California Publications in American Archaeology and Ethnology 37, pp.117-254. Global Land Cover Facility 2004 Earth Science Data Interface. Retrieved March 24th, 2004 from http://glcf.umiacs.umd.edu/index.shtml Gradmann, R. 1906 Beziehung zwischen Pflanzengeographie und Siedlungsgeschichte. Geographische Zeitschrift 12:305-325. GRASS Development Team June 21st, 2005 Geographic Resources Analysis Support System. Retrieved June 22nd, 2005 from http://grass.itc.it/ Green, Stanton W. 1990 Approaching Archaeological Space: An Introduction to the Volume. In Interpreting Space: GIS and Archaeology, edited by Kathleen M. S. Allen, Stanton W. Green, and Ezra B. W. Zubrow, pp.3-8. Taylor & Francis, New York. Grimes, W. F. 1945 Early Man and Soils of Anglesey. Antiquity, 19, pp.169-174. Hägerstrand, T. 1952 The Propagation of Innovation Waves. Lund Studies in Geography, Series B, Human Geography 4:3-9.

168

Haggett, Peter 1966 Locational Analysis inhuman Geography. St. Martin’s Press, New York. Harris, T. M. and G. R. Lock 1996 Multi-dimensional GIS exploratory approaches to spatial and temporal relationships within archaeological stratigraphy. In Interpreting the past, edited by H. Kamermans and K. Fennema, pp.307-316. Analecta Prehistorica Leidensia 28, Leiden University Press. Hasenstab, Robert J. 1996 Settlement As Adaptation: Variability in Iroquois Village Site Selection As Inferred Through GIS. In New Methods, Old Problems: Geographic Information Systems in Modern Archaeological Research, Occasional Paper No.23, edited by Herbert D. G. Maschner, pp.223-241. Center for Archaeological Investigations, Southern Illinois University at Carbondale. Hellmich, M. 1923 Die Besiedlung Schlesiens in vor - und frűhgeschichtlicher Zeit. Preuss und Jűnger, Breslau. Hodder, Ian 1977 Some New Directions in the Spatial Analysis of Archaeological Data at the Regional Scale (Macro). In Spatial Archaeology, edited by David. L. Clarke, pp.223-351. Academic Press, London. 1984 Archaeology in 1984. Antiquity 58:25-32.

169

Hodder, Ian and Clive Orton 1976 Spatial Analysis in Archaeology. Cambridge University Press, Cambridge. Hodge, Frederick W. (ed.) 1907-1910 Handbook of American Indians North of Mexico, 2 pts. Bureau of American Ethnology, Bulletin 30. Washington, D.C. Hogg, A. H. 1943 Native Settlements of Northumberland. Antiquity, 17, pp.136-147. Jensen, John R 2000 Remote Sensing of the Environment: An Earth Resource Perspective. Prentice Hall, New Jersey. Jet Propulsion Laboratory January 24th, 2000 UTM Zones. Retrieved February 9th, 2004 from http://shookweb.jpl.nasa.gov/validation/UTM/default.htm Johnson, Ian 2005 Studying Angkor: Integration of GIS, GPS, Remote Sensing and Contemporary Observation. In Reading Historical Spatial Information from around the World: Studies of Culture and Civilization Based on Geographic Information Systems Data, The 24th International Research Symposium, pp.188-205. International Research Center for Japanese Studies, Japan. Kluckhohn, Clyde 1939 The Place of Theory in Anthropological Studies. Philosophy of Science, Vol. 6, no.

170

3, pp.328-344. 1940 The Conceptual Structure in Middle American Studies. In The Maya and Their Neighbors, edited by C. L. Hay and others, pp.41-51. Appleton-Century, New York. Kosok, Paul 1965 Life, Land and Water in Ancient Peru. Long Island University Press, New York. Kroeber, Alfred 1939 Cultural and Natural Areas of Native North America. University of California Publications in Anthropology, Vol. 38. Kvamme, Kenneth L. 1993 Spatial Analysis and GIS: An Integrated Approach. In Computing the Past: Computer Applications and Quantitative Methods in Archaeology – CAA92, edited by Jens Andresen, Torsten Madsen, and Irwin Scollar, pp.91-103. Aarhus University Press, Aarhus. 1999 Magnetic Survey at Navan Fort, Northern Ireland. Retrieved September 23rd, 2004 from http://www.cast.uark.edu/~kkvamme/navan/navan.htm Lambers, Karsten 2004 The Geoglyphs of Palpa (Peru): Documentation, Analysis, and Interpretation. Ph.D. dissertation, University of Zurich. Launhardt, W. 1882 Die Bestimmung des zweckmäßigsten Standortes einer gewerblichen Anlage. Zeitschrift des Vereins deutscher Ingenieure 26:107-116.

171

Leica Geosystems GIS & Mapping Division 2002a ERDAS Field Guide, Sixth Edition. ERDAS LLC, Atlanta, Georgia. 2002b

IMAGINE OrthoBASE User's Guide: Including IMAGINE OrthoBASE Pro. ERDAS

LLC, Atlanta, Georgia. Lillesand, Thomas M., Ralph W. Kiefer, and Jonathan W. Chipman 2004 Remote Sensing and Image Interpretation, fifth edition. John Wiiley & Sons, New York. Lo, C. P. and Albert K. W. Yeung 2002 Concepts and Techniques of Geographic Information Systems. Prentice Hall, New York. Lock, G. R. and Z. Stančič [eds.] 1995 Archaeology and Geographic Information Systems: A European Perspective. Taylor & Francis, London. Longley, Paul A., Michael F. Goodchild, David J. Maguire, and David W. Rhind 2001 Geographic Information Systems and Science. John Willey & Sons, Ltd, Chichester. Mackinder, H. J. 1904 The Geographical Pivot of History. The Geographical Journal 23(4):421-437. 1909 Geographical Conditions Affecting the British Empire: I. The British Islands. The Geographical Journal 33(4):462-476.

172

Madry, Scott L. H. and Lynn Rakos 1996 Line-of-Sight and Cost-Surface Techniques for Regional Research in the Arroux River Valley. In New Methods, Old Problems: Geographic Information Systems in Modern Archaeological Research, Occasional Paper No.23, edited by Herbert D. G. Maschner, pp.104-126. Center for Archaeological Investigations, Southern Illinois University at Carbondale. Marble, Duane F. 1990 The Potential Methodological Impact of Geographic Information Systems on the Social Sciences. In Interpreting Space: GIS and Archaeology, edited by Kathleen M. S. Allen, Stanton W. Green, and Ezra B. W. Zubrow, pp.9-21. Taylor & Francis, New York. Marble, Duane F., Hugh Calkins, and Donna Peuquet (eds.) 1984 Basic readings in Geographic Information Systems. SPAD Systems, Williamsville, NY. Markham, Sir Clements R. 1856 Cuzco: A Journey to the Ancient Capital of Peru. Chapman and Hall, London. 1871 On the Geographical Positions of the Tribes Which Formed the Empire of the Yucas. Journal of the Royal Geographical Sociey 41:281-338. 1892 A History of Peru. Charles H. Siegel, Chicago. 1910 The Incas of Peru. E. P. Dutton, New York.

173

Martin, Paul, Carl Lloyd, and Alexander Spoehr 1938 Archaeological Works in the Ackman-Lowry Area, Southwestern Colorado, 1937. Field Museum of Natural History Anthropological Series 23(2):217-304. Chicago. Martin, Paul and John Rinaldo 1939 Modified Basket Maker Sites, Excavations at a Mogollon Village, Western New Mexico, 1939. Field Museum of Natural History Anthropological Series 23(3):305-499. Chicago. Maschner, Herbert D. G. (ed.) 1996 New Methods, Old Problems: Geographic Information Systems in Modern Archaeological Research, Occasional Paper No.23. Center for Archaeological Investigations, Southern Illinois University at Carbondale. Maschner, Herbert D. G. 1996 Geographic Information Systems in Archaeology. In New Methods, Old Problems: Geographic Information Systems in Modern Archaeological Research, Occasional Paper No.23, edited by Herbert D. G. Maschner, pp.1-21. Center for Archaeological Investigations, Southern Illinois University at Carbondale. McGee, R. Jon and Richard L. Warms 1996 Anthropological Theory: An Introductory History. Mayfield Publishing Company, Mountain View, CA.

174

Michczyński, Adam, Peter Eeckhout, and Anna Pazdur 2003

14 C

Absolute Chronology of Pyramid III and the Dynastic Model at Pachacamac,

Peru. Radiocarbon, Vol. 45, Nr. 1, pp.59-73. Middendorf, E. W. 1893-1895 Peru, 3 vols. Robert Oppenheim, Berlin. Mori, Hirohisa n.d. Globalbase Project. Retrieved April 8th, 2005 from http://globalbase.sourceforge.jp/home/en/ 2005 Development of the distributed GIS architecture for archaeology. In Reading Historical Spatial Information from around the World: Studies of Culture and Civilization Based on Geographic Information Systems Data, The 24th International Research Symposium, pp.106-115. International Research Center for Japanese Studies, Japan. Mortillet, Gabriel de 1897 Formation de la nation française. Alcan, Paris. Moseley, Michael E. 1975 The Maritime Foundations of Andean Civilization. Cummings Publishing Company, Menlo Park, California. Nilsson, Sven 1868 The Primitive Inhabitants of Scandinavia, third edition, translated by J. Lubbock. Longsman, Green, London.

175

Nolan, J. L. 1980 Prehispanic Irrigation and Polity in the Lambayeque Sphere, Peru. Ph.D. dissertation, Columbia University. Ormsby, Tom, Eileen Napoleon, Robert Burke, Carolyn Groessl, and Laura Feaster 2001 Getting to Know ArcGIS Desktop: Basics of ArcView, ArcEditor, and ArcInfo. ESRI Press, New York. Orton, Clive 2005 Adding Values to GIS – Some Spatial-analytical Techniques and Their Applications. In Reading Historical Spatial Information from around the World: Studies of Culture and Civilization Based on Geographic Information Systems Data, The 24th International Research Symposium, pp.148-160. International Research Center for Japanese Studies, Japan. Palerm, Angel and Eric R. Wolf 1957 Ecological Potential and Cultural Development in Mesoamerica. In Studies in human Ecology, Pan American Union Socail Sciences Monograph, no. 3, pp.28-42. Washington, D.C. Paredes B., Ponciano 1991 Pachacamac: Murallas y Caminos Epimurales. Boletín de Lima, No. 74: 85-95. Paredes B., Ponciano and Régulo G. Franco J. 1985 Excavaciones en La Huaca Pintada o El Templo de Pachacamac. Boletín de Lima 7(41):78-84.

176

Patterson, Thomas C. 1966 Pattern and Process in the Early Intermediate Period Pottery of the Central Coast of Peru. University of California Publications in Anthropology, Vol. 3. University of California Press, Berkeley. 1971 Central Peru: Its Population and Economy. Archaeology 24:316-321. Petrie, Sir W. M. F. 1899 Sequences in Prehistoric Remains. Journal of the Royal Anthropological Institute of Great Britain and Ireland 29:295-301. 1939 The Making of Egypt. Sheldon, London. Phillips, Philip, James A. Ford, and James B. Griffin 1951 Archaeological Survey in the lower Mississippi Alluvial Valley, 1940-47. Papers of the Peabody Museum, Vol. 25. Cambridge, Massachusetts. Pitt-Rivers, A. H. L. F. 1887 Excavations in Cranborne Chase, Vol. 1. Privately printed. 1888 Excavations in Cranborne Chase, Vol. 2. Privately printed. 1892 Excavations in Cranborne Chase, Vol. 3. Privately printed. 1898 Excavations in Cranborne Chase, Vol. 4. Privately printed. Proulx, Donald A. 1985 An Analysis of the Early Cultural Sequence of the Nepeña Valley, Peru. Research Report, No. 25. Department of Anthropology, University of Massachusetts, Amherst.

177

Raper, J. (ed.) 1989 Three dimensional applications in geographical information systems. Taylor & Francis, London. Ravines, Rogger n.d. Pachacamac: Santuario Universal. Editorial Los Pinos E.I.R.L., Lima, Peru. Ray, Christopher 1991 Building a Scale Reconstruction Model of the Sun Temple at Pachacamac. In Pachacamac: A reprint of the 1903 edition by Max Uhle. University Museum Press, University of Pennsylvania. Reiss, Wilhelm and Alphons Stüdel 1880-1887 The Necropolis of Ancón in Peru, 3 vols. Berlin. Rostworowski, Maria 1992 Pachacamac y el Señor del los Milagros: Una Trayectoria Milenaria. Instituto de Estudios Peruanos, Lima. Rowe, John H. 1954 Max Uhle, 1856-1944, a Memoir of the Father of Peruvian Archaeology. University of California Publications in American Archaeology and Ethnology 46(1). Berkeley. Ruggles, Clive L. N. and David J. Medyckyj-Scott 1996 Site Location, Landscape Visibility, and Symbolic Astronomy: A Scottish Case Study. In New Methods, Old Problems: Geographic Information Systems in Modern

178

Archaeological Research, Occasional Paper No.23, edited by Herbert D. G. Maschner, pp.127-146. Center for Archaeological Investigations, Southern Illinois University at Carbondale. Ruiz, Alvaro, Gerbert Ascensios, Keith Carlson, Nathan Craig, Winifred Creamer, and Jonathan Haas 2005 Mapping on Different Scales and GIS in the Norte Chico: Enhancements to traditional archaeological Excavation. Paper presented at the 33rd Annual Midwest Conference on Andean and Amazonian Archaeology and Ethnohistory, University of Missouri, Columbia, MO. February 26-27, 2005. SAN (Servicio Aerofotográfico Nacional) n.d. Nuestro Equipamiento. Retrieved March 24th, 2004 from http://www.fap.mil.pe/Grupos%20Aereos/diraf/docs/equipamiento.htm Schäffle, G. F. 1878 Bau und Leben des sozialen Korpers 3. Tübingen. Schaedel, Richard P. 1951 The Lost Cities of Peru. The Scientific American, Aug. 1951, pp.18-23. Schliz, A. 1906 Der schnurkeramische Kulturkreis und seine Stellung zu der anderen neolithischen Kulturformen in Sudwestdeutchland. Zeitschrift für Ethnologie 38:312-345.

179

Schreiber, Katharina J. 1999 Regional Approaches to the Study of Prehistoric Empires: Examples from Ayacucho and Nasca, Peru. In Settlement Pattern Studies in the Americas: Fifty Years since Virú, edited by Brian R. Billman and Gary M. Feinman, pp.160-171. Smithsonian Institution Press, Washington, D.C. Shimada, Izumi 1991 Pachacamac Archaeology: Retrospect and Prospect. In Pachacamac: A reprint of the 1903 edition by Max Uhle. University Museum Press, University of Pennsylvania. 2004 Pachacamac (Ychsma). In Enciclopedia Archeologica, edited by Massimo Bray. Instituto della Enciclopedia Italiana, Roma. Shimada, Izumi, Rafael Segura, John Jones, María Rostworowski, Hirokatsu Watanabe 2003 The Pachacamac Archaeological Project: Results of the First Season and Their Implications. Paper presented at the 22nd Annual Northeast Conference on Andean Archaeology and Ethnohistory, Harvard University, Cambridge, MA. November 1-2, 2003. Silverman, Helaine 2002 Ancient Nasca Settlement and Society. University of Iowa Press, Iowa City. Squier, E. George 1877 Peru: Incidents of Travel and Exploration in the Land of the Incas. Macmillan and Co., London.

180

Stanish, Charles 1999 Settlement Pattern Shifts and Political Ranking in the Lake Titicaca Basin, Peru. In Settlement Pattern Studies in the Americas: Fifty Years since Virú, edited by Brian R. Billman and Gary M. Feinman, pp.116-128. Smithsonian Institution Press, Washington, D.C. 2003 Ancient Titicaca : The Evolution of Complex Society in Southern Peru and Northern Bolivia. University of California Press, Berkeley. Stephens, John L. 1837

Incidents of Travel in Egypt, Arabia Petraea and the Holy Land, 2 vols. Harper,

New York. 1838

Incidents of Travel in Greece, Turkey, Russia and Poland, 2 vols. Harper, New

York. 1841

Incidents of Travel in Central America, Chiapas and Yucatan, 2 vols. Harper,

New York. 1843

Incidents of Travel in Yucatan, 2 vols. Harper, New York.

Steward, Julian H. (ed.) 1946-1950 The Handbook of South American Indians, 6 vols. Bureau of American Ethnology, Bulletin 143. Washington, D.C. Steward, Julian H. 1947 American Culture History in the Light of South America. Southwestern Journal of Anthropology 3:85-107.

181

1949 Cultural Causality and Law: A Trial Formulation of the Development of Early Civilizations. American Anthropologist 51:1-27. 1955 Theory of Culture Change. University of Illinois Press, Urbana. Steward, Julian H. and Frank M. Setzler 1938 Function and Configuration in Archaeology. American Antiquity 4(1):4-10. Strong, William Duncan 1936 Anthropological Theory and Archaeological Fact. In Essays in Anthropology, edited by R. H. Lowie, pp.359-368. University of California Press, Berkeley. Sugimoto, Tomohiko February 2nd, 2002 3D Landscapes CG & Maps. Retrieved March 15th, 2005 from http://www.kashmir3d.com/index-e.html Thioulouse, Jean, Daniel Chessel, Sylvain Dolédec, and J. M. Olivier 1997 ADE-4: A Multivariate Analysis and Graphical Display Software. Statistics and Computing 7(1):75-83. Thomas, Julian 2001 Archaeologies of Place and Landscape. In Archaeological Theory Today, edited by Ian Hodder, pp.165-186. Polity Press, Cambridge. Tilley, Chris 1994 A Phenomenology of Landscape: Places, Paths and Monuments, first edition. Berg, Oxford.

182

Tomlin, Charles Dana 1990 Geographic Information Systems and Cartographic Modelling. Prentice Hall, New Jersey. Trigger, Bruce G. 1986 Prospects for a World Archaeology. World Archaeology 18(1):1-20. 1989 A History of Archaeological Thought. Cambridge University Press, Cambridge. Tschauner, Hartmut 2001 Socioeconomic and Political Organization in the Late Prehispanic Lambayeque Sphere, Northern North Coast of Peru. Ph.D. dissertation, Harvard University. Tschudi, Johann 1869 Reisen Durch Súd-Amerika, 5 vols. Leipzig. Uhle, Max 1903 Pachacamac: Report of the William Pepper, M. D., LL. D., Peruvian Expedition of 1896, translated by C. Grosse. Department of Archaeology, The University of Pennsylvania, Philadelphia. Von Thünen, J. H. 1826 Die isolierte Staat in Beziehung auf Landwirtshaft und Nationalökonomie. Pergamon Press, New York. Wahle, E. 1915 Urwald und offense Land in ihrer Bedeutung für die Kulturentwickelung. Archiv für Anthropologie, N. S. 13:404-413.

183

Weber, Alfred 1909 Über den Standort der Industrie: Reine Theorie des Standorts. Tübingen. Westcott, Konnie L. and R. Joe Brandon (eds.) 2000 Practical Applications of GIS for Archaeologists: A Predictive Modeling Toolkit. Taylor & Francis, London. Wheatley, David 1996 The Use of GIS to Understand Regional Variation in Earlier Neolithic Wessex. In New Methods, Old Problems: Geographic Information Systems in Modern Archaeological Research, Occasional Paper No.23, edited by Herbert D. G. Maschner, pp.75-103. Center for Archaeological Investigations, Southern Illinois University at Carbondale. Wheatley, David and Mark Gillings 2002 Spatial Technology and Archaeology: The Archaeological Applications of GIS. Taylor & Francis, New York. Willey, Gordon R. 1953 Prehistoric Settlement Patterns in the Virú Valley, Peru. Bureau of American Ethnology, Bulletin 155. Washington, D.C. 1959 Aerial Photographic Maps as Survey Aids in Virú Valley. In The Archaeology at Work, edited by Robert F. Heizer, pp.203-207. Harper & Brothers, New York. Willey, Gordon R. and Jeremy A. Sabloff 1993 A History of American Archaeology, Third Edition. W. H. Freeman and

184

Company, New York. Williams, Patrick Ryan 2002 A Re-examination of Disaster Induced Collapse in the Case of the Andean Highland States: Wari and Tiwanaku. World Archaeology 33(3). 2003 Hydraulic Landscapes and Social Relations in the Middle Horizon Andes. In The Reconstruction of Archaeological Landscapes through Digital Technologies, edited by M. Forte and P. R. Williams, pp.163-172. BAR International Series 1151, 2003, Oxford. Williams, Patrick Ryan, Deborah Blom, Nicole Couture, Christopher Dayton, John Janusek, and Benjamin Vining 2003 Visualizing the Urban and Monumental Components of the Tiwanaku State: New Perspectives from Geophysics in the Andean Altiplano. Paper presented at the 22nd Annual Northeast Conference on Andean Archaeology and Ethnohistory, Harvard University, Cambridge, MA. November 1-2, 2003. Wilson, David J. 1983 The Origins and Development of Complex Prehispanic Society in the Lower Santa Valley, Peru: Implications of Theories of State Origins. Journal of Anthropological Archaeology 2, pp.209-276. 1988 Prehispanic settlement patterns in the lower Santa Valley, Peru: a regional perspective on the origins and development of complex North Coast society. Smithsonian, Washington, D.C.

185

Winsborough, Barbara, John Jones, Lee Newsom, Izumi, Shimada, Rafael Segura, and María Rostworowski 2005 Paleoenvironmental Reconstruction at Pachacamac: Integrating Diatom, Pollen, Macrobotanical, and Archaeological Data. Paper presented at the 33rd Annual Midwest Conference on Andean and Amazonian Archaeology and Ethnohistory, University of Missouri, Columbia, MO. February 26-27, 2005. Woolridge, S. W. and D. L. Linton 1933 The Loam-terrains of Southeast England and their Relation to its Early History. Antiquity, 7, pp.297-310. Worboys, M. F. 1995 GIS: a computing perspective. Taylor & Francis, London. World Heritage Committee, UNESCO 1999 Convention Concerning the Protection of the World Cultural and Natural Heritage. 23rd session at Paris, UNESCO Headquarters, July 5-10, 1999. Retrieved November 27, 2004 from http://whc.unesco.org/archive/repbur99.pdf Ychsma Project 2005a Topographic and Planimetric Survey: Project Description. Retrieved March 12th, 2005 from http://www.ulb.ac.be/philo/ychsma/en/topodescription.html 2005b Current results of Topography, Mapping, and CAD Processing. Retrieved March 12th, 2005 from http://www.ulb.ac.be/philo/ychsma/en/avancement.html 2005c Up-to-date map of Pachacamac, December 2004 (Directors: P. Eeckhout and C.

186

Farfán; Survey and Mapping: V. Decart) Retrieved March 12th, 2005 from http://www.ulb.ac.be/philo/ychsma/en/pdf/21.planen2004.pdf

APPENDICES

187

APPENDIX A: HARDWARES AND SOFTWARES

In order to make it easier for the interested users to replicate or consult my work, I have kept it in mind that I use major hardware and software that have larger market share. The specification of each device is listed below.

Table A-1. List of hardware and software utilized. Image Scanner

PC 1

GRAPHTEC, CS2000 Model

Dell, WORKSTATION PWS340

Processor

Intel, Pentium 4 2.53GHz (uniprocessor)

RAM

1.024GB

Graphic Card

NVIDIA, Quadro4 700 XGL

Operation System

Microsoft, Windows XP Professional + Service Pack 2

GIS Software

ESRI, ArcGIS 8.3 (ArcInfo License)

Image Processing

Leica Geosystems, ERDAS Imagine 8.6

Software

PC 2

Model

No brand (assembled; PC/AT compatible)

Processor

Intel, Pentium 4 1.8GHz Northwood (uniprocessor)

RAM

1.024GB (512MB DDR SDRAM PC2700 CL3 x 2)

Graphic Card

ATI Technologies, RADEON 9200 ATLANTIS

Operation System

Microsoft, Windows XP Professional + Service Pack 2

GIS Software

ESRI, ArcGIS 8.3 (ArcView License) + 3 extensions of Spatial Analyst, 3D Analyst, Geostatistical Analyst

Image Processing

Adobe, Photoshop CS and Illustrator CS

Software GPS Control Receiver GPS Rover Receiver Software for Data

Leica Geosystems, GS20 Professional Data Mapper (Firmware 1.15.29) + Leica Geosystems, AT501 Antenna + Generic Tripod Leica Geosystems, GS20 Professional Data Mapper (Firmware 1.15.29) + Leica Geosystems, AT501 Antenna + Leica, GLS111 Plumbing Pole

Leica Geosystems, GIS DataPRO

Post-processing Plotter

Hewlett Packard, Designjet 755CM and Designjet 5500PS

188

APPENDIX B: PROCEDURES MANUAL

B.1. How to georeference a scanned image

STEP 1

Start ArcMap. You will be prompted to choose three options with which you

wish to start using ArcMap: (1) “A new empty map,” (2) “A template,” or (3) “An existing map.” Since you do not have either template or existing map, choose the first. STEP 2

Open the scanned image in ArcMap by selecting “Add Data” from the “File”

menu and locating the file path. The image will be displayed together with a warning message (Figure B-1). This message will always come up when you add a new data layer without any spatial reference information specified. Hit “OK.”

Figure B-1. A warning message.

STEP 3

If you cannot find the “Georeferencing” toolbar either plugged in the

application window or floating on the screen, select “Toolbars > Georeferencing” in the “View” menu. The toolbar will appear somewhere on the screen (Figure B-2). You can drag and move it as you want.

Figure B-2. The “Georeferencing” toolbar of ArcMap.

189

STEP 4

Now you define a series of control points in order to resample the image.

Control points are defined at the points that are easily recognized on the original image and have the exact coordinates. In my case, I used the points at which the X and Y grids intersect each other (49 points in total). Click the “Add Control Points” icon that is located second right in the “Georeferencing” toolbar. The arrow cursor will be turned into a crosshair. STEP 5

Click at the point where you want to place a control point. In so doing, you

may want to use the “Zoom In” and/or “Pan” tools so that you can more easily recognize the exact place where you should define the control point. When you click at a point, a light green cross will be marked at the spot and a black line will stretch from the spot (Figure B-3). Then, you right-click and select “Input X and Y” from the small pop-up list that will appear right next to the crosshair. If you miss the spot and want to try again, you can undo it by selecting “Cancel Point.”

Figure B-3. Locating the control point. The point will be marked at the spot with a light-green crosshair.

190

STEP 6

Correctly type in the X-Y coordinates in the “Enter Coordinates” dialog window

and hit “OK.” Repeat this as many times as the number of control points you have. A defined control point will be marked as a red crosshair (Figure B-4). Sometimes the image may disappear from the map display window after defining a control point. On such an occasion, click the “Full Extent” icon in the “Tools” toolbar. The image will come up again. If you want to cancel the point after defining it, click the “View Link Table” icon rightmost in the “Georeferencing” toolbox, select the point that you want to erase from the list, and press the erase button labeled “X.”

Figure B-4. The 49 defined control points marked by red crosshairs.

STEP 7

The coordinate information can be retrieved by clicking the “View Link Table”

icon in the “Georeferencing” toolbar. All of the defined control points are listed in a table and can be saved as a text file (Figure B-5; Table B-1, B-2, B-3, B-4). You had better save the file constantly not to loose the point information by accident.

191

Figure B-5. The defined control points are listed in a table. The list can be saved as and loaded from a text file in this window.

STEP 8

Click the “Georeferencing” dropdown arrow and select “Rectify.” In the “Save

as” dialog window that will appear, modify the “Cell size” to enlarge or reduce the spatial resolution of and assign the name and type of the “Output Raster” image. In my case, since the four quadrangles had to be resampled in the same resolution and bonded into one piece of map later, I unified the cell size as 0.32, which was the coarsest. You should leave the “Resample Type” as it is (“Nearest Neighbor” selected) unless you have specific preference. For the file type of the output raster image, choose ESRI GRID. Once you click “OK,” resampling program will be launched. Resampling may take a while if the original image size is very large. When the resampling process is completed, click the “File” menu and click “Exit.” Click “No” if prompted to save your changes.

192 Table B-1. The 49 control points for georeferencing of 30-K. X Source

Y Source

X Map

Y Map

Residual

6.307099

29.561526

291000.000000

8648000.000000

29.857500

5.980104

294000.000000

8645000.000000

0.36332 0.12054

29.872520

29.556375

294000.000000

8648000.000000

0.93985

6.292318

5.966282

291000.000000

8645000.000000

1.21617

10.230032

25.632553

291500.000000

8647500.000000

0.40041

14.157615

21.698869

292000.000000

8647000.000000

0.19427

18.081249

17.770097

292500.000000

8646500.000000

0.09255

22.008736

13.842631

293000.000000

8646000.000000

0.41824

25.932476

9.912612

293500.000000

8645500.000000

0.37043

25.942550

25.632676

293500.000000

8647500.000000

0.07692

22.013729

21.701316

293000.000000

8647000.000000

0.17398

14.151299

13.838885

292000.000000

8646000.000000

0.08350

10.218862

9.905024

291500.000000

8645500.000000

0.31779

10.230100

29.565098

291500.000000

8648000.000000

0.74921

25.927417

5.982773

293500.000000

8645000.000000

0.59743

25.943710

29.562524

293500.000000

8648000.000000

0.24473

10.220064

5.975161

291500.000000

8645000.000000

0.29852

6.302440

25.628951

291000.000000

8647500.000000

0.03306

29.861245

9.907420

294000.000000

8645500.000000

0.38551

29.872607

25.627670

294000.000000

8647500.000000

0.70745

6.289903

9.898699

291000.000000

8645500.000000

1.00456

14.165186

29.563711

292000.000000

8648000.000000

0.61121

22.003646

5.982497

293000.000000

8645000.000000

0.55531

22.020076

29.562638

293000.000000

8648000.000000

0.33148

14.148801

5.977570

292000.000000

8645000.000000

0.35722

6.298668

21.694973

291000.000000

8647000.000000

0.43892

29.862664

13.840383

294000.000000

8646000.000000

0.13698

29.867459

21.696036

294000.000000

8647000.000000

0.81911

6.292640

13.833749

291000.000000

8646000.000000

0.49240

18.090084

29.562683

292500.000000

8648000.000000

0.16755

18.074865

5.980068

292500.000000

8645000.000000

0.31988

6.295939

17.766294

291000.000000

8646500.000000

0.25251

29.866168

17.770197

294000.000000

8646500.000000

0.19594

14.160031

25.631200

292000.000000

8647500.000000

0.20360

22.006390

9.913773

293000.000000

8645500.000000

0.63905

22.015054

25.632522

293000.000000

8647500.000000

0.11695

14.149825

9.907450

292000.000000

8645500.000000

0.15591

10.227244

21.699984

291500.000000

8647000.000000

0.18764

25.933794

13.842594

293500.000000

8646000.000000

0.34888

25.938789

21.698892

293500.000000

8647000.000000

0.41429

10.221263

13.838796

291500.000000

8646000.000000

0.28076

18.087616

25.631333

292500.000000

8647500.000000

0.10959

18.077580

9.910027

292500.000000

8645500.000000

0.24635

10.223672

17.770203

291500.000000

8646500.000000

0.35700

25.937287

17.772641

293500.000000

8646500.000000

0.22251

18.085109

21.700083

292500.000000

8647000.000000

0.10181

18.079930

13.840209

292500.000000

8646000.000000

0.18235

14.153941

17.770150

292000.000000

8646500.000000

0.16283

22.010110

17.772608

293000.000000

8646500.000000

0.29374

Total RMS Error

0.43977

193 Table B-2. The 49 control points for georeferencing of 30-L. X Source

Y Source

X Map

Y Map

Residual

6.158426 29.686478

29.412780 5.788753

294000.000000 297000.000000

8648000.000000 8645000.000000

0.23731 0.33440

29.715390

29.386246

297000.000000

8648000.000000

0.78455

6.133564

5.806488

294000.000000

8645000.000000

0.78285

10.084121

25.477320

294500.000000

8647500.000000

0.46051

14.002285

21.535080

295000.000000

8647000.000000

0.36572

17.924852

17.602631

295500.000000

8646500.000000

0.22529

21.844907

13.666274

296000.000000

8646000.000000

0.31882

25.766193

9.727567

296500.000000

8645500.000000

0.16087

25.787367

25.460109

296500.000000

8647500.000000

0.11226

21.854968

21.530109

296000.000000

8647000.000000

0.17459

13.992468

13.672616

295000.000000

8646000.000000

0.21647

10.062377

9.739876

294500.000000

8645500.000000

0.21952

10.091011

29.413736

294500.000000

8648000.000000

0.88879

25.762499

5.796214

296500.000000

8645000.000000

0.34526

25.793931

29.392991

296500.000000

8648000.000000

0.21992

10.061288

5.806109

294500.000000

8645000.000000

0.70658

6.149885

25.479815

294000.000000

8647500.000000

0.66633

29.693939

9.722273

297000.000000

8645500.000000

0.15628

29.715059

25.455160

297000.000000

8647500.000000

0.29944

6.131158

9.738912

294000.000000

8645500.000000

0.81699

14.013624

29.408804

295000.000000

8648000.000000

0.50802

21.837566

5.803749

296000.000000

8645000.000000

0.89226

21.872371

29.396468

296000.000000

8648000.000000

0.79140

13.980069

5.801196

295000.000000

8645000.000000

0.46843

6.147427

21.540236

294000.000000

8647000.000000

0.66006

29.700042

13.656190

297000.000000

8646000.000000

0.16574

29.709159

21.519217

297000.000000

8647000.000000

0.59902

6.137458

13.676122

294000.000000

8646000.000000

0.35102

17.944974

29.401230

295500.000000

8648000.000000

0.66671

17.911210

5.801215

295500.000000

8645000.000000

0.28936

6.142112

17.610009

294000.000000

8646500.000000

0.37021

29.700436

17.589833

297000.000000

8646500.000000

0.47551

14.007247

25.472592

295000.000000

8647500.000000

0.18741

21.841308

9.732529

296000.000000

8645500.000000

0.27041

21.861407

25.465144

296000.000000

8647500.000000

0.10048

13.987709

9.737453

295000.000000

8645500.000000

0.01444

10.073721

21.540006

294500.000000

8647000.000000

0.37853

25.772595

13.662825

296500.000000

8646000.000000

0.30063

25.782541

21.525070

296500.000000

8647000.000000

0.29874

10.066230

13.675335

294500.000000

8646000.000000

0.11708

17.935030

25.470080

295500.000000

8647500.000000

0.28563

17.914990

9.735117

295500.000000

8645500.000000

0.17972

10.069889

17.610072

294500.000000

8646500.000000

0.32662

25.776272

17.595158

296500.000000

8646500.000000

0.22409

17.930080

21.532542

295500.000000

8647000.000000

0.24630

17.920177

13.670241

295500.000000

8646000.000000

0.37348

13.997634

17.605307

295000.000000

8646500.000000

0.11703

21.850032

17.600182

296000.000000

8646500.000000

0.36978

Total RMS Error

0.44157

194 Table B-3. The 49 control points for georeferencing of 31-K. X Source

Y Source

X Map

Y Map

Residual

6.117956 29.861008

29.204058 5.881759

291000.000000 294000.000000

8645000.000000 8642000.000000

0.70305 0.92071

29.641996

29.432983

294000.000000

8645000.000000

1.84077

6.362439

5.617872

291000.000000

8642000.000000

2.30618

10.079728

25.320100

291500.000000

8644500.000000

0.79479

14.036241

21.430422

292000.000000

8644000.000000

0.55825

17.990348

17.531490

292500.000000

8643500.000000

0.91387

21.943307

13.652014

293000.000000

8643000.000000

0.81709

25.906523

9.768322

293500.000000

8642500.000000

0.73350

25.752599

25.472825

293500.000000

8644500.000000

0.21888

21.869909

21.508347

293000.000000

8644000.000000

0.39989

14.109809

13.577380

292000.000000

8643000.000000

1.02446

10.240356

9.601060

291500.000000

8642500.000000

0.79640

10.042706

29.246489

291500.000000

8645000.000000

0.64596

25.943188

5.839114

293500.000000

8642000.000000

0.60093

25.717118

29.398097

293500.000000

8645000.000000

0.74229

10.283155

5.669171

291500.000000

8642000.000000

1.46848

6.156981

25.276666

291000.000000

8644500.000000

0.69332

29.824888

9.800689

294000.000000

8642500.000000

0.26674

29.673514

25.509662

294000.000000

8644500.000000

0.77537

6.315692

9.552947

291000.000000

8642500.000000

1.18488

13.961133

29.285331

292000.000000

8645000.000000

0.53207

22.028043

5.798317

293000.000000

8642000.000000

0.49547

21.798531

29.362508

293000.000000

8645000.000000

0.47729

14.201078

5.717918

292000.000000

8642000.000000

1.37223

6.193763

21.349513

291000.000000

8644000.000000

0.94828

29.784190

13.730852

294000.000000

8643000.000000

0.16663

29.709463

21.583061

294000.000000

8644000.000000

0.44692

6.272864

13.485351

291000.000000

8643000.000000

0.85673

17.884895

29.324056

292500.000000

8645000.000000

1.06246

18.111547

5.761553

292500.000000

8642000.000000

0.95495

6.240574

17.408423

291000.000000

8643500.000000

1.23888

29.743957

17.649985

294000.000000

8643500.000000

1.13209

13.999171

25.357925

292000.000000

8644500.000000

0.54967

21.980153

9.727953

293000.000000

8642500.000000

1.23605

21.833704

25.434514

293000.000000

8644500.000000

0.06359

14.155129

9.642398

292000.000000

8642500.000000

0.37166

10.116283

21.391649

291500.000000

8644000.000000

0.76485

25.866051

13.694573

293500.000000

8643000.000000

0.55437

25.787232

21.547608

293500.000000

8644000.000000

0.41059

10.196215

13.539074

291500.000000

8643000.000000

1.13337

17.914612

25.395571

292500.000000

8644500.000000

0.23234

18.069341

9.686354

292500.000000

8642500.000000

0.51018

10.154069

17.454274

291500.000000

8643500.000000

0.60481

25.819966

17.608525

293500.000000

8643500.000000

1.68421

17.950428

21.467555

292500.000000

8644000.000000

0.51460

18.027758

13.611339

292500.000000

8643000.000000

0.49781

14.072373

17.490529

292000.000000

8643500.000000

1.02121

21.908077

17.574351

293000.000000

8643500.000000

0.61159

Total RMS Error

0.90451

195 Table B-4. The 49 control points for georeferencing of 31-L. X Source

Y Source

X Map

Y Map

Residual

6.572395 30.048812

29.705047 6.037402

294000.000000 297000.000000

8645000.000000 8642000.000000

0.50265 0.49579

30.127398

29.619814

297000.000000

8645000.000000

0.27689

6.483541

6.123923

294000.000000

8642000.000000

0.62939

10.483777

25.762444

294500.000000

8644500.000000

0.22411

14.397536

21.819997

295000.000000

8644000.000000

0.38477

18.308687

17.872549

295500.000000

8643500.000000

0.20725

22.220007

13.932495

296000.000000

8643000.000000

0.39414

26.136414

9.983605

296500.000000

8642500.000000

0.39660

26.187409

25.702512

296500.000000

8644500.000000

0.32359

22.248761

21.792550

296000.000000

8644000.000000

0.42552

14.367532

13.961010

295000.000000

8643000.000000

0.29856

10.425081

10.042617

294500.000000

8642500.000000

0.23047

10.498893

29.693943

294500.000000

8645000.000000

0.40946

26.122541

6.052459

296500.000000

8642000.000000

0.48940

26.200385

29.638507

296500.000000

8645000.000000

0.61290

10.412359

6.114764

294500.000000

8642000.000000

0.16927

6.552068

25.775106

294000.000000

8644500.000000

0.51359

30.063907

9.973308

297000.000000

8642500.000000

0.58017

30.114950

25.687065

297000.000000

8644500.000000

0.37426

6.493784

10.056205

294000.000000

8642500.000000

0.82117

14.426146

29.676433

295000.000000

8645000.000000

0.49654

22.196139

6.068768

296000.000000

8642000.000000

0.41048

22.276497

29.647589

296000.000000

8645000.000000

0.18104

14.341238

6.098861

295000.000000

8642000.000000

0.20833

6.537593

21.850133

294000.000000

8644000.000000

0.59238

30.072493

13.906276

297000.000000

8643000.000000

0.79150

30.097446

21.761175

297000.000000

8644000.000000

0.58451

6.511136

13.993770

294000.000000

8643000.000000

0.82509

18.351454

29.660081

295500.000000

8645000.000000

0.51488

18.265937

6.082374

295500.000000

8642000.000000

0.25609

6.527428

17.917649

294000.000000

8643500.000000

0.03124

30.083920

17.828671

297000.000000

8643500.000000

0.55955

14.411298

25.746200

295000.000000

8644500.000000

0.41343

22.209981

9.998611

296000.000000

8642500.000000

0.41172

22.262462

25.717527

296000.000000

8644500.000000

0.20038

14.352461

10.024852

295000.000000

8642500.000000

0.51879

10.468684

21.834906

294500.000000

8644000.000000

0.24848

26.146210

13.917387

296500.000000

8643000.000000

0.35623

26.172623

21.777352

296500.000000

8644000.000000

0.47379

10.441115

13.980182

294500.000000

8643000.000000

0.88217

18.336343

25.729905

295500.000000

8644500.000000

0.49175

18.280110

10.011254

295500.000000

8642500.000000

0.37673

10.456169

17.904937

294500.000000

8643500.000000

0.32191

26.158670

17.843741

296500.000000

8643500.000000

0.36154

18.322498

21.804920

295500.000000

8644000.000000

0.17362

18.294997

13.945061

295500.000000

8643000.000000

0.17645

14.382503

17.888762

295000.000000

8643500.000000

0.17788

22.235132

17.858825

296000.000000

8643500.000000

0.09664

Total RMS Error

0.44896

196

B.2. How to clip and combine together parts of raster images

STEP 1

Start ArcMap. Open the GRID image of interest, which was resampled in the

previous process of georeferencing, by selecting “Add Data” from the “File” menu and locating the file path. Although the warning message to inform the absence of spatial reference information will come up again, it is just because the image has not been projected yet. Hit “OK.” STEP 2

Click the “Spatial Analyst” dropdown arrow in the “Spatial Analyst” toolbar

and click “Options” (Figure B-6). Then, select the “General” tab in the “Options” dialog box. If you do not find the toolbar either plugged in the application window or floating on the screen, select “Toolbars > Spatial Analyst” in the “View” menu.

Figure B-6. The “Spatial Analyst” toolbar of ArcMap.

STEP 3

Fill in the “Working directory” with the path of directory in which you want to

work. Because we will not use this GRID file after this process, “Analysis mask” has no need to be created. Leave it as it is. Then, in the “Analysis Coordinate System” section, make sure that the upper option, “Analysis output will be saved in the same coordinate system as the input (or first raster input if there are multiple inputs),” is selected. Click the “Extent” tab. STEP 4

Be sure to select “As Specified Below” from the “Analysis extent” dropdown

textbox and fill in the four boxes (“Top,” “Bottom,” “Left,” and “Right”) with the values corresponding to the extent of the image that you would like to clip out. The four

197

coordinates for each of my quadrangles are listed below (Table B-5). When you finish typing in the coordinates, click the “Cell Size” tab.

Table B-5. The extent of the scanned map images.

Map

STEP 5

Top

Bottom

Left

Right

30-K

8648000

8645000

291000

294000

30-L

8648000

8645000

294000

297000

31-K

8645000

8642000

291000

294000

31-L

8645000

8642000

294000

297000

Select “Same as Layer [layer name]” from the “Analysis cell size” dropdown

textbox. Leaving other items unchanged, click “OK.” STEP 6

Click the “Spatial Analyst” dropdown arrow and select “Raster Calculator.”

Double-click the GRID file name (“rectify30-k” in my case) in the “Layers” window and make sure that the selected file name will be listed in the box right below. Then, click “Evaluate” (Figure B-7). Calculation program will be launched. The process may take a while depending on the image size.

Figure B-7. The Raster Calculator. You can perform mathematical calculations using operators and functions, execute selection queries, and type in Map Algebra syntax.

198

STEP 7

Once the calculation process is complete, the resultant GRID image will be

added as a new layer in the table of contents. The colors for the attribute values (0 and 1) are randomly chosen, so your result may be different from the graphic below (Figure B-8). You can change the colors as you like.

Figure B-8. The resultant GRID image after calculation process. Note that the margins around the quadrangle have been cut off.

STEP 8

Right-click on the “Calculation” layer (new GRID image) in the table of

contents and select “Save As Layer File.” Specify the file path and name, and click “Save.” In addition, layer file is not the GRID image itself, but a file that contains the file path of the main body of image dataset and other layer property information. STEP 9

Repeat the processes from Step 1 to 8 as many times as the number of the

images you would like to bond together (in my case, 4 times; 30-K, 30-L, 31-K, and 31-L). STEP 10 Display all of the clipped images by opening their layer files from “Add Data” in the “File” menu (Figure B-9).

199

Figure B-9. All of the resultant GRID images displayed in the same map display window. They have to fit each other.

STEP 11 Click the “Spatial Analyst” dropdown arrow in the “Spatial Analyst” toolbar and click “Options.” Then, select the “Extent” tab in the “Options” dialog. If you want to change your working directory, you can do it in the “General” tab. STEP 12 Type in the extent of the envisioned image so that all of the clipped images you want to combine are involved (in my case, 8648000 for top, 8642000 for bottom, 291000 for left, and 297000 for right). Then, click “OK.” STEP 13 Click the “Spatial Analyst” dropdown arrow and select “Raster Calculator.” Build your expression in the textbox at the bottom of the window following the syntax: “merged image name = merge ([clipped image name 1], [clipped image name 2], [clipped image name 3], … [clipped image name n])” (Figure B-10). You can fill in the parenthesized names of clipped images by double-clicking the layer names listed in the “Layers” window. Then, click “Evaluate.” Calculation program will be launched.

200

Figure B-10. Raster Calculator. Merge function combines all of the clipped image datasets into a large GRID image.

STEP 14 Once the calculation process is completed, the resultant GRID image will be displayed in the map display window (Figure B-11). Save the layer as a layer file if you want to (see Step 8). Click the “File” menu and click “Exit.” Click “No” if prompted to save your changes.

Figure B-11. A new image dataset consisting of the four clipped GRID images.

201

B.3. How to define projection and plane coordinate system

STEP 1

Start ArcToolbox. Double-click the “Define Projection Wizard (coverages, grids,

TINs)” in the directory tree (“Data Management Tools > Projections > Define Projection Wizard (coverages, grids, TINs)”) (Figure B-12).

Figure B-12. The Define Projection Wizard (coverages, grids, TINs).

STEP 2

Select “Define the coordinate system interactively” and hit “Next.”

STEP 3

Specify the file path of the image dataset for which you want to define a

projection and plane coordinate system. Then, click “Next.” The procedures described below are exclusively for the maps projected by the Provisional South American Datum 1956 (PSAD56) and the UTM Zone 18 South (UTM18S). Prior to this operation, you should know about the specific ellipsoidal model or datum and plane coordinate system of your own image dataset.

202

STEP 4

Select “UTM” from the “Projections” list and click “Next.”

STEP 5

Choose “meters” from the “Units” dropdown textbox and “18” from “Zone.”

Type in “10000000” in the “Y Shift (optional)” textbox. Then, hit “Next.” STEP 6

Check “Datum” and select “PROV. SOUTH AMERICAN 1956 - Peru” from the

“Datum” list. Click “Next.” STEP 7

Make sure of the contents of “Summary of your input” and click “Finish.” Click

the “Tools” menu and click “Exit.” STEP 8

Start ArcMap. Open the GRID image that you merged in the previous section

again by selecting “Add Data” from the “File” menu and locating the file path. Before the image is displayed, a dialog message will pop up (Figure B-13). Then, select “Build pyramids” and hit “OK.” Pyramids are versions of a raster image dataset, varying from coarse to fine resolution. They are referenced to improve the drawing speed of raster layers when you zoom in and out (Ormsby et al. 2001:119). Building pyramids may take a length of time in proportion to the size of your raster dataset.

Figure B-13. A dialog message prompting to build pyramids.

STEP 9

You may notice that the scale (1:43,157) in the “Standard” toolbar that has

been whited out so far is now active and that the display of “Unknown Units” on the

203

status bar has been turned into “Meters” (Figure B-14). This means that the image can be used as a map. Save the layer as a layer file if you want to (see B.2. Step 8). Click the “File” menu and click “Exit.” Click “No” if prompted to save your changes.

Figure B-14. The resultant GRID dataset of topography map scanned, georeferenced, and projected.

B.4. How to reproject a raster image

STEP 1

Every data layer in a GIS overlay needs to be georeferenced by the same

projection and coordinate system. Therefore, the downloaded SRTM-arc3 DEM which is projected by WGS84 should be reprojected by the same combination of PSAD56 and UTM18S whereby our topography maps are projected. Start ERDAS IMAGINE 8.6. You will be prompted to choose “Classic Viewer” or “Geospatial Light Table.” Choose the former and click “OK.” STEP 2

Click the “DataPrep” icon located third left on the menu bar (Figure B-15).

204

“Data Preparation” dialog window will appear.

Figure B-15. ERDAS IMAGINE 8.6 main menu bar.

STEP 3

Click “Reproject Images.” “Reproject Images” dialog window will open.

STEP 4

Locate the input and output files and click the “earth” icon beside the

“Categories” dropdown textbox (Figure B-16). “(Edited) Projection Chooser” dialog window will open.

Figure B-16. The “Reproject Images” dialog window.

STEP 5

Select “Custom” tab. Define the parameters should be defined as follows

(Figure B-17): Projection Type

: UTM

Spheroid Name

: International 1909

Datum Name

: PSAD (Peru)

UTM Zone

: 18

NORTH or SOUTH

: South

205

Figure B-17. The “Projection Chooser” dialog window.

STEP 6

Click “Save” to save the parameters. Type in “PSAD56” and “Peru” respectively

in the “Save as” textbox and “In Category” dropdown textbox of the “Save Projection” dialog. Then, hit “OK.” STEP 7

Hit “Yes” if a dialog that asks “The Projection category Peru does not exist. Do

you create a new category with this name to save the item PSAD56?” comes up. Then, type in an appropriate name for the new category (e.g., “peru-psad56”) and click “OK.” You will get back to the “Project Images” dialog window. STEP 8

Respectively from the “Categories” and “Projection” dropdown lists, select

“Peru” and “PSAD56” which you just defined in the previous few steps. STEP 9

Make sure that “meters” is being selected from the “Units” dropdown list and

the “Ignore Zero in Stats” checkbox is checked. STEP 10 Select “Nearest Neighbor” for “Resample Method.” Then, choose either “Rigorous Transformation” or “Polynomial Approximation.” The former is a slow but more rigid process in terms of geometric fidelity that directly uses the original mathematical formula of projections for reprojection without approximation, while

206

the latter is a fast and commonly accepted process that uses polynomials to approximate the transformation between map projections. If you do not have specific preference, choose “Polynomial Approximation.” STEP 11 Once you select “Polynomial Approximation,” the lower portion of the dialog will be active. However, leave the parameters unchanged and click “OK” (Figure B-18). The resampling process will begin.

Figure B-18. The “Reproject Images” dialog window.

STEP 12 After the resampling process is complete, click the leftmost “Viewer” icon on the menu bar and open the Viewer (Figure B-15). Display the resultant DEM in the Viewer to see if it was properly reprojected.

207

B.5. How to clip a subset from raster image

STEP 1

Start ERDAS IMAGINE 8.6. You will be prompted to choose “Classic Viewer” or

“Geospatial Light Table.” Choose the former and click “OK.” STEP 2

Click the “Viewer” icon located leftmost on the menu bar (Figure B-15). A

“Viewer” window will open. Open the DEM reprojected in the previous section. STEP 3

Choose “Inquire Box” from the “Utility” menu. The “Inquire Box” window will

open (Figure B-19).

Figure B-19. The input DEM displayed in a Viewer and the “Inquire Box” dialog window.

208

STEP 4

Click the “DataPrep” icon located third left on the menu bar (Figure B-15).

“Data Preparation” dialog window will appear. STEP 5

Click “Subset Image” to open the “Subset” dialog window.

STEP 6

Locate the input and output files and select “Float Single” from the “Output”

dropdown list of “Data Type” section (Figure B-20). Then, hit “OK.”

Figure B-20. The “Subset” dialog window.

B.6. How to perform geometric corrections of aerial photographs (Phase I)

STEP 1

Start ERDAS IMAGINE 8.6. You will be prompted to choose “Classic Viewer” or

“Geospatial Light Table.” Choose the former and click “OK.”

209

STEP 2

Click the “OrthoBASE” icon located second right on the menu bar (Figure

B-15). “OrthoBASE Startup” dialog window will appear. STEP 3

All the parameters you input can be saved as an OrthoBASE project in a

binary file with the .blk extension called “block file.” If you have a previously created project, you can resume working on it here. Since you get started with a new project this time, select “Create a new OrthoBASE project” and hit “OK.” STEP 4

Name the new project and save it in an appropriate folder.

STEP 5

Once you created a new project, you get started with defining some basic

properties for the block file. First, you select a geometric model that corresponds to the type of camera that obtained the images. Choose “Frame Camera” from the list in the textbox and click “OK” (Figure B-21). The “Block Property Setup” dialog will open.

Figure B-21. The “Model Setup” dialog window.

STEP 6

Next, you define spatial reference information. Click the “Set Projection”

button to open the “Projection Chooser” dialog and click the “Custom” tab of the dialog. Select “UTM,” “International 1909,” “PSAD56(Peru),” “18,” and “South”

210

respectively from the “Projection Type,” “Spheroid Name,” “Datum Name,” “UTM Zone,” and “NORTH or SOUTH” dropdown textboxes (Figure B-22). Then, press “OK.”

Figure B-22. The “Projection Chooser” dialog window.

STEP 7

Confirm the projection parameters that you input and hit “Next.”

STEP 8

Make sure that “Meters,” “Meters,” and “Degree” are being selected

respectively from the “Horizontal Units,” “Vertical Units,” and “Angle Units” dropdown textboxes. Then, click “Next.” STEP 9

For “Rotation System,” “Omega, Phi, Kappa” is the most commonly used

convention, recommended by ISPRS, whereas other two systems are primarily used respectively in Germany and China. Make sure that “Omega, Phi, Kappa” system is being selected. For “Photo Direction,” “Z-axis for normal images” should be selected when you use aerial photographs. The other option, “Y-axis for close range images,” should be selected when you use ground-based photography. Check the “Define Average Fly Height (meters)” checkbox and type in “1600” in the textbox (Figure B-23). Then, hit OK. Now, the “Block Property Setup” is completed. The “OrthoBASE” main window will open.

211

Figure B-23. The “Set Frame-Specific Information” dialog window.

STEP 10 Now you are going to define the parameters necessary for orthorectification of the images in order. They include imagery location, camera information, fiducial mark measurements, GCP measurements, and so forth. Select “Add Frame” from the “Edit” menu or click the “Add Frame” icon on the menu bar to add the images to the list. Prior to following this step, be sure to confirm that the image files are not read-only and thus can be modified. The added images will be listed in the dialog. You will see some cells on the right of the image list which are shaded in red or green (Figure B-24). The red columns are labeled as “Pyr.” (Compute Pyramid Layers), “Int.” (Interior Orientation), “Ext.” (Exterior Information), “DTM” (Digital Terrain Model), and “Ortho” (Orthorectification). Some parameters need to be defined for each category in order.

Figure B-24. The OrthoBASE Pro main window.

212

STEP 11 First, click either cell in the “Pyr.” Column or choose “Compute Pyramid Layers” from the “Edit” menu to build pyramids. The “Compute Pyramid Layers” dialog will appear. Select “All Images Without Pyramids” and hit “OK” to start computing pyramid layer for the image. A gauge at the bottom of the dialog shows the progress of computation as the pyramid layer is created. When it is completed, the formerly red cells in the “Pyr.” column will turn into green (Figure B-25).

Figure B-25. The OrthoBASE Pro main window (Pyramids created).

STEP 12 Before you set up the parameters for “Int.” and “Ext.,” you need to specify your camera model (or “Sensor” model). Click the “Show and edit frame properties” button second left on the menu bar or choose “Frame Editor” from the “Edit” menu. The “Frame Editor (626.tif)” dialog will open. In the parenthesis will be inserted the image file name. STEP 13 Hit the “New” button to open the “Camera Information” dialog. In the “General” tab, type in “Unknown,” “PAP2004 626 and 649,” and “152.67” respectively in the “Camera Name,” “Description,” and “Focal Length (mm)” textboxes (Figure B-26). Since we are not sure of the X-Y coordinates of the principal point, assuming that it is located accurately at (0, 0) with no displacement, leave the “Principal Point xo (mm)” and “Principal Point yo (mm)” textboxes as they are. Then, click the “Fiducials” tab.

213

Figure B-26. The “Camera Information” dialog window.

STEP 14 Type in “4” in the “Number of Fiducials” textbox and modify the numbers in the table as shown in Table B-6. Then, click “OK.” You will get back to the “Frame Editor (626.tif)” dialog.

Table B-6. The four hypothetical fiducials.

Row # (Fiducial Points)

Film X (mm)

Film Y (mm)

1

-106.000

-106.000

2

106.000

106.000

3

-106.000

106.000

4

106.000

-106.000

STEP 15 Now you are going to measure the fiducial marks in the image in order to define Interior Orientation (“Int.”). Select the “Interior Orientation” tab and click the “Open viewer for image measurement” icon, located leftmost in the “Viewer Fiducial Locator” icon group (Figure B-27). The “Main View” window will open on top of the “Frame Editor” dialog with the “Over View” that shows the entire image and the “Detail View” that shows a part of the image.

214

Figure B-27. The Viewer Fiducial Locator.

STEP 16 Since the data strip (e.g., usually focal length, clock, level bubble, altimeter, etc.) is located on the right-hand side in 626.tif, the image needs to be rotated 180° degree relative to the photo-coordinate system. Select the second right icon of the “Fiducial Orientation” icon group (Figure B-28). Then, the square boxes called “Link Cursors” and adjacent crosshairs displayed in the “Over View” and “Main View” windows will move to the upper right corner of the image. This is the approximate location of the first fiducial. For 649.tif, in addition, the photo-coordinate system parallels to the image orientation with the data strip on the left; therefore, the image does not need to be rotated. The leftmost icon should be chosen.

Data strip on the right (626.tif)

Data strip on the left (649.tif)

Figure B-28. Fiducial Orientations.

STEP 17 The “Link Cursor” and crosshair are useful tools to resize and reposition the area of interest. When you want to resize the area, drag a corner of the square and adjust it smaller (to zoom in) or larger (to zoom out). To reposition, drag a crosshair or

215

the center of the square to the desired location and release the mouth button. Using these tools, locate the first fiducial marks so as to view it in the “Detail View” window. STEP 18 Click the “Place Image Fiducial” icon located center of the “Viewer Fiducial Locator” icon group. Then, place your cursor, which was turned into a crosshair, in the center of the first fiducial point and click. The spot will be marked by a small circled green cross with number 1 (“#1”), and the “Image X” and “Image Y” columns will be filled out with the file CellArray, which is a set of coordinates measured from the image in pixels (Figure B-29). Since the display will automatically move to the approximate location of the next fiducial point, repeat this step until you mark all of the 4 fiducials. If you miss the spot that you wished to mark, you can drag the circled green cross to the desired location later.

Figure B-29. The Fiducial Locator.

216

STEP 19 Once you finish marking all fiducials, the display will get back to the first fiducial, and RMSE (Root Mean Square Error) will be calculated and reported in the rightmost section of the “Interior Orientation” tab (Figure B-30). It is generally said that RMSE should be ideally less than a pixel. However, since we input theoretical values of “106.000” or “-106.000” for the film coordinates of fiducial points (Step 14) to complement the lack of information of our camera report, RMSE will inevitably be substantial (4.17 pixels for 626.tif) even though I accurately locate all fiducials.

Figure B-30. Measured fiducials and RMSE.

STEP 20 Click “Next.” You’re going to repeat the same process for 649.tif. Be sure to confirm that the leftmost of the “Fiducial Orientation” icon group is being selected. When you finish marking all fiducials for 649.tif, push off the “Open viewer for image

217

measurement” icon to close the view windows. STEP 21 Click the “Exterior information” tab. Since the exterior information is not available in our camera report, leave all of the parameters in this tab as they are and hit “OK” to close the “Frame Editor” dialog (Figure B-31). You will get back to the OrthoBASE Pro main window. Note that the formerly red “Int.” column of the image list turned into green.

Figure B-31. Exterior Information.

STEP 22 Next, you are going to define ground control points in order to determine the exterior orientations of the images. Select “Point Measurement” from the “Edit” menu or click the “Point Measurement” icon, third left on the menu bar. Then, the “Point Measurement (Left view: 626.tif Right view: 649.tif)” window will open (Figure B-32). STEP 23 Click the “Add” button at the upper right corner of the window to add a new row to the Point # list in the lower portion of the window. In this new row, click the “Type” and “Usage” columns and select “Full” and “Control” respectively from their pop-up lists. A Full GCP is the point that has X, Y, and Z coordinates, and Control stands for a control pint. STEP 24 Using the “Select Tool” (arrow icon) in the Point Measurement tool palette to resize and reposition the display, locate the first GCP for 626.tif in the “Detail View.”

218

Then, click the “Create Point” icon (cross icon) and mark the spot. As with the case of fiducial points, the GCP will be marked by a small circled green cross with Point ID. Concurrently, X and Y File coordinates will be measured and listed with the image number and name in the lower right window. Repeat the same process for 649.tif. Additionally, the image 626.tif is displayed on the left-hand side and 649.tif on the right.

Figure B-32. The “Point Measurement (Left view: 626.tif Right view: 649.tif)” window.

STEP 25 Type in the “Description,” “X Reference,” “Y Reference,” and “Z Reference” textboxes according to Table B-7 and Figure B-33. Note that some of the GCPs show only in either 626 or 649.tif.

219 Table B-7. The 10 Ground Control Points for triangulation. Point ID

Description

X Reference

Y Reference

Z Reference

626/649

1

Panamericana E

293053.030594

8644971.451042

32

626/649

2

Puente Lurín

294124.830563

8645145.555089

20

649

3

Piramide V

293395.079902

8645003.121138

28

626/649

4

Mamacuna

293010.554617

8644706.332327

18

626/649

5

Piramide XIV

293757.006375

8644685.831222

40

649

6

Cementerio

293554.620823

8644341.724585

30

626/649

7

Templo del Sol

293289.876711

8644015.938500

74

626/649

8

Coliseo

292886.856044

8643849.684808

8

626/649

9

Camacho Primero

294524.620987

8643777.783216

12

649

10

Panamericana W

292049.206456

8644873.060726

9

626

Figure B-33. Ground Control Points (Phase I).

STEP 26 Repeat Steps 23 through 25 for all GCPs (Figure B-33). When you finish placing all GCPs, click the “Automatic Tie Point Collection Properties” icon located second left on the second row in the Point Measurement tool palette. The “Automatic Tie Point Generation Properties” dialog opens.

220

STEP 27 Make sure that “All available” and “Exterior/Header/GCP” are being selected respectively for “Images Used” and “Initial Type.” The “Image Layer Used for Computation” property should be set to “1.” Then, type “15” in the “Intended Number of Points Per Image” field and confirm that the “Keep All Points” checkbox is off (unchecked). Click “Run” to start creating tie points. When the process is completed, you will get back to the “Point Measurement” window (Figure B-34).

Figure B-34. Tie points to be automatically computed.

STEP 28 Click “Save” and “Close.” You will be returned to the OrthoBASE Pro main window. STEP 29 Select “Triangulation Properties” from the “Edit” menu. The “Aerial Triangulation” dialog will open.

221

STEP 30 Click the “Point” tab in the “Aerial Triangulation” dialog. Then, choose “Same weighted values” from the “Type” dropdown list and click “Run.” The triangulation process will be launched. When the process is completed, the “Triangulation Summary” dialog will be generated and opened (Figure B-35).

Figure B-35. The “Triangulation Summary” dialog window.

STEP 31 Click “Update” to update the exterior orientation parameters that were left blank. In case that you entered the exterior orientation parameters during the measurement of fiducials, those values will be replaced with new values computed by IMAGINE OrthoBASE based on the control and tie points in the images processed. STEP 32 You may wish to save this report for future reference. You can save it into a text file by clicking “Report” (see Appendices F.1). The Triangulation Report will open in a separate Text Editor dialog (Figure B-36). Choose “Save as” from the “File” menu of the dialog and name the report. STEP 33 Click “Close” to close the “Triangulation Summary” dialog. You will be returned to the “Aerial Triangulation” dialog.

222

Figure B-36. Triangulation Report.

STEP 34 Click “Accept” to accept the triangulation parameters and hit “OK.” Note that the “Ext.” column of the OrthoBASE Pro main window turned into green (Figure B-37).

Figure B-37. The OrthoBASE Pro main window (Triangulation completed).

223

STEP 35 Click the “Ortho Resampling” icon located rightmost on the menu bar of the OrthoBASE Pro main window to open the “Ortho Resampling” dialog. STEP 36 Select “DEM” from the “DTM Source” dropdown list and make sure that “Meters” is being selected for “Vertical Units.” Then, click the “DEM File Name” dropdown list to select “Find DEM.” Locate the small DEM prepared in the previous section (B.5. “How to clip a subset from raster image”). Once the DEM file is appropriately located, the “Output Cell Sized” and the file extent textboxes will be automatically filled out (Figure B-38). Click the “Advanced” tab.

Figure B-38. The “Ortho Resampling” dialog window (Phase I).

224

STEP 37 Click the “Resample Method” dropdown list and choose “Bilinear Interpolation.” Then, click the checkbox next to “Ignore Value” and leave the value of 0 as it is. Hit “Add” to open the “Add Single Output” dialog. STEP 38 By default, OrthoBASE Pro assumes that you only want to generate an orthoimage for the first image in the block file, which is 626.img in our case. Therefore, 649.tif needs to be added manually. Select “649.tif” from the “Input File Name” and specify the name of the output file in the “Output File Name” textbox. Then, click the “Use Current Cell Sizes” checkbox and hit “OK” (Figure B-39).

Figure B-39. The “Add Single Output” dialog window.

STEP 39 Confirm that 649.tif in the block file were added to the CellArray in the “Ortho Resampling” dialog window. Then, click “OK.” The resampling process will begin and a status dialog window will open, tracking the process (Figure B-40).

Figure B-40. The status dialog window.

STEP 40 When the status dialog reaches 100% complete, hit “OK” to dismiss it. Open

225

the resultant orthoimages in the Viewer to see if they were successfully processed (Figure B-41). Then, Save the block file.

Figure B-41. The resultant orthoimages 626 and 649.

B.7. How to digitize ground features in orthophotos and topography map

STEP 1

Start ArcCatalog. Move to an appropriate folder in which you wish to create

new shapefiles so that the contents in the folder will be displayed in the lower right portion of the main window. Make sure that the “Contents” tab is being selected (Figure B-42).

226

Figure B-42. The ArcCatalog main window.

STEP 2

Select “New” > “Shapefile” from the “File” menu. The “Create New Shapefile”

dialog window will open (Figure B-43).

Figure B-43. The “Create New Shapefile” dialog window.

STEP 3

Fill in the “Name” textbox and select an appropriate feature type from the

227

“Feature Type” dropdown textbox. Contour lines and archaeological structures are saved as Polyline features and labeled respectively as “contour” and “architectures,” while elevation points are saved as a Point feature and labeled as “elevation.” Then, click the “Edit” button to define spatial reference information. The “Spatial Reference Properties” dialog window will open. STEP 4

Press “Select” to open the coordinate system locator and select “Projected

Coordinate Systems” > “Utm” > “Other GCS” > “Prov. S. Amer. Datum UTM Zone 18S.prj.” Make sure that “Prov. S. Amer. Datum UTM Zone 18S.prj” is displayed in the “Name” textbox and click “Add.” STEP 5

Confirm the parameters of the coordinate system shown in the “Details”

window of the “Spatial Reference Properties” dialog. Then, hit “OK” (Figure B-44). You will get back to the “Create New Shapefile” dialog window.

Alias: Abbreviation: Remarks: Projection: Transverse_Mercator Parameters: False_Easting: 500000.000000 False_Northing: 10000000.000000 Central_Meridian: -75.000000 Scale_Factor: 0.999600 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) Geographic Coordinate System: Name: GCS_Provisional_S_American_1956 Alias: Abbreviation: Remarks: Angular Unit: Degree (0.017453292519943295) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Provisional_S_American_1956 Spheroid: International_1924 Semimajor Axis: 6378388.000000000000000000 Semiminor Axis: 6356911.946127946500000000 Inverse Flattening: 297.000000000000000000

Figure B-44. The “Spatial Reference Properties” dialog window.

228

STEP 6

Confirm that the names of the projected and geographic coordinate systems

(“PSAD_1956_UTM_Zone_18S” and “GCS_Provisional_S_American_1956”) are displayed in the “Description” window of the “Create New Shapefile” dialog and check “Coordinate will contain Z values. Used to store 3D data.” Then, click “OK.” STEP 7

A new shapefile will be created and shown in both the folder tree (left) and

contents window (right) of the ArcCatalog main window. STEP 8

The on-screen digitizing is to be performed on the topography map layer and

the orthophotos created in the previous sections. In so doing, the Editor toolbar of ArcMap is very useful. Click the ArcMap icon on the menu bar of ArcCatalog to start ArcMap. You will be prompted to choose three options with which you wish to start using ArcMap: (1) “A new empty map,” (2) “A template,” or (3) “An existing map.” Since you do not have either template or existing map, choose “A new empty map.” STEP 9

Select “Add Data” from the “File” menu and locate the file paths of the

orthoimages 626 and 649, the layer file of topography map (see B.3. Step 9), the three shapefiles (elevation points, contour lines, and architectures) to open them as six separate data layers (Figure B-45). STEP 10 If you cannot find the “Editor” toolbar in the ArcMap main window, select “Toolbars > Editor” from the “View” menu to call it up. The “Editor” toolbar will appear somewhere on the screen. You can drag it into the menu bar of ArcMap or wherever you wan to place. Then, in the toolbar click the “Editor” down-arrow button and select “Start Editing.” The toolbar will be made enabled (Figure B-46). Make sure that “Create New Feature” is being selected from the “Task” dropdown list.

229

Figure B-45. The two orthoimages and topography map.

Figure B-46. The “Editor” toolbar of ArcMap.

STEP 11 Click the “Sketch Tool” icon and select from the “Target” dropdown list the shapefile that you wish to modify. The cursor will change from the black arrow (Edit Tool) to a black crosshair accompanied by a blue dot (Sketch Tool), while “Create New Feature” and “Sketch Tool” are being selected. By clicking, place the vertices to trace the ground features in the orthophotos and contour lines and elevation points in the topography map (Figure B-47). You may want to use Zoom In and Out tools and Pan tool to locate and resize the area of interest.

230

Figure B-47. On-screen digitizing.

STEP 12 In case that you miss the spot at which you wish to place a vertex, you can undo it by pressing “Z” while you are holding “Ctrl” key in your keyboard. If you want to modify a vertex or a set of vertices afterward, you can also delete or move them to the right positions. First, double-click the feature of interest to display its edit sketch. Then, in order to delete a vertex, right-click on the vertex that you want to delete and select “Delete Vertex” from the pop-up menu. In order to move it, left-click on the vertex and drag it to the desired position. Besides these, you can use a series of useful functions. Consult the ArcGIS Desktop Help for more advanced operations. STEP 13 When you finish digitizing for all of the three shapefiles, click the “Editor” down-arrow button and choose “Stop Editing.” All the vertices will be saved in the

231

shapefiles. Be sure to save the work in progress in order not to loose it by accident. You can save it by selecting “Save Edits” from the “Editor” dropdown list.

B.8. How to change projection and coordinate system

STEP 1

Start ArcToolbox. Double-click the “Project Wizard (shapefiles, geodatabase)”

in the directory tree (“Data Management Tools > Projections > Projection Wizard (shapefiles, geodatabase)”) (Figure B-48).

Figure B-48. Project Wizard (shapefiles, geodatabase).

STEP 2

Locate the input file path that you wish to re-project. Make sure that the file

name with its absolute file path and current coordinate system will appear when you select the file (Figure B-49). In this case, “GCS_WGS84_84” is displayed as the current

232

coordinate system. Then, hit “Next.” In addition, you can choose multiple files and re-project them at a time.

Figure B-49. Selection of the input shapefile.

STEP 3

Locate the directory in which the output file is to be saved and name the file.

Then, click “Next.” STEP 4

Press the “Select Coordinate System” button. The “Spatial Reference

Properties” dialog window will come up. STEP 5

Click “Select” and specify the projected coordinate system. For UTM18S

projected by PSAD56, for instance, select “Projected Coordinate Systems” > “Utm” > “Other GCS” > “Prov. S. Amer. Datum UTM Zone 18S.prj.” Then, click “Add.” You will get back to the “Spatial Reference Properties” window. STEP 6

Confirm the projection parameters that you input and click “OK” (Figure B-50).

You will be returned to the “Project Ward (shapefile, geodatabase)” window.

233

Alias: Abbreviation: Remarks: Projection: Transverse_Mercator Parameters: False_Easting: 500000.000000 False_Northing: 10000000.000000 Central_Meridian: -75.000000 Scale_Factor: 0.999600 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) Geographic Coordinate System: Name: GCS_Provisional_S_American_1956 Alias: Abbreviation: Remarks: Angular Unit: Degree (0.017453292519943295) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Provisional_S_American_1956 Spheroid: International_1924 Semimajor Axis: 6378388.000000000000000000 Semiminor Axis: 6356911.946127946500000000 Inverse Flattening: 297.000000000000000000

Figure B-50. The “Spatial Reference Properties” dialog window.

STEP 7

The same information will be displayed again in the “Details” textbox. If it is

correct, hit “Next.” STEP 8

Click the “Set Transformation” button to open the “Geographic Coordinate

System Transformations” dialog window. Input (“Converting from”) and output GCS (“to”) will be automatically filled out. Confirm them and hit “OK” (Figure B-51).

Figure B-51. The “Geographic Coordinate System Transformations” dialog window.

234

STEP 9

Make sure that the “Geographic Transformation” column in the textbox is

filled out with the geographic transformation that you just chose in the previous step (“PSAD_1956_To_WGS_1984_1”) and click “Next” (Figure B-52).

Figure B-52. Selection of the geographic transformation(s).

STEP 10 The next two dialog windows will be displayed just to show (1) the estimated output extent based on the input dataset and (2) summary of your input. In the first window (“Coordinate extents for the output dataset”; Figure B-53), you can modify the values if you want to. Then, click “Next” and “Finish” in the second window. When the transformation process is completed, select “Exit” from the “Tools” menu to close ArcToolbox. STEP 11 Start ArcMap. Open the output shapefile and superimpose it over other data layers with the same projection and coordinate system. Be sure that you can do so with no problem (Figure B-54).

235

Figure B-53. Coordinate extents for the output dataset.

Figure B-54. DGPS points superimposed over the orthophoto 649.

236

B.9. How to create a new DEM out of multipoint and line features

STEP 1

Start ERDAS IMAGINE 8.6. You will be prompted to choose “Classic Viewer” or

“Geospatial Light Table.” Choose the former and click “OK.” STEP 2

Click the “DataPrep” icon located third left on the menu bar (Figure B-15).

“Data Preparation” dialog window will appear. STEP 3

Click “Create Surface” to open the “3D Surfacing” dialog window (Figure B-55).

Figure B-55. The “3D Surfacing” dialog window.

STEP 4

Click the “Read Points” icon leftmost on the menu bar to open the “Input Data”

dialog window. STEP 5

In order to read out the 3D coordinate information of DGPS points, first of all,

check “Point Data” and choose “Shapefile” from the “Source File Type” dropdown list. Then, locate the source file of DGPS points, check “Attribute For Z,” and select from the dropdown list the column in which attribute data for elevation are stored (“ORTHOHEIGHT”) (Figure B-56). Once you hit “OK,” the “Surfacing” dialog window will come up, and the reading process will be launched. STEP 6

When the reading process is completed, hit “OK” to dismiss the dialog. The

237

Data input above will be displayed in the window in a tabular format (Figure B-57). Click the “Read Points” icon again.

Figure B-56. The “Input Data” dialog window (Point Data to be read out).

Figure B-57. The 3D coordinate information of DGPS points.

STEP 7

Repeat the steps 5 and 6 to read out the elevation points shapefile.

STEP 8

In order to read out the contour lines shapefile, which is a Polyline feature,

238

choose “Breakline Data” and select “Shapefile” from the “Source File Type” dropdown list. Then, locate the source file, choose from the “Attribute For Z” the column in which attribute data for elevation are stored (“Z”), and select “Soft Link” from the “Breakline Type” dropdown list. Hit “OK” (Figure B-58). Once you hit “OK,” the “Surfacing” dialog window will come up, and the reading process will be launched.

Figure B-58. The “Input Data” dialog window (Breakline Data to be read out).

STEP 9

When the reading process is completed, hit “OK” to dismiss the dialog. The

Data input will be added to the table in the “3D Surfacing” window. Then, click the “Perform Surfacing” icon rightmost on the menu bar to open the “Surfacing” dialog window. STEP 10 Specify the output file name and path and select “Non-linear Rubber Sheeting” from the “Surfacing Method” dropdown list. Then, click the “Ignore Zero In Output Stats” checkbox to enable it, select “Float Single” from the “Output Data Type” dropdown list, and hit “OK” (Figure B-59). The surfacing process will begin and a status dialog window will open, tracking the process.

239

Figure B-59. The “Surfacing” dialog window.

STEP 11 When the surfacing process is completed, click “OK” to dismiss the status dialog.

B.10. How to perform geometric corrections of aerial photographs through an automated DTM extraction (Phase III)

STEP 1

Follow Steps 1 through 24 of B.6 “How to perform geometric corrections of

aerial photographs (Phase I).” STEP 2

Type in the “Description,” “X Reference,” “Y Reference,” and “Z Reference”

fields with reference to Table B-8 and Figure B-60. Note that some of the GCPs show only in 649.tif.

240 Table B-8. The 21 Ground Control Points for triangulation (Phase III). Point ID

Description

X Reference

Y Reference

Z Reference

626/649

1

USHNU

293372.708199

8644386.205184

34.28727

626/649

2

PWR13

293458.514662

8644464.173309

38.34640

626/649

3

ROOM

293364.250519

8644472.623991

34.94863

626/649

4

POST

293301.566633

8644346.418556

34.65171

626/649

5

WLCNR1

293210.208724

8644379.107112

33.35287

626/649

6

WLEND

293219.315473

8644522.881528

30.42555

626/649

7

WLCNR2

293538.484720

8644559.424469

25.99706

626/649

8

WLCNR3

293679.974777

8644566.494306

28.43733

649

9

STORAGE1

293743.986557

8644444.755863

31.45113

649

10

PINTADO

293483.472668

8644294.829542

51.38184

626/649

11

TMPLSOL

293381.621784

8643981.531162

93.69171

626/649

12

MAMACONA

292980.354831

8644629.715267

16.40138

626/649

13

STORAGE2

293666.297344

8645057.536914

36.65530

626/649

14

TAURICHUMPI

294036.872321

8644966.682700

51.75549

649

15

QUIPUHOUSE

293910.408524

8644726.965556

54.14340

649

16

PWR2

293622.660137

8644727.110304

51.62682

626/649

17

PWR1

293357.441795

8644637.207526

40.56282

626/649

18

STORAGE3

293210.846044

8644704.791648

31.22612

626/649

19

EASTEND

294020.506083

8644479.351763

42.91762

649

20

PWR3

293731.638615

8644785.403537

68.24042

649

21

STORAGE4

293440.298909

8644936.131793

34.93833

626/649

STEP 3

Repeat Steps 23 through 25 of B.6 for all of 21 GCPs (Figure B-60). When you

finish placing all GCPs, click the “Automatic Tie Point Collection Properties” icon located second left on the second row in the Point Measurement tool palette. The “Automatic Tie Point Generation Properties” dialog will open. STEP 4

Make sure that “All available” and “Exterior/Header/GCP” are being selected

respectively for “Images Used” and “Initial Type.” The “Image Layer Used for Computation” property should be set to “1.” Then, type “30” in the “Intended Number of Points Per Image” field and confirm that the “Keep All Points” checkbox is off (unchecked). Click “Run” to start creating tie points. When the process is completed, you will get back to the “Point Measurement” window.

241

Figure B-60. The 21 Ground Control Points for triangulation (Phase III).

STEP 5

Follow Steps 28 through 34 of B.6.

STEP 6

In the “OrthoBASE Pro” main window, click the “DTM Extraction” icon located

second right on the menu bar. The “DTM Extraction” dialog window will open. STEP 7

Select “Single DTM Mosaic” for “Output Form” and name the output DTM file

(Figure B-61). The “Single DTM Mosaic” option creates a single DTM from all of the image pairs in the block file, whereas the “Individual DTM Files” option creates multiple DTMs as individual files. Be sure to create the DTM in the IMAGINE image file (*.img) format. STEP 8

Click the “Make Pixels Square” checkbox and type “1” in the “DTM Cell Size X”

242

(Figure B-61). The “DTM Cell Size Y” field will be updated automatically. It is generally said that the output cell size for a new DTM should be ten times the ground resolution of original imagery. Since our photo images have a ground resolution of ca. 0.1 m, it follows that the recommended cell size is ca. 1 m. Confirm that “Meters” is being selected for the units. STEP 9

Type “5” in the “Trim the DTM Border by” field and hit “Enter” key. This

percentage stands for the area that is less accurate and thus is to be trimmed before the DTM generation process. 2.5% will be removed from each of the four sides of the overlap between orthophotos 626 and 649. Then, press the “Advanced Properties …” button (Figure B-61). The “DTM Extraction Properties” dialog window will open on the “General” tab.

Figure B-61. The “DTM Extraction” dialog window.

STEP 10 Since the spatial reference information (“Output Projection,” “Spheroid,” “Zone Number,” and “Datum”) and the unit measurements (“Horizontal Units” and “Vertical Units”) are inherited from the block file, you do not need to modify them unless you wish to change them (Figure B-62).

243

Figure B-62. The “DTM Extraction Properties” dialog window (“General” tab).

STEP 11 Type “10” in the “Reduce DTM Correlation Area by” text field and hit “Enter” key. This function is used to get rid of the extraction of erroneous DTM mass points that may be present at the extreme edges of the input images. The reduction of correlation area works in the exactly same manner as the trim percentage. 5% will be removed from each of the four sides of the overlap between orthophotos 626 and 649. Then, press the “Reduce” button (Figure B-62). STEP 12 IMAGINE OrthoBASE Pro provides two methods to evaluate the quality of the resultant DTM: creation of Contour Map(s) and DTM Point Status image(s). The Contour Map is a three-dimensional shapefile that represents the topographic variation in the output DTM, while the DTM Point Status image is a raster image that illustrates the quality associated with the correlated DTM postings, which are to be categorized as being “Excellent,” “Good,” “Fair,” “Isolated,” and “Suspicious.” In order to create the former, click the “Create Contour Map” checkbox and confirm that the “Contour Interval” field is set to “3.” By default, the contour interval is automatically computed as being three times the DTM cell size. Then, click the “Remove Contours Shorter Than” checkbox and type “5” in the text field next to it. By default, this value

244

is also automatically computed as being five times the DTM cell size. For the DTM Point Status image to be created, click the “Create DTM Point Status Output Image” checkbox (Figure B-62). STEP 13 Click the “Image Pair” tab and hit the “View” icon to open the three views on top of the “DTM Extraction Properties” dialog window : the Block Graphic View, the Left Image View, and the Right Image View (Figure B-63). The bold black outline in the Block Graphic View and the highlighted area in the Left and Right Image Views illustrate the area of overlap between the orthophotos 626 and 649. Confirm that the image pair listed in the CellArraytable at the bottom of the dialog window is set to be active with the “Active” column checked by “x.”

Figure B-63. The “DTM Extraction Properties” dialog window (“Image Pair” tab).

STEP 14 Click the “Area Selection” tab and hit the “View” icon to open the three views

245

on top of the “DTM Extraction Properties” dialog window: the Main View, the Over View, and the Detail View (Figure B-64). In this tab, you digitize some regions of different topography and land use and classify them into several categories called “Region Strategies” (e.g., “Rolling Hills,” “High Mountains,” “Forest,” “Low Urban,” “Flat Areas,” and so forth). The automated DTM extraction will be performed differently in response to these categories. In this case, you define only one region that encompasses the Pacific Ocean.

Figure B-64. The “DTM Extraction Properties” dialog window (“Area Selection” tab).

STEP 15 Right-click in the Main View and choose “Zoom Out By X.” In the “Reduction” dialog that will com up, type “5” and press “Enter” key. Then, hit “OK.” This setting will change the ratio of reduction scale between the views so that more of the image pair will be displayed in the Main View compared to the default value of 3%.

246

STEP 16 Locate the area of interest using the Link Cursor and Link Box in the same manner as you defined the fiducials and GCPs. Then, click to select the “Create Polygon Region” icon from the Area Selection tool palette (leftmost in the second row) and start digitizing (Figure B-65).

Figure B-65. The region to be excluded.

STEP 17 When you finish digitizing, click in the “Region Description” cell in the second row of the CellArray and type “Pacific Ocean.” Then, click in the “Region Strategy” cell in the same row and select “Excluded Are” from the pop-up list. STEP 18 Click the “Accuracy” tab and hit the “View” icon to open the Block Graphic View on top of the “DTM Extraction Properties” dialog window. In this tab, you read out point data and external DEM with appropriate coordinate information to check the accuracy of the output DTM. Click the “Show Image ID” checkbox to display in the

247

Block Graphic View. STEP 19 Click the “Use Block GCPs” and “Use Block Tie Points” checkboxes. The points defined and evaluated in the preceding steps (see Steps 23 through 27 of B.6) will be displayed in the Block Graphic View and listed in the CellArray at the bottom of the dialog window. Then, click the “Use External DEM” checkbox and locate the file path and name. The file extent will be illustrated with its name (“gps-elev-cont.img”) in the Block Graphic View (Figure B-66). Make sure that the “Elevation Units” is being set to “Meters” and hit “OK.” You will be returned to the “DTM Extraction” dialog window.

Figure B-66. The “DTM Extraction Properties” dialog window (“Accuracy” tab).

STEP 20 Click “Run” in the “DTM Extraction” dialog window to begin DTM extraction (Figure B-61). You will be returned to the “OrthoBASE Pro” dialog window, which will track the progress of DTM extraction with its status. Once the process is completed,

248

the cells in the “DTM” column of the OrthoBASE Pro main window will turn into green (Figure B-67).

Figure B-67. OrthoBASE Pro main window (DTM extraction completed).

STEP 41 Click the “Ortho Resampling” icon located rightmost on the menu bar of the OrthoBASE Pro main window to open the “Ortho Resampling” dialog. STEP 42 Select “DEM” from the “DTM Source” dropdown list and make sure that “Meters” is being selected for “Vertical Units.” Then, click the “DEM File Name” dropdown list to select “Find DEM.” Locate the DTM prepared above. Once the DEM file is appropriately located, the “Output Cell Sized” and the file extent textboxes will be automatically filled out (Figure B-68). STEP 43 Hit “Add” to open the “Add Single Output” dialog. Select “649.tif” from the “Input File Name” and specify the name of the output file in the “Output File Name” textbox. Then, click the “Use Current Cell Sizes” checkbox and hit “OK” (Figure B-39). You will get back to the “Ortho Resampling” dialog window. STEP 44 Confirm that 649.tif in the block file were added to the CellArray in the “Ortho Resampling” dialog window and click the “Advanced” tab. In the “Advanced” tab, click the “Resample Method” dropdown list and choose “Bilinear Interpolation.” Then, click the checkbox next to “Ignore Value” and leave the value of 0 as it is. Click “OK.” The

249

resampling process will begin and a status dialog window will open, tracking the process (Figure B-40).

Figure B-68. The “Ortho Resampling” dialog window (Phase III).

STEP 45 When the status dialog reaches 100% complete, hit “OK” to dismiss it. Open the resultant orthoimages in the Viewer to see if they were successfully processed (Figure B-69). Then, Save the block file.

250

Figure B-69. The resultant orthoimages 626 and 649 (Phase III).

251

APPENDIX C: FIELD DRAWINGS

252

C-33 C-29

C-36 C-32

C-34 C-35

C-31

C-30

C-28

C-37

C-27

C-25

C-14 C-22

C-13

C-24

C-21

C-15

C-10 C-26

C-16 C-23 C-20 C-19 C-18

C-12 C-11

C-9

C-2

C-17 C-3 C-7

C-4

C-8 C-6

C-5

Figure C-1. The 36 quadrangles of field drawings (Scale = 1:8,500).

8644300

POINT00011

8644250

The Painted Temple Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South

Scale = 1:700 293400

253

Figure C-2. The Painted Temple (Scale = 1:700).

293450

8644300 8644250

The Old Temple of Pachacamac Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

293550

293600

254

Figure C-3. The Old Temple of Pachacamac (Scale = 1:800).

Scale = 1:800

8644200 8644150

Miscellaneous Structure A Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

Scale = 1:550

293200

255

Figure C-4. Miscellaneous Structure A (Scale = 1:550 ).

8643850 8643800

Cemetery A Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

Scale = 1:500 293400

256

Figure C-5. Cemetery A (Scale = 1:500).

293450

8644100

South Entrance to Sector I Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

293650

293700

257

Figure C-6. South Entrance to Sector I (Scale = 1:600 ).

Scale = 1:600

8644150

Miscellaneous Structure B Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

Scale = 1:500 293800

258

Figure C-7. Miscellaneous Structure B (Scale = 1:500).

293850

259 293950

8644150

8644200

293900

Cemetery B Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

Figure C-8. Cemetery B (Scale = 1:700 ).

Scale = 1:700

8644450

POINT00010

8644400

Miscellaneous Structure C Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

293700

260

Figure C-9. Miscellaneous Structure C (Scale = 1:800 ).

Scale = 1:800

293750

293750

293800

8644550

M

o

d

e

rn

R

o

a

d

8644500

Miscellaneous Structure D Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

261

Figure C-10. Miscellaneous Structure D (Scale = 1:500).

Scale = 1:500

293800

293850

293900

8644450 8644400

Miscellaneous Structure E Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

262

Figure C-11. Miscellaneous Structure E or Eeckhout's "B14" (Scale = 1:650).

Scale = 1:650

8644500

POINT00022

8644450

Miscellaneous Structure F Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

293950

263

Figure C-12. Miscellaneous Structure F or Eeckhout's "B13" (Scale = 1:650).

Scale = 1:650

294000

264 293850

293900

8644550 8644500 8644450

Miscellaneous Structure G Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

Figure C-13. Miscellaneous Structure G or Eeckhout's "B12" (Scale = 1:600).

Scale = 1:600

ad o R n er d o

8644600

M

Miscellaneous Structure H Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

293850

Scale = 1:500

293900

265

Figure C-14. Miscellaneous Structure H or Eeckhout's "B11" (Scale = 1:500).

8644650

Miscellaneous Structure I Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10

Scale = 1:500

8644600

Meters

293950

266

Figure C-15. Miscellaneous Structure I or Eeckhout's "B10" (Scale = 1:500).

294000

267 293100

Pukio A and Miscellaneous Structure J Provisional South American Datum 1956

8644450

293050

Universal Transverse Mercator Zone 18 South 0

5

10

20

Scale = 1:700

8644350

8644400

Meters

Figure C-16. Pukio A and Miscellaneous Structure J (Scale = 1:700).

8644300

Miscellaneous Structure K Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 8644250

0

5

10

20 Meters

293050

268

Figure C-17. Miscellaneous Structure K (Scale = 1:700).

Scale = 1:700

293100

8644350

269

South Entrance to the Pilgrims' Plaza Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10

Scale = 1:400

Mo

d er

n R oa d

8644300

Meters

293250

Figure C-18. South Entrance to the Pilgrims' Plaza (Scale = 1:400).

293300

293350

293400

293450

8644400

M

od

e

rn

R

oa

d

"Ushnu" Provisional South American Datum 1956 8644350

2.5

5

10 Meters

Scale = 1:600

270

Figure C-19. "Ushnu" (Scale = 1:600).

Universal Transverse Mercator Zone 18 South 0

293150

293200

293250

293300

8644450

293100

M od er n R oa 8644400

d

Intensively Looted

The Pilgrims' Plaza Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

271

Figure C-20. The Pilgrims' Plaza (Scale = 1:950).

Scale = 1:950

Ea

ste

r n-

W

e est

rn

St r

ee t

8644500

M o d e rn R o a d

8644450

Miscellaneous Structure L (West) Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

293250

272

Figure C-21. Miscellaneous Structure L (West) (Scale = 1:700 ).

Scale = 1:700

293300

8644550

Ea

st

er

W n-

es

te

rn

St

re

et

8644500

Miscellaneous Structure L (East) Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

293350

273

Figure C-22. Miscellaneous Structure L (East) (Scale = 1:700).

Scale = 1:700

293400

274 293450

8644500

POINT00002

8644450 8644400

The Pyramid With Ramp XIII Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

Figure C-23. The Pyramid With Ramp XIII (Scale = 1:552).

Scale = 1:552

8644550

The Pyramid With Ramp XII Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

293450

275

Figure C-24. The Pyramid With Ramp XII (Scale = 1:600).

Scale = 1:600

293500

276 8644700

293400

293450

8644650

N or th er n -S o u th er n S tr ee t

8644600

Ea

st

er

W n-

es

te

rn

St

re

et

Miscellaneous Structure M Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

Figure C-25. Miscellaneous Structure M (Scale = 1:550).

Scale = 1:550

277 293600

293650

8644600 8644550 8644500

M

o

r de

n

Ro

ad

Miscellaneous Structure N Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

Figure C-26. Miscellaneous Structure N or Eeckhout's "B3" (Scale = 1:800).

Scale = 1:800

8644700

Ea

st

er

W n-

es

te

rn

St

re

et

8644650

Miscellaneous Structure O Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

293600

278

Figure C-27. Miscellaneous Structure O (Scale = 1:700).

Scale = 1:700

293650

279

The House of the Quipus Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

Scale = 1:600

8644800 8644750

M

od

n er

Ro

ad

POINT00017

293900

Figure C-28. The House of the Quipus (Scale = 1:600).

293950

280

The Palace of Tauri Chumpi Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20

Scale = 1:800

8645000

Meters

8644900

8644950

POINT00016

293950

294000

Figure C-29. The Palace of Tauri Chumpi (Scale = 1:800).

294050

293850

293900

293950

8644950 8644900

Miscellaneous Structure P Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

281

Figure C-30. Miscellaneous Structure P (Scale = 1:600).

Scale = 1:600

293750

293800

293850

8644900

M o d e rn R o a d

Miscellaneous Structure Q Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

282

Figure C-31. Miscellaneous Structure Q (Scale = 1:500).

Scale = 1:500

293800

293850

8645000 8644950

Miscellaneous Structure R Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

283

Figure C-32. Miscellaneous Structure R (Scale = 1:500).

Scale = 1:500

8645100

293600

293650

293700

8645050

d Mo

er

n

Ro

ad

Miscellaneous Structure S Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

284

Figure C-33. Miscellaneous Structure S (Scale = 1:600).

Scale = 1:600

293600

293650

M

o

d

e

rn

R

o

a

d

8645000

Pukio B Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

285

Figure C-34. Pukio B (Scale = 1:600).

Scale = 1:600

293600

M

o

d

e

rn

R

o

a

d

293550

8644950

Miscellaneous Structure T Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

286

Figure C-35. Miscellaneous Structure T (Scale = 1:600).

Scale = 1:600

287

Miscellaneous Structure U 8645050

Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

Scale = 1:500

8645000

293450

Figure C-36. Miscellaneous Structure U (Scale = 1:500).

293500

293300

293350

M

od

e

rn

R

oa

d

8644750

Pukio C 8644700

Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

2.5

5

10 Meters

288

Figure C-37. Pukio C (Scale = 1:500).

Scale = 1:500

289

APPENDIX D: DIGITIZED MAPS

290

D-16 D-13

D-11 D-14

D-7

D-8 D-15

D-6

D-10

D-12

D-17

D-18

D-9 D-5

D-19

D-2

D-3

D-4

Figure D-1. The 18 quadrangles of digitized maps (Scale = 1:8,500).

T he Paint ed T emple Prov isional Sout h American Dat um 1956 Univ ersal T ransv erse Mercat or Zone 18 Sout h 0

5

10

20 Met ers

Sc ale = 1:750

Int e nsiv e ly Loot e d Area

8644300

POI NT 00011

4

5

8644250

5 293400

291

Figure D-2. T he Paint ed T emple (Scale = 1:750 ).

0

293450

Int ensiv ely Loot ed

8644300

35

4

5

8644200

50

T he Old T emple of Pachacam ac Prov isional Sout h American Dat um 1956 Univ ersal T ransv erse Mercat or Zone 18 Sout h

5 10

5

20 Met ers

Sc ale = 1:1,200 4 293550

293650

292

Figure D-3. T he Old T emple of Pachacam ac (Scale = 1:1,200 ).

0 0

5

3

0

293300

5

293500

5

0

5

8644100

65

8

5

60

4 5

POI NT 00012 8644000

75

POI NT 00013

8 0

T he T emple of t he Sun Prov isional Sout h American Dat um 1956 Univ ersal T ransv erse Mercat or Zone 18 Sout h 0

10

20

40 Met ers

4

0

Sc ale =1:1,500 3

293

Figure D-4. T he T em ple of t he Sun (Scale = 1:1,500 ).

5

T he Pilgrims' Plaz a and Pukio I Prov isional Sout h American Dat um 1956 Univ ersal T ransv erse Mercat or Zone 18 Sout h 0

10

20

40 Met ers

Sc ale = 1:2,200

10

15

8644400

Int ensiv ely Loot ed

Int ensiv ely Loot ed

2

5

8644300

4 293100

293200

293400

5 293500

50

294

Figure D-5. T he Pilgrims' Plaz a and Pukio I (Scale = 1:2,200 ).

0 3 293300

292900

293000

2

1

8644650

5

8644700

0

POI NT 00014

T he Conv ent o f Mam acona Prov isional Sout h American Dat um 1956 Univ ersal T ransv erse Mercat or Zone 18 Sout h 0

5

10

20 Met ers

295

Figure D-6. T he Conv ent of Mamacona (Scale = 1:900 ).

Sc ale = 1:900

296

The Palace of Tauri Chumpi Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20

Scale = 1:800

8645000

Meters

8644900

30

POINT00016

0 4 294000

Figure D-7. Palace of Tauri Chumpi (Scale = 1:800).

293600

293700

T he T emple of t he Monkey Prov isional Sout h American Dat um 1956 Univ ersal T ransv erse Mercat or Zone 18 Sout h 0

5

10

20 Met ers

M

od

er

d n-

ay

R

oa

Sc ale = 1:800

d

4

5

8644850 8644800

55

35

297

Figure D-8. T he T em ple of t he Monkey (Scale = 1:800 ).

298

Ro

ad

The Pyramid With Ramp I

ay

Provisional South American Datum 1956

-d

Universal Transverse Mercator Zone 18 South

rn

0

5

10

20

Scale = 1:801

Mo

de

Meters

8644700

N or th er n -S ou th er n S tr ee t

POINT00020

8644600

Ea

st

e

rn

W

es

t

293350

Figure D-9. The Pyramid With Ramp I (Scale = 1:801).

n er

St

re

et

293400

40

T he Py ramid Wi t h Ramp II Prov isional Sout h American Dat um 1956

POI NT 00019

Univ ersal T ransv erse Mercat or Zone 18 Sout h 0

5

10

20 Met ers

Sc ale = 1:800

35

8644700

Ea

st

n er

es -W

t

n er

St

re

et

8644650

293500

299

Figure D-10. T he Py ram id Wit h Ram p II (Scale = 1:800 ).

293600

300

The Pyramid With Ramp III 4 0

Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

Scale = 1:900

Intensively Looted Area

M o d e rn -d a y R a

8644900

o d

8644800

45

55

POINT00023

293700

Figure D-11. The Pyramid With Ramp III (Scale = 1:900).

293750

293200

293250

T he Py ramid Wi t h Ramp IV and Pukio II Prov isional Sout h American Dat um 1956 Univ ersal T ransv erse Mercat or Zone 18 Sout h 0

5

10

20 Met ers

Sc ale =1:800

M

od

er

n

-d

ay

R

oa

d

8644750 8644700

2

0

301

Figure D-12. T he Py ram id Wit h Ram p IV and Pukio II (Scale = 1:800 ).

The Pyramid With Ramp V and VIII Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20

Scale =1:800

Meters

de Mo

r n-

d ay

R oa

d

8645000 8644950

293300

302

Figure D-13. The Pyramid With Ramp V and VIII (Scale = 1:800 ).

293400

293400

293500

2

5

8644950

POI NT 00024

8644900

M

od

er

da n-

y

Ro

ad

T he Py ramid Wi t h Ramp VI Prov isional Sout h American Dat um 1956 Univ ersal T ransv erse Mercat or Zone 18 Sout h 0

5

10

20 Met ers

303

Figure D-14. T he Py ram id Wit h Ram p VI (Scale = 1:900 ).

Sc ale = 1:900

8644900

304

Mo

d

n er

-d

ay

Ro

ad

N th

8644800

or en -S ou th er n St re et

M

e od

rn

-d

ay

R

oa

d

The Pyramid With Ramp VII Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

Scale = 1:1,000 293350

Figure D-15. The Pyramid With Ramp VII (Scale = 1:1,000 ).

293400

305 294000

de

r

o y R

ad

8644900

8645000

Mo

da n-

35

T he Py ramid Wi t h Ramp IX Prov isional Sout h American Dat um 1956 Univ ersal T ransv erse Mercat or Zone 18 Sout h 0

5

10

20 Met ers

Figure D-16. T he Py ram id Wit h Ram p IX (Scale = 1:600 ).

Sc ale = 1:600

293800

293900

50

8644750

POI NT 00017

Mo de rn-day

Ro ad

8644700

40

T he Py ramid Wi t h Ramp XI, XIV, and t he Ho use of t he Quipus Prov isional Sout h American Dat um 1956

Int e nsiv e ly Loot e d

Univ ersal T ransv erse Mercat or Zone 18 Sout h 0

5

10

20 Met ers

306

Figure D-17. T he Py ram id Wit h Ram p XI, XIV, and t he House of t he Qui pus (Scale = 1:900 ).

Sc ale = 1:900

The Pyramid With Ramp XII Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

Intensively Looted

te

rn

-W

te

rn

S

10

20 Meters

et

Scale = 1:800

8644600

s Ea

es

e tr

5

8644550

Intensively Looted

293450

307

Figure D-18. The Pyramid With Ramp XII (Scale = 1:800 ).

293550

8644500

308

8644450

POINT00002

The Pyramid With Ramp XIII Provisional South American Datum 1956 Universal Transverse Mercator Zone 18 South 0

5

10

20 Meters

Scale = 1:650

M 293450

Figure D-19. The Pyramid With Ramp XIII (Scale = 1:650).

o

d

er

n

-d

ay

R

o

ad

309

APPENDIX E: SUPPORT DOCUMENTATION

E.1. Triangulation Report (Phase I) The Triangulation Report With OrthoBASE The output image x, y units: pixels The output angle unit: degrees The output ground X, Y, Z units: meters The Input Image Coordinates image ID = 1 Point ID x y 1 16748.997 4790.024 3 20124.980 4118.995 4 16514.001 7459.998 6 22416.009 10511.977 7 20343.005 14036.006 8 16138.995 15931.998 10 7286.978 6876.992 11 17011.906 4425.255 12 19906.066 5789.936 13 20229.320 8201.934 14 16000.814 8271.001 15 16573.234 8462.563 16 16772.355 8553.924 17 13268.749 9741.052 18 13836.166 10397.409 19 13400.384 10555.490 20 13734.775 10753.928 21 14491.254 14180.944 22 21839.342 16293.611 23 19448.443 17436.635 24 15062.529 19273.236 25 20836.641 21008.219 Affine coefficients from file (pixels) to film (millimeters) A0 A1 A2 B0 B1 117.2375 -0.010009 -0.000055 -116.3554 -0.000054

Point ID 1 2 3 4 5 6 7 8 9 11 12

image ID = 2 x 5220.007 15421.026 8546.994 4977.027 12240.004 10471.021 7920.003 4122.029 20188.999 5494.698 8291.935

y 5267.996 3080.008 4798.008 7862.012 7553.998 11037.987 14477.998 16401.007 15799.010 4932.635 6383.337

B2 0.010003

310 13 14 15 16 17 18 19 20 21 22 23 24 25

8489.618 4443.904 5011.525 5211.104 1607.564 2150.255 1694.414 2023.980 2608.031 9873.458 7504.502 2916.087 8765.172

8724.391 8643.435 8854.679 8949.765 10013.796 10689.420 10837.195 11047.403 14553.642 16786.416 17946.842 19874.309 21640.781

Affine coefficients from file (pixels) to film (millimeters) A0 A1 A2 B0 B1 -130.9891 0.010010 0.000059 116.2636 0.000059

B2 -0.010007

THE OUTPUT OF SELF-CALIBRATING BUNDLE BLOCK ADJUSTMENT the no. of iteration =1 the standard error = 2.6427 the maximal correction of the object points = 6.40827 the no. of iteration =2 the standard error = 2.7828 the maximal correction of the object points = 1.49397 the no. of iteration =3 the standard error = 2.7828 the maximal correction of the object points = 0.01529 the no. of iteration =4 the standard error = 2.7828 the maximal correction of the object points = 0.00030 The exterior orientation parameters image ID Xs Ys Zs OMEGA 1 292528.9148 8644297.6213 1596.6391 0.8628 2 293767.4164 8644169.0723 1586.3949 3.0996 The interior orientation parameters of photos image ID f(mm) xo(mm) yo(mm) 1 152.6700 0.0000 0.0000 2 152.6700 0.0000 0.0000 The residuals of the control points Point ID rX rY rZ 1 -1.4708 0.9663 -0.2459 2 3.9289 5.3663 4.2818 3 -0.4955 0.7518 0.7092 4 -3.5780 1.6884 -3.8498 5 1.7816 -1.2814 -0.4370 6 -0.6518 -3.4619 -1.3658 7 2.7899 -1.6466 2.0674 8 -1.3957 -4.1972 4.3773 9 -6.4992 2.3582 -3.6729 10 5.5906 -0.5439 -1.8643 aX

aY

aZ

PHI KAPPA 2.2114 174.3117 -1.5205 -4.3046

311 -0.0000 mX 3.4274

Point ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

0.0000 mY 2.7011

-0.0000 mZ 2.7600

The coordinates of object points X Y 293051.5598 8644972.4173 294128.7594 8645150.9214 293394.5844 8645003.8730 293006.9766 8644708.0208 293758.7880 8644684.5498 293553.9690 8644338.2627 293292.6666 8644014.2919 292885.4603 8643845.4876 294518.1218 8643780.1414 292054.7971 8644872.5169 293081.3050 8645006.2817 293358.2022 8644838.6247 293365.4081 8644591.3505 292947.7622 8644630.7250 293004.1820 8644605.2241 293023.7038 8644594.0601 292654.2076 8644507.7783 292706.2288 8644434.3745 292659.8520 8644422.3628 292692.3093 8644398.5090 292736.7272 8644038.2190 293454.3106 8643760.2812 293208.2300 8643661.7377 292745.8597 8643509.2676 293313.2356 8643287.4333 The total object points = 25

The residuals of image points Point 1 1

Image 1 2

Vx 0.017 -0.034

Vy -0.661 0.692

Point 2

Image 2

Vx -0.042

Vy 0.068

Point 3 3

Image 1 2

Vx -0.002 0.014

Vy 0.111 -0.108

Point 4 4

Image 1 2

Vx 0.086 -0.049

Vy -0.085 0.099

Point 5

Image 2

Vx -0.021

Vy -0.013

Point 6 6

Image 1 2

Vx 0.031 -0.090

Vy -2.157 2.165

Point

Image

Vx

Vy

Z

Overlap 31.7541 2 24.2818 1 28.7092 2 14.1502 2 39.5630 1 28.6342 2 76.0674 2 12.3773 2 8.3271 1 7.1357 1 32.1176 2 24.8520 2 25.4998 2 11.0467 2 10.0084 2 9.1522 2 8.5050 2 7.8826 2 7.7169 2 7.7583 2 3.9498 2 3.6752 2 -2.4516 2 -1.6755 2 -7.7659 2

312 7 7

1 2

-0.027 -0.011

-0.558 0.540

Point 8 8

Image 1 2

Vx -0.199 0.266

Vy 9.528 -9.247

Point 9

Image 2

Vx 0.076

Vy 0.019

Point 10

Image 1

Vx -0.066

Vy -0.000

Point 11 11

Image 1 2

Vx 0.004 0.040

Vy 0.828 -0.858

Point 12 12

Image 1 2

Vx 0.000 0.008

Vy 0.175 -0.182

Point 13 13

Image 1 2

Vx -0.000 0.006

Vy 0.142 -0.146

Point 14 14

Image 1 2

Vx -0.002 0.039

Vy 0.969 -0.979

Point 15 15

Image 1 2

Vx -0.004 0.057

Vy 1.432 -1.447

Point 16 16

Image 1 2

Vx -0.001 0.008

Vy 0.212 -0.214

Point 17 17

Image 1 2

Vx -0.002 0.010

Vy 0.281 -0.279

Point 18 18

Image 1 2

Vx 0.010 -0.049

Vy -1.366 1.351

Point 19 19

Image 1 2

Vx 0.007 -0.034

Vy -0.956 0.943

Point 20 20

Image 1 2

Vx 0.007 -0.032

Vy -0.912 0.899

Point 21 21

Image 1 2

Vx 0.059 -0.107

Vy -3.800 3.684

Point

Image

Vx

Vy

313 22 22

1 2

0.001 -0.002

-0.076 0.075

Point 23 23

Image 1 2

Vx 0.007 -0.007

Vy -0.311 0.302

Point 24 24

Image 1 2

Vx 0.063 -0.044

Vy -2.465 2.324

Point 25 25

Image 1 2

Vx 0.010 -0.005

Vy -0.344 0.329

The image residuals of the control points The image ID = 1 Point ID Vx Vy 1 0.017 -0.661 3 -0.002 0.111 4 0.086 -0.085 6 0.031 -2.157 7 -0.027 -0.558 8 -0.199 9.528 10 -0.066 -0.000 RMSE of 7 points: mx=0.087, my=3.707 The image ID = 2 Point ID Vx Vy 1 -0.034 0.692 2 -0.042 0.068 3 0.014 -0.108 4 -0.049 0.099 5 -0.021 -0.013 6 -0.090 2.165 7 -0.011 0.540 8 0.266 -9.247 9 0.076 0.019 RMSE of 9 points: mx=0.100, my=3.179

E.2. Triangulation Report (Phase III) The Triangulation Report With OrthoBASE The output image x, y units: pixels The output angle unit: degrees The output ground X, Y, Z units: meters The Input Image Coordinates image ID = 1 Point ID x y 1 20442.018 10241.030 2 21258.977 9372.952

314 3 4 5 6 7 11 13 14 15 19 20 21 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

20284.998 19766.990 18812.000 18763.009 21947.992 21763.983 21286.990 16252.992 22760.992 22744.976 20118.992 18500.994 20587.998 17011.906 19906.066 20229.320 16000.814 16573.234 16772.355 13268.749 13836.166 13400.384 13734.775 14825.329 16854.451 14282.445 14383.936 15379.454 14491.254 15506.836 21839.342 19448.443 15059.140 21757.576 22532.717 22544.568 18932.854 22880.701 18681.000 16390.537 20836.641 19033.758

9387.985 10695.000 10464.972 9041.970 8304.990 11025.982 14339.975 8263.954 3275.996 6549.958 7756.033 7261.970 4733.014 4425.255 5789.936 8201.934 8271.001 8462.563 8553.924 9741.052 10397.409 10555.490 10753.928 12097.276 12449.760 12601.379 12864.513 13319.292 14180.944 14670.696 16293.611 17436.635 18374.217 19451.395 19784.490 20136.434 20142.123 20622.215 20890.916 20973.570 21008.219 22232.594

Affine coefficients from file (pixels) to film (millimeters) A0 A1 A2 B0 B1 117.2375 -0.010009 -0.000055 -116.3554 -0.000054

Point ID 1 2 3 4 5 6 7 8 9

image ID = 2 x 8628.982 9421.986 8492.982 7947.015 7027.977 7052.210 10132.009 11525.012 12207.083

y 10721.022 9896.005 9881.007 11150.994 10896.982 9491.956 8888.991 8741.997 9874.494

B2 0.010003

315 11 13 14 15 16 17 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

9746.991 8802.977 4707.990 11079.016 14691.997 13650.989 10848.989 8358.998 6872.982 14882.009 11867.993 8979.006 5494.698 8291.935 8489.618 4443.904 5011.525 5211.104 1607.564 2150.255 1694.414 2023.980 3065.951 5077.089 2484.054 2569.309 3564.049 2608.031 3623.908 9873.458 7504.502 2966.759 9734.684 10492.582 10495.005 6869.727 10810.987 6581.240 4201.601 8765.172 6867.464

11527.982 14800.970 8645.987 4134.988 4715.993 7049.011 7223.995 8282.002 7747.986 9384.994 6546.972 5403.983 4932.635 6383.337 8724.391 8643.435 8854.679 8949.765 10013.796 10689.420 10837.195 11047.403 12438.345 12837.706 12934.721 13207.759 13686.788 14553.642 15076.647 16786.416 17946.842 18924.549 20016.545 20352.441 20716.361 20757.453 21213.197 21542.969 21664.146 21640.781 22959.904

Affine coefficients from file (pixels) to film (millimeters) A0 A1 A2 B0 B1 -130.9891 0.010010 0.000059 116.2636 0.000059

B2 -0.010007

THE OUTPUT OF SELF-CALIBRATING BUNDLE BLOCK ADJUSTMENT the no. of iteration =1 the standard error = 1.2428 the maximal correction of the object points = 25.13078 the no. of iteration =2 the standard error = 1.0823 the maximal correction of the object points = 20.64444 the no. of iteration =3 the standard error = 1.0874 the maximal correction of the object points = 0.26716

316

the no. of iteration =4 the standard error = 1.0874 the maximal correction of the object points = 0.00077 The exterior orientation parameters image ID Xs Ys Zs OMEGA 1 292529.1880 8644310.7551 1596.9849 0.4654 2 293765.5117 8644181.4972 1593.2947 2.6974 The interior orientation parameters of photos image ID f(mm) xo(mm) yo(mm) 1 152.6693 0.0000 0.0173 2 152.6693 0.0000 0.0173 The residuals of the control points Point ID rX rY rZ 1 -0.0958 -0.9264 -1.1888 2 0.2540 -0.4440 -0.9803 3 0.1155 -0.6207 0.3308 4 -0.0727 -0.2220 -1.1615 5 -0.1858 -0.4982 -1.2655 6 0.3085 -0.3619 0.5370 7 -1.3439 3.8548 10.2575 8 0.1604 4.1639 1.0274 9 -1.6274 3.1226 0.5569 11 0.4175 -0.7664 -1.1417 13 -0.0876 -1.0229 -1.0281 14 0.4782 -0.3724 0.7440 15 0.1710 -0.0376 -1.2963 16 0.2509 -1.0548 -0.4922 17 0.2646 -1.1399 -0.3781 19 0.0558 -0.1668 -2.2175 20 0.1980 -0.3646 -0.3640 21 0.1518 -0.3648 -0.0755 22 -0.0867 -1.1212 -0.2288 23 0.3329 -1.4995 -0.5994 24 0.3411 -0.1574 -1.0359 aX -0.0000 mX 0.5153

Point ID 1 2 3 4 5 6 7 8 9 11 13 14 15

aY -0.0000 mY 1.5710

The coordinates X 293372.6124 293458.7686 293364.3660 293301.4940 293210.0229 293219.6239 293537.1408 293680.1352 293742.3591 293483.8902 293381.5341 292980.8330 293666.4684

aZ -0.0000 mZ 2.4283

of object points Y 8644385.2788 8644463.7294 8644472.0033 8644346.1966 8644378.6089 8644522.5196 8644563.2793 8644570.6582 8644447.8784 8644294.0632 8643980.5083 8644629.3429 8645057.4993

Z

Overlap 33.0984 2 37.3661 2 35.2794 2 33.4902 2 32.0874 2 30.9625 2 36.2546 2 29.4648 1 32.0080 1 50.2402 2 92.6636 2 17.1454 2 35.3590 2

PHI KAPPA 1.9103 174.2388 -1.7102 -4.3573

317 16 17 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

294037.1232 293910.6732 293622.7159 293361.4386 293210.9979 294020.4194 293731.9715 293440.6400 293088.2172 293363.8527 293370.7363 292954.8387 293011.0025 293030.4361 292662.4252 292714.1073 292667.9312 292700.2045 292798.3756 292999.5233 292738.1372 292745.8362 292842.7546 292743.9586 292842.4936 293458.4018 293213.1603 292760.8939 293423.4357 293496.9673 293494.8864 293135.2704 293523.2946 293102.7900 292870.5049 293317.3522 293124.3945 The total object

8644965.6279 8644725.8257 8644726.9435 8644636.2776 8644704.4269 8644478.2306 8644783.9040 8644935.9744 8645004.8416 8644837.5555 8644591.3634 8644630.9432 8644605.4897 8644594.3478 8644508.8191 8644435.6995 8644423.7897 8644400.0119 8644251.3332 8644196.2744 8644205.0545 8644177.0036 8644121.2913 8644041.2467 8643981.6392 8643763.5439 8643665.6287 8643606.3298 8643443.2939 8643403.7519 8643368.0113 8643395.1959 8643316.7255 8643321.2137 8643331.0304 8643292.6451 8643183.5217 points = 50

The residuals of image points Point 1 1

Image 1 2

Vx 0.023 -0.060

Vy -1.257 1.266

Point 2 2

Image 1 2

Vx 0.015 -0.043

Vy -0.733 0.745

Point 3 3

Image 1 2

Vx -0.005 -0.011

Vy -0.423 0.425

Point 4 4

Image 1 2

Vx 0.018 -0.028

Vy -0.368 0.369

Point

Image

Vx

Vy

51.2633 53.7653 49.4093 40.1988 31.1506 42.6888 67.6410 33.9025 37.5248 32.5596 34.8790 18.6086 17.9370 17.2287 16.0546 16.0465 15.8450 16.1227 15.6936 19.6200 15.1289 15.5642 15.6376 14.8176 14.9441 19.1553 12.5808 12.5884 11.5981 11.2630 10.8632 11.4557 10.7705 11.0556 12.9361 10.2484 12.3917

1 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

318 5 5

1 2

0.021 -0.038

-0.631 0.630

Point 6 6

Image 1 2

Vx -0.005 -0.040

Vy -1.208 1.224

Point 7 7

Image 1 2

Vx -0.126 0.237

Vy 2.320 -2.349

Point 8

Image 2

Vx 0.001

Vy 0.047

Point 9

Image 2

Vx 0.021

Vy 0.034

Point 11 11

Image 1 2

Vx 0.005 0.018

Vy 1.066 -1.091

Point 13 13

Image 1 2

Vx -0.015 0.044

Vy 2.139 -2.138

Point 14 14

Image 1 2

Vx -0.017 0.036

Vy 0.659 -0.670

Point 15 15

Image 1 2

Vx 0.010 -0.068

Vy -0.941 1.002

Point 16

Image 2

Vx -0.004

Vy -0.012

Point 17

Image 2

Vx -0.004

Vy -0.013

Point 19 19

Image 1 2

Vx 0.027 -0.066

Vy -0.796 0.830

Point 20 20

Image 1 2

Vx 0.005 -0.070

Vy -1.555 1.596

Point 21 21

Image 1 2

Vx -0.001 0.053

Vy 1.290 -1.327

Point 22

Image 2

Vx 0.000

Vy -0.013

Point 23

Image 2

Vx -0.005

Vy -0.017

Point

Image

Vx

Vy

319 24 24

1 2

0.013 -0.010

0.126 -0.134

Point 25 25

Image 1 2

Vx 0.001 0.012

Vy 0.249 -0.258

Point 26 26

Image 1 2

Vx 0.001 0.022

Vy 0.479 -0.498

Point 27 27

Image 1 2

Vx -0.001 0.014

Vy 0.357 -0.366

Point 28 28

Image 1 2

Vx -0.002 0.031

Vy 0.788 -0.796

Point 29 29

Image 1 2

Vx -0.003 0.052

Vy 1.344 -1.359

Point 30 30

Image 1 2

Vx -0.000 0.006

Vy 0.159 -0.161

Point 31 31

Image 1 2

Vx -0.002 0.014

Vy 0.396 -0.392

Point 32 32

Image 1 2

Vx 0.007 -0.035

Vy -1.003 0.992

Point 33 33

Image 1 2

Vx 0.004 -0.018

Vy -0.513 0.506

Point 34 34

Image 1 2

Vx 0.003 -0.014

Vy -0.407 0.402

Point 35 35

Image 1 2

Vx -0.011 0.037

Vy 1.173 -1.154

Point 36 36

Image 1 2

Vx -0.008 0.025

Vy 0.790 -0.781

Point 37 37

Image 1 2

Vx 0.012 -0.032

Vy -1.049 1.026

Point 38 38

Image 1 2

Vx -0.003 0.009

Vy 0.291 -0.285

320

Point 39 39

Image 1 2

Vx 0.020 -0.046

Vy -1.569 1.536

Point 40 40

Image 1 2

Vx 0.030 -0.058

Vy -2.068 2.007

Point 41 41

Image 1 2

Vx -0.012 0.022

Vy 0.835 -0.811

Point 42 42

Image 1 2

Vx 0.009 -0.012

Vy -0.511 0.504

Point 43 43

Image 1 2

Vx -0.007 0.007

Vy 0.335 -0.326

Point 44 44

Image 1 2

Vx -0.012 0.011

Vy 0.553 -0.524

Point 45 45

Image 1 2

Vx -0.010 0.008

Vy 0.414 -0.401

Point 46 46

Image 1 2

Vx 0.015 -0.011

Vy -0.593 0.575

Point 47 47

Image 1 2

Vx 0.004 -0.002

Vy -0.136 0.132

Point 48 48

Image 1 2

Vx -0.014 0.009

Vy 0.541 -0.516

Point 49 49

Image 1 2

Vx 0.027 -0.017

Vy -1.018 0.983

Point 50 50

Image 1 2

Vx 0.037 -0.020

Vy -1.325 1.257

Point 51 51

Image 1 2

Vx -0.023 0.012

Vy 0.818 -0.768

Point 52 52

Image 1 2

Vx 0.006 -0.003

Vy -0.205 0.196

Point

Image

Vx

Vy

321 53 53

1 2

-0.033 0.014

1.094 -1.031

The image residuals of the control points The image ID = 1 Point ID Vx Vy 1 0.023 -1.257 2 0.015 -0.733 3 -0.005 -0.423 4 0.018 -0.368 5 0.021 -0.631 6 -0.005 -1.208 7 -0.126 2.320 11 0.005 1.066 13 -0.015 2.139 14 -0.017 0.659 15 0.010 -0.941 19 0.027 -0.796 20 0.005 -1.555 21 -0.001 1.290 24 0.013 0.126 RMSE of 15 points: mx=0.036, my=1.195 The image ID = 2 Point ID Vx Vy 1 -0.060 1.266 2 -0.043 0.745 3 -0.011 0.425 4 -0.028 0.369 5 -0.038 0.630 6 -0.040 1.224 7 0.237 -2.349 8 0.001 0.047 9 0.021 0.034 11 0.018 -1.091 13 0.044 -2.138 14 0.036 -0.670 15 -0.068 1.002 16 -0.004 -0.012 17 -0.004 -0.013 19 -0.066 0.830 20 -0.070 1.596 21 0.053 -1.327 22 0.000 -0.013 23 -0.005 -0.017 24 -0.010 -0.134 RMSE of 21 points: mx=0.064, my=1.026

322 VITA Graduate School Southern Illinois University Go Matsumoto

Date of Birth: March 21, 1972

1962 Evergreen Terrace Drive East Apt.#194-3, Carbondale, Illinois 62901 Kanda University of International Studies, Japan Bachelor of Arts, English Language, March 1995 Thesis Title: Pachacamac GIS Project: A Practical Application of Geographic Information Systems and Remote Sensing Techniques in Andean Archaeology Major Professor: Dr. Izumi Shimada Publications: 1994. A Comparative Study of Japanese Case Particles “ni” and “de”: Spatial Recognition and Identity of Japanese People. In Semantics Vol.3, edited by Misato Tokunaga, pp.66-98. Kanda University of International Studies, Japan.