I wish to thank my adviser, Dr. Bill Hazelton, for material and spiritual as well as economic support ...... Lake Erie in North America (Skinner and Porter, 1995; Press and Siever, 1998). 4. Ponds A pond ...... Mills, J.P., and I. Newton, 1996. A new ...
CONSTRUCTING A PRIOR INFORMATION BASE FOR RIVER MAPPING FROM DIGITAL IMAGES AND DEMS BY AN ADVANCED IMAGE INTERPRETATION SYSTEM
DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Department of Civil and Environmental Engineering and Geodetic Science of The Ohio State University
By Ali Can Demirkesen, MS
***** The Ohio State University 2001
Dissertation Committee: Dr. N. W. J. Hazelton, Adviser Dr. A. F. Schenk
Approved by
Dr. A. F. Habib Adviser Geodetic Science Graduate Program
ABSTRACT The purpose of this dissertation is to describe the construction of a prior information-base (interpretation of river characteristics) for river mapping from digital representations, such as remotely sensed digital images and DEMs, by an advanced image interpretation system. More reliable prior information availability in an advanced image interpretation system enables GIS and remote sensing facilities to locate rivers in an easier, more accurate and more straightforward way. In this study, the author proposes a prior information-base including some rules and facts for river mapping from the use of both remotely sensed multi-spectral images and DEMs. These rules not only allow waterrelated applications in both GIS and remote sensing to be more accurate, but also construct the information-base for river mapping by an advanced image interpretation system. These rules were constructed as a synthesis from searching the literature and experiments with both digital images and DEMs. These rules are employed in an advanced image interpretation system which requires (1) a prior information-base (2) a working memory (3) an inference module (Caelli and Bischof, 1997; Schenk and Zilberstein, 1990). A prior information-base is formed by a set of rules (qualitative or quantitative or relationships). A working memory has the basic function of holding features in the form of spatial data and their attribute values. These spatial and attribute
ii
data are used by interpreters to activate the rules. Inference module refers to software and hardware that connect the user's questions to the prior information-base and instruct the user (interpreter) about the process. In this dissertation, a prior information-based river mapping was implemented and the proposed rules were tested studying both images and DEMs in IDRISI, as well as RiverTools.
iii
Dedicated to my parents, my mother Hatice and my father Emin
iv
ACKNOWLEDGMENTS I wish to thank my adviser, Dr. Bill Hazelton, for material and spiritual as well as economic support by providing me with a TA position and intellectually encouragement, and enthusiasm which made this dissertation possible, and for his patience in correcting both my stylistic and scientific errors. I thank Dr. Terry Caelli for material and spiritual support, stimulating discussions and suggestions with creative ideas. I also thank Dr. Toni Schenk and Dr. Ayman Habib. They both served as my dissertation committee members and also greatly helped me complete this dissertation by correcting both stylistic and scientific errors and by giving intellectual ideas with constructive criticism. I would like to thank Dr. Burhard Schaffrin for his valuable discussions and comments. I would like to thank my uncle, Dr. Aksel Ozturk, and his wife Nancy who helped me join Geodetic Science at The Ohio State University. And also thank Dr. Huseyin Demirel, and Dr. Ayhan Alkis, who all greatly supported my progress in my academic life. I also wish to thank those who helped me handle various computer problems, especially my friends Sinan Kaplan, Anthony Githuku and Patrick Gakure.
v
VITA September 9, 1967 .....Born in Derinkuyu, Nevsehir, Turkey. 1984............................Technical high school of cadastral surveying Ankara, Turkey. 1984-1989 ..................Worked as a surveying technician in surveying companies Istanbul, Turkey. 1989............................B.S. Surveying, Yildiz Technical University (YTU), Istanbul, Turkey. 1990-1991 ..................Worked as a cadastral control engineer for government, Bursa, Turkey. 1991-1993 ..................Worked as a TA at YTU. 1993............................MS Geodesy and Photogrammetry, YTU. 1997............................MS Geodetic Science, The Ohio State University (OSU) Columbus, OH. 1994-Present ..............Scholar at OSU supported by the Turkish government and Dept. of Civil and Env. Eng. and Geodetic Science Geodetic Science Section, OSU. PUBLICATIONS Demirkesen, A. C. and B. Schaffrin, “Map conflation: spatial point data merging and transformation,” GIS/LIS ‘96 Proceedings, pp. 393-403, (1996). FIELD OF STUDY Major Field: Geodetic Science and Surveying. Minor Field: GIS and computer mapping. vi
TABLE OF CONTENTS Abstract………...…………………………………….…………………………..
ii
Dedication.................................................................................…...............….…..
iv
Acknowledgment.......................................................................…...............…...…
v
Vita……………………………………………………………………………….
vi
List of Tables……………………………………………………………………..
xii
List of Figures……………………………………………………………………
xiii
CHAPTERS: 1. INTRODUCTION………...…………………………………….……………
1
1.1 Problem to be solved………...………...……………………………………..
1
1.1.1 Building the prior information-base…..…………………...………….….
3
1.1.2 Advanced image interpretation systems……….…………...…………….
6
1.2 Why is the information-base important……...……………...…………………
12
1.3 What was done in this study………………………………………….……....
13
1.4 Prior information base vs. knowledge base
15
1.5 Summary.……………………………………………………………………..
15
2. PRIOR INFORMATION ABOUT WATER BODIES…...…...…………..
17
Introduction…………...…………………………………………………………...
17
2.1 Water………………………………...……………………………………….
19
2.2 Where water comes from and goes (hydrologic cycle).………………….……
19
2.3 Uses of water and its importance……..…......………………………………..
21
2.4 Problems of water and its significance…...….…..…….……………………..
21
2.5 Classification of water bodies….……………………………………………..
22
2.5.1 Surface water bodies……..………...………………………………………..
22
vii
2.5.1.1 Standing water bodies (oceans, seas, and lakes)....……...………………
22
2.5.1.2 Flowing water bodies (streams)…..…………...………...………………
25
a. Nature of streams according to their magnitudes…...….…...…….………
26
b. Nature of streams according to their channel properties….………………
27
c. Nature of streams according to their main channel patterns……………...
27
d. Nature of streams according to their drainage patterns...…………………
28
e. Nature of streams according to their deposits..…………………………...
31
2.5.2 Ground waters..………...…………………………………………………...
33
a. Water table……..………...…………………..………………………….……
34
b. Soil moistures.…..…….……………………...……………………………….
34
c. Springs…………….…..…...………………………..………………………...
35
d. Wells..………………...………………………………………………………
36
2.6 Summary…………...…...……………………………………………………..
37
3. PRIOR INFORMATION ABOUT HYDROLOGIC SURFACE ANALYSIS…………………………………………………………………….
38
Introduction………………………………………………………………………..
38
3.1 Hydrologic surface analysis…...……...……………………………………….
39
3.2 Components of hydrologic surface analysis………………………….………..
39
a. Hydrologic surface…………..………………………………………………..
39
b. Hydrologic cycle………..………………………………...…………………..
43
c. Hydrograph……..…………………………...………………………………...
44
3.3 Analysis of streams...………………………...………………………………..
46
3.4 Map representation of stream drainage networks…...………………………...
49
3.5 Summary………………………...…………………………………………….
51
4. SYNTHESIS OF PRIOR INFORMATION BASE FOR RIVER MAPPING FROM DIGITAL IMAGES AND DEMs….…………………...
52
Introduction………………………………………………………………………..
52
4.1 Overview of prior information-base for river mapping………….………...…..
53
4.2 Properties of our information-base…………………………………………….
56
viii
4.3 Geometric information about lakes…….……………………………………...
57
4.4 Geometric information about rivers………….………………………………..
59
4.5 Radiometric information about water bodies…….……………………………
64
4.6 Distinguishing rivers from roads……………………………...……………….
65
4.7 Summary………………………………………....……………………………
68
5. AN APPLICATION OF PRIOR INFORMATION BASED RIVER MAPPING FROM DIGITAL IMAGES AND DEMs………………………
69
Introduction………………………………………………………………………..
69
5.1 Materials……………………………………………………………………….
70
5.1.1 Hardware…………………………………………………………………..
70
5.1.2 Software…………………………………………………………………...
70
5.1.3 Description of the multi-spectral image data sets………………...……….
71
5.1.4 Description of the DEM data set…………………………………………..
72
5.2 Methods………………………………………………………………………..
72
5.2.1 List of the tested basic rules for rivers…………………………………….
73
5.2.2 A tool for river finding and testing the rules in IDRISI …...……………...
75
5.2.3 Operators used in the tool…………………………………………………
76
5.2.4 Prior information-based feature extraction of water bodies……………….
77
5.2.5 Processing a DEM with RiverTools……………………………………….
82
5.2.6 Extracting river networks from DEMs with RiverTools…………………..
83
5.3 Results…………………………………………………………………………
85
5.3.1 Results from IDRISI……………………………………………………….
88
5.3.2 Results from RiverTools…………………………………...……………...
97
5.3.3 Results of testing the basic rules………………………………………….
100
5.4 Discussions…………………………………………………………………….
101
5.4.1 Discussions of the results of the tested basic rules……………...………...
101
5.4.2 Discussions of overall results……………………………………………...
103
5.5 Summary…...………………………………………………………………….
107
6. CONCLUSIONS AND SUGGESSIONS…………………………….………
109
ix
6.1 Summary……………………...……………………………………………….
109
6.2 Conclusions……………………………………………………………………
110
6.3 Research contributions………………………………………………………...
111
6.4 Suggestions for future work…………………………………………………...
112
APPENDICES: A. INTERPRETATION OF HYDROLOGIC SURFACE CHARACTERISTICS FROM REMOTELY SENSED DIGITAL IMAGES ………………...……………...……………………………………..
114
A.1 Remote sensing and remotely sensed digital imagery………………………...
115
A.2 Energy source and sensors…………………………………………………….
116
A.3 Wavelengths……………………………………………………………….….
117
A.4 Spectral response patterns-signatures…………………………………………
118
A.5 The visual keys for image interpretations……………...………………….….
120
A.6 Image interpretation of landform components………………...……………...
121
A.7 Surface topography in images………………………………………………...
123
A.8 Erosion and gullies in images………………………...……………………….
123
A.9 Drainage pattern and textures in images……………………………………...
124
A.10 Stream channels in images…………………………...……………………...
126
A.11 Wetlands in images…………………………...………………………….….
127
A.12 Image classification or segmentation……………………...………………...
127
A.12.1 Supervised classification……………………………………………….
128
A.12.2 Unsupervised classification…………………………………………….
129
A.12.3 Principle component analysis…………………………………...……
129
A.12.4 Filtering images………………………………………………………
130
A.13 Understanding rivers from digital representations…………...……………
130
A.14 Modeling a flood hydrograph using images…………………………………
133
A.15 Summary…………………………………………………………………….
134
B. INTERPRETAION OF HYDROLOGIC SURFACE CHARACTERISTICS FROM DEMs………………………………………. x
135
B.1 Digital elevation models……………………………………...……………….
135
B.2 Quality of DEMs……………………………………………………………...
137
B.3 Warntz network……………………………………………………………….
139
B.4 Estimating elevations from DEMs……………………………………………
142
B.5 Computing slopes, aspects, and curvatures from DEMs……………………...
143
B.6 Extracting hydrologic surface characteristics from DEMs………………...….
147
B.6.1 Flow directions in DEMs………………………………………………….
148
B.6.2 Generating a depressionless DEM……………………………...…………
150
B.6.3 Finding flow accumulation………...……………………………………...
151
B.6.4 Determining watersheds…………………………………………………..
152
B.6.5 Determining stream networks……………………………………………..
152
B.6.6 Assigning ranks to the streams……………………………………………
153
B.7 Mapping of stream channels……………...…………………………………...
154
B.8 Summary……………………...……………………………………………….
154
BIBLIOGRAPHY………………………………………………………….…….
155
xi
LIST OF TABLES TABLE Table 5.1
PAGE Results of the tested basic rules in multi-spectral images.………...………100
xii
LIST OF FIGURES FIGURE Figure 1.1
PAGE Building a prior information-base for river mapping……….……... 4
Figure 1.2
A decision tree for interpretation of water bodies from imagery…..
6
Figure 1.3
Components of an advanced image interpretation system……..…..
8
Figure 1.4
A formation of an advanced image interpretation system structure..
10
Figure 2.1
Positional change of a meandering stream channel……..………….
22
Figure 2.2
Mirror Lake, an artificial lake at The Ohio State University…….…
23
Figure 2.3
Classification of water bodies…………………….………………..
24
Figure 2.4
Main channels of streams…………………………….…………….
28
Figure 2.5
Drainage patterns of streams…………..…………………………...
30
Figure 2.6
Features of a stream deposit, shown in an alluvial valley………….
32
Figure 2.7
Relations of surface water with ground water……………………...
33
Figure 2.8
Ground water………………………………………………….……
34
Figure 2.9
Three kinds of soil moistures: gravitational, hygroscopic, and capillary water…………………………………………….………..
35
Figure 2.10
An artesian well……………………………………….…………...
36
Figure 3.1
Slope and aspect on a hydrologic surface in a DEM……..………..
40
Figure 3.2
A profile of surface curvature in a DEM…………………….…….
40
xiii
Figure 3.3
A periodic surface curvature profile………………………………..
41
Figure 3.4
Water cycle and runoff showing water circulation and storage……
44
Figure 3.5
A continuous discharge hydrograph with two peaks………..……...
45
Figure 3.6
Discharge hydrograph elements……………………….…………...
45
Figure 3.7
Directions of water flows on a hill slope and in the ground……..…
46
Figure 3.8
Flood control components of streams used by a hydrograph……....
48
Figure 3.9
A map representation of a stream drainage network………..……...
49
Figure 3.10
A hierarchical watershed drainage Strahler's order…………….…..
50
Figure 4.1
Natural characteristics of rivers and lakes…………………….…...
61
Figure 4.2
An information representation as a decision tree from the SPOT multi-spectral image data………………………………………….
64
Figure 4.3
Relations between rivers, lakes, and roads………………….……...
65
Figure 5.1
A river mapping implementation in an advanced image interpretation system………………………………………….……
73
Figure 5.2
A tool for river finding and testing rules in IDRISI system…….….
75
Figure 5.3
Prior information-based feature extraction of water bodies………..
77
Figure 5.4
Results from image #1 in IDRISI…………………………………..
88
Figure 5.5
Results from image #2 in IDRISI…………………………………..
92
Figure 5.6
Results from image #3 in IDRISI…………………………………..
93
Figure 5.7
Results from DEM in RiverTools…………………….……………
97
Figure A.1
Landsat imagery system…………………………………….……...
116
Figure A.2
The electromagnetic spectrum……………………………….…….
118
xiv
Figure A.3
Three types of interactions between electromagnetic energy and material……………………………………………………….……
118
Spectral signature differences between water, soil, and vegetation……………………………………………….…………
119
Figure A.5
Spectral response patterns-spectral signatures………….…………
119
Figure A.6
Cross sections of gullies formed by erosion……………………….
123
Figure A.7
Drainage textures of streams……………………………….……...
125
Figure A.8
Images and DEMs for visual interpretation of rivers……………...
131
Figure A.9
Understanding multi-spectral image concept………………….…...
132
Figure B.1
Profile views of a pit, peak, and depressionless surface from a DEM, respectively……………………………………………..…...
137
Figure B.2
A pass line between two hills………………………………….…...
140
Figure B.3
A pale in a pass line…………………………………………..…….
140
Figure B.4
A Warntz network……………………………………………..…...
140
Figure B.5
Extracting hydrologic surface characteristics from DEMs…….…..
148
Figure B.6
Elevations of grid cells as input data by a DEM…………………..
150
Figure B.7
Flow directions of cells using elevations…………………………..
150
Figure B.8
Flow accumulation running from each grid cell…………………...
150
Figure B.9
Critical flow level of stream channels…………………………..
150
Figure B.10
Ordering streams by Strahler and Shreve methods, respectively…..
153
Figure A.4
xv
CHAPTER 1 INTRODUCTION
1.1 Problem to be solved Interpreted very broadly, all decision-making environments operate on the basis of matching a set of perceived conditions to a set of pre-established rules. In effect, we undertake a pattern recognition exercise between the data perceived and the preestablished rules. When we match the conditions to the rules, we can then make decisions based upon those rules. Therefore, the creation of a set of rules is the first stage of building a decision support system. The rules for making the decision do not have to be of the form “IF … THEN … ELSE,” as in traditional Expert Systems, although this helps the computer implementation of the rules considerably. The rules can also express behavior of objects in the real world, certain characteristics of objects, and a number of other pieces of information concerning the real world. Two important components of making the decision are being able to understand what is going on and being able to try different approaches to the problem. Understanding what is going on helps in predicting the consequences of various actions. Being able to try different approaches, whether by simulation or otherwise, allows
1
experimentation to find the optimal solution, as well as leading to the potential for testing different approaches so as to determine their effectiveness and so find the optimal solution. Humans are particularly well-suited to pattern recognition tasks, owing to the way that their brains are organized. Computers, on the other hand, are better suited to direct computational tasks. Clearly, the best approach is a human-computer hybrid, allowing each of the components to undertake the work for which it is better suited. This means that we try to use the computer to undertake the computationally intensive work, while the human undertakes the pattern recognition work. To make this hybrid approach work, it is necessary to have a clear understanding of what the human mind needs to undertake its pattern-recognition work. What is required is the ability to present the mind with patterns that are meaningful in terms of the experience that the mind already has. To achieve this, it is critical that the computer component does its work so that suitable patterns are produced for the human mind to consider. The problem that is tackled in this dissertation is the process of building a system to allow a computer to support a human-based, pattern recognition system. To keep the system sufficiently simple for the limited time and resources available, a single-problem system was developed. The single problem domain chosen was recognizing rivers from images, as it involves sufficiently complex rules, but not too many for the resources available for this project. It
2
is also a significant problem in the real world, and there is already a good body of knowledge on the subject. This facilitates the generation of the rules and other information for the proposed system. Further, rivers are complex entities that occur across a wide range of terrains, have different appearances in images depending upon location, development, etc., and there is a great deal of ‘common knowledge’ about rivers in people’s minds.
1.1.1 Building the prior information base In this study, not all rules are of the “IF … THEN … ELSE” type. Some are implicit in the way that preliminary data are presented to the user (the human component), being an encouragement to experiment with the preliminary data. Others are encapsulated in the order in which preliminary analyses are undertaken As a consequence, we can’t call the computer system a ‘rules base,’ as there are things there other than ‘rules.’ Similarly, a large amount of the system’s knowledge about rivers resides in the user, so the computer system can’t be called a ‘knowledge base.’ The best compromise is to term it an ‘information base,’ implying data structured for a specific purpose. Assembling the information base ahead of the processing of any specific image data creates the ‘prior information base.’ This is then applied to any supplied image in order to simplify the task of selecting rivers from the image. The results of the computer’s
3
analysis are presented to the user, who decides which part of the image are rivers and which parts are not. A computer system has a structure with four main components: input, processes, output, and a feedback link between output and input. A system environment controls these four components. One example of this is an expert system (e.g., a rule-based advanced image interpretation system), which is computer software which represents experienced prior information of a subject by solving and making inferences (Jackson, 1999). The prior information base is built from an extensive body of prior information about the water domain. This prior information is stored in an information-base separately from the inference module, both of which compose the core of the system. The prior informationbase contains the prior information usually in the form of facts and rules (Jackson, 1999; Caelli and Bischof, 1997). Figure 1.1 shows a formation of the prior information-base.
Inferences from the literature (learning from the literature search)
Communication with experts (learning from survey)
A PRIOR INFORMATION-BASE: (Interpretation of river characteristics for river mapping as rules and facts) • Information about the natural characteristics of rivers (e.g., where and how rivers run). • Geometric information about rivers (e.g., river drainage patterns and shapes). • Radiometric information about rivers (e.g., signatures, thresholds, colors, and attributes). • Relation with other objects (e.g., distinguishing rivers from other linear objects, such as roads, edges and ridges of rocky areas, boundaries of vegetation fields).
Experiments from multi-spectral images and DEMs (learning from experiments)
Figure 1.1: Building a prior information-base for river mapping. 4
The real world is infinitely complex, so we must develop models, which are an abstraction of reality, in order to comprehend it. We try to build the essential features of the real world into the model, while keeping it simpler than reality (Hall and Day, 1977). Mismatches between the model and reality appear as systematic errors. A model should have a good representation of the functional attributes of the real system. However, a model cannot have all the attributes of the real system; there must be some simplification of reality; otherwise, it would not be a model. One way of constructing a model is by using a
computer system. We may classify models into four primary
categories, depending upon how we use them for the representation of phenomena. These categories are as follows. (1)
Conceptual (formal description).
(2)
Iconic (scaled picture) which is an image replica of real systems.
(3)
Analog (graphs, trees, networks) which is a symbolic representation of reality. Analog models are more abstract than iconic models. For example, a simple graph can be employed for a logical representation of spatial and temporal relationships between phenomena. These symbolic (diagrammatic) models are also used to represent more abstract relationships between time series river maps overtime.
(4) Mathematical (quantitative) models, such as geometric models that are models of complex real systems. A more abstract model is used with aid of mathematics. The 5
components of quantitative models are tied together by sets of mathematical expressions, such as equations or inequalities.
1.1.2 Advanced image interpretation systems Image interpretation methods or systems deal with linking “remotely sensed data” to a “domain prior information” (e.g., information about rivers) by means of a user interface. In this study, prior information is used to determine and constrain as well as control the decision support process. The rules and facts used are typically explanation-based and require a precise definition of information types and interpretation of digital images and DEMs (see figure 1.2), (Caelli and Bischof, 1997).
Land cover in an image scene IS-A Non-water
Surface water body
IF…
IS-A Lake
River
IF…
Figure 1:2: A decision tree for interpretation of water bodies from imagery. One of the most important targets in computer vision is to develop advanced image interpretation systems for GIS or remote sensing applications. These systems are made to be consistent with human domain knowledge (in this case, knowledge about rivers) in terms of sensing and visualization as well as recognition. To do this, we encode images, extract features, and associate their attributes with specific types of prior information.
6
Image interpretation is commonly perceived as a process of labeling image data in the form of image regions or features with respect to the domain prior information. We can represent this prior information explicitly or we can encode it implicitly using algorithms and constraints in an advanced image interpretation system. An example of explicit information representation (feature models) can be seen in digital image interpretation systems, where rivers, roads, buildings etc., are specific image features, linked to the urban domain information. In the implicit information representation (image models), we do the feature extraction process directly from a digital representation of the imaging process, such as determination of hydrologic features like rivers and lakes. In both cases, implicit and explicit information representations, we concentrate on image interpretation in the problem domain of how extracted image features are connected to the domain prior information. Therefore an advanced image interpretation system is used to solve this specific interpretation problem, on which domain information representation relies. The system is understood as a complete representation system that covers encoding and feature extraction methods, information representation, domain prior information and rule generation. Feature extraction from imagery is one of the most active areas of computer vision and GIS. In this research, varieties of boundary-driven and region-driven methods are used for extraction of water bodies from multi-spectral images. In boundary-driven methods, we extract features, such as edges, lines, corners or curves that are typically derived by filtering models. In this approach, we model or organize differential operators in various
7
ways for finding rivers. In region-based methods, we use clustering, filtering, and statistical methods (e.g., Dempster-Shafer Theory and Fuzzy Set Theory) for finding rivers and lakes. Boundary-based and region-based methods can also be combined into an adaptive hierarchical feature extraction or segmentation model which partitions images into regions as a function of how these partitions can minimize the statistical variations within feature regions. To do these, a rule-based advanced image interpretation system can be established as in figure 1.3. However, the main focus of this research is to establish “a prior information-base (production rules and facts)” for feature extraction of water bodies (see “prior information-base” in figures 1.1, 1.3, and 1.4). There are basically five components of an expert image interpretation system (Caelli and Bischof, 1997). They are sensors, primary encoders, feature extraction, rule generation, and information representation (see figure 1.3).
Sensors
Prior information-base • Experience: numerical attributes • Regulations: facts, rules • Equations: algebraic forms • Schematics: maps, drawings
Primary encoders
Prior info-based feature extraction
Rule generation
Rule/Evidence Evaluation
Information representation • Tuples • Trees • Lattices • Networks • Objects • Formal definitions and descriptions
Outcomes • Interpretation and queries • Map generation, verification and validation
Figure 1.3: Components of an advanced image interpretation system (Caelli and Bischof, 1997, p.4). 8
a.
Sensors
In this study, the Landsat TM sensor for multi-spectral images is employed. There are, of course, a number of other efficient sensor types (Schott, 1997; Huang and Jensen, 1997; Jensen, 1996; Lillesand and Kiefer, 1994; Richards, 1993). b.
Primary encoders
We assume that primary encoders were done to the data (Graham and Barrett, 1997; Caelli and Bischof, 1997). For example, noise reduction, rectification and preprocessing an image are considered primary encoding functions. c.
Prior information-based feature extraction of rivers from multi-spectral data
A number of pattern recognition methods for extraction of water features are employed in this study, such as threshold methods and filtering methods, as well as statistical methods (Wilson; 1997; Graham and Barrett, 1997; Caelli and Bischof, 1997; Schott, 1997; Huang and Jensen, 1997; Richards, 1993; Ton et al, 1991; Moller-Jensen, 1991; Wharton, 1987). d.
Rule generation
If-Then-Else production rules and facts for river features are constructed as a prior information-base (Caelli and Bischof, 1997; Huang and Jensen, 1997). The purpose of rule generation is to generate rules from observations, guided by the information representations that are required for a specific expert system, such as a hydrological information system. Rule generation techniques are used for development and
9
optimization of image interpretation systems. Rules can be known explicitly or encoded implicitly in a specific algorithm and can also constrain an expert system (Huang and Jensen, 1995, 1997; Jackson, 1999; Caelli and Bischof, 1997; Rich and Knight, 1993).
Core of a rule-based expert system
Working Memory (Facts)
Inference Module
Prior Info. Base (Rules)
Explanatory or User Interface
Information Acquisition Procedure
Computer usable production rules (IF-THEN RULES) as prior information are stored here to generate a prior information-base.
Information acquisition by an interpreter • Inferences from the literature • Communication with experts • Experiments from imagery
Data Base
Figure 1.4: A formation of an advanced image interpretation system structure. Rule generation encompasses problems in inductive and deductive “learning”. Induction refers to the process of generalizing from known examples. Deduction refers to learning to “reason” about data from given models. And both “learning” techniques are involved in explanation-based learning. Induction learning is commonly used in applications of rule generation problems in computer vision. General inductive rule generation techniques can be also used in image interpretation. The techniques can be classified into unsupervised and supervised “learning”. In supervised “learning”, the outcomes are defined only implicitly in the
10
specific algorithms and built-in constraints, and they are used for different images explicitly (Caelli and Bischof, 1997). e.
Information representation of water bodies (rules for water bodies)
There are a number of ways in which expert information can be captured and recorded for use by a prior information-based analysis system (Jackson, 1999; Caelli and Bischof, 1997; Huang and Jensen, 1997; Rich and Knight, 1993; Richards, 1993). The production rule has the simplest form, and is as follows: IF (condition) THEN (inference) The "condition" in the rule is a logical expression, which can be either TRUE or FALSE. If it is true then the inference is justified; otherwise no information is provided by that rule. "Condition" can be a simple logical expression or can be a compound logical statement in which several components are linked through the logical OR and AND operations. These operations are defined as follows (Richards, 1993). The composite condition (condition 1 and condition 2) is true only if condition 1 and condition 2 are both true. The composite condition (condition 1 or condition 2) is true if either condition 1 or condition 2 or both of them are true. Additionally, the rule-based information systems can also use the logical NOT operation, such as “not (condition) is false if (condition) is true,” and vise versa. As a greatly simplified example of rule representation of information using Landsat MSS imagery (Wilson, 1997; Huang and Jensen, 1997; Richards, 1993; Ton et al, 1991; Moller-Jensen, 1990; Wharton, 1987): 11
If (band 7/band5 > threshold) then (vegetation) If (band7/band4 < 1) then (water) If not (water) and not (vegetation) then other If (water and specular surface) then (lake) If (water and diffuse surface or volume) then (open water)
1.2
Why is the prior information base important? •
Advanced image interpretation systems (expert systems) that use a prior information-base (in the form of IF-THEN-ELSE rules, such as "IF Condition THEN Inference"), have been recently put into wide use, although there is not one for river mapping commercially available yet. Image interpretation is usually associated with the labeling process of image data in the form of image regions or features regarding a domain prior information-base. A domain prior informationbase is formed by a set of rules that define features in an image. Therefore, the accuracy and reliability of the extracted features depend on the prior informationbase in the image interpretation system. The prior information-base is required to be well defined for GIS or Remote Sensing applications.
•We need broad approaches in dealing with complex problems, such as water-related information extraction from digital imagery.
12
•We need pattern recognition of water bodies in remotely sensed imagery. •Existing techniques are very problem-specific. •Turning a "map" into "information". • For moving expertise from the user to the machine (or vice versa) for shared cognitive responsibility and decision support. More reliable prior information availability in the system makes river mapping easier and more straightforward as well as more accurate.
1.3
What was done in this study
In this study, a prior information base was built to explore the process of building prior information bases in circumstances where there is shared cognitive responsibility for making decisions between the user, the computer and the system designer. The first stage was developing a set of rules and general knowledge about rivers. Additional information about how rivers can be identified in images was also collected. This collection process is presented in Chapter 2, where river characteristics, properties an d behavior are discussed, and in Chapter 3, where hydrological surface analysis is discussed. The synthesis of all these rules and information is discussed in Chapter 4. The approach is to consider the question of the human-computer division of cognitive labor, so that the two components’ performances are optimized, both separately and together. 13
The product of the synthesis was the implementation of software tools and procedures for using the prior information base, which is discussed in Chapter 5. The software packages RiverTools (in association with the ENVI remote sensing package), together with the Idrisi GIS and image processing system, were used for the actual work on the data. To give a very brief example of this process, there are many guidelines for analyzing remotely sensed images for rivers, e.g. water absorbs infra-red radiation, so it appears dark in that part of the spectrum. However, while this is true in general, it does not translate into exact reflectance values for all images, leaving the level of reflectance that gives the best representation of ‘rivers’ up to the user. The software tools developed take the analysis of infra-red data for water detection a certain distance along the path, without user involvement. Then the current image is presented to the user, who adjusts the viewing controls until the image ‘looks like a river’ to them. The concept of what a river looks like is something in the user’s head and in the hands of an expert, the system will produce good results. The system developed here, however, is set up to run as many possible scenarios as can be done, with the user selecting the particular image thresholding for each that gives the best-looking image (consistent with their concept of ‘river’). These images are then combined, together with images that have been chosen because they exclude large parts of the area of interest. These images are then used to maximize the potential of finding the river, by combining many different detection methods. The user fine-tunes each method on the basis of their concept of ‘river’, while the computer races through the computations to be done to obtain the various results.
14
The finished result, from the point of view of the system, is that rivers are located in a variety of terrain types fairly easily. Different rules and information come to the fore in different cases, and some measures of how good different methods are in the various scenes are provided. This is discussed in Chapter 6.
1.4
Prior Information Base vs. Knowledge Base
A study of this dissertation will indicate that there is much in the concept of a prior information base, which is common with what is commonly known as a knowledge base. In the prior information base are information, rules and methods for applying the information and rules. This could be considered as constituting knowledge. So why not call it a knowledge base? In the early stages of writing this dissertation, it was impressed upon the author by a senior faculty member that circumstances at that point in the research did not justify use of the term ‘knowledge base.’ As a consequence, and as a matter of expedience, the term ‘prior information base’ was selected. Many readers may wish to mentally substitute ‘knowledge base’ for ‘prior information base,’ and this will not affect the content or reading of the dissertation. There is an explanation in Section 1.1.1 for this term, but the definitions are not precise at present.
1.5
Summary
The purpose of this dissertation research was to describe the construction of a prior information-base (interpretation of river characteristics) for river mapping, from digital 15
representations by an advanced image interpretation system. This advanced system requires: (1)
a prior information-base;
(2)
a working memory; and
(3)
an inference module.
The prior information-base was built to include a set of rules and facts (qualitative or quantitative or relationships) for river mapping from the use of both remotely sensed multi-spectral images and DEMs. In order to test out the rules and facts, the prior information-based river mapping was implemented in the IDRISI GIS and the RiverTools system. Consequently, these rules of the research outcomes were constructed as a synthesis from searching the literature and experiments from both multi-spectral images and DEMs. These rules constructing the information-base enable both GIS and remote sensing facilities to locate rivers in an easier and straightforward way, as well as more accurate.
1.6
What’s next…
Chapter 2 explores the collection and systemization of the rules and information that were used to develop the river detection system. An extensive discussion of rivers and their behavior is provided, which provides the basis of the prior information base.
16
CHAPTER 2 PRIOR INFORMATION ABOUT WATER BODIES
Introduction In this chapter, in addition to general information about water, we classify water bodies that are useful to build the prior information-base for river mapping. This chapter is navigated as follows: 2.1 Definition of water 2.2 Where water comes from and goes (hydrologic cycle) • Precipitation • Condensation • Interception • Evapotranspiration • Direct surface runoff (overland flow) • Inter-flow (trough flow) • Infiltration (percolation) 2.3 Uses of water and its importance • Domestic use • Industrial use • Agricultural use • Power use • Transportation use • Foods and raw material use • Recreational use 2.4 Problems of water and its significance • Problems of ocean and sea roads • Problem of streams • Problems of shifting channels • Problems of water rights • Problem of pollution • Problem of ground water Contd.
17
2.5 Classification of water bodies
2.5.1 Surface water bodies
2.5.1.1 Standing water bodies • Oceans, seas, lakes, ponds, pools, and swamps
2.5.2 Ground water bodies
2.5.1.2 Flowing water bodies • Streams • • • •
2.6 Summary of the chapter
Water Table Springs Soil moisture Wells
Our Interest
2.5.1.2 Streams
Physical sizes
Channel properties
Channel patterns
Drainage patterns
Great Rivers
Ephemeral
Straight
Dendritic
Medium Rivers
Intermittent
Meandering
Trellis
Braided
Rectangular
Creeks, Brooks, Rivulets
Perennial
Deposits
Flood plains And levees Terraces Alluvial fans
Radial Deltas Annular Centripetal Pinnate Parallel Multi-basin Deranged Artificial
18
2.1 Water According to the Webster’s dictionary, water is the liquid that descends from the clouds as rain, issues from the ground as springs, and forms streams, lakes, and seas. It occurs in three states. (1) crystalline ice (the solid state), (2) water (the liquid state), and (3) water vapor (the gaseous state). Pure water consists of one oxygen atom and two hydrogen atoms, H2O. It freezes at 0o C and boils at 100o C. It has a maximum density at 4° C.
2.2 Where water comes from and goes – the hydrologic cycle Water comes from precipitation or issues from the earth. When precipitation occurs, it not only joins water bodies but some goes back to the atmosphere. This is called the hydrologic cycle (the water cycle). There are six main components of the water cycle. They are precipitation, interception, condensation, evapotranspiration, direct surface runoff and interflow, and infiltration (Kendall et al, 1962; Goodie, 1993; Skinner and Porter, 1995; Press and Siever, 1998). 1. Precipitation has mainly four main forms: rain, snow, hail, and sleet. They all feed or create water bodies. All come from clouds in the sky and fall onto the ground surface. 2. Condensation is the process of cooling water vapor (gas) and forming droplets of precipitation in the sky. The precipitation droplets come together and eventually fall. 3. Interception Precipitation sometimes does not drop directly on the Earth’s surface. The branches and foliage of plants may intercept it. Precipitation may flow by other paths to reach the ground surface, with some evaporating during the trip.
19
4. Evapotranspiration Because of the sun’s heat, there is a significant loss of water in a drainage watershed by evapotranspiration, often more than the amount of water leaving the watershed as stream discharge. There are two components of the evapotranspiration. Evaporation is the direct loss of water to the atmosphere from water bodies, soil and other surfaces as a result of physical processes. It is the process of turning liquid water into gaseous water vapor. Transpiration is the biological process of evaporation of water from the leaves of plants through openings known as stomata. 5. Direct surface runoff (overland flow) When precipitation hits the ground, it flows along the surface of the ground if the ground cannot absorb the water any more. The water runs along the surface until it forms a stream or finds its way into a lake, sea, or ocean. Thus, surface runoff creates a stream almost immediately after it rains since flowing water is much faster than infiltration. 6. Interflow (throughflow) is also called saturated overland flow. In some locations, vertical and horizontal percolation saturates the soil throughout its depth. This causes water to emerge and become overland flow. 7. Infiltration (percolation or base flow) occurs when the precipitation percolates into the ground and goes down to the water table, which is the top of the saturated zone. The water table can rise until it reaches the surface. The available water in the water table is called groundwater. Infiltration can generate lakes, streams and swamps. Such water bodies are usually fed by precipitation, but if there is no rainfall, then the level of rivers and lakes falls until it reaches the level of the surrounding water table. Water levels in streams or lakes are usually higher than the surrounding water table. 20
2.3 Uses of water and its importance The uses of water and its importance reflect the significance of this study. There are a number of uses of water (Kendall et al, 1962). 1. Domestic use: (e.g., drinking, cooking, washing, bathing, and gardening). 2. Industrial use: (e.g., cleaning, making steam, and cooling). 3. Agricultural use: (e.g., irrigation). 4. Power use: (e.g., generating electric energy). 5. Transportation use (e.g., transportation on oceans, seas and great rivers). 6. Food and raw material use (e.g., fish, plants, sponges, pearls and magnesium). 7. Recreational use (e.g., resting, swimming, rafting and fishing).
2.4 Problems of water and its significance Water can be useful, but it can also be harmful. There are many water-related problems (Kendall et al, 1962). 1. Problems of oceans and seas (e.g., problems of trade on the ocean and sea roads). 2. Problems of streams (e.g., carrying diseases and destroying the natural balance). 3. Problems of floods (e.g., soil erosion, destroying buildings, boundaries and roads). 4. Problem of shifting channels (e.g., problem of property boundaries) (figure 2.1). 5. Problems of soil erosion (e.g., depletion of arable land, removal of top soil) 6. Problem of water rights (e.g., problem of the people living in neighbor regions). 7. Problem of pollution (e.g., pollution of environment by dumping sewage, trash, and industrial wastes into streams).
21
8. Problem of ground water (e.g., excessive spring water may cause erosion, destroy natural vegetation cover and change ecosystems). Properties A & B
New channel
Old channel
A B
Boundary
Figure 2.1: Positional change of a meandering stream channel.
2.5 Classification of water bodies 2.5.1 Surface water bodies 2.5.1.1 Standing water bodies 1. Oceans The ocean is a huge saline water body that covers about 70 per cent of the Earth’s surface. The oceans are the Pacific, Atlantic, Indian, Southern and Arctic oceans. 2. Seas A sea is a standing water body, which is smaller than an ocean. Seas are tucked into the land but connected with oceans (e.g., the Mediterranean and Arabian Seas). 3. Lakes A lake is a considerable inland body of standing water, indeed. It can be an expanded part of a river, or a reservoir formed by a dam or a lake basin (lakebed) whether or not covered by water. A lake basin is a depression occupied by a lake at some time, i.e., an area to which stream drainage channels reach with water. In this study, we assume lakes have water in them at the time of image capture.
22
Considering the size of seas and oceans, lakes are smaller standing water bodies and they are not necessarily interconnected. The existence of a lake requires two things: (1) a basin that has no low-level outlet channel and (2) enough water to fill the basin or to flood a part of the basin. All continents have many lakes, ranging from small to great ones. Great lakes are occupied at low elevations compared to small lakes at mountainous elevations. Examples of Great Lakes are the Lake Superior Lake Michigan, Lake Huron and Lake Erie in North America (Skinner and Porter, 1995; Press and Siever, 1998). 4. Ponds A pond is a small body of water, usually smaller than a lake and larger than a pool. It is either naturally or artificially confined.
Figure 2.2: Mirror Lake, an artificial lake, at The Ohio State University.
5. Pools A pool is a small, rather deep body of fresh water. They are artificially created for either swimming or irrigation purposes. They are usually smaller than ponds. 6. Swamps A swamp is an area of saturated land. It is sometimes partially or intermittently covered with water. In other words, it is waterlogged and imperfectly drained land. It is unsuitable for agriculture without artificial drainage. It usually supports natural vegetation, shrubs and trees, as well as grassy marsh. 23
Water bodies (watersheds, basins, or catchments)
Surface water bodies
Ground water bodies
Flowing water bodies
Standing water bodies
Water table
Oceans
Springs
Seas
Great Lakes
Wells
Lakes
Medium Lakes
Soil moistures
Ponds
Small Lakes
Gravitational
Hygroscopic
Pools Our Interest Swamps
Physical sizes
Capillary
Streams
Channel properties
Channel patterns
Drainage patterns
Great Rivers
Ephemeral
Straight
Dendritic
Medium Rivers
Intermittent
Meandering
Trellis
Braided
Rectangular
Creeks, Brooks, Rivulets
Perennial
Deposits
Flood plains And levees Terraces Alluvial fans
Radial Deltas Annular Centripetal Pinnate Parallel Multi-basin Deranged Artificial
Figure 2.3: Classification of water bodies. An information representation of water bodies like a decision tree.
24
2.5.1.2 Flowing water bodies - Streams A stream means continuous running water. It can be either large or small flowing water. For instance, a river is a large stream while a creek is a small stream. ‘River’ is used to denote the main stream channel having running water or larger branches of a stream drainage system. From the hydrological point of view, the most geomorphic characteristics of stream courses determine their discharges to a water basin. Their characterizing elements are so complex that it is difficult to describe them individually. The mobility of a stream course and the amount of discharge both change over a wide ranges and result in complex phenomena. Streams develop drainage patterns in watershed areas. Streams, including their tributaries and their main channels, form a water basin and themselves deposit eroded materials into the basin. Then, streams form different patterns in the watershed areas since geological structure varies from place to place. When we recognize a stream drainage pattern, we may also deduce the landform type of the region occupied by the stream drainage. For instance, while a dendritic stream drainage pattern occurs on a homogeneous floodplain, a trellis stream drainage pattern forms in a region of ridges and valleys. Furthermore, we can see streams having different characteristics and behaviors in arid, semi-arid or humid climate regions. The streams in dry lands usually are intermittent rather than permanent and they are smaller. These intermittent streams may cause many significant problems. They run onto settlements as flood water and destroy property and 25
lives. Therefore, protection measures must be taken to protect life, towns and roads. These difficulties come from the amount and distribution of precipitation. This is a climatic problem. (Morisawa, 1968; Argialas, 1985; Argialas and Lyon, 1988; Skinner and Porter, 1995; Press and Siever, 1998).
(a) Nature of streams according to their magnitudes Streams can be classified as great rivers, rivers and creeks in terms of their physical size. 1. Great rivers are already known and defined (e.g., the Nile in Africa, the Colorado and Mississippi in North America, the Amazon in South America, and the MurrayDarling in Australia). 2. Rivers are natural streams with a considerable volume. They run permanently or seasonally. In geomorphology, it is also called a stream channel. A stream channel is a hollow bed, which is a natural body of a stream course either with running water or without any water. A deeper part of a moving water body in a channel has a considerable current that erodes a deeper passage. Rivers are one of the main environmental, geological and geographical features on the land surface. As rivers of all sizes carve bedrock and erode the landscape, they transport and deposit sand, gravel and mud into a water basin. 3. Creeks are also called streamlets, brooks or rivulets, and are a small stream. They are smaller than a river.
26
(b) Nature of streams according to their continuity Because of precipitation and channel size, streams can be large and permanent or small and intermittent. Thus, streams can be classified into three categories in terms of continuity of running water in their channels. 1. Ephemeral streams are small and show up only immediately after a rain. 2. Intermittent streams carry water for part of the year. 3. Perennial streams are large streams that carry water all year.
(c) Nature of streams according to their main channel patterns Stream channel patterns appear with three different characteristics. These are straight, meandering and braided channels (see figure 2.4). 1. Straight channels segments are rare. They usually occur for only short stretches before the channel has many curves. Any stream channel is usually meandering, not straight. A stream channel is said to be meander if the real distance from a point A to another point B along the channel is equal to, or more than, 1.5 times the linear distance between the points A and B. 2. Meandering channels This pattern has an S-shaped channel flowing as curves and bends called meanders. The name comes from the Meandrous (Menderes River in Turkey). They can be seen on fine sands, silt, or mud or easily eroded bedrocks. Some streams have deeply eroded meanders in V-shaped deep valleys that have practically no floodplains. Some other streams may meander on wide floodplains bounded by steep, rocky valley walls. Over time, meandering rivers may create an oxbow lake as they move slowly down a valley. 27
3. Braided channels Some streams may have many channels rather than a single one. These channels can be interlaced with multi-line flows. This is called a braided channel pattern. The braided streams split apart and then rejoin creating bars or small islands in the channel. The geometry of the pattern resembles braided hair. Braids are found in many settings, from broad valleys in lowlands to stream deposits in wide, valleys adjacent to mountain ranges.
Before cutoff
After cutoff Oxbow lake
A straight channel.
A meandering channel.
A braided channel.
Figure 2.4: Main channels of streams.
(d) Nature of streams according to stream drainage patterns We can observe many stream drainage patterns as flow lines of running surface waters. Streams form these patterns as they run over and erode the land. Stream drainage patterns are all destructive drainage patterns resulting from the erosion of the land surface. Drainage patterns can be classified into 11 categories (see figure 2.5). 1. Dendritic is the most common stream drainage pattern It looks like blood vessels or tree-like branching channels in many directions (e.g., Sabina River in Texas and Mississippi River in Mississippi). This pattern shows up on homogeneous, uniform soil and rock materials. 28
2. Trellis is a kind of modification of dendritic pattern with parallel tributaries and short parallel gullies, which occur at nearly right angles (e.g., Hiwassee River in Tennessee). This pattern shows up on a folded bedrock structure, dissected coastal plains, folded and faulted sedimentary rocks, in which the main parallel channels follow the strike of the beds. 3. Rectangular pattern is a variation of the trellis pattern. The tributaries join the main stream at almost right angles (e.g., Batoka Gorge of Zambezi River in Zambia). This pattern shows up on the outcropping edges of folded sedimentary rocks (weak or resistant) with long and roughly parallel belts. This pattern forms rectangular shapes that are controlled by bedrock cracks. 4. Radial is also called a centrifugal pattern. Channels radiate out, like the spokes of a wheel, from a topographically high area, such as a dome or a volcanic cone. They show up on volcanoes, isolated hills, and dome-like landforms. 5. Annular is a circular pattern that occurs most frequently as a result of erosion on structural domes. This pattern is a curved trellis pattern and nearly concentric. This kind of pattern develops on topographic forms generally similar to radial patterns. However, in this case, the bedrock joints or bedrock fracturing control the parallel tributaries. Granite or bedrock sedimentary domes may develop this type of pattern. 6. Centripetal pattern is the opposite of the radial stream pattern. Flows are directed toward a central point. They occur in areas of limestone sinkholes, glacial kettle holes, volcanic centers, outwash terraces, alluvial beach ridges, sand dunes and other depressions.
29
Valley
Ridge
Valley Ridge
Dendritic stream drainage pattern.
Trellis stream drainage pattern.
Volcano
Radial stream drainage pattern.
Dome Water Basin
Annular stream drainage pattern.
Parallel stream drainage pattern.
Rectangular stream drainage pattern
Centripetal stream drainage pattern.
Thermokarst stream drainage pattern.
Artificial stream drainage pattern.
Figure 2.5: Drainage patterns of streams.
30
Pinnate stream drainage pattern.
Deranged stream drainage pattern.
7. Pinnate (feather-like or angular) is a modification of dendritic pattern, but its secondary tributaries are evenly and closely spaced and parallel. It indicates a high silt content of soil and bedrock features control its tributaries. 8. Parallel Tributaries join the mainstream at roughly the same angle as parallels. This occurs on homogeneous, gentle, uniformly sloping sedimentary surfaces whose main collector streams may indicate a fault or fracture. This is also found at tilted coastal or lake plains. 9. Termokarst or multi-basin: this pattern develops in poorly drained, fine-grained sediments and in organic materials in regions of thick soil and especially in permafrost. Freezing causes many cracks to develop polygonal shapes and depressions. Streams crossing this area may connect these rounded depressions and create multi-basin stream patterns. Note that rivers can also disappear by running underground in karst area and then reappear as surface flow. This can be observed as discontinuity of rivers in terrain. Indeed, rivers are continuous. 10. Deranged is a disordered pattern of randomly directed streams, ponds, and wetland areas; and an instance of multi-basin drainage pattern. It occurs on glacial tilted areas and flood plains. 11. Artificial is artificially constructed, as for irrigation; a pattern characterized by straight lines. It carries water into agricultural fields.
(e) Nature of streams according to their deposits Stream deposits When a stream loses energy because of a decrease in gradient, velocity, discharge and transporting power, it deposits sediments. Especially, stream deposits 31
occur in channel margins (edges), valley floors, mountain fronts and on the margin of a lake or the ocean. Stream deposits form four kinds of landforms. They are floodplains and levees, terraces, alluvial fans, and deltas (see figure 2.6) (Press and Siever, 1998). Terraces (abandoned flood plains) Flood plains
Mountains
Flood (over bank flow) Alluvial fan
Mountains (Valley margins, uplands) Stream channel flow Natural levees
Figure 2.6: Features of a stream deposit, shown in an alluvial valley.
1. Floodplains and levees As stream channel water moves over the floor of a valley, it creates a floodplain. Floodwaters deposit sediments (point bars) when the stream overflows its banks. A levee is a broad, low ridge of fine alluvium built along the side of a channel by sediment-laden floodwater. The abrupt decrease in the current of floodwater results in sudden and rapid deposition of load of sediments along the margins of the channel, building up the natural levees with finer silt and clay settling out in the standing water covering the floodplain. 2. Terraces are uncontrolled floodplains formed when a stream runs above the level of its present channel and floodplain. Most streams have alluvial terraces along their courses
32
and above the floodplain. They sometimes occur at different heights. A terrace is a landform, rather than a deposit. 3. Alluvial fans are low and cone-shaped deposits of soil sediment. Streams run into an abrupt widening as they leave a mountain front or an open valley. This forms alluvial fans on top of terraces in front of mountains or valleys. 4. Delta is a body of sedimentary materials deposited into standing waters (e.g., ocean, sea or lake) at the mouth of a stream. When a steam enters the standing water, its speed and ability to transport sediment decrease, so it deposits its load of sediments into the standing water. This sedimentary deposit forms a triangular shape resembling the Greek letter delta, ∆.
2.5.2 Ground waters Although groundwater is not visible, it is as significant as surface water since this moisture is needed for the growth of natural vegetation and crops as well as industrial use. When precipitation hits the ground, it passes through the soil layer downwards into the bedrock and reaches a zone of saturation, the ground water zone. The top of this zone is called water table. Thus, ground water is a part of the moisture from the air that sinks into the upper portion of the earth’s crust. Groundwater may resurface via springs or wells (Kendall et al, 1962; Skinner and Porter, 1995; Press and Siever, 1998). Spring
Rivers/Lakes
Well
Ground surface
Bedrock Saturation zone
Water table
Figure 2.7: Relations of surface waters with ground water.
33
(a) Water table The saturation zone in the Earth’s crust is an area where all spaces (such as pores, cracks and gaps in soil or bedrock) are filled by moisture as water moves down from the surface to the underground (see figures 2.7 and 2.8). The moisture fills the saturation zone consisting of soil particles, gaps and cracks of the bedrock.
Vegetation Soil Weathered bedrock
Unsaturated zone
Rock Pores with air and water
Water table
Porous bedrock
Saturated zone
Pores containing water Rock
Figure 2.8: Ground water (Skinner and Porter, 1995; Press and Siever, 1998).
The top of the saturation zone is called the water table. It intersects the surface in low spots where swamps, ponds, springs or lakes occur. The water table usually lies closer to the surface in humid regions than arid areas and the shape of water table is a restrained image of the surface topography.
(b) Soil moisture Soil moisture is the amount of available water in the soil. It is an important factor in the growth of natural vegetation and crop. Soil moisture is held in the soil in three ways: gravitational, hygroscopic, and capillary water (see figure 2.9). 34
Soil particles
Gravitational water among soil particles
Capillary water
Film of hygroscopic water
Figure 2.9: Three kinds of soil moistures: gravitational, hygroscopic, and capillary water (Kendall et al, 1962).
1. Gravitational water moves downward, i.e., toward the saturation zone, by gravity. If gravitational water is too abundant, the soil becomes waterlogged. This will kill crops, although such drowned soil can support several types of swamp vegetation. 2. Hygroscopic water exists as an extremely thin film around each soil particle. Each particle is wrapped. This is often referred to as “unavailable water” since plants cannot obtain it, as they cannot generate sufficient osmotic pressure to pull it from the soil particles. 3. Capillary water is the water which moves by capillary action through the pores. Its ability to move up is useful for crops.
(c) Springs When ground water issues at the surface it creates either springs or seeps. Springs can be categorized as hydrostatic, artesian, deep-seated, fracture or tubular (Kendall et al, 1962).
35
1. Hydrostatic springs form the majority of springs and occur where the land surface intersects the water table in permeable rocks, either because of a break in slope or at a point of contact with an underlying impermeable bed. 2. Artesian springs are special case where the water emerges under pressure through a break in an overlaying impermeable layer, similar to artesian wells. 3. Deep-seated springs are associated with volcanic landforms and emerge under pressure from cracks. They may be hot and include gaseous emissions. 4. Fracture springs emerge from fractures, such as cracks in porous rocks but differ from the deep-seated type. They result from gravitational, rather than volcanic, forces. 5. Tubular springs are similar fracture springs except that they follow more or less rounded channels due to features, such as pipes in limestones, caverns in lava.
(d) Wells Wells themselves do not bring their waters to the surface. People must dig or drill a well until they reach the water table. Therefore, they must use some pumping method for bringing the water to the surface. However, an artesian well does not require pumping. Figure 2.10: An artesian well.
The water comes to the surface by its own pressure (see figure 2.10).
36
2.6 Summary In this chapter, we have covered general information about water bodies and their classifications, and in particular we have reviewed natural characteristics of rivers and their formal definitions. In the next chapter, we will review information about hydrologic surface analysis that is useful for building the prior information-base for river-related environmental GIS applications.
37
CHAPTER 3 PRIOR INFORMATION ABOUT HYDROLOGIC SURFACE ANALYSIS Introduction In this chapter, we explore information about hydrologic surface analysis that is useful for construction of the prior information-base for river-related environmental GIS applications. We also explain components of hydrologic surface analysis and their formal definitions in addition to map representation of streams. This chapter is organized as follows. 3.1 Hydrologic surface analysis
3.3 Analysis of streams 1. Profile of streams 2. Discharge of streams 3. Flood control of streams
3.2 Components of hydrologic surface analysis 3.2.1 Hydrologic surface 1. Slope 2. Aspect 3. Profile of surface curvature 4. Planform curvature 5. Ridge 6. Valley (stream) 7. Valley (glacier) 8. Channel 9. Floodplain 10. Flow direction 11. Stream discharge 3.2.2 Hydrologic cycle 3.2.3 Hydrograph 1. Channel precipitation 2. Direct surface runoff (overland flow) 3. Inter-flow (through flow) 4. Infiltration (percolation)
3.4 Map representation of stream drainage networks • Tree structures • Pour points • Stream channels • Junctions • Interior links • Exterior links • Watersheds • Watershed area • Watershed boundaries (divides) • Watershed slopes
3.5 Summary of the chapter
38
3.1 Hydrologic surface analysis Hydrology is the science dealing with the characteristics, movements, distributions, and circulation of water on land or within land. In other words, it is a study of water that is available in the Earth, on land surface, in soil, under rocks, and in the atmosphere. Hydrology analyzes how water occurs and how it behaves. In hydrologic surface analysis, we undertake the following types of work. •
Determine volumetric flow rates against time and predict maximum and minimum flow rates by measuring or computing as well as synthesizing hydrographs. So we need to know hydrologic cycle information about hydrologic surface areas.
•
Determine the height, timing and inundation of floods.
•
Locate streams from images. •
Determine surface characteristics from DEMs.
These works are employed for many fields, such as agriculture and forestry, civil and environmental engineering, and urban and regional planning.
3.2 Components of hydrologic surface analysis There are mainly three components for hydrologic surface analysis. They are the hydrologic surface, hydrologic cycle and hydrograph.
(a) Hydrologic surface The hydrologic surface is a physical structure of a ground surface that shows the characteristics of a surface flow across the ground surface. The surface flow characteristics depend on topography of the ground surface. The hydrologic surface includes the following concepts (see figures 3.1, 3.2 and 3.3). 39
1. Slope: The maximum change in elevation from each cell in a DEM. The surface slope determines the energy of flow. A steeper slope results in greater energy. 2. Aspect: The direction of the maximum change in elevation from each cell in the DEM. It is also called as slope direction. 0
Aspect
0
45 0
α
rise
Slope = tan α = rise / run Aspect = α
α DEM grid cells
run
Figure 3.1: Slope and aspect on a hydrologic surface in a DEM.
3. Profile of surface curvature: The curvature of a surface in the slope direction. It shows where the surface is convex and concave causing acceleration or deceleration of flow. During accelerated flow, the stream gains energy and its power can transport particles (sediments). While a convex profile indicates an erosion area, a concave profile reduces the flow speed and the stream loses energy. Then stream deposition can occur.
Flow direction Convex surface Positive profile curvature Increasing slope Erosion
Zi, Cell heights
Concave surface Negative profile curvature Decreasing slope Deposition Basin
Figure 3.2: A profile of surface curvature in a DEM.
4. Planform (contour) curvature: The curvature of a surface vertical to the slope direction. Planform curvature reveals where the surface is convex or concave causing
40
divergence and convergence respectively. In a DEM, a periodic surface curvature profile can be decomposed into oscillations (k=integers) with different frequencies and different amplitudes by means of Fourier series as follows (Habib, 2000).
(Amplitude spectrum)
Z
Flow direction (Phase spectrum, frequencies)
X ∆X0 Water basin
Lmin
Figure 3.3: A periodic surface curvature profile.
∞
Z ( x ) = ∑ C k Cos( k =1
∞ 2πkx 2πkx 2πkx ) − Φ k ) = ∑ ( a k Cos + bk Sin L L L k =1
where C k = ( a k2 + bk2 )1 / 2 = amplitude spectrum, Φ = tan −1 (bk / a k ) = phase spectrum, L = length of the profile,
λk = wave length of the k th oscillation = 2π f k = 1 / λk = k / L = frequency, k = oscillations (integers), and
a k & bk = amplitude coefficients.
41
L = L/k , 2πk
5. Ridge: A range of hills or mountains or uplands or the upper part of such a range. Ridge is an extended elevation between valleys. It is top of mountains with long and narrow crest (peak). Therefore, divergent flow leaves a ridge. 6. Valley (stream): The entire area between the tops of the slopes on either side of a stream. Therefore, a convergent flow causes concentration of runoff and indicates a valley. 7. Valley (glacier): A glacier that is smaller than a continental glacier or an icecap and that flows mainly along well-defined valleys in mountainous regions. 8. Channel: The bottom of a valley where water runs. 9. Floodplain: A flat area about level with the top of the channel. It lies on either side of the channel in wide valleys. Floodplains may be narrow or absent in steep valleys. 10. Flow direction: The direction of running water across a surface determined by the aspect and the slope at each location in a DEM. 11. Stream discharge: The volume of runoff per unit area is measured as stream flow and is divided by the contributing basin area. For turbulent flow, depth may be estimated by combining the continuity equation (Mitchell, 1991): Q = WDV where Q = quantitative volume of flow per second (m3s-1), W = average width of channel (m), D = average depth of channel (m), V = average velocity of flow (ms-1), and the Manning equation:
42
2
V=
1
0.454 3 2 R S n
where R = coefficient that is the cross-sectional area of flow (m2) divided by the wetted perimeter (m). S = coefficient that is the hydraulic gradient (tangent of slope angle or mm-1). n = surface roughness factor (Manning’s coefficient of resistance). A basic n value is defined for some materials, such as topsoil, rock, fine and coarse gravel, surface irregularity and vegetation, by Mitchell (1991). This is an empirical value that changes according to the degree of permeability, density of vegetation, roughness of land. Channel size, cross section, alignment, and hydraulic radius increase turbulence and retard flow.
(b) Hydrologic cycle We have already reviewed the hydrologic cycle (water cycle) in chapter 2. A drainage system occurs by means of the water cycle. The water cycle is shown in figure 3.4 including precipitation, condensation, evapotranspiration and infiltration. The water cycle is a process of reforming precipitation repeatedly and describing the movement of water, which comes from precipitation and issues from the Earth.
43
SKY Evapotranspiration
Precipitation
Ground surface Transpiration
Channel storage
Evaporation Interception
Surface storage
RUNOFF Direct surface flow
Infiltration
Soil water storage
Inter flow
Percolation
Ground water storage
Ground water flow
Figure 3.4: Water cycle and runoff showing water circulation and storage (Viessman et al, 1989).
(c) Hydrograph A hydrograph is a mechanism for recording the changing level of water bodies, such as streams, reservoirs and ponds on a chart or graph. A hydrograph is a continuous graph showing the characteristics of stream flow with respect to time. Thus, hydrography is a description and study of water bodies, such as seas, lakes, and streams, i.e., it is the measurement of water levels and investigation of the behavior of streams especially with reference to the control or the utilization of water bodies. For hydrologic analysis of surface water flow, a hydrograph is used to illustrate the flow rate at all points in time during precipitation and after ice and snow melt.
44
In the hydrograph (see figure 3.5), the concentration curve is the rising portion of the hydrograph. The crest segment (peak segment) is the region in the vicinity of the peak. The recession (separation) is the falling portion. The shape of a hydrograph depends on precipitation pattern characteristics and basin properties. There are mainly four inputs to the overall flow pattern recorded by the hydrograph. Discharge
Crest segment
Storm period hydrograph
Inflection point
Beginning of direct surface runoff
End of direct surface runoff
Rising limb
Inflection point
Falling limb
Time
Figure 3.5: A continuous discharge hydrograph with two peaks (Viessman et al, 1989).
•
Channel precipitation begins with inception of rainfall and ends with the storm. Its distribution with respect to time is highly correlated with the storm pattern (see figure 3.6). Discharge Inception
End of raindrop Max. ratio of runoff to infiltration at mid-slope point (inflection point) Direct surface runoff Inter flow
Channel Precipitation
Ground water flow
A B Time
Figure 3.6: Discharge hydrograph elements (Viessman et al, 1989).
45
2. Direct surface runoff (overland flow) When precipitation hits the ground surface, it flows downward along two paths as surface flow and subsurface flow (see figure 3.7). The first one is also called runoff. Direct surface flow is the first water to reach the main stream. Precipitation
Ground surface
Path 1 Direct surface flow Infiltration
Evapotranspiration
Path 2 Inter flow Path 3 Ground water flow
Water table
Watershed or basin, i.e., a stream or lake
Percolation
Figure 3.7: Directions of water flows on a hill slope and in the ground.
3. Inter-flow (through-flow) is also called saturated overland flow. In some locations, vertical and horizontal infiltration drenches the soil throughout its depth. This water merges with direct surface flow and becomes overland flow. 4. Infiltration, percolation and groundwater flow
Infiltrated water either flows
downhill within the soil zone or penetrates towards the groundwater table. The first is known as runoff or through-flow and the second is known as infiltration or percolation. Some infiltrated water percolates to the groundwater table and either reaches the stream or penetrates to deeper subsoil aquifers. The process can be slow, often taking years.
3.3 Analysis of streams We have already seen stream classifications in chapter 2. Here we will analyze the behaviors of streams.
46
1.
Stream Profile is the side view of a stream from its head (source point) to its mouth (discharge point), showing elevation versus distance from the head to the mouth as a chart. The stream profile is controlled at its lower end by a stream’s base-level elevation at which point it disappears as it enters a large standing water body, such as a sea. Thus, changes in natural base level affect the stream profile in predictable ways. If the regional base level rises (e.g., as sea levels rise), then the stream profile will show the effects of sedimentation as the stream forms channel and floodplain deposits to reach the new base-level elevation. Constructing a dam in a stream can also create a new local base level.
2.
Stream Discharge The size of a stream’s flow can be measured by its discharge, that is the volume of water that passes a given point in a given time as it flows through a channel of a certain width and depth. In short, discharge of a stream is the volume of flow per unit time, expressed as Q = W ⋅ D ⋅ V (Mitchell, 1991). Where Q is the discharge, quantitative volume of flow per second (m3s-1), W is the average width of the channel (m), D is the average depth of the channel (m), V is the velocity of the stream (ms-1) (distance traveled per second). The product of width and depth of the stream (WD) may be called cross section of the stream. Stream discharge is subject to change over time. Studies of changes in discharge, velocity, channel dimensions and topography, especially slope along the entire length of a stream from the head to the mouth, have revealed a large-scale and a long-term balance. Therefore, a stream has a dynamic equilibrium between erosion of the streambed sedimentation in the channel and floodplain over its entire length. This
47
equilibrium is controlled by several factors: topography, climate, stream flow (discharge and velocity), resistance of rock to weathering and erosion. 3. Flood control of streams: for flood control of streams, we measure the amount of runoff flowing in a stream, and plot a graph of stream discharge as a function of time. This can be called the discharge hydrograph. It indicates the sequence of associations between runoff and other components of the physical characteristics of the watershed (see figure 3.8). Precipitation
Transient
Precipitation characteristics
Type
Permanent
Path
Speed
Duration
Interception and detention Evapotranspiration
Evaporation
Transpiration
Infiltration
Storage capacity
Bedrock
Soil
Land use
Crop
Watershed area characteristics
Vegetation
Stream drainage patterns
Pattern type
Density
Main channel geometry
Size
Slope
Impermeable surfaces
Roughness
Shape
Slope
Order
Aspect
Size
Size
Height
Figure 3.8: Flood control components of streams used by a hydrograph (McCuen, 1998; Mitchell, 1991; Viessman et al, 1989).
48
3.4 Map representation of stream drainage networks A drainage system is a network area, upon which water falls and runs. Every topographic rise between two stream drainage patterns in a watershed area forms a divide (a watershed boundary or a ridge) of high land along which all precipitation is shed as runoff down either side of the rise. In other words, stream valleys and drainage basins are separated by divides, which are ridges, uplands or mountain ranges. A divide separates a stream with its tributaries from adjacent stream systems and so forms the drainage watershed boundary. The watershed or catchment is surrounded by divides, whence all water runs into the main stream body of the draining area. In a map representation (see figures 3.9 and 3.10), stream drainage networks may include the following components.
A branch of stream
A junction (node)
Stream channel Interior links
Pour point (outlet) Watershed area
Exterior links
Watershed boundary Junctions An outlet junction (the lowest junction)
A watershed (a lake with a stream drainage)
Figure 3.9: A map representation of a stream drainage network.
•
Tree structures: drainage systems can be mostly visualized as a tree structure, with a dendritic pattern like blood vessels in the brain.
49
•
Pour points: the base of the tree (stream drainage pattern) is called the pour point or outlet. A pour point is the lowest point of a watershed into which water flows.
•
Stream channels: the branches of the tree are called stream channels.
•
Junctions: junctions are also called nodes. They are the intersections of two stream channels.
•
Interior links: the branches of a stream channel linking two successive junctions, or a link of a junction and outlet, which has minimum height in the network.
•
Exterior links: they are the outermost branches of a stream channel. They have no tributaries. Watershed, basin, catchment, or contributing area
Stream network watershed Pour points, or outlets Watershed boundary
Figure 3.10: A hierarchical watershed drainage with Strahler’s order (Mitchell, 1991, p.249)
•
Watershed: also called a basin, catchment, or contributing area. A watershed is a drainage basin, which is a tract of land filled from both surface runoff and ground water into a water body such as a stream, lake, pond, or swamp. 50
•
Watershed areas: a large watershed may also contain many smaller sub-watersheds. Streams convey both surface and groundwater coming from higher areas due to precipitation. Both surface and subsurface water are drained to an area by a surface or subsurface flow. This area is called watershed area.
•
Watershed boundaries (divides): a direct surface runoff in a watershed flows downhill until it reaches a stream. The lines separating the land surface into watersheds are called divides. These normally follow ridges and mountains and can be delineated by using contour maps, field surveys, or stereograph pairs of aerial photographs to identify slope directions.
•
Watershed slope: the gradient of a drainage basin and of its channels has effects on the surface runoff process of that drainage region. Most stream channel profiles exhibit the characteristic of decreasing slope proceeding in a downstream direction.
3.5 Summary In this chapter, we have reviewed information about hydrologic surface analysis that is useful for construction of the prior information-base for river-related environmental GIS applications. We also have explained components of hydrologic surface analysis and their formal definitions in addition to a map representation of streams. In the next chapter, we will build our prior information-base for river mapping including rules and facts. The prior information-base is synthesized from information coming from chapters 2 and 3 in addition to experimental results from multi-spectral images.
51
CHAPTER 4 SYNTHESIS OF A PRIOR INFORMATION BASE FOR RIVER MAPPING FROM DIGITAL IMAGES AND DEMs Introduction In this chapter, we synthesize a prior information-base from information coming from chapters 2 and 3 in addition to experimental results using multi-spectral images. The prior information-base includes rules and facts about water bodies. In this chapter, we overview a prior information-base and describe our prior information-base. Then, we represent our rules and facts in terms of geometric, radiometric and natural characteristics of rivers in images. This chapter is navigated as follows. 4.1 Overview of a prior information-base 4.2 Properties of our prior information-base • The key concepts • Methodology • Different • Generic • A basic assistance tool 4.3 Geometric information about lakes 4.4 Geometric information about rivers 4.5 Radiometric information about water bodies 4.6 Distinguishing rivers from roads 4.7 Summary of the chapter
52
4.1 Overview of a prior information-base for river mapping For more efficient image interpretation, in contrast to a photogrammetric mapping system that requires the stereo-pairs of images, an advanced image interpretation system in a GIS environment requires both images and DEMs together with a prior information-base (interpretation of river characteristics) for river mapping. We cannot interpret rivers efficiently by just processing images alone or by just processing DEMs alone. For instance, as we may not easily extract water features from complex, low-resolution images, flat running water channels, such as deltas and/or river channels in settlement areas can not be easily extracted from DEMs. Extracted channels from DEMs also do not show where water is. So, in order to determine where water is, we use the reflectance information of water in images. On the other hand, multi-spectral images come from color sensors in satellites. A specific color characterizes the signature of each pixel. A set of numbers represents the combination of the reflectance values of materials in pixels. The satellite’s sensor records the digital values for different intensities at each given wavelength band of the light. These numbers create a spectral signature for each pixel. During this process, some images, which may be called “fuzzy images,” may not have clear visibility of rivers owing to small scale, noises, reflectance problem of materials, complexity of materials, and mixed pixels. Also some roads and rivers appear in images with similar reflectance and shape. Moreover, the shape and reflectance of either edges or ridges of any rocky areas, mountain areas, tectonics and faults, in addition to some boundaries of vegetation fields, may appear similar to the shape and reflectance of rivers as linear features. 53
Additionally, some water features, which may have different reflectance than all water features do, may not appear as water features. In this case, we apply DEMs to discriminate rivers from other linear features (e.g., roads) using slope and 3-D location information. Therefore, for efficient and effective feature extraction of rivers, we combine image information, such as color and shape as well as pattern, with DEM information, such as slope and water channels. However, the image information is considered as essential for feature extraction of water bodies, because in this study, what we are doing is basically image interpretation for river mapping. Thus, DEMs are employed to help us determine exactly where rivers can be when we do image interpretation. Drainage pattern recognition of rivers (Argialas and Lyon, 1988) can be done by means of human vision and/or computer vision via an advanced image interpretation system with a prior information-base. We interpret rivers from digital representations, such as images and DEMs, using a set of rules related to colors (multi-spectral reflectance), gray tones (panchromatic reflectance), shapes, patterns, and textures of rivers in images (Caelli and Bischof, 1997; Wilson, 1997; Huang and Jensen, 1997; Richards, 1993; Ton et al, 1991; Moller-Jensen, 1990; Wharton, 1987), in addition to elevations, slopes and aspects in DEMs (Band, 1986; Jenson, 1985; O’Callaghan, 1984; Mark, 1983; Haralick, 1983). In other words, rivers may be efficiently and effectively recognized from a combination of both images and DEMs using advanced image interpretation, together with a set of rules related to information about the radiometric, geometric, and natural characteristics of rivers. The rules are used to construct river features for more reliable map representation. 54
We represent information about rivers by means of rules. We also make generalizations about the behavior of rivers for determining flow directions of rivers in a region. Furthermore, geometric characteristics of rivers are often determined by the physical structure of the ground surface in the areas where the rivers flow (explained in detail in chapters 2 and 3). For instance, knowing the geometric characteristics of all rivers in a region, we can interpret and generalize characteristics of the physical structure of the region. Water flows from higher energy to lower energy in terms of gravity and elevation. So, we can make decisions about where high elevations (mountain area) and low elevations (areas of plain and valley) are located in the region. Using this information together with river drainage pattern, we can also make decisions about soil characteristics for agricultural purposes (Press and Siever, 1998; Skinner and Porter, 1995; Argialas, 1985; Morisawa, 1968). With digital images, we discriminate rivers from other linear features using the prior information-base. Then, we extract them using advanced image interpretation techniques, such as filtering and classification methods, as well as extracting linear features pixel by pixel using attributes (threshold values). Next, from DEMs, we extract water channels for river mapping. Then, for verification we combine and analyze these results of images and DEMs by an overlay. So, for efficient river mapping, we use images and DEMs together with the prior information-base in a GIS environment integrated with remote sensing, rather than using photogrammetry, which requires stereo-pairs of images and image interpretation by the operator. More reliable river interpretation is crucial to human society. Once we recognize rivers using the prior information-base, we can extract river features more reliably and 55
accurately. Then we can establish many efficient GIS models, such as a hydrologic model for hydrologic purposes, an agricultural model for irrigation, and a fire-fighting model for environment (e.g., for protecting settlements or forest areas from fires).
4.2 Properties of our prior information-base 1. The key concepts •
Shared cognitive responsibility and its support.
•
Building the fundamental prior information-base as rules and facts.
•
Minimizing the computing needs and maximizing cognitive workload of the system operator, and vice versa for the computer part of the system.
2. Methodology •
We build the prior information-base from fundamental principles.
•
We make the use of it flexible and user-friendly.
•
We test the different parts of the prior information-base for their usefulness.
•
We make the machine do the big computations as simple tasks, and we make the operator do the complex interaction analysis of the higher-order results.
•
We run it on different data sets to test and calibrate the system.
3. Different Our prior information-base involves “Shared Cognitive Responsibility.” The difference between existing Expert Systems (Huang and Jensen, 1997) and this system can be shown as follows. •
Part of the expertise (in the form of rules) is in the machine.
•
The system is interactive and semi-automated. 56
•
The results are shared with the operator.
•
An interpreter adapts the system to the specific problem.
•
Machine's analysis is designed to facilitate how the expert works.
•
The analysis works to human and machine strengths.
4. Generic The system is a generic prior information-base for a wide range of circumstances, based on fundamental information of what's happening in the real world. 5. A basic assistance tool We build a basic assistance tool for the interpreter to use to speed analysis and decision-making.
4.3 Geometric information about lakes A lake is a large area of water, usually non-salty water, surrounded by land. There are a number of river and lake-related rules and facts that not only construct water features but also constrain river map representations (Press and Siever, 1998; Skinner and Porter, 1995; Ramirez, 1992; Argialas, 1985; Morisawa, 1968). These rules are as follows. 1. Boundaries of lakes must be closed on themselves in a map representation. In other words, the start and the end points of the boundary of a lake must be the same, creating a closed polygon. 2. Boundaries of lakes cannot overlap with another lake. Lakes join each other but they do not intersect each other, and the outermost border of the joint lakes is used as the boundary. Thus, two lakes cannot have the same location. 3. Contours and rivers cannot cross lakes. 57
4. Size of lakes varies from small to large. Large lakes tend to occur at comparatively low elevations, while small lakes are usually at higher elevations, such as a craterlake. Low elevation lakes usually obtain more water during precipitation and/or snowmelt. 5. Lakes must have an elevation lower than the surrounding contours (elevations). Note that crater-lakes at the top of mountain have also lower elevation than the surrounding elevations. 6. Lakes have flat (specular) surfaces. They appear as flat planes in DEMs. Thus, a standing water body must have a constant elevation. 7. Lakes strongly absorb the near-infrared wavelengths. Thus, they appear dark in panchromatic images and they appear dark blue or black in color composite images, especially the near infrared images with 0.76-0.90 microns of wavelengths. However, very small ponds may appear bright white on both color and panchromatic images. Reflectance of lakes can also differ from that of rivers because of the different color of the water. So, the reflectance indicates the geometry of the lake in an image. 8. The most important lakes must take precedence for placement in a map representation in case of reducing the map scale. 9. The outermost contour of an island must have an elevation higher than the surrounding water (e.g., lake and river) elevation. 10. In a map representation, contours must be perpendicular to break-lines when they intersect them.
58
4.4 Geometric information about rivers A stream is a natural flow of water moving across land between banks (land along the side of the stream), and narrower than a river. It may be flowing continuously or intermittently. A river is a wide, natural stream of water flowing between banks into a lake, into another wider stream, or into the sea. So a river must have a connection with either a standing water body or another larger river that is connected to a standing water body. Every river or segment (tributary) of the river has its drainage basin, consisting of the total area that contributes water to the river. A river basin is an area from which all the water flows into the same river. The line that separates adjacent drainage basins is called a divide. A riverbed is the ground over which a river flows between its banks. A watershed is the high land separating two river systems, from which each has its origin with many little tributaries. Additionally, we have seen more details in the classification of rivers as a decision tree in chapter 2 and their formal definitions. We show some rules and facts about rivers as follows. 1. Rivers cannot cross each other. They join each other. 2. Rivers cannot cross standing water bodies. 3. Rivers can intersect contour lines only at the inflection points of contours. 4. Rivers must have a connection with a standing water body; otherwise, they will be standing water bodies. Rivers must discharge into a water body. As occasional exceptions, they may run from a lake into land (e.g., Chicago River), but this is very rare. Also running waters may issue from the earth, run for a while and then disappear
59
somewhere without discharging into any standing water body. We can see examples of this in Australia and Canada. 5. Rivers cannot have coincident paths; they join each other. 6. Rivers cannot have sharp turns. Rivers have smooth turns due to the acceleration of flowing water and natural dispersion of water. Even smooth turns occur after waterfalls. 7. Rivers cannot be exactly straight lines with an invariant width along their lengths. Rivers mostly are meandering and they have both irregular shapes and widths along their paths, depending on the inhomogeneous physical structure of the underlying land. 8. Rivers run from high areas downhill to low areas, so flowing water accumulates in a valley. In other words, rivers flow from higher energy states towards lower energy states. Thus, the main river channel has the local lowest elevation. The origin of the river must be the highest point along its path. 9. Rivers segments must have an invariant slope (except for waterfalls, such as Niagara Falls), derived from the intersected contours with the rivers. 10. Rivers have certain drainage patterns that we have seen in chapter 2. The most common one is dendritic (tree-like). 11. Rivers must have contiguity. Rivers are continuous, long and skinny things in terrain. 12. When rivers flow in flat plain areas (e.g., Boston River), they create braided or meandering or straight channel patterns. Rivers in flat areas have many fewer tributaries or may not have tributaries (sub-channels) joining the main river at all. However, rivers usually have more sub-tributaries in rough areas than in flat areas. 60
Oxbow lake
A
C
River 2
D River 1
Split point
An oxbow lake can occur after a river changes its path. Joint point
B Rivers cannot have sharp turns.
Tributaries of a river can join the river from all directions depending on surface topography.
Rivers have smooth turns.
Rivers join each other, not intersect each other. Rivers can also split into several directions, then join each other to make a loop. But a river cannot run as a loop itself without a split and a join.
E
A loop sub-channel with the same elevation Dome or Volcano Main river
F
Split and joint of Niagara River itself. A loop of Niagara River
Lake Erie
An annular river drainage pattern.
Lake Ontario Split
Connecticut
G
Connecticut River
Joint
River 1
H River 2
River N
Gulf of St Lawrence in Atlantic Ocean Island
River 3
Long Island
Manicouagan River
Atlantic Ocean St Lawrence River
A lake with an island in its center makes a loop, or joining rivers make a loop. For instance, a loop of water reservoir of Manicouagan River in Quebec, Canada.
Not all rivers discharge into a standing water body. An overloaded standing water body causes a river running towards the land and also creates a bay. For instance, the Chicago River runs from Lake Michigan though the land.
Great Lakes Region in North America
Atlantic Ocean
St Marys River Lake Superior
St Lawrence River Lake Ontario
Lake Huron
Niagara River Lake Michigan
St Claire River Lake Erie
Lakes can be linked with each other through rivers creating a multi-basin river drainage pattern. In Great Lakes region in North America, Lake Superior, Lake Huron, Lake Erie and Lake Ontario are connected with the St Marys River, the St Claire River and the Niagara River, respectively. These rivers run eastward and finally discharge into the Atlantic Ocean through the St Lawrence River. This multibasin region is fed by water of icemelt and snowmelt in addition to plenty of rain in the region.
Figure 4.1: Natural characteristics of rivers and lakes.
61
I
13. Rivers can be found in vegetated area in either desert or rocky area. 14. Rivers make S-turns rather than sharp turns. This meandering pattern can be seen in flat areas or in valleys in mountain areas. This type of river can cause an oxbow lake in flat areas but it is almost impossible to cause an oxbow lake in valleys in mountain areas (e.g., oxbow lakes and meanders of Animas River in Durango, Colorado). 15. Tributaries join the main river with mostly acute angles in the direction the river is running. This creates the dendritic river drainage pattern (e.g., Sabine River in Texas). But right angle joins are also possible. This causes the trellis river drainage pattern (e.g., Hiawassee River in Tennessee) or rectangular river drainage pattern (e.g., Zambezi River in Livingstone, Zambia). 16. The origin of rivers must be the highest point along its path. Many rivers discharge into standing water bodies, such as lakes, seas or oceans, and the direction of discharge is the flow direction. If a river has no discharge and a connection with a standing water body, then it will be a standing water body, with w few exceptions mentioned previously. 17. In arid areas, the river channel having the lowest rank in the stream hierarchy dries first. Thus, the main river channel dries after all its tributaries have dried. 18. The main river channel obtains the most water in a watershed area during any precipitation or snowmelt. Therefore, we need to measure hierarchically in case of a flood disaster. This hierarchical protection may be also applied in sub-watershed area. If we predict where, when, how long, how much, type of precipitation and the amount of snowmelt occur, we can protect flood disaster efficiently using this information. The amount of water in rivers is subject to change according to the 62
climate. This also changes the widths of rivers. The water pressure at the origin of a river affects the amount of water in the river channel, which also affects the river width. Moreover, the sediments rivers carry can make the river narrower because rivers may not able to carry all the sediments in their channels and they will leave the heavier sediments at the sides. This will cause the river to be narrower. 19. Rivers in deep valleys in mountain areas, as well as forested areas, carry more water (such areas often have a humid climate) than rivers in flat and low vegetation areas (such areas often have a dry climate). Note that humid areas usually receive much more precipitation than arid areas. 20. Flowing waters can split into multiple streams in the flow direction, especially in flat delta areas. A delta is an area of low land shaped like a triangle where a river divides into branches towards standing water bodies. For instance, the delta of the Nile River in Egypt occurs when it discharges into the Mediterranean Sea. Rivers get larger and also split into branches when they get close to discharge into a standing water body, often as their slope gets flatter. 21. Rivers can link a standing water body to another creating a multi-basin river drainage pattern (see Great Lakes in figure 4.2). Connection can be also made by a canal, an artificial waterway. 22. Rivers cannot make loops themselves, but their sub-channels can form an annular drainage pattern. A loop sub-channel occurs below dome-like mountains. This drainage pattern is called annular. A loop sub-tributary usually has much the same elevation along its length, so its running direction is uncertain; however, it re-joins
63
the main river channel. A loop also occurs by split and join of a river (see Niagara River in figure 4.2). 23. The most important rivers must take precedence for placement in a map representation in case of reducing the map scale.
4.5 Radiometric information about water bodies Rivers and lakes have better visibility in the near infrared Bands with 0.76-0.90 micron wavelengths in multi-spectral images. A greatly simplified rule representation of information using Landsat TM (multi-spectral) imagery is as follows: (Wilson, 1997; Richards, 1993; Ton et al, 1991; Moller-Jensen, 1990; Wharton, 1987). If (band 7/band5 > threshold) then (vegetation) If (band7/band4 < 1) then (water) If not (water) and not (vegetation) then other If (water and specular surface) then (lake) If (water and diffuse surface or volume) then (open water) Additionally, some rules for SPOT multi-spectral imagery were constructed (Huang and Jensen, 1997) as in figure 4.2. Note that neither of these rules will work for all images, although they work very well for certain specific images. Images have many different complexities in terms of threshold and reflectance. Band_1 ≤ 77
> 77
Band_3 ≤ 44 Water
Bare soil > 44 Vegetation
• • •
Rule 1: if Band_3 ≤ 44 then class water Rule 2: if Band_1 > 77 then class bare soil Rule 3: if Band 1 < 52 AND Band 3 > 63 then class vegetation
Figure 4.2: An information representation using a decision tree from SPOT multi-spectral image data (Huang and Jensen, 1997).
64
4.6 Distinguishing rivers from roads In addition to enhancing image classification for extracting water bodies, if we know where rivers and roads are in a region, we can establish a better fire-fighting model in forest areas. Thus, these rules are very useful information for fire-fighting in forest areas in addition to discriminating rivers from roads for river mapping. 1. As meandering rivers mostly run perpendicular to the slope directions (parallel to contour lines), roads also extend perpendicular to the slope directions. Flowing water bodies can intersect the contour lines only at the inflection points of contours (Ramirez, 1992). Roads can be seen as concave and/or convex surface in DEMs, but rivers never run as concave or convex surfaces. In other words, road paths can go up and down, but rivers always go downward.
Peak
Peak
Peak
Downward slope direction Downward slope direction
Downward slope direction This is a road, not a river since the meandering occurs on flat areas or in valleys.
Lake
This is a meandering river or a road on downhill or in a valley.
Lake
A radial river, it may not be a road since roads have certain slopes.
Lake
Downward slope direction Downward slope direction
Downward slope direction This is a road, not a river since the meandering occurs on flat areas or in valleys.
This is a Crater Lake at the top, and a meandering river or a road on downhill or in a valley.
Figure 4.3: Relations between rivers, lakes and roads.
65
A centripetal river, it may not be a road since roads have certain slopes.
2.
While roads often form loops, no river can make a loop itself, loops only occurring as sub-channels in an annular drainage pattern. A loop sub-channel occurs below dome-like mountains (see figure 4.2).
3.
River sections must have a relatively constant slope (except for rivers having a waterfall) derived from the intersected contours with rivers. Roads have also certain slopes. But, roads usually go up and down while rivers run only downward.
4.
Roads can have sharp turns, while rivers have smooth turns, owing to the acceleration and natural dispersion of water (see figure 4.2).
5.
Roads can be straight lines, but rivers cannot be exactly straight lines with a standard constant width.
6.
Roads have standard widths and shapes, while rivers usually have irregular widths and shapes along their paths, such as irregular meandering owing to the nature of inhomogeneous physical structure of land. In other words, rivers can be wider and narrower along their length, while roads keep their specific widths along their paths.
7.
In large settlement areas, such as urban areas, rivers are usually wider than roads. In rural areas, rivers and roads can have similar widths.
8.
Roads cannot have a braided texture, but rivers can.
9.
Turning roads have curves with specific radii, while meandering rivers do not have curves with specific radii. In other words, roads usually have regular curves or patterns, while rivers have irregular curves or patterns. This is because roads are artificial, while rivers are natural and their form depends on the characteristics of the land. 66
topographic
10. Stream drainage patterns are very different to road network patterns. For instance, roads network do not have the dendritic river drainage pattern. 11. Roads can intersect other roads, but rivers cannot intersect other rivers. Rivers join each other and they have a connection with a standing water body. Roads can also join each other. 12. Roads usually connect one settlement to another. Most rivers discharge into a standing water body, such as a lake or sea. 13. Rivers and roads cannot join each other, but they can have coincident paths. They can intersect each other using a bridge. 14. Rivers usually appear darker (black) than roads, which appear brighter (white), on panchromatic images. However, some smaller tributaries of rivers can appear similar to roads on panchromatic imagery. 15. In images of the near infrared wavelengths, the spectral response pattern of water is very different to the spectral reflectance of road. For instance, since water strongly absorbs the near infrared waves, it appears in an image as strongly darker than roads. Thus rivers appear usually black or strongly dark blue in color images, especially in the near infrared bands with 0.76-0.90 micron wavelengths (e.g. Landsat TM band 4). However, in some images having different wavelengths, small rivers, such as creeks, appear similar to roads, usually with a bright white color.
67
4.7 Summary In this chapter, we have constructed a number of basic rules and facts of our prior information-base as a synthesis from information coming from chapters 2 and 3, while adding some of the published experimental results using multi-spectral imagery. This provides the basis for the PIB, from which tools and algorithms can be developed. In the next chapter, we will discuss the application of a prior information-based river mapping system for using digital images and DEMs to test out the fundamental rules for river mapping developed in this chapter.
68
CHAPTER 5 AN APPLICATION OF PRIOR INFORMATION-BASED RIVER MAPPING FROM DIGITAL IMAGES AND DEMs
Introduction In this chapter, we test a number of fundamental rules using three different multi-spectral Landsat TM images. To do this, we do an implementation of prior information-based river mapping from digital representations using IDRISI, creating a tool for river finding and testing out the basic rules. Additionally, we do a DEM application for extraction of river channels using the RiverTools package. Materials and methods for these implementations are explained in this chapter. Results and discussions are given at the end of the chapter. This chapter is organized as follows. 5.1 Materials 5.1.1 Hardware 5.1.2 Software 1. Overview of IDRISI 2. Overview of RiverTools 5.1.3 Description of the image data sets 5.1.4 Description of the DEM data set
5.3 Results 5.3.1 Results from IDRISI 5.3.2 Results from RiverTools 5.3.3 Results of testing the basic rules 5.4 Discussions 5.5 Summary of the chapter
5.2 Methods 5.2.1 List of the tested basic rules 5.2.2 A tool for river finding and testing images in IDRISI 5.2.3 Operators used in the tool 5.2.4 Prior information-based feature extraction of water bodies 1. On-screen digitizing method 2. Threshold method 3. Filtering methods 4. Classification methods 5.2.5 Processing a DEM with RiverTools 5.2.6 Extracting river networks from a DEM by RiverTools
69
5.1 Materials • Images and a DEM data set belonging to different areas, with different sizes and resolutions were used because an image and a DEM belonging to the same area with the same resolution could not be readily found. Thus, for further analysis, it was not possible to overlay the results of extracted river features from the image with the river channels derived from the DEM.
• Another problem was that the mapping of river channels from the DEM had an RTV format, proprietary to RiverTools and RiverTools has no option to export its vector data as a DXF file for AutoCAD or as a shapefile for ArcView to allow further processing. Thus, it was not possible to combine the extracted river features from the image with the river channels from the DEM. IDRISI has options to import and export its vector data in DXF and shapefiles, and the next version of RiverTools (currently version 2.0) will have these conversion options, some time in late 2001.
5.1.1 Hardware In order to operate IDRISI and RiverTools, a PC with Windows NT 4.0 operating system and Intel Pentium III Processor with 566 MHz speed and 64 MB Random Access Memory (RAM) was used.
5.1.2 Software IDRISI 32 was used for feature extraction of water bodies and RiverTools version 2.0 was used for extraction of water channels. 1. Overview of IDRISI system IDRISI is a remote sensing, image-processing and GIS package designed to provide comprehensive analysis of satellite and aircraft remote 70
sensing data. It provides a powerful, innovative and user-friendly environment to display and analyze images of any size and data type. With its combined file- and band-based approach to image processing, IDRISI allows us to work with entire image files, individual bands, or both. IDRISI can analyze single-band or multispectral images. (IDRISI manual, 1997).
2. Overview of RiverTools RiverTools is software for digital terrain and river network analysis. Using RiverTools, we are able to import DEM data and delineate river basins for further study. We can perform in-depth analysis of watersheds of interest (RiverTools manual, 2000).
5.1.3 Description of the multi-spectral image data sets In this study, we analyzed three data sets of multi-spectral images. They are: (1) Bighorn Basin, Wyoming with a size of 512x512x6 Bytes and BSQ format; (2) Canon City, Colorado with a size of 640x400x6 Bytes and BSQ format; and (3) Boulder City, Colorado with a size of 615x816x6 Bytes and BSQ format. They are small subsets from 30m resolution Landsat TM (Thematic Mapper) scenes. These image consists of 6 multispectral bands. They are as follows. Band 1 = 0.45-0.52 microns wavelengths (Visible Blue Band) Band 2 = 0.52-0.60 microns wavelengths (Visible Green Band) Band 3 = 0.63-0.69 microns wavelengths (Visible Red Band) Band 4 = 0.76-0.90 microns wavelengths (Near Infrared Band) Band 5 = 1.55-1.75 microns wavelengths (Mid Infrared Band) Band 7 = 2.08-2.35 microns wavelengths (Mid Infrared Band) 71
Note that Band 6 of 11.5 microns wavelengths (Thermal IR Band) has been removed from this data set because it has a larger pixel size (120 m) than the rest of the bands. Suppose an RGB (Red, Green, and Blue) color composite is the composite of Band 3, Band 2, and Band 1 of a multi-spectral image of Landsat TM. Thus, Band 3, Band 2, and Band 1 color composite image is a close approximation of the true color image and it approximates the way this area would look to a human observer. Note that the true color composite image does not give clear visibility of water bodies. A Landsat TM Band 4 gray value image has the best visibility of water features relative to other bands, because water features usually appear black or strongly dark gray in the near infrared bands (0.760.90 microns of wavelengths), since water strongly absorbs the near infrared. Thus a false color composite image must include Band 4 for better visualization of water bodies.
5.1.4 Description of the DEM data set The DEM data set is a part of Cumberland Basin in Kentucky. This DEM data set is very small, but it is useful for illustrating how drainage networks can be extracted from DEMs using RiverTools.
5.2 Methods Figure 5.1 (overleaf) shows the process of a river mapping implementation by an advanced image interpretation system.
72
DEM
Multi-spectral imagery
RiverTools System
IDRISI system
Preprocessing imagery
Extraction of stream channels
Prior info-based feature extraction of water bodies
Vectorization
Vectorization
Prior information-base: • Multi-spectral image classification, threshold, and filtering methods • Decision trees and taxonomies • Rules and facts • Information representations (qualitative, quantitative)
Overlay
Spatial analysis and verification with ground control points
Experimental results will improve the prior information-base.
River map
Figure 5.1: A river mapping implementation in an advanced image interpretation system.
5.2.1 List of the tested basic rules for rivers Rule #1 Reflectance of water bodies must be less than 10% in multi-spectral images (see signatures in Lillesand and Kiefer, 1994). Rule #2 We must work with at least Band 4 and/or Band 5 as we study with Landsat TM images since rivers are more visible in the near infrared bands. Note that water bodies appear as darker in the near infrared bands and Band 4 is the best for water bodies. Rule #3 If ((multiplication of classified Band_4 with classified Band_5) is within a threshold) then water. The threshold (color value, 0-255) is defined for reclassification
73
by the interpreter interactively. In this work, a fuzzy classifier was used, determining possibility of membership within the specified thresholds. Rule #4 Rivers cannot have sharp turns. They have smooth turns. Rule #5 Rivers cannot cross each other. They join each other. Rule #6 Rivers cannot cross a standing water body. They join each other. Rule #7 Rivers must have contiguity. Rivers are continuous, long and skinny things in images. Note that Landsat TM images have 30m resolution. Rule#8 Rivers cannot appear as exactly straight lines with invariant width along their paths. They meander (S-shaped), often irregularly due to the structure of topography. Rule #9 Rivers must have certain drainage patterns, mostly dendritic and meandering (see classification of streams in chapter 2). Rule #10 A river cannot run as a loop. Rivers can split into separate streams and rejoin itself later, creating a loop (e.g., Niagara River). Rivers can also just split into distinct streams (e.g., deltas). Rule #11 Rivers must be linked to a standing water body because they need to discharge into it. However, as exceptions to this, a stream may issue from the Earth and run for a while and then disappear somewhere (e.g., springs causing streams). Rule #12 The origin of a river must be the highest point along its path. Rule #13 Rivers accumulate in a valley since they run downhill and flow from higher energy configurations towards lower energy configurations. Rule #14 The main channels of rivers receive more water than their tributaries during precipitation. Rule #15 Rivers may be found in vegetated areas in desert and rocky areas. 74
Rule #16 Lakes must have flat (specular) surfaces with a constant elevation. Thus, they appear as flat planes in DEMs.
5.2.2 A tool for river finding and testing the rules in IDRISI Figure 5.2 shows a methodology of a tool to find rivers and test out river-related rules in IDRISI. Multi-spectral image
Classified set from Band 4
Classified set from Band 5
Multiply Class_4 withClass_5
Check water bodies with: • Radiometric rules, • Geometric rules, • Relation with other objects
Rule #3 (river finding rule)
Testing the rules
Filter the multiplied Class image by: • Edge enhancement filter, • Edge detection filter, etc.
Reprocess the filtered image for editing and verification by operators: • Distance, • Reclass, • Group, • Area, • Perim, • Overlay, • Buffer.
River map
Figure 5.2: A tool for river finding and testing the rules in IDRISI.
75
Decision making to test the rules is interactive between the user and machine. Finding rivers is semi-automated by rule #3 (river finding rule) and the amount of automation depends on the resolution of the images and the visibility of rivers in the images. The more complicated and low resolution the image, the more interactive work is required by the operator. The higher the resolution and river visibility the more automated the extraction of water bodies. The worst case is on-screen digitizing of the rivers, which is often the current situation.
5.2.3 Operators used in the tool 1. Fuzzy evaluates the possibility that each pixel belongs to a fuzzy set by evaluating any of a series of fuzzy set membership functions. The Sigmoidal, J-shaped and Linear functions are controlled by four points ordered from low to high on the measurement scale (IDRISI manual, 1997), i.e., it is a fuzzy-based reclassification operator. 2. Distance measures the Euclidean, “straight-line,” distance between each cell and the nearest of a set of target features. 3. Reclass classifies the data or attribute (threshold) values into new categories, based on rigid threshold values. 4. Group finds contiguous groupings of identical integer cells in an image. 5. Area finds the areas associated with each integer category on an integer image. 6. Perim measures the perimeter of each category in a grouped integer image. 7. Overlay produces a new image from the two input images. New values result from applying operations, such as addition, subtraction, multiplication and division. 76
8. Buffer calculates the buffer zones of a user-defined width about the target areas defined in an input image.
5.2.4 Prior information-based feature extraction of water bodies Figure 5.3 shows a methodology for a river mapping application by an advanced image interpretation system. Shape and reflectance of either edges or ridges of any rocky areas, mountain areas, tectonics and faults, as well as some boundaries of vegetation fields, can appear similar in the shape and reflectance to rivers in terms of color and shape as linear features, especially in small-scale, low-resolution images. Roads also sometimes appear similar to rivers. Multi-spectral image
Visualization of a best false color composite image for water bodies of the multi-spectral image
Determination of water bodies Using the prior information-base (rules): • River finding rule • Visual inspection (user's decision) • Spectral rule inspection • Geometric rule inspection • Relation with other objects
Extraction of water bodies by: • On-screen digitizing method • Threshold method • Classification methods • Filtering methods, and etc.
Editing and verification
Using operations: Buffering, Distance, Reclassification, Grouping, Area, Perimeter, Overlaying, and etc.
Verified water features
Extracted river channels
Overlay for spatial analysis and verification with ground control points
River map
Figure 5.3: Prior information-based feature extraction of water bodies.
77
DEM
In order to distinguish rivers from other linear features in images, we apply a prior information-base for river mapping. This prior information-base allows us to understand and recognize rivers in digital representations in terms of location and 3-D information, color, shape, and pattern information as well as relation information with other linear objects, such as roads, edges of rocky areas, and boundaries of vegetated fields. Thus, once we see linear objects in an image, especially a low-resolution image, we make a decision as to whether or not they are rivers, using our knowledge coming from the prior information-base, for instance, if (condition) then (inference). Thus, before we start feature extraction in order to determine water bodies, we need to visualize and analyze the image using our knowledge coming from the prior information-base (rules and facts). For this reason, we use the following steps. (1) Opening and displaying Landsat TM data. (2) Reviewing image colors: analyzing color images. (3) Cursor location/value for finding Min and Max threshold values to extract regions of interest, such as water bodies. (4) Examine spectral plots for examining the spectral characteristics of images. (5) Applying the rules (e.g., the river finding rule and the other rules) to recognize and determine water bodies. After determining rivers and lakes in images we extract them from the images. There are a number of methods to extract linear features from images (Caelli and Bischof, 1997; Wilson, 1997; Huang and Jensen, 1997; Ton and Jain, 1991; Schenk and Zilberstein, 1990). For instance, in order to extract water bodies we can use one of those methods, which are supervised multi-spectral image classification methods, filtering methods (e.g., high pass filters, or edge detection methods), and threshold methods, as well as on-screen 78
digitizing methods. In this study we employed the combination of the threshold method, classification methods and filtering methods, in addition to heads-up digitizing, since a combination of them is more promising for extracting rivers and lakes. Note that the focus of this study is on constructing a prior information-base for a specific task, rather than feature extraction per se. 1. On-screen digitizing method After displaying the image, we visually determined the rivers and lakes in the image using our knowledge coming from the prior informationbase. Then, we digitized the rivers from the displayed zoom window using the tool for defining regions of interest as a polyline feature in IDRISI. This method is efficient for extracting rivers and lakes, although it is interactive and time-consuming, as well as including some digitizing errors, such as dangling points, overshoot and undershoot. Nevertheless, digitizing does not take much time since an image usually does not hold many rivers and lakes. We used this extraction method to help verify the threshold, classification and filtering methods, rather than as the main method used, as it is this method that we are trying to supersede in this work. 2. The threshold method is similar to extraction of features pixel by pixel, according to reflectance information of features. In this method, for instance, we used Min Threshold=7 and Max Threshold=15 to extract the regions of interest (ROI) in Band 4 of the first multi-spectral image data set (Bighorn Basin). We determined Min and Max threshold values using the cursor location/value and the spectral information, as well as visual inspection, according to knowledge coming from the prior information-base.
79
The result is promising, although this method may include mistakes of Min and Max boundary values of the threshold, since there are no unique threshold values for all multispectral images. This method is fully automated and it is quick to extract water features. However, due to the similar reflectance of other objects in the image, the result needs further processing to complete the extraction of features. In other words, when we extract water features, because of the similar reflectance, this also includes some other, unwanted, features that are not water. We need to eliminate those unwanted features using operators, such as Area, Group, Perim and Overlay, in IDRISI. Additionally, in this method, due to the fact that the reflectance of some water features is quite different from the reflectance of most of the water features, these water features of different reflectance are not extracted with all the other water features. Thus, we need to add the water features, which are not extracted, using further process, such as combining and/or overlaying with other extracted water features. 3. Filtering methods: filtering (convolution) is employed for many purposes (IDRISI manual, 1997). For instance, the following were useful for this study. •
Minimum and maximum filters are good for mathematical morphology,
•
Median filter is good for random noise removal. Low pass smoothing filters (e.g., Gaussian) and adoptive box filters are also good for correcting "salt-and-pepper" random noise, as well. However, note that these filters may also remove rivers from images in the meantime since the rivers have high pass brightness.
•
Edge enhancement filters (e.g., Laplacian) are good for accentuating areas of change in continuous surfaces. 80
•
High-pass filters extract areas of abrupt change relative to those of gradual change.
•
Sobel edge detector extracts the edges between features or areas of abrupt changes.
•
We also detect vertical and horizontal lines (edges) by filtering. The following are the convolution kernels which are used in this study: −1 −1 −1 − 1 16 − 1 −1 −1 −1
−1 2 −1 −1 2 −1 −1 2 −1
−1 −1 −1 2 2 2 −1 −1 −1
A high-pass filter for edge detection
A filter for vertical line detection
A filter for horizontal line detection
− 1/ 9 − 1/ 9 − 1/ 9 − 1 / 9 17 / 9 − 1 / 9 − 1/ 9 − 1/ 9 − 1/ 9 Laplacian filter for edge enhancement
The Sobel edge detector output value is determined by the equation: newvalue = x 2 + y 2 where x = the result of applying the Kx kernel, and y the result of applying the Ky kernel. Kx =
−1 0 1 −2 0 2 −1 0 1
4. Classification methods
Ky =
1 2 1 0 0 0 −1 − 2 −1
There are two main classification methods in IDRISI:
unsupervised and supervised classification. Unsupervised classification uses statistical techniques to group n-dimensional data into their spectral classes, such as K-Means and Isodata in IDRISI (IDRISI manual, 1997). In this study these methods were not used. Instead, we used some classification operations supported by Distance, Area, Group, Perim, Reclass, and Overlay operators in IDRISI. Supervised classification, such as the threshold and the screen digitizing methods, requires that the user selects training areas for use as the basis for classification. Various 81
comparison methods are then used to determine if a specific pixel qualifies as a class member. IDRISI provides a broad range of different supervised classification methods, including parallelepiped, maximum likelihood, minimum distance, mahalanobis distance, binary encoding, and spectral angle mapper (IDRISI manual, 1997). However, in this study, we only used the threshold method and the screen digitizing method for extraction of water bodies with IDRISI, since these two methods were sufficient for the purposes of our research.
5.2.5 Processing a DEM with RiverTools RiverTools imports DEMs, then automatically delineates watersheds and calculates a variety of information from the DEM. In general, the main menu options of Prepare, Extract, Display, and Analyze guide the steps of this processing (RiverTools manual, 2000). 1. Preparing a DEM RiverTools imports both digital elevation data and flow grids. It provides tools to prepare data for further processing. Flow grids from ArcInfo can be converted to RiverTools flow grids. Individual DEMs can be patched together (mosaicked) to form a new DEM that covers the area of the basin of interest. 2. Extracting information from DEMs RiverTools processes the DEM and extracts a flow grid and a vector river network. This extraction processing results in a full data set of files containing information about the DEM. These files are used by RiverTools for display and analysis routines. Some of these preprocessing routines are computationally intensive and may take some time when processing large data sets.
82
RiverTools prints detailed information about what it is doing in the output log of the main window. 3. Displaying DEMs RiverTools provides many types of displays including density plots, shaded aspect and shaded relief plots, surfaces, contours, river network and river sources/junctions, basin maps and basin boundaries, and multi-layer plots which are combinations of the others. Map projections can also be applied to these plots. 4. Analyzing DEMs RiverTools contains tools for the quantitative analysis of the many measurements that can be made with tools in the Extract menu. These tools let the operator perform various statistical analyses and allow the creation of different kinds of plots that show how different measurements are related to one another. Many of these measurements can be used in other types of hydrologic models.
5.2.6 Extracting river networks from DEMs with RiverTools The DEM data, a small part of watershed in Cumberland, Kentucky, is useful for illustrating how drainage networks can be extracted from DEMs. This data set was completely preprocessed with routines in the Extract menu. Results are shown at the end of this chapter. The river network of the main basin Strahler streams of order 3 or higher is drawn on the Shaded Relief image. RiverTools performs some conditioning of a raw DEM by filling sinks (pits). The result is called a depressionless DEM, which is necessary for RiverTools to extract an accurate flow grid. When the File > Import DEM dialog is selected, RiverTools automatically creates a depressionless DEM as the RTG file with the corresponding RTI header file. It automatically extracts a flow grid from
83
DEM data sets. We also created and displayed the river network vector plots and a mask of the basin of the small DEM data set of Kentucky River basin area. With RiverTools, we process four initial steps to extract a flow grid from a DEM and identify a particular basin. These extraction steps are done in the order of their layout in the RiverTools Extract menu. In brief, the four extraction steps are as follows. (1) Extract > Flow Grid creates a raster-formatted flow grid that shows how water moves from each pixel in the DEM. (2) Extract > Basin Outlet allows us to select the basin we want to analyze by specifying the outlet of the basin. This step is optional if we want to extract all of the basins in the DEM. In this case, we proceed to the next step. (3) Extract > RT Treefile creates a vector-formatted version of the flow grid for either our specified basin outlet or for all basins in the DEM. (4) Extract > River Network applies the pruning method and threshold we choose to determine attributes for every link and Strahler stream in the selected basin. Several other auxiliary files are generated in this step. Briefly, steps for delineating rivers are as follows. 1. Opening and viewing DEMs Open and view the DEM data set by selecting File > Open Data Set. 2. Extracting and displaying a flow grid Select Extract > Flow Grid to extract the flow grid file with the extension RTG. Select Display > Density Plot to plot density the flow grid. 3. Selecting a basin and basin outlet Choose a basin outlet from the DEM data set by selecting Extract > Basin Outlet. 84
4. Creating a tree-file in RiverTools: Use the basin outlet just saved to create the RiverTools treefile. Select Extract > RT Treefile to extract the tree-file (link-file) with the extension RTV. 5. Extracting and displaying river network files The last extraction step begins by selecting Extract > River Network to extract river network. Select Display > River Network to display the River Network.
5.3 Results • We may not see all existing water features in every multi-spectral image, especially in low-resolution images. Thus, we need to use a proper color composite image of selected bands to visualize water features better so that feature extraction will be more reliable and we are able to reduce uncertainties. For instance for water bodies, the color composite image must include a near infrared band of 0.76-0.90 microns wavelengths (e.g., Band 4 of Landsat TM images).
• Better visualizations of bands as gray or color values in terms of water bodies are as follows: Band 4 > Band 5 > Band 7 > Band 3 > Band 2 > Band 1, respectively, where ">" means "better than".
• A false color composite image has better visualization than the true color composite image in terms of water bodies. However, a false color composite image must include the near infrared Band 4 for better visualization of water bodies.
• For efficient river mapping, the use of images and DEMs together with a prior information-base (interpretation of river characteristics) for rivers is very promising to understand and recognize rivers as well as to extract them from digital 85
representations (see experimental results at the end of the chapter. Thus, efficient river mapping in a GIS environment integrated with remote sensing, in contrast to photogrammetry that requires stereo-pairs of images, requires the use of both images and DEMs together with a prior information-base for river mapping.
• In terms of extracting river channels, a DEM itself is not efficient in settlement areas because of buildings. In other words, efficiency relies on quality of DEMs, which may not always be sufficiently accurate.
• A DEM itself does not tell us exactly where water is. Extracted river channels from DEMs do not show where there is water, and they do not give us sufficient information about lakes. For instance, every flat area in a DEM may not be a lake. To extract river channels in flat areas from DEMs is difficult. •
On the other hand, low-resolution images, which do not have clearly visible rivers, are not efficient for river mapping. In images, shape and reflectance of either edges or ridges of any rocky areas, mountain areas, tectonics and faults, in addition to some boundaries of vegetated fields, may appear similar to the shape and reflectance of rivers as linear features, especially in low-resolution images, and roads also sometimes appear similar to rivers. Thus, we need to apply DEMs to figure out where river channels (valleys) are. In order to recognize rivers we also apply the prior information-base for river mapping.
•
Owing to the reflectance similarities of water bodies with other objects in images, when we extract features automatically by using the threshold method, we also extract various other objects which are not water bodies. We can also see that some 86
water bodies are not extracted because those water features have different reflectance characteristics than water bodies generally have. In order to cope with this problem, we apply other classification methods that can be done by operators, such as Area, Group and Perim, in IDRISI. As a last resort, we can use on-screen digitizing. •
In other words, for reliable feature extraction of water bodies, we need to use a combination of the threshold method, filtering method and classification method (e.g., grouping, area, and perimeter operations) with heads-up digitizing as the final resort if we cannot extract the river more easily. This result should also be combined with the results coming from the DEM, since extracted river channels from the DEM should be consistent with the extracted rivers from the image. Final results should be checked with “ground truth” points to see how well our map agrees with reality. “Ground truth” points can be located by GPS (Global Positioning System) and/or Total Station (a ground survey instrument).
87
5.3.1 Results from IDRISI
RGB False Color Composite Bands 7,4,3 of Bighorn Basin in Wyoming Landsat TM Multi-spectral Image. Note that, in this composite image, dark line area is water (river), green area is vegetation, and red area is ground and/or cultural features.
Figure 5.4: Results from image #1 in IDRISI.
(Continued)
88
Figure 5.4: Continued
Band 1
Band 2
Band 3
Band 4
Band 5
Band 7
RGB False Color Composite Bands 1,2,7.
RGB True Color Composite Bands 3,2,1.
RGB Bands 4,5,7.
RGB Bands 5,7,4.
RGB Bands 7,4,3.
RGB Bands 7,4,4. (Continued)
89
Figure 5.4: Continued
Extracted river by the threshold method. Blue area should be water. Green areas should not be water.
Overlay of RGB 7,4,3 with the river extracted by the threshold method.
Extracted river by the on-screen digitizing method.
Overlay of RGB 7,4,3 with the river extracted by the digitizing.
Overlay of the threshold river (red) with the digitized river (yellow).
Fuzz_4 reclassified from Band 4.
Fuzz_5 reclassified from Band 5.
Multiplication of Fuzy_4 with Fuzz_5.
Filtered image of (Fuzz_4xFuzz_5) by edge enhancement and edge detection filters.
Classifying the filtered image.
Buffering the classified image.
90
Grouping the buffered image. (Continued)
Figure 5.4: Continued
Area of the buffered image.
Reclassified perimeter image as a Boolean image (final river map).
Grouped area image.
Vegetation from Band_1 by the normalized vegetation index.
Reclassified set from vegetation distance. Water should be inside the vegetation area in rocky or desert area.
91
Perimeter of grouped area image.
Distance from vegetation cells.
RGB False Color Composite Bands 7,4,3 of Boulder City, Colorado Landsat TM Multi-Spectral Image. Note that, in this composite image, dark areas are water (lakes), green area is vegetation, and red area is ground and/or cultural features.
Reclassified Band 4.
Reclassified Band 5.
Figure 5.5: Results from image #2 in IDRISI.
92
Multiplication of Reclassified Band 4 and Reclassified Band 5
RGB False Color Composite Bands 7,4,3 of Canon City, Colorado Landsat TM Multi-Spectral Image. Note that, in this composite image, dark areas are water (river and small lakes) but shaded dark cliff areas are not water, green area is vegetation, and red area is ground and/or cultural features.
Band 1
Band 2
Band 3
Band 4
Figure 5.6: Results from image #3 in IDRISI.
93
(Continued)
Figure 5.6: Continued
Band 5
Band 7
Digitized river from color composite image RGB 7,4,3.
Reclassified Band 4.
Sigmoid fuzzy reclassifier applied to from Band 2.
Reclassified Band 5.
Filtering the multiplied image of Reclass_4 with Reclass_5. (Continued)
Multiplication of Reclass_4 with Reclass_5.
94
Figure 5.6: Continued
Area of multiplication image.
Grouped area of multiplication image.
Reclassified the grouped area.
Overlay of the digitized river and the reclassified group area of the multiplied image.
Summation of Reclass_4 and Reclass_2.
Area of summation image.
Grouped area of summation image.
Reclassified grouped area. (Continued)
95
Figure 5.6: Continued
Overlay of the digitized river and the reclassified group area of the summation image.
96
5.3.2 Results from RiverTools
Shaded relief of a small DEM data set of Kentucky River Drainage in Cumberland, Kentucky.
Density plot of the DEM.
Wire mesh of the DEM.
Sub-watershed areas in the DEM with Strahler order 2 of streams.
Contour lines of the DEM.
Histogram of the DEM.
Figure 5.7: Results from DEM in RiverTools.
(Continued)
97
Figure 5.7: Continued
Value zoom of interest in the DEM.
Line profile of a white line in the DEM.
Flat (blue) and peak (white) areas in the DEM.
Surface zoom of interest in the DEM.
Channel profile of a white channel in the DEM.
A flood image for flood control. No flooding area with current elevation 274m. (Continued)
98
Figure 5.7: Continued
Flooded areas with the current water elevation 355m.
Flooded areas with the current water elevation 411m.
Links of the river network.
Links of the river network with the Strahler order 3 and higher.
Value and surface zooms of the white window in the drainage network.
Flooded water in the drainage network with the current water elevation 376m.
99
5.3.3 Results of testing the basic rules We tried out a subset of the rules in the prior information-base during the river mapping implementation process, using three different multi-spectral Landsat TM image data sets. Quality and effectiveness results of the application of the basic rules, as to what worked and what worked where, as well as how good they were, are given in Table 1. We graded the work of the rules for the image data sets as follows: Excellent = (80-100)%, Good = (60-80)%, Average = (40-60)%, Weak = (20-40)%, and Non-applicable (NA) = (Not Work)= (0-20)%. Note that, as some rules work excellent for the images, they are nonapplicable for the DEM and vise versa.
Rule #1 Rule #2 Rule #3 Rule #4 Rule #5 Rule #6 Rule #7 Rule #8 Rule #9 Rule #10 Rule #11 Rule #12 Rule #13 Rule #14 Rule #15 Rule #16
Bighorn Basin Image #1 Excellent Excellent Good Excellent Excellent Excellent Excellent Excellent NA NA NA NA NA NA Excellent NA
Boulder City Image #2 Excellent Excellent Excellent Excellent Excellent Excellent NA NA NA NA NA NA NA NA Average NA
Canon City Image #3 Excellent Excellent Average Excellent Excellent Excellent Excellent Excellent NA NA NA NA NA NA Average NA
Table 5.1: Results of the tested basic rules in multi-spectral images.
100
Usage in DEM NA NA NA NA Excellent Excellent Excellent NA Excellent NA NA Excellent Excellent Excellent NA Excellent
5.4 Discussions 5.4.1 Discussions of the results of the tested basic rules Rule #1 This rule worked well for all three images, since we can see that at least 80% of the extracted water bodies from all images have a reflectance of less than 10% when we inspect the signatures. This rule is non-applicable for the DEM. Rule #2 This rule as worked well for all the images, since Band 4 (NIR Band) exhibits the best visualization of water bodies. In this band, water bodies appear darker, while cultural features and vegetation areas appear lighter. The second best band is Band 5 (Mid-Infrared Band). Third best is Band 7 (Mid Infrared Band). Rule #3 This is a river finding rule. Multiplication of a fuzzy-reclassified set from Band 4 with a fuzzy-reclassified set from Band 5 in IDRISI shows at least 70% of water bodies clearly in the first image, 90% in the second image, and 50% in the third image. Note that our images have the worst cases with 30m resolution. This rule will be better for images having higher resolutions. This rule is non-applicable to the DEM. Alternatively, we may also use other river finding rules, such as if ([reclassified set from Band 4] plus [reclassified set from Band 2] is within a threshold) then water. This rule may work as well as rule #3 does, in certain conditions. Rule #4 This rule worked well for all images since we see that rivers have no sharp turns in all images. Even the lakes in image #2 have smooth boundaries. However, extracted river channels from the DEM do have sharp turns because the result from the DEM is a model that simplifies the real river network. So, it is of limited applicability for the DEM.
101
Rule #5 This rule worked well for all images since we can see that water bodies have no intersection with themselves. This rule is very useful to distinguish rivers from roads. This rule also worked well for the DEM, since we see that there is no intersection of river channels by themselves. Rule #6 This rule worked well for all images and the DEM. The reason for this is the same as in rule #5. Rule #7 This rule worked well for images #1 and #3 since we see at least 90% continuity of rivers in both images. However, this is not applicable for the image #2 since lakes are found in separate locations. This rule worked well for the DEM since the extracted watershed is continuous and connected with each river channel. Rule #8 This rule worked well for image #1 and image #3 since we see at least 90% of irregularity and meandering of rivers in both images. There is no river having any straight line in either image. However, this rule is not applicable for image #2 since there is no obvious river in the image. This rule is also useful for distinguishing rivers from roads. However, the DEM is a model and there are many straight lines in the watershed extracted from the DEM. So this rule is of limited applicability to the DEM. Rule #9 This rule has not worked in all three images since we cannot see the whole drainage pattern in each image, owing to the low resolution of the images. This rule worked well for the DEM, since we see a tree-like drainage pattern in the extracted river network. Rule #10 This rule is not applicable for all three images and the DEM, since there is no obvious river loop (splitting and rejoining river) in all images and the DEM.
102
Rule #11 This rule has not worked for all three images and the DEM since we do not see any river linking to a standing water body in images and the DEM, owing to the limited extent of the images and DEM. Rule #12 This rule is not applicable to images. It worked well for the DEM. Rule #13 This rule worked well for the DEM. This rule is very useful for exploring where rivers can be found by means of the DEM since rivers accumulate in valleys. Rule #14 This rule worked well for the DEM, but it is not applicable to images. Rule #15 This rule worked well for the image #1 since at least 90% of the river is in the middle of the vegetated area in the largely desert-covered image area. It had average success for image #2 and image #3, since we see that at least (40-50)% of water bodies are surrounded by the vegetation areas in both images (see color composite images RGB 7,4,3 for all three images at the results from IDRISI system). This rule is not applicable to the DEM, since the DEM does not show land cover. Rule #16 This rule is useful for exploring lakes from DEMs and worked well for the DEM (see flat and peak areas in the DEM results from RiverTools).
5.4.2 Discussions of overall results •
Advanced image interpretation systems use a domain prior information-base (e.g., IFTHEN production rules for river mapping), such as "IF Condition THEN Inference” to define water bodies in images. Image interpretation is usually associated with the labeling process of image data in the form of image regions or features regarding the domain prior information-base. A river domain prior information-base is formed by a set of rules that define rivers in an image. Therefore, the accuracy and reliability of 103
the extracted river features depend on the river domain prior information-base in the image interpretation system and the river domain information-base is required to be well defined for water-related GIS or remote sensing applications. •
In this study, we form a river domain prior information-base for river mapping using the nature of river characteristics and digital representations, such as images and DEMs. We show that efficient and effective river mapping and river-related GIS applications requires the use of images and DEMs, together with a prior informationbase about river characteristics in terms of radiometric and geometric information, as well as relations with other linear objects. The prior information-base includes preinformation about reflectance, color, shape, pattern and texture of rivers in addition to relations with other hydrologic features in images.
•
Rivers and lakes play an important role in the water cycle. Thus, the precise dynamics of their environment need to be well understood for use in GIS applications. Rivers can be confused with other linear objects, such as roads and various landforms. Thus, cartographers and hydrologists need to visualize and measure, as well as analyze, the 3-D geometry of rivers and lakes to improve understanding and recognizing them from digital representations, such as images and DEMs.
•
One way to achieve this is to use IDRISI for analyses of multi-spectral images, and to use RiverTools for analyses of digital terrain and river networks. Conventionally, cartographers and hydrologists spent many hours making measurements by hand from topographic maps to extract rivers. But there are a number of easy ways in which the rivers and lakes can be recognized and captured. They can then be mapped by the use 104
of the prior information-base in an advanced image interpretation system (this system can be a combination of IDRISI and RiverTools, together with a prior informationbase). The prior information-base allows us to understand and recognize rivers from digital representations for efficient river mapping and revision of river maps, as well as river-related GIS applications. •
In summary, we used the prior information-base to understand and recognize water bodies in digital representations. Once we determined water bodies, we extracted water features automatically and/or interactively from visualized multi-spectral images, using the river finding tools in IDRISI. These features can be vectorized as polylines for rivers and polygons for lakes, and exported as either DXF or shapefiles for further work in AutoCAD and ArcView, respectively. In case of raster extraction, raster to vector or vector to raster conversion is also possible in IDRISI. Vector to vector or image to image integration can also be done by overlay or map conflation techniques for GIS analysis with IDRISI. This is useful for combining IDRISI data sets with RiverTools data sets.
• The rivers extracted from multi-spectral images by IDRISI can be compared to the river channels automatically extracted from a DEM by RiverTools. This can be used for updating river maps or monitoring riverbank changes. Multi-scaled map production and revision of maps are possible. Quality control and assurance can also be sought by either spatial analysis or proper map conflation techniques (Schaffrin, 1999; Demirkesen and Schaffrin, 1996; Goodchild and Gopal, 1994). However, in this study, we were not able to analyze the overlay of the results of the two systems 105
(IDRISI and RiverTools) since we were not able to find a straightforward way to export their file formats to each other. •
In this study, our intention was to use image and DEM data belonging to the same area with the same resolution. Then we would analyze the results from both the image and the DEM, since the extracted river channels from the DEM should be consistent with the extracted rivers from the image. Unfortunately, we were not able to do this because (1) we were not able to find image data and a DEM data set belonging to the same area; (2) RiverTools has no option to export its own vector data to any other format for further processing, although IDRISI system has an option to convert to/from a number of vector file formats (e.g., DXF and shapefiles). Nevertheless, although we used different data sets in this study, we have shown we have constructed our prior information-base as a synthesis, as well as how we used the prior information-base for river mapping from digital representations. We have tested the fundamental rules. Results are very promising in terms of understanding and recognizing rivers from digital representations. This was on of the objectives of the study.
•
In prior information-based feature extraction of water bodies, we used some methods to extract water bodies from multi-spectral images. They are the threshold method, classification methods, filtering methods and the screen digitizing method. Although all methods include some errors, the use of a combination of them is promising and reduces uncertainties. However, the results of these methods should also be compared
106
to the extracted river channels from the DEM and final verification should be checked with ground control points that can be collected by GPS or Total Station methods. •
After producing verified river maps, we also need to monitor the changes in rivers. In other words water bodies are subject to change due to the human actions and natural forces. Thus, we need to do map revisions by a proper map conflation technique.
•
In this study, we used 30m resolution Landsat TM multi-spectral imagery. This is satisfactory for 1:100 000 - 1:250 000 scaled maps considering the resolution of our eyes is 0.2mm in paper maps. But 30m resolution images are not efficient for river maps considering errors and error propagation during map production process. We recommend the use of multi-spectral images having at least 5-10m resolution depending on the map scale and the purpose of the map. This will be adequate for 1:5 000 - 1: 25 000 scaled paper maps and the rules would work better. Note that a river having 1m width is subject to drying out. One-meter resolution multi-spectral images provided by AVIRIS (Advanced Visible Infra-Red Imaging System) and other commercial imaging systems can provide 5-10m resolution multi-spectral images, and these are starting to become available. In these high-resolution images, the automation of map production would be more accurate and more reliable because of clear reflectance.
5.5 Summary In this chapter, we have tested a number of fundamental rules using three different multispectral Landsat TM images, using an implementation of a prior information-based river
107
mapping from digital representations in IDRISI. Results and discussions are given at the end of the chapter. In the next chapter, we will summarize the dissertation. Conclusion and suggestions for future research will be presented.
108
CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS
6.1 Summary The purpose of this dissertation was to describe the construction of a prior informationbase (interpretation of river characteristics) for river mapping from digital representations, such as remotely-sensed digital images and DEMs, by an advanced image interpretation system. The main reason for this is that more reliable prior information availability in an advanced image interpretation systems enables GIS and remote sensing facilities to locate rivers for river mapping in an easier, more accurate and more straightforward way. Note that our main objective is to build a prior informationbase, rather than feature extraction of water bodies per se. However, we have shown how the prior information-base can be used for feature extraction of water bodies from Landsat TM multi-spectral images. In this study, we built a prior information-base including rules and facts for river mapping from multi-spectral images and DEMs. These rules not only allow water-related applications in both GIS and remote sensing to be more accurate, but also construct the information-base for river mapping by an advanced image interpretation system. Rules were constructed as a synthesis from searching the literature and experiments with both 109
digital images and DEMs. A prior information-based river mapping system was implemented and a number of basic rules were tested, studying both multi-spectral images in IDRISI and DEMs in RiverTools.
6.2 Conclusions Using the prior information-base and the advanced image interpretation system, we can do efficient river mapping and revision of river maps, as well as river-related GIS applications. The essential information about river characteristics for efficient river mapping from digital representations is contained in the dissertation. In order to obtain meaningful information about river characteristics, the author searched and reviewed the literature, such as atlases of the world, books and papers related to cartography, remote sensing, physical geography, geology and hydrology, in addition to experimental results. Meaningful rules and facts were generated for description and interpretation of river characteristics. From literature searches, taxonomies, classifications of rivers, and their relationship to other objects, such as roads, edge of rocky areas, and boundaries of vegetation fields in terms of color, shape, and patterns in images, were developed. The prior information is useful to help understand and recognize water features in digital representations, especially in low-resolution images, and to extract those water features for efficient river mapping. This reduces the uncertainties in digital representations, since more reliable prior information availability in an advanced image interpretation system enables GIS and remote sensing applications to work with better river data. 110
These rules and facts are promising in recognizing and understanding rivers from digital representations, such as images and DEMs for river mapping. They can be used effectively in any advanced image interpretation system for river mapping and riverrelated GIS applications. In other words, if this prior information-base is converted into a computerunderstandable form, it can be employed in any advanced image interpretation system. Thus this system will increase efficiencies for river map revision and flood mapping. This is an important issue, since rivers are subject to change caused by human actions (e.g., usage of waters and constructing dams) and natural forces (e.g., climate and abrupt changes of the Earth, such as erosion, earthquake and heavy precipitation). Using this advanced image interpretation system, we can construct flood models and process riverrelated environmental GIS applications more reliably and more efficiently.
6.3 Research contributions • We classified water bodies and exhibited their formal definitions in chapter 2 and we provided information about natural characteristics of hydrologic surfaces in chapter 3. These provide useful information for water-related researchers, in addition to the construction of the prior information-base. •
We built our prior information-base, including rules and facts for river mapping, in chapter 4. We tested a number of basic rules in chapter 5. These rules and facts show fundamental characteristics of water bodies in nature and in digital representations.
•
We provided more reliable prior information availability for any advanced image interpretation system, which would enable GIS and remote sensing systems to locate 111
rivers in an easier, more accurate and more straightforward way. The prior information-base also allows users to understand and recognize rivers better in terms of understanding images and DEMs, as well as understanding the natural characteristics of rivers. •
We built a set of basic, generic tools in IDRISI for finding rivers and testing the rules, as well as for helping users make better decisions. Using this advanced image interpretation system, we can construct flood models and process river-related environmental GIS applications more reliably and more efficiently.
6.4 Recommendations for future work •
We recommend the use of multi-spectral images having 5-10m resolution, rather than Landsat TM images of 30m resolution. However, choice of imagery resolution depends on the map scale and the purpose of the map, as well as financial issues. Note that a river having a 1m width can easily dry out, and 30m resolution imagery for river mapping is not really satisfactory. Making a one pixel error in feature extraction means 30m error in a river, when we consider digitizing errors and mistakes in threshold values, as well as error propagation during the map production process.
•
We recommend the use of false color composite images, rather than a true color composite image, for better visualization of water bodies. The false color composite image must include a near infrared band of 0.76-0.90 microns wavelengths.
•
For feature extraction of water bodies, we recommend the use of a combination of threshold methods, filtering methods, and statistical methods for defining regions of 112
interest, such as points, polylines and polygons. This result should also be compared to extracted channels from the DEM. Operators such as Area, Group and Perim in IDRISI are very useful for extraction of rivers from imagery. As a last resort, onscreen (heads-up) digitizing can be used to fill in missing stretches of river. •
For verification and validation of the river map products, “ground truth” data must be compared to the river map after the river mapping, to confirm the degree of reliability of the map produced. For this reason, “ground truth” points on water features must be located by Global Positioning System (GPS) or other surveying instruments, such as a Total Station. Then, we must compare these points to the corresponding points in the produced map.
•
For spatio-temporal analysis, we should keep monitoring river maps, since water bodies are always subject to change because of human actions and natural affects over time.
113
APPENDIX A INTERPRETATION OF HYDROLOGIC SURFACE CHARACTERISTICS FROM REMOTELY SENSED DIGITAL IMAGES In this appendix, in order to show where and how rivers appear in images, we provide an overview of the interpretation of hydrological surface characteristics from remotely sensed digital images. This appendix is navigated as follows. A.1 Remote sensing and remotely sensed imagery A.2 Energy source and sensors A.3 Wavelengths A.4 Spectral response patterns-signatures A.5 The visual keys for image interpretation • Shape, size, tone, texture, pattern, shadow and site A.6 Image interpretation of landform components A.7 Surface topography in images A.8 Erosion and gullies in images A.9 Drainage patterns and textures in images A.10 Stream channels in images A.11 Wetlands in images A.12 Image classification or segmentation • Supervised classification • Unsupervised classification • Principle component analysis • Filtering images A.13 Understanding rivers from digital representations A.14 Modeling a flood hydrographs using images A.15 Summary of the chapter
114
A.1 Remote sensing and remotely sensed digital imagery Remote sensing is a science associated with extracting information about an object without physical contact with it. In other words, remote sensing is a field of study of observation from a distance. Information is acquired from an object, area or phenomenon without touching it. Our eyes are the best examples of a remote sensing device. We are able to gather information about our environment by sensing the amount and the nature of the reflectance of visible light energy from some external source, such as the sun as it reflects off objects in our field of view. Thus, our eye (sensor) and brain (processor) are key elements for the remote sensing system that makes human vision possible. The visible light energy emitted and/or reflected from an illuminated object is detected by sensitive cells in the eye linked to a high-speed real time processor (brain). The human eye is sensitive to both the intensity of light energy received and the frequency of the light. Thus, the eye differentiates both ranges of brightness and the color or tone. The brain operates as a data bank where images are stored. Remote sensing technology is therefore an imitation of human eye and brain function. In remote sensing, a digital image is divided into a two-dimensional array of picture elements (pixels) with each pixel having an integer value associated with image brightness. Color or multiple band images can be thought of as being made up of layers of two dimensional arrays. Each layer represents brightness in a different spectral band (Idrisi manual, 1997; Schott, 1997; Jensen, 1996; Castlemen, 1996; Lillesand and Kiefer, 1994; Richards, 1993; Gonzalez and Woods, 1993; Barrett and Curtis, 1992).
115
A.2 Energy source and sensors Sensors are required to record and measure the reflectance of materials. The use of sensors is one of the most involved and significant aspects of remote sensing. Thus, sensor design and calibration is critical to obtaining better resolution of remotely sensed images. Sensors can be grouped into two classes, passive and active. Passive sensors measure ambient levels of an existing source of energy. The most common remote sensing devices use passive sensors, for which the Sun is the energy source. But not all passive sensors use energy from the Sun. For example, thermal infrared and passive microwave sensors measure natural Earth energy emissions. Active sensors use their own source energy. The most common active sensor is a flash photograph. More explanations about sensors can be found in many remote-sensing books (e.g., Schott, 1997; Jensen, 1996; Lillesand and Kiefer, 1994; Richards, 1993; Barrett and Curtis, 1992). In this study, we use Landsat TM multi-spectral images from a passive sensor. LANDSAT is an American satellite. Satellite
The
Earth
Observation
Company Total field of
(EOSAT) operates this system. This is a
Satellite motion
private company, which sells digital and photographic data and imagery. They
30 m
provide full or quarter scenes for
185 km 185 km
distribution, as well as photographic Figure A.1: LANDSAT imagery system.
116
products of multi-spectral scanner (MSS) and thematic mapper (TM) scenes in false color and panchromatic (black and white) modes. There have been 7 Landsat satellites, the first one launched in 1972. Landsat 6 failed and was lost on launch, but Landsat 7 is operating well. Landsats 4 and 5 are about to be permanently switched off. Landsat uses two multi-spectral sensors, MSS and TM. The MSS gathers imagery in four spectral bands. They are red, green, blue and near infrared. The TM gathers imagery with seven bands. They are red, green, blue, near infrared, two mid-infrared and one thermal infrared. The MSS has a spatial resolution of 80 meters and TM has 30 meter resolution. Both sensors gather images 185 km wide, passing over locations at 9:45 a.m. local time, returning every 16 days (Idrisi manual, 1997).
A.3 Wavelengths Most remote sensing devices use electromagnetic energy. However, the electromagnetic spectrum is very wide and not all wavelengths can be efficiently collected or used for remote sensing applications For instance, the Earth’s atmosphere causes major absorption and scattering of the short wavelengths, so ultra-violet and X-rays have little use in remote sensing. The near infrared (NIR), green and red wavelengths are very efficient for measuring and recording ground surface interactions with minimal atmosphere effect and are very important for feature extraction of water bodies. Figure A.2 shows the electromagnetic spectrum with wavelengths.
117
0.4 Ultra Violet (UV)
0.5 Blue
0.6
Green
0.7
(µm) Near Infrared (NIR)
Red
Visible Wavelength (µm)
Wavelength (µm) 10-6
10-5
10-4
10-3
10-2
10-1
1
10
102
Ultraviolet Visible Near-Infrared Mid-Infrared Thermal
103
104
105
106
Microwave
107
108
109
Television and radio
X-rays γ-rays Cosmic rays
Figure A.2: The electromagnetic spectrum (Adapted from Lillesand and Kiefer, 1994).
A.4 Spectral response patterns – signatures There are three types of interactions Light source, sun
between electromagnetic energy and Reflection
materials on the ground (see figures A.3 Ground surface
and
A.4).
They
are
reflection, Absorption
absorption
and
transmission.
Transmission
The
reflected component, over a range of
Figure A.3: Three types of interactions between electromagnetic energy and material.
wavelengths, is used to characterize a spectral response pattern, which is also called a signature. 118
The signature, which can be represented in a graph form, is a description of the degree to which energy is reflected in different regions of the spectrum. It is also equivalent to human color perception (Idrisi manual, 1997). The eye has the capability of detecting spectral response patterns since it is a multi-spectral sensor. That is, the eye senses in more than one place in the spectrum. The eye has a complex function, including three different types of detectors associated with red, green and blue (RGB) wavelength regions. These three colors are called the additive primary colors. The other colors can be generated from these three hues. Thus, the eye responds to mixtures of these three colors to produce a sense of other colors. For instance, in the 4th graph (purple) in figure A.5, we can see reflectance of blue and red regions of the visible spectrum. This bi-model pattern is not a specific color in the spectrum. However, we perceive this mixture as purple.
Reflectance % Dry bare soil (gray-brown)
40 Vegetation (green)
20 Water (clear)
0
1.6
0.4
Wavelength (µm)
2.6
Figure A.4: Spectral signature differences between water, soil and vegetation. Adapted from Lillesand and Kiefer, 1994. Reflectance %
B
G
R
B
G
R
B
G
R
B
G
R
B
G
R
B
G
R
Wavelength (µm)
Bright Red
Dark Green
Yellow
Purple
White
Figure A.5: Spectral response patterns-spectral signatures (Idrisi manual, 1997).
119
Dark Gray
A.5 The visual keys for image interpretation 1. Shape is the geometric outline of an object. This outline gives us information about the nature and the geometry of the object in the image. 2. Size is the magnitude of an object or a single dimension of the object, such as the length of a river. Once we look into an image, our eye-brain cognitive system automatically assigns a scale to the object in the image. We simply recognize one or two objects (e.g., a river, lake, house, road or car). Then, knowing their size, we can figure out the dimensions of other objects in the same image and recognize the extent or coverage of the entire image. 3. Tone is the brightness level in a panchromatic image or the color level of brightness in multi-spectral image. For instance, using color tone we distinguish water from soil since water has different tone than the soil. 4. Texture describes the structure of the variation in brightness within an object. Vegetation and water in certain spectral bands may have the same mean brightness, but they may have different textures. Therefore, we can differentiate them from each other easily using the texture knowledge. 5. Pattern are shapes with identifiable geometric or periodic attributes, such as dendritic stream drainage pattern. Patterns can be man-made (roads) or natural (streams). The extraction of information from pattern data requires some pre-knowledge or learning process (e.g., machine or human learning). For instance, roads can intersect each other while rivers join each other. As another example, roads may have sharp turns;
120
however, streams can not have sharp turns. Thus, roads and streams can be seen in different patterns in images. 6. Shadows are usually difficult to use since it can be hard to see objects in deep shadows. However, shadows can be used to figure out the height of an object. 7. Site is the geographic location of a target or the location of one object relative to another. To recognize objects using site information requires pre-knowledge about objects. For instance, in order to determine types of trees in a forest (e.g., evergreen or not), we need to know where and what types of trees are located at the forest.
A.6 Image interpretation of landform components The Earth’s features may be divided into landforms so that each form presents separate and distinct surface characteristics, such as topography, rock materials and water body conditions. First order forms include continents and ocean basins. Second order forms are mountain ranges which are relief features. Third order formations include valleys, basins, ridges and cliffs. Thus, hydrologic surface characteristics have a close relationship with the landforms of soils, rocks, and vegetation in remotely sensed imagery. We should consider all landforms when we interpret the hydrologic surface characteristics from images (Mitchell, 1991). Hydrologic surface analysis is sensitive to many interrelationships of hydrologic surface and subsurface in landform conditions. The hydrologic surface factors may include geology, hydrology, soil and minerals. The factors in geology can be rock type, faults, fractures and attitudes of beds. The factors in hydrology can be drainage pattern and density, ground water flow, direct surface runoff, flow capacity and quality. The 121
factors in soil can be soil type, distribution, textural composition, organic content, moisture content, depth to bedrock and depth to water table. The factors in vegetation can be vegetation type, association, height and density. The factors in minerals can be mineral type, grade and distribution (Lillesand and Kiefer, 1994; Mitchell, 1991). A hydrologic surface analysis has mainly two purposes. First, the analysis provides the necessary information about hydrologic surface factors. Second, the analysis defines sensitivity to the interactions of these factors. The technology of remote sensing and wide availability of remotely sensed images have allowed us to reach these two goals. The visual pattern elements examined in image analysis include topographic form, drainage patterns, gully characteristics, erosion features, landform boundaries, color or image tone, landuse pattern and distribution, vegetation pattern and distribution, and any other special features available. However, in this study, we are interested in distinguishing rivers from other objects in images. To interpret and identify rivers in landforms more accurately, it is necessary to know and to be able to define each of their characteristic pattern elements. The image analyst interprets the image coverage of the entire site and then identifies the characteristics of the pattern elements. This interpretation process covers an analysis of topography, type and texture of the drainage pattern, image tone and/or color, gullies or other erosion features, vegetation and land use, as well as miscellaneous features, such as fractures and outcrops.
122
A.7 Surface topography in images Each landform and bedrock type have their own topographic structures in terms of size and shape on an image. We can see a distinct topographic change at the boundary between two different landforms. In the image, keys in the landform sections, small-sectional drawings of idealized landform surfaces and sub-surfaces, indicates the forms of hills, the relationship of hilltops to depressions, and the relative steeps of slopes. The topography of the land is described by its degree of dissection and continuity. Typical descriptions include: flat; flat table rocks; massive hills; soft, rounded hills; steep, rounded hills; karst topography; terracing; parallel ridges; saw-toothed ridges; bold, dome-like hills; A-shaped hills; parallel laminations; undulating; snakelike ridges; conical hills; and pitted plains. Other descriptions show the planimetric shape of a landform, such as fan-shaped, star-shaped, crescent-shaped or drumlin-shaped.
A.8 Erosion and gullies in images Gullies
are
the
smallest
drainage features viewed on
Sand/gravel
Silt
Clay
aerial images. They may be as small as a meter wide and a
Figure A.6: Cross sections of gullies formed by erosion.
hundred meters long. They develop from the erosion of unconsolidated material by stream runoff and occur where raindrops do not infiltrate the ground. That is, they are formed when sheet runoff collects in channel flow and, eroding the bottom, forms the first order drainage system. 123
Gullies collect and flow across the surface in creeks. These rivulets enlarge and generate a particular shape, based on the material in which they are formed. Short gullies with V-shaped cross-section indicate sand and gravel material; U-shaped cross-section gullies show silty soils. Long gullies with smoothly rounded cross sections form in silty clay and clay soils. Thus, as the gullies erode through the surface soils, they adopt characteristic cross-sectional shapes, which reflect the textural composition and cohesiveness of the surrounding soils. Identification and recognition of gully shapes aids in the interpretation and mapping of general soil materials. Gullies occurring in certain landforms may be described as having white fringes around their planimetric outline. They mostly occur in limestone, dolomite, or glacial till and indicate the presence of relatively lighter, exposed subsurface profiles.
A.9 Drainage patterns and textures in images Drainage patterns can be determined according to their pattern type and texture or density. Drainage systems are a most significant identifier of landforms. Drainage pattern analysis provides a great deal of knowledge related to the parent rock and soil materials, since they affect the amount of runoff and how the water runs off or drains from a landform surface. In addition to images, topographic maps or DEMs are also used for drainage analysis. We can use drainage patterns and texture seen on aerial image as indicators for identification of landform and bedrock type and also soil characteristics and site drainage conditions. There are a number of stream drainage patterns. Drainage patterns are grouped as regional or local, depending on the scale of image observed, but standard 1:20 000 stereo124
pairs (or DEMs) give effective coverage for regional analysis. Images at scales smaller than 1:20 000 can be supplemented by maps that allow us to study and identify regional patterns, because clues to landform identification commonly occur at the regional rather than the local level. Drainage patterns are classified by their density of dissection, or texture, and by their pattern type. Drainage texture can be classified into three types. They are fine, medium, and coarse based on 1:20 000 scaled-image (Denegre, 1994). 1. Fine-textured pattern In a 1:20 000 scaled-image, they have average spacing between tributaries and first order streams are less than 0.5 inch. Fine-textured patterns usually show high levels of surface runoff, impervious bedrock, and soils of low permeability.
Fine-textured drainage pattern.
Coarse-textured drainage pattern.
Medium-textured drainage pattern.
Figure A.7: Drainage textures of streams.
2. Medium-textured pattern describes channel spacing in which most first-order streams are from 0.5 to 2 inches apart. The amount of runoff is medium compared to fine and coarse textures. Soil textures are normally neither fine nor coarse but contain different textures of materials.
125
3. Coarse-textured pattern has first-order streams that are over 2 inches apart and carry relatively little runoff. These textures usually show a more resistant bedrock which is permeable and which weathers to form coarse, permeable soils.
A.10 Stream channels in images As stream channels make their way along the bottom of a valley, they may run straight in some stretches and snake their way along various irregular paths in others, sometimes splitting into multiple channels. The channel may flow along the center of the floodplain or hug one edge of the valley. In addition to straight stretches, the two other types of channel patterns are meandering and braided. The patterns that river channels take fall into one of three categories: braided, when a channel divides into several parts; meandering, when the channel twists; and least frequently, straight. Any stream may take each of these three patterns at various places along its channels, and sometimes it may be difficult to separate one pattern from another. A meandering stream may have straight and braided reaches. Definitions are also likely to be arbitrary, though generally a stream is said to be straight if the reach is straight for at least ten times the width of the channel along that reach. It is said to meander if the distance from any point A along its channel to another point B further down the channel is equal to, or more than, 1.5 times the distance from A to B measured along the valley. The ratio of channel distance to valley distance is known as the sinuosity ratio.
126
A.11 Wetlands in images The objective of wetland study is to monitor the hydrology of coastal areas, lakes, watersheds and marsh areas using multi-spectral images, since evaluation of surface water is very important for seasonal change to flooding and variation of water reserves in dams and reservoirs. As mentioned before, surface water is prominent in near-infrared images, and from Landsat and SPOT imagery water bodies as small as one hectare can be located, and rivers only 20 meter across have been directly traced (Barrett and Curtis, 1992). In coastal zones, many important features can be recognized and mapped at 1:100 000 or better. This also includes the marsh/water interface, the upper wetland boundary and different plant communities within the marshy area. Successful estimates of water quality have been made through a variety of means, such as regressions of a “quantitative brightness” parameter (the cube root of the product of standardized Landsat data in Bands 4, 5, and 6) against observed sediment levels in North American lakes.
A.12 Image classification or segmentation Image classification is also called computer-aided image interpretation. Image classification fundamentally is based on detection of spectral signatures, spectral response patterns of land cover classes. The classification results depend on two things: (1) the available signatures of land cover classes in the band set; and (2) the ability to distinguish one signature from other signatures accurately. A set of sample locations is identified by visiting them in the field. Then land cover in the field is compared to that
127
which was mapped in the image for the same location. Statistical accuracy evaluations are derived for the whole study area and for individual classes. There are basically two types of image classifications, supervised and unsupervised. Supervised classification detects already known specific types of land cover. Unsupervised classification attempts to define all land use categories existing in the image at a certain level of generalization.
A.12.1 Supervised classification In this method, examples of specific classes, such as land cover types of interest in the image, are identified. These are called training sites. A statistical method is used to identify the reflectance characteristics for each class. This procedure is also called signature analysis. It involves means, variances and co-variances over all bands. After statistical characterization is done for each class, the image is classified by examining the reflectance for each pixel in each spectral band. Then decision making about the signature is done. There are several techniques used as classifiers in image processing systems, such as Erdas Imagine and Idrisi. Many image processing packages provide more than one classifier. These include parallelepiped, minimum distance to means, and maximum likelihood. Each of these uses a different logic for assigning pixels to classes. Furthermore, a new class of supervised classifiers is designed for hyperspectral image processing. This process involves Spectral Angle Mapper and minimum distance to means hyper-spectral classifiers.
128
A.12.2 Unsupervised classification This method does not use any advance information. The method examines the data and makes a segmentation of natural spectral groupings, or clusters, available in the image. The analyst identifies land cover classes using these clusters through both region and ground truth visits. The logic of unsupervised classification is known as cluster analysis. Unsupervised classification produces spectral classes rather than information classes. It classifies pixels or features with similar reflectance patterns. Thus, the analyst needs to reclassify spectral classes into classes. For instance, the system may identify classes for rivers and lakes. The analyst may later regroup these when generating a watershed area as a class.
A.12.3 Principal component analysis Principal component analysis (PCA) is a linear transformation technique associated with factor analysis. It generates a new set of images from given a set of image bands. The generated new images, known as components, are not correlated with one another and ordered according to their amount of variance, which is computed from the original band set. PCA is used for data compaction in imagery. For a multi-spectral image band set, it is used to find the first two or three components that explain virtually all of the original variability in reflectance values. The remainder of the components are often dominated by noise. Thus, by ignoring the ‘noisy’ components, the volume of data is reduced without loss of significant information.
129
PCA is also used for time series image analysis, for updating purposes or monitoring changes in the environment. For this purpose, multi-spectral images are taken at different times or dates. The bands from different-dated images cover the same area. Changes between two different-dated images tend to emerge in various components in PCA.
A.12.4 Filtering images In this study, we used filtering (convolution) techniques for exploring rivers, such as edge enhancement (e.g., Laplacian filter) and edge detection (e.g., high-pass filter and Sobel edge detector) (IDRISI manual, 1997).
A.13 Understanding rivers from digital representations Rivers are complex and skinny in digital representations. Images may be low-resolution and we can not see water bodies clearly. Thus, we need DEM information to determine where water will flow to, so as to be able to extract water body features. However, DEMs sometimes cannot show where water is. This depends upon the quality of DEMs and the topography of the surface. The following images and DEMs show complexity of rivers in digital representations in terms of where rivers and lakes are.
130
Figure A.8: Images and DEMs for visual interpretation of rivers.
131
Figure A.9: Understanding multi-spectral image concept (Schott, 1997).
132
A.14 Modeling a flood hydrograph using images Runoff is a hydrologic component and most often used by hydrologists and water resource planners to give them the ability to forecast peaks on the stream hydrograph accurately. This ability is much sought because of its significance for hazard advice and warnings, as well as in river management and control. The most important objectives are the timely and accurate estimate of runoff at any given point in a drainage basin. The tools traditionally available for this include many equations and models, as well as stream gauges that measure stream flow or reservoir volume directly in real time. Today, runoff cannot be measured directly by remote sensing techniques, but the role of remote sensing is to help us estimate equation coefficients and model parameters including: (1) Quantity of infiltration or/and percolation into the ground; (2) direct surface runoff; and (3) channel flow. Five stages have been proposed (Barrett and Curtis, 1992) for the development of watershed models to estimate crest in a hydrograph accurately. 1. Delineation of the physical boundary of the watershed area Aerial and satellite imagery is convenient unless a high incidence of cloud necessitates radar imagery. A low angle of solar illumination is best, as the evidence of shadows is especially useful in watershed determination. 2. Identification of the watershed topography Stereoscopic aerial image (multispectral radar images where slopes and shallows are determinable) or DEMs are analyzed for rectangular slope elements (areas and mean angles of the slope). 3. Determination of stream channel geometry Near infrared imagery, such as Landsat MSS Band 7 can be used both for high water level determination and analysis of 133
stream patterns into rectangular line segments. Digital photogrammetric or image processing procedures can be used for the analysis of stream pattern cross-section at low water levels. 4. Determination of hydraulic roughness of slope elements and stream channels Radar is the most direct information source on slope roughness, since the wavelengths employed are of the same order as the surface variations. Visible image analysis, such as aerial photo, Landsat and SPOT images can be used to determine larger scale landscape or landform elements. Channel roughness may be evaluated by imagery under clean water conditions in blue and green regions of visible spectrum (approx. 0.5 µm). Where water is muddy, it is much harder to analyze. 5. Segmentation and delineation of regions for impermeable and saturated surfaces In cases of impermeable or saturated soils, additional surface flow occurs. Impermeable surfaces may be identified and evaluated by infrared images, considering their low thermal passiveness, which may be indicated by sharp, rectangular outlines. Wet soil appears dark in visible images, and on radar at wavelengths in excess of 20cm, showing ability to integrate subsurface moisture values.
A.15 Summary In this appendix, we have reviewed image interpretation of hydrologic surface characteristics. In the next appendix, we will consider interpretation of hydrologic surface characteristics from DEMs.
134
APPENDIX B INTERPRETATION OF HYDROLOGIC SURFACE CHARACTERISTICS FROM DEMs
In this appendix, in order to show where and how river channels are found in DEMs, we provide an overview of the interpretation of hydrological surface characteristics from DEMs. This appendix is navigated as follows.
B.1 Digital elevation models (DEMs) B.2 Quality of DEMs B.3 Warntz network • Six critical locations of the Warntz network (pits, peaks, ridges, valleys, passes and pales) B.4 Estimating elevations from DEMs B.5 Computing slopes, aspects and curvatures from DEMs B.6 Extracting hydrologic surface characteristics from DEMs • Flow directions in DEMs • Generating a depressionless DEM • Finding flow accumulation • Determining watersheds • Determining stream networks • Assigning rank order to streams B.7 Mapping of stream channels B.8 Summary of the chapter
B.1 Digital elevation models Digital elevation models (DEMs) are frequently employed for digital representation of a topographic surface. They are commonly in the form of a raster or regular grid of spot 135
heights, or by a random set of elevation points connected by a triangulation (a TIN). DEMs can also contain elevation data stored like a digital image, where numerical values represent elevations. (Habib, 2000; Hazelton, 2000; Wilson and Gallant, 2000) A DEM with breaklines, or additional topographical information and hydrologic characteristics, is called digital terrain model (DTM). A DTM is a discrete representation of the Earth’s surface (or part thereof). The digital surface model is assumed continuous and single-valued, i.e., not containing any spikes, pits, cliffs or faults. A single-valued surface can be defined by a function as z = f(x, y), where z is unique for any (x, y) pair. With these constraints, a DTM is often called a 2.5-D model, instead of a full 3-D model. The most common source for DTMs is remotely sensed stereo images, such as aerial photos. The DTM can also represents hydrologic surface characteristics of the terrain. Hydrologic surface characteristics include peaks, ridges and valleys. DTMs are generated from DEMs, although they can be generated by means of traditional ground survey, photogrammetry or digitizing contour maps. Although ‘DTM’ is a more generic term for any digital representation of a topographic surface, it is less widely used than ‘DEM’ for a digital surface representation. DEMs are indispensable components of any GIS and are used for many GIS applications. DEMs are also very useful for recognition of hydrologic surface patterns. There are a number of applications of DEMs in GIS. For instance: visualization of the terrain surface; attribute determinations of the terrain, such as elevation at any point, slope and aspect; finding features on the terrain, such as drainage basins and watersheds, drainage networks and channels, peaks and pits, and other landforms; and modeling hydrologic functions, energy flux and the like. 136
In summary, the purpose of DEMs is to represent the Earth’s surface in a computer. While analog representations of the terrain consist of contour lines and spot elevations, the digital representation of surface includes discrete 3-D points. Advantages of digital representation of the terrain include the ability to extract different products, such as contour lines, perspective views, volume determination, slope lines, aspects, surface shading, drainage channels, as well as the ability to integrate this information with other GIS applications.
B.2 Quality of DEMs DEMs commonly use a matrix or raster representation of a continuous surface. Therefore the accuracy of a DEM depends in part upon the resolution of the grid of points, that is, the distance between sampling points of the continuous surface. The data types used, such as integer or floating point, also influences accuracy. Sampling errors and quality of measurements also influence DEM quality. Errors in DEMs may be seen as pits (sinks or depression) or peaks (spikes).
Figure B.1: Profile views of a pit, peak and depressionless surface from a DEM, respectively.
These errors may come from the discrete representation of the surface by a grid. This error is proportional to the grid size. There may be errors in the reference points used to generate DEMs, and in the interpolation techniques used. One way of reducing these 137
errors in DEMs is to use a wide-angle camera to generate stereo photography for DEMs, or use a normal angle camera to generate an ortho-photo with relief displacement measurements (Habib, 2000). We may also detect errors and problem areas by visual inspection of DEMs. One way to do this is to check a perspective view of the DEM. In this case, blunders may be seen as spikes (or peaks) and pits (or sinks). Another method for error inspection is stereosuperimposition in an analytical plotter. In this case, the DEM data is placed into an analytical plotter and displayed over the 3-D stereo-model from the aerial photography. Erroneous or inaccurate points can then be determined and corrected. We can obtain more reliable detection or inspection when we use a larger image scale to create the reference stereo-model than the scale used to generate DEMs (Habib, 2000). Pits commonly occur in natural features and may be determined from flow directions. A pit is an area surrounded by higher elevation values, and is also referred to as a depression. A depression is an area of internal drainage. Some of these depressions may be real and natural, especially in glacial or karst areas, although many pits are irregular in DEMs. Such errors, particularly pits, should be removed before deriving any surface information. Pits, being areas of internal drainage, may produce unwanted results when computing flow direction. Thus, we need to create DEMs without depression by removing or filling pits. The count of pits in DEMs is usually higher for coarse resolution DEMs. This can cause a problem in areas of low vertical relief. DEMs may also include horizontal striping, which comes from systematic sampling errors when creating the DEM. This occurs in flat areas using integers (ESRI, 1994).
138
In summary, the advantages of a raster DEM compared to other surface representations are that it has a simple data structure that is similar to digital images and it allows faster computation by array algebra. Disadvantages of raster DEMs include: problems with integrating and handling break lines in the regular grid; they require large amounts of memory for storing data; and they need a large system of equations for interpolation. However, breaklines can be extracted by several methods, using cell heights in DEMs (Habib, 2000; Hazelton, 2000).
B.3 Warntz network A Warntz network is a method for simplifying the representation of the terrain, by reducing it to a network of connected key topographic features. The Warntz network helps to define water flow over the surface. These six key features are as follows. 1. Pit is also called a sink (or a depression), an area surrounded by higher elevation values. 2. Peak is a point with the highest elevation in a region. 3. Ridge is defined as an ascending slope line from a pass that reaches a second pass or peak. Divergent flow comes from a ridge. 4. Valley is defined as a descending slope line from a pass that reaches a second pass or pit. Convergent flow causes concentration of runoff and indicates a valley. 5. Pass is a transit between two hills. It may be a valley (see figure B.2). 6. Pale is a bump (or a small hill) in a valley or pass line.
139
Consequently, hills, valleys, ridges, lakes and passes are distinct elements of the surface. The ridge and valley lines are slope lines having maximum gradients. So, streams flow from a peak through a pass to a pit (see figure B.3).
Peaks of two hills
A pass line
Figure B.2: A pass line between two hills. A pale that is a bump in a pass line (valley) A pass line Flow direction 90
90
89
95
90
89
88
87
DEM
Figure B.3: A pale in a pass line (or a valley).
Watershed area
Ridge Channel (valley) Watershed area
Peak Pass Ridge Ridge Peak
Watershed area
Ridge (watershed boundary) Channel
Figure B.4: A Warntz network (Wilson and Gallant, 2000; Wilcox and Moellering, 1995; Warntz, 1966).
140
Many scientists have revised the Warntz network model and many algorithms have been developed to construct the Warntz network from a grid DEM (Wilcox and Moellering, 1995). These algorithms locate the six components (see figure B.4), which are important for surface representation, visualization and hydrologic surface analysis in hydrology and geomorphology as well as computer mapping and GIS . In hydrology, channel networks and ridges have a close relation with the Warntz network. In terrain analysis and geomorphology, the Warntz network is mostly employed to generalize topographic surfaces. Peaks, pits, ridges, valleys (stream courses or channels), passes and pales construct critical locations and characteristics of a continuous topographic surface. In other words, they are points at which the magnitude of the surface gradient is zero. Thus, the Warntz network is composed of ridges, valleys, pits, peaks, passes and pales, the critical points of the surface. Critical points can also be classified as pits, peaks and passes by examining the Hessian matrix of second derivatives (Wilcox and Moellering, 1995). Thus, a formal definition of these six components of a topographic surface network, covering ridges and valleys, can be done by derivatives, critical points at which both x and y partial second derivatives are zero. Flow lines connect the critical points to each other. An ascending slope line direction from an arbitrary point (x,y) can be represented by a vector-valued transformation function with one variable (t) from the real numbers (R) to the real plane (R2): r S (t ) = R → R 2
141
r In other words, S (t ) = ( x (t ), y (t )) , S is a vector-valued transformation function with a parameter (t) that yields 2-D vector (x(t),y(t)) as from R to R2. Increasing slope line direction An ascending slope line is a line from a pit point to a peak on the surface. An ascending slope line direction from the initial point, (x,y) can be denoted by ∇ operator as follows: r r ∂f ( x, y ) ∂f ( x, y ) S ' (t ) = ∇( S (t )) = ∇f ( x (t ), y (t )) = ( , ) ∂x ∂y r with an arbitrary initial (starting) value, S (0) = ( x (0), y (0)) . Decreasing slope line direction A descending slope line is a line from a peak to a pit point. A descending slope line direction from a initial point (x,y) can be denoted by -∇ operator as follows: r r S ' (t ) = −∇( S (t )) where r r ∂f ( x, y ) ∂f ( x, y ) ∂S (t ) ∂S (t ) ∇f ( x, y ) = ( , )=( , ). ∂x ∂y ∂x ∂y
B.4 Estimating elevations from DEMs Basically, we can estimate a plane equation Z=a+bx+cy using the 4 nearest grid points in a 2x2 window. We define an origin (x=0, y=0) in the middle of the 2x2 window, and give the 4 neighboring points with the (x, y) co-ordinates (-1, -1), (-1, 1), (1, -1) and (1, 1) where coefficient values a, b and c are as follows:
142
Z4
Z = a + bx + cy a = ( z1 + z2 + z3 + z4 ) / 4
Z3 S
Z1
b = (− z1 + z2 − z3 + z4 ) / 4
Z2
c = (− z1 − z2 + z3 + z4 ) / 4
A 2x2 window.
The coefficient can also be solved using larger neighborhoods, e.g., the nearest 9 points, but we should possibly weight those further away, as they have a smaller influence on the interpolated value. After determining the coefficient values a, b and c, any elevation (Zx,y) can be determined from the plane equation above (Hazelton, 2000).
B.5 Computing slopes, aspects and curvatures from DEMs Given a 3x3 window DEM (Z-matrix) with grid spacing S (pixel size or distance between the two neighbor elevation points in DEMs), the least square fit plane at Zi,j (central point as origin) can be written (Chrisman, 1997) as the best fit plane:
Z = a + bx + cy
zi −1, j +1
zi , j + 1
zi +1, j +1
Z7
Z8
Z1
zi −1, j
zi , j
zi + , j
Z6
Z9
Z2
zi −1, j −1
zi , j −1
zi +1, j −1
Z5
Z4
Z3
A 3x3 window in a DEM.
143
where Zi,j is the origin coordinates with (i=0,j=0) and the other (i,j) coordinates are (-1, 1), (0,1), (1,1), (-1,0), (1,0), (-1,-1), (0,-1) and (1,-1), respectively in the 3x3 window. The coefficient values of a, b and c are found from the least squares best fit as follows. a = ( zi−1, j −1 + zi−1, j + zi−1, j +1 + zi , j −1 + zi , j + zi , j +1 + zi+1, j −1 + zi+1, j + zi+1, j +1 ) / 9 b = (( zi −1, j +1 + 2 zi , j +1 + zi +1, j +1 ) − ( zi −1, j −1 + 2 zi , j −1 + zi +1, j −1 )) / 2 S c = (( zi +1, j −1 + 2 zi +1, j + zi +1, j +1 ) − ( zi −1, j −1 + 2 zi −1, j + zi −1, j +1 )) / 2 S slope_gradient_tangent = b + c 2
2
aspect _ angle = arctan(c / b)
These equations can also be written (Wilson and Gallant, 2000) by derivatives of the topographic surface. These derivatives show the rate at which elevation changes with respect to locations x and y as follows:
b = Zx =
∂z z 2 − z6 ≈ ∂x 2S
c = Zx =
∂z z8 − z4 ≈ ∂y 2S
Z xx =
∂ 2 z z 2 − 2 z9 + z 6 ≈ ∂x 2 S2
Z yy =
∂ 2 z z8 − 2 z9 + z4 ≈ ∂y 2 S2
Z xy =
∂ 2 z − z7 + z1 + z5 − z3 ≈ ∂x∂y 4S 2 144
p = z x2 + z 2y
q = p +1
slope = b 2 + c 2 = z x + z y = 2
2
p
aspect = a tan( z y / z x ) = a tan(c / b)
Curvatures are found from the second derivatives (Wilson and Gallant, 2000). Curvature is the rate of change of a first derivative, such as slope and aspect in particular direction. There are two most used curvatures. One is the contour (or plan) curvature, denoted by Kc. Contour curvature is the rate of change of aspect along a contour. The second is the profile curvature, denoted by Kp. Profile curvature is the rate of change of slope down a flow line, i.e., the rate of change of the gradient, and is important for finding changes in flow velocity and sediment transport processes. Plan curvature measures topographic convergence and divergence and thus the propensity of water to converge as it flows across the land. There is also tangential curvature, denoted by Kt. Surface curvature in a DEM is the curvature of a line formed by the intersection of a plan and topographic surface. The curvature of a line is the reciprocal of radius of curvature, so a gentle curve has a small curvature value and a tight curve has a large curvature value. Contour curvature is the curvature in the horizontal plane of a contour line, while profile curvature is the curvature
145
in the vertical plane of a flow line. Tangential curvature is the curvature in an inclined plane perpendicular to both the direction of flow and the surface.
Kp =
z xx z x2 + 2 z xy z x z y + z yy z y2 pq
Kc =
2
z xx z y2 + 2 z xy z x z y + z yy z x2 p
Kt =
3
3
2
z xx z y2 + 2 z xy z x z y + z yy z x2 pq
1
Profile curvature formed from vertical slope curves
Contour curvature formed from curves in a horizontal plane
Tangential curvature
2
From these formulas, profile curvature is negative for slope increasing downhill (convex flow profile on upper slopes) and positive for slope decreasing downhill (concave on lower slopes). Contour curvature is negative for diverging flow (on ridges) and positive for converging flow (in valleys). Total curvature denoted by K is sometimes used as a measure of surface curvature. This curvature measures the curvature of itself, not the curvature of a line across the surface in some direction. It can be positive or negative, with zero curvature indicating that the surface is either flat or the convexity in one direction is balanced by the concavity in another direction, as at a saddle. Total curvature is found as K = z xx2 + 2 z xy2 + z 2yy
Total curvature
In addition to their use in modeling flow characteristics, curvatures can be used to delineate geomorphic units (Wilson and Gallant, 2000). Contour curvature can be used to differentiate between ridges, valleys and hillslopes, whereas profile curvature can 146
differentiate between upper (convex) slope and lower (concave) slopes (Wilson and Gallant, 2000; Wilcox and Moellering, 1995; Warntz, 1966).
B.6 Extracting hydrologic surface characteristics from DEMs Drainage networks, as hydrologic surface characteristics, can be extracted from DEMs. The geometry of the surface shows how water flows across the surface. Tools for hydrologic surface analysis are employed to determine hydrologic characteristics of a surface. Using a DEM, we can delineate a drainage system and evaluate the drainage system characteristics for any given location, and we can determine the upslope area contributing to a given location and the downslope path where water flows (Wilcox and Moellering, 1995; ESRI, 1994; Band, 1986; Jenson, 1985; O’Callaghan and Mark, 1984; Mark et al, 1984; Mark, 1983; Haralick, 1983). Figure B.5 demonstrates a flowchart showing the process of extracting surface flow characteristics (or hydrologic knowledge), such as watershed boundaries and stream networks, from DEMs. Watersheds and stream networks generated from DEMs are used as base maps for many hydrologic models. These models can be used for determining height, timing and inundation of a flood, as well as locating areas and solving pollution problem of water bodies, or predicting the effects of landscape or landform changes (ESRI, 1994).
147
DEM
Flow direction
Is there any pit? For flow direction from depressionless DEM by checking pits
Yes
No
Fill pits
Depressionless DEM
Watersheds
Streams
Flow path length
Flow accumulation
Total flow length
Figure B.5: Extracting hydrologic surface characteristics from DEMs (ESRI, 1994).
B.6.1 Flow directions in DEMs Flow direction on a surface is always in the steepest downhill direction. If the flow direction of each cell is known, we can
1 √2
1
8
into any given cell. This knowledge can be
Diagonal distance between two cells.
used to define watershed boundaries and stream networks.
148
X
3 4
1 7
determine which and how many cells flow
2
6
5
Eight flow directions for a cell location, x given.
The direction of flow across a surface is determined by the aspect at each location. There are eight directions relating to the eight adjacent cells into which flow could run. Flow direction can be determined by computing the direction of the steepest descent, or maximum drop from each cell. Maximum drop is ratio of difference in elevation to the distance between cell centers. Maximum drop = change in z value / distance The distance may be found using cell centers, where cell size is 1 unit. Thus, the distance between two orthogonal cells is 1 and the distance between two diagonal cells is the square root of 2, that is 1.414216…. If the descent to all eight neighboring cells is the same, the adjacency of the given location is enlarged until the maximum descent is found. Once we find the steepest descent direction at every location, we assign a direction value for each cell, from one to eight, to produce output cells showing the flow directions as in figure B.7. This can be done using an Arc/Info Grid function called flowdirection as follows: flow_dir = flowdirection (elevation)
If all eight adjacent cells have higher elevations than the processing cell, the processing cell is a pit that has lowest elevation of all its neighbors. It has an undefined flow direction, since there is no definite direction for flow out of the cell. For accurate flow direction assignment across the surface, the pits must be filled first, because when water runs across the surface, it first fills the pits, then runs on over the surface. Filling the pits generates a depressionless DEM (ESRI, 1994).
149
26
24
23
24
19
16
5
5
5
5
6
7
25
22
19
16
15
17
5
5
5
6
7
7
23
18
15
12
13
16
4
4
5
6
7
6
21
19
18
8
11
9
3
3
5
5
6
7
23
20
16
7
5
6
5
4
5
5
6
7
25
18
11
5
4
5
4
4
4
4
6
8
Figure B.6: Elevations of grid cells as input data by a DEM.
Figure B.7: Flow directions of cells using elevations.
0
0
0
0
0
0
0
1
1
1
3
0
0
3
7
8
1
0
0
0
0
19
0
1
0
0
1
1
23
0
0
2
3
6
35
8
7
8 19 23 6
Figure B.8: Flow accumulation running from each grid cell.
35
8
Figure B.9: Critical flow level of stream channels.
B.6.2. Generating a depressionless DEM A DEM without any pits is called a depressionless DEM. This is required for finding flow direction since the presence of pits may generate inaccurate flow directions. Finding flow directions is the first step in the extraction of hydrologic information about the 150
surface, so the DEM should be correct. Otherwise, any error in this first step propagates on to the next steps. There may be legitimate pits in the data and the landscape, but it is important for understanding the surface morphology to know what features are really pits on the surface and which are data errors (or noise) (ESRI, 1994). Steps for generating a depressionless DEM 1. Find flow directions by a function (e.g., flowdirection). 2. Find sinks by a function (e.g., sink). 3. Find contributing area by a function (e.g., watershed). 4. Find depth of sinks, if they are within z-limit (height threshold) then proceed. 5. Fill sinks to the value of the minimum neighbor cell in the watershed of each sink by a function (e.g., zonalfill). 6. Iterate starting step 1 until finding no more sinks in step 2, or no more sinks within the {z-limit} in step 4. Eliminating sinks usually requires three iterations. The reason for several iterations is that as we fill areas, new pits occur in their boundary neighbors (ESRI, 1994).
B.6.3 Finding flow accumulation Accumulation of rainfall volume can be found (see figures B.6 to B.9) as follows. (1) Set each cell value to zero. (2) Starting at each cell, add one to it (rainfall) and transfer part (or all) of that rainfall (to allow for some infiltration, if desired) to the cells downstream of it, following the directions indicated in the network. (3) To simulate stream channels, assume a channel begins only when the accumulated water passing through a cell reaches some critical value. So, small tributaries will be ignored and channels will only show up 151
when the flow reached a high volume. In figures B.8 and B.9, channels started as volume reached the threshold value of 6 (Hazelton, 2000; ESRI, 1994).
B.6.4 Determining watersheds A watershed is defined as an attribute of each point on the network, which identifies the region upstream of that point. A watershed is also called a basin, catchment, or contributing area. It is the upslope area contributing flow to a given cell location in a DEM. A watershed is a drainage basin, that is a tract of water land drained of both surface runoff and ground water (Hazelton, 2000; ESRI, 1994; Band, 1986; Mark et al, 1984). The watershed boundary is a key requirement for almost all hydrologic surface modeling. We employ grid map algebra to combine these boundaries with soil and land use information to generate summary statistics for input to basin models to estimate sediment loss or flood height. To find a watershed, we begin at the specified cell and label all cells which drain to it, then all which drain to those, etc., until the upstream limits of the basin are defined. The watershed is then the polygon formed by the labeled cells (or its exterior boundary) (Hazelton, 2000; ESRI, 1994).
B.6.5 Determining stream networks To draw the drainage network, connect the flow moves with arrows. A zero on the edge of the array is interpreted as a channel, which flows off the area. Since in natural systems, small quantities of water generally flow overland, not in channels, we may want to accumulate water as it flows downstream through the cells so that channels begin only 152
when a threshold volume is reached (see figure B.9 where the threshold is 6) (Hazelton, 2000). We delineate stream networks from a DEM by employing the output of the function, e.g., flowaccumulation (ESRI, 1994). Flow accumulation is the count of upslope cells that run into each cell. We delineate the stream network by using a grid algebraic expression with a threshold to the results of the function flowaccumulation (ESRI, 1994).
B.6.6 Assigning rank order to the streams Ranking a stream is an assignment of a numeric order to links in a stream network. This assignment can be done using a method for identification and hierarchical classification of streams based on their tributaries. As a result of ordering streams we can infer some characteristics of streams. Direct surface flow of water dominates the first order streams, which have no upstream concentrated flow. The first order flow is the highest hierarchical level stream with the highest assignment number. The function streamorder, has two methods we can use to assign hierarchical levels to a stream (see figure B.10). These are Strahler (1957) and Shreve (1966) ordering methods (ESRI, 1994). The exterior links of the stream network are always assigned the highest order, 1, in these two methods. 1
1 1
1
2
1 2
1
2
2
1
3
2
1
5
3
1
1
Figure B.10: Ordering streams by Strahler and Shreve methods, respectively.
153
B.7 Mapping of stream channels Having found the flow accumulation running from each grid cell, we find the critical flow level (see figures B.8 and B.9). This critical flow level gives us a map of stream channels as stream links in raster format. This raster map can be converted into a vector map format by vectorization (ESRI, 1994; Bishop and Church, 1992). A function, streamlink, is used for assigning unique values to each link in a raster linear network. We can take advantage of this by attaching related attribute knowledge to each segment of a stream individually. We convert a raster linear network into a vector map by using a function streamline. The function streamline is a program primarily designed for vectorization of stream networks, or any other grid showing a raster linear network for which directionality is known. Thus, in the output coverage, all arcs represent downstream flow (ESRI, 1994).
B.8 Summary In this appendix, we have seen an overview of the interpretation of hydrological surface characteristics from DEMs in order to show where and how river channels are found in DEMs.
154
BIBLIOGRAPHY Argialas, D.P., 1985. A structural approach towards drainage pattern recognition. Ph.D. Dissertation, The Ohio State University, Columbus, Ohio, USA. Argialas, D.P., and J.G. Lyon, 1988. Quantitative description and classification of drainage patterns. Photogrammetric Engineering and Remote Sensing, 54 (4), 505-509. Argialas, D.P., and C.A. Harlow, 1990. Computational image interpretation models: an overview and a perspective. Photogrammetric Engineering and Remote Sensing, 56 (6), 871-886. Band, L.E., 1986. Topographic partition of watershed with digital elevation models. Water Resources Research, Vol. 22, No. 1. pp. 15-24. Barret, E.C., and L.F. Curtis, 1992. Introduction to environmental remote sensing. Chapman and Hall : New York. Benosky, C.P., and C.J. Merry, 1995. Automated extraction of watershed characteristics using spatial analysis techniques with application to groundwater mapping. Journal of Hydrology, Vol. 173, pp. 145-163. Bishop, G.D., and M.R. Church, 1992. Automated approaches for regional runoff mapping in the Northeastern United States. Journal of Hydrology, Vol. 138, pp. 361-383. Burrough, P.A., and R.A. McDonnell, 1998. Principles of geographical information systems. Oxford University Press Inc.: New York. Caelli, T., and W.F. Bischof, 1997. Machine learning and image interpretation. Plenum Press: New York. Castleman, K.R., 1996. Digital image processing. Prentice Hall Inc.: New Jersey. Chrisman, N., 1997. Exploring geographic information systems. John Wiley and Sons Inc.: New York. Demirkesen, A.C., and B. Schaffrin, 1996. Map conflation: spatial point data merging and transformation. Proceedings GIS/LIS ‘96 Conference, Denver, pp. 393-403. 155
Denegre, J. (Ed), 1994. Thematic mapping from satellite imagery. A guide book. International Cartographic Association. Elsevier Science Ltd.: New York. ESRI (Environmental Systems Research Institute), 1994. Cell-based modeling with Grid. Arc/Info version 7. Gonzalez, R.C., and R.E. Woods, 1993. Digital image processing. Addison Wesley Publishing Company Inc.: New York. Goodchild, M.F., and S. Gopal, 1994. Accuracy of spatial databases. Taylor and Francis Inc.: Bristol, PA. Goodie, A., 1993. The nature of the environment. Third edition. Blackwell Publishers: Cambridge, MA. Graham, D., and A. Barrett, 1997. Knowledge-based image processing systems. Springer: New York. Habib, A., 2000. Photogrammetric mapping: class notes, The Ohio State University, Dept. of Civil and Env. Eng. and Geodetic Science, Geodetic Science Section. Hall, C.A.S., and J.W. Day, 1977. Ecosystem modeling in theory and practice. An introduction with case histories. John Wiley and Sons Inc.: New York. Haralick, R.M., 1983. Ridges and valleys on digital images. Computer vision, graphics, and image processing, Vol. 22, pp. 28-38. Hazelton, N.W., 2000. Large scale and topographic mapping: class notes, The Ohio State University, Dept. of Civil and Env. Eng. and Geodetic Science, Geodetic Science Section. Huang, X., and J.R. Jensen, 1997. A machine-learning approach to automated knowledge-base building for remote sensing image analysis with GIS data. Photogrammetric Engineering and Remote Sensing, 63 (10), 1185-1194. IDRISI manual, 1997. Clark University: Worcester, Massachusetts. Jackson, P., 1999. Introduction to expert systems. Addison Wesley Inc.: New York. Jensen, J.R., 1996. Introductory image processing: a remote sensing perspective. Prentice Hall Inc.: New Jersey. Jenson, S.K., 1985. Automated derivation of hydrologic basin characteristics from digital elevation model data. Proceedings Auto-Carto 7, pp. 301-310. 156
Kartikeyan, B., K.L. Majumder, and A.R. Dasgupta, 1995. An expert system for land cover classification. IEEE Trans Geoscience and Remote Sensing, 33 (1), 58-66. Kendall, H.M., R.M. Glendinning, and C.H. MacFadden, 1962. Introduction to geography, Third edition. Harcourt, Brace and World Inc.: USA. Kudrat, M., A.K. Tiwari, and S.K. Saha, 1997. Modeling sediment yield for prioritization of sub-watersheds using remote sensing and GIS techniques. Geocarto International, Vol.12, No.2, pp. 31-37. Laurini, R., and D. Thompson, 1995. Fundamentals of spatial information systems. Academic Press Ltd.: New York. Lillesand, T.M., and R.W. Kiefer, 1994. Remote sensing and image interpretation. John Wiley and Sons Inc.: New York. Liu, X. and Ramirez, J.R., 1997. Automatic extraction of hydrographic objects in digital orthophoto images. Proceedings GIS/LIS ‘97 Conference, Cincinnati, OH. McCuen, R.H., 1998. Hydrologic analysis and design. Prentice Hall Inc.: New Jersey. Mark, D.M., 1983. Automated detection of drainage networks from digital elevation models. Proceedings Auto-Carto 6, pp. 288-297. Mark, D.M., J. Dozier, and J. Frew, 1984. Automated basin delineation from digital elevation data. Geo-Processing, Vol. 2, pp. 299-311. Mehldau, G., and R.A. Schowengerdth, 1990. A C-extension for rule-based image classification systems. Photogrammetric Engineering and Remote Sensing, 56 (6), 887892. Mills, J.P., and I. Newton, 1996. A new approach to the verification and revision of largescale mapping. ISPRS Journal of Photogrammetry & Remote Sensing. No.51, pp. 17-27. Mitchell, C.W., 1991. Terrain factors in hydrology, pp. 249-262. In: Terrain evaluation. John Wiley & Sons Inc.: New York. Moller-Jensen, L., 1990. Knowledge-based classification of an urban area using texture and context information in Landsat TM imagery. Photogrammetric Engineering and Remote Sensing, 56 (6), 899-904. Moore, I.D., 1996. Hydrological modeling and GIS. In: GIS and environmental modeling: process and research issues, pp. 143-148. 157
Morisawa, M., 1968. Streams: their dynamics and morphology. McGraw Hill Book Company: New York. Mueksch, M.C., 1994. Airborne hyperspectral algorithms to determine tropical and morphological status of lakes, rivers and coastal waters, Proceedings SPIE-Aerospace Conference, Orlando, Florida, USA. Mueksch, M.C., 1996. Monitoring and assessing natural lake environments for LakeGIS, Proceedings GIS/LIS’96 Conference, Denver, pp. 30-36. National Geographic, 1995. Atlas of the World. Revised 6th edition. Washington, D. C. Newby, P.R.T.,1996. Digital images in the map revision process. ISPRS Journal of Photogrammetry & Remote Sensing. No.51, pp. 188-195. Newson, M., 1992. Land, water and development: river basin systems and their sustainable management. Routledge: New York. Nicholin, B., and R. Gabler, 1987. A knowledge-based system for the analysis of aerial images. IEEE Trans Geoscience and Remote Sensing, GE-25, 317-328. O’Callaghan, J.F., and D.M. Mark, 1984. Extraction of drainage networks from digital elevation model. Computer vision, graphics, and image processing, Vol. 28, pp. 323-344. Petrie, G., 1997. The current situation in Africa regarding topographic mapping and map revision from satellite images. ITC Journal 1997-1, pp. 49-63. Press, F., and R. Siever, 1998. Understanding Earth. Second edition. W. H. Freeman and Company: USA. Ramirez, J.R., 1988. A map representation theory for the evaluation of digital exchange formats. The Ohio State University, Department of Geodetic Science and Surveying, Report No. 389, pp. 32-75. Ramirez, J.R., and D. Lee, 1991. The development of a cartographic model for consistent and efficient map production. Final Report USGS Grant No. 14-08-0002-G1884, The Center for Mapping, The Ohio State University. Ramirez, J.R., 1992. Modern cartographic theory: an introduction. Unpublished. The Ohio State University, Columbus, OH. Ramirez, J.R., 1995a. Revision of hypsographic data: a conceptual framework. Mobile Mapping Symposium, The Ohio State University Center for Mapping & Department of Geodetic Science and Surveying, pp. 153-162. 158
Ramirez, J.R., 1995b. Map revision and new technologies: a general framework and two proof of concepts. Proceedings 17th International Cartographic Conference, pp. 924-932. Ramirez, J.R., 1996a. Spatial data revision: toward an integrated solution using new technologies. International Archive of Photogrammetry and Remote Sensing, Vienna, Austria. Ramirez, J.R., 1996b. Spatial data revision: current research and its influence in GIS. PLANS’96, Position, Location, and Navigation Symposium, Atlanta Georgia, pp. 137144. Ramirez, J.R., 1997a. Revision of geographic data sets: development and implementation of a new solution, Proceedings GIS/LIS’97 Conference, Cincinnati, USA. Ramirez, J.R., 1997b. Geographic data revision: a framework. Generation of up-to-date data for GIS. GIM Magazine, April, 1997. Ramirez, J.R., 1997c. A framework for revision of geographic datasets. Proceedings 1997 ACSM/ASPRS Annual Convention, Seattle, Washington, pp. 115-122. Rich, E., and K. Knight, 1993. Artificial intelligence: an introduction. Addison-Wesley Inc.: New York. Richards, J.A., 1993. Remote sensing digital image analysis: an introduction. SpringerVerlag: New York. RiverTools manual, 2000. Research Systems Inc.: Boulder, Colorado. Schaffrin, B., 1999. Adjustment computations: class notes, The Ohio State University, Dept. of Civil and Env. Eng. and Geodetic Science. Geodetic Science Section. Schenk, A., and O. Zilberstein, 1990. Experiments with a rule-based system for interpreting linear map features. Photogrammetric Engineering and Remote Sensing, 56 (6), 911-917. Schmoldt, D.L., and H.M. Rauscher, 1996. Building knowledge-based systems for natural resource management. Chapman and Hall Inc.: New York. Schott, J.R., 1997. Remote sensing: the image chain approach. Oxford University Press: New York. Schowengerdt, R.A., 1989. A general-purpose expert system for image processing. Photogrammetric Engineering and Remote Sensing, 55 (9), 1277-1284.
159
Silfer, A.T., G.J. Kinn, and J.M. Hasset, 1986. A geographic information system utilizing the triangulated irregular networks as a basis for hydrologic modeling. Proceedings AutoCarto 8, pp. 129-136. Siram, R.D., 1997. Intelligent systems for engineering: a knowledge-based approach. Springer-Verlag: London. Skinner, B.J., and S.C. Porter, 1995. The dynamic earth: an introduction to physical geology. Third edition. John Wiley and Sons Inc: New York. Tin-Seong, K., 1995. Integrating GIS and remote sensing techniques for urban land-cover and land-use analysis. Geocarto International, Vol.10, No.1, pp. 39-49. Tomlin, C.D., 1990, Geographic information systems and cartographic modeling. Prentice Hall Inc.: New Jersey. Ton J., J. Sticklen, and A.K. Jain, 1991. Knowledge-based segmentation of Landsat images. IEEE Trans Geoscience and Remote Sensing, 29 (2), 222-232. Turban, E., 1995. Decision support and expert systems: management support systems. Prentice Hall Inc.: New Jersey. Viessman, W., G.L. Lewis, and J.W. Knapp, 1989. Introduction to hydrology. Third edition. Harper and Row Inc.: New York. Warntz, W., 1966. The topology of a socio-economic terrain and spatial flow. Papers of the Regional Science Association, Vol. 17, pp. 47-61. Wharton, S.W., 1987. A spectral-knowledge-based approach for urban land cover discrimination. IEEE Trans Geoscience and Remote Sensing, GE 25 (3), 272-282 Wilson J.P., and J.C. Gallant, 2000. Terrain analysis: principles and applications. John Wiley and Sons, Inc.: New York. Wilson, P.A., 1997. Rule-based classification of water in Landsat MSS images using the variance filter. Photogrammetric Engineering and Remote Sensing, 55, 1277-1284. Wilcox, D., and H. Moellering, 1995. Pass locations to facilitate the direct extraction of Warntz networks from grid digital elevation models. Proceedings Auto-Carto 12, pp. 2231. Worboys, M.F., 1995. GIS: a computing perspective, Taylor and Francis Inc.: Bristol, PA.
160
Wright, R.D., and P.L. Wright, 1995. The Tijuana river watershed GIS project: a binational consortium. Proceedings GIS/LIS’95 Conference, Nashville, pp. 1046-1055.
161
