Topographic Maps: Rediscovering an Accessible ...

Topographic Maps: Rediscovering an Accessible Data Source for Land Cover Change Research Ron McChesney and Kendra McSweeney

ABSTRACT: Given some limitations of satellite imagery for the study of land cover change, we draw attention here to a robust and often overlooked data source for use in student research: USGS topographic maps. Topographic maps offer an inexpensive, rapid, and accessible means for students to analyze land cover change over large areas. We demonstrate our visualassessment based method using 1:24,000 topographic maps to examine urban expansion and surface mining shrinkage over thirty years in Ohio. Compared with alternative methodologies, the simple method we propose yields surprising robust results. The pedagogic potential of historic USGS topographic maps for land cover change research is just one reason to justify their maintenance in federal depositories. Key words: land cover change, methodology, USGS topographicmaps, urban, mining

Ron McChesney is completing his doctoral dissertationin urban growth modelingat Ohio State University, Columbus. His MA and BA degrees were in Economics at the University of New Hampshire and the University of Colorado.His work is in the areas of population, land cover and land use, transportation, economic, and urban geography. Kendra McSweeney is an Assistant Professor of Geography at Ohio State University, Columbus. Her research interests include local peoples' response to landscape change, and the mnicroeconoinicand microdemographic foundations of resource use, especially in tropical environments. She has written and presented on these themnes in popular media and to school groups.

INTRODUCTION

Land cover change is a theme that cross-cuts virtually all areas of geographic research and scholarship (NRC 1997).' Whether examining past changes in city morphology in order to model urban growth (e.g., Clarke 2000; Landis and Zhang 1998; Fischer and Nikjamp 1987), estimating climate change parameters based on changing surface characteristics (Gurney et al. 1993), or assessing anthropogenic impacts on forests to focus biogeographic sampling (e.g., Egan and Howell 2001), much of our work and teaching is based on assessments of land cover change. Up to about twenty years ago, these efforts would have relied on the interpretation of aerial photographs or comparison of hand-drawn maps. Today, satellite imagery and/or digital data sources are more likely to be our methodological default, since both are now sufficiently fine-grained to trace such changes from neighborhood to global scales. An increasing share of class time is correspondingly spent training our students in the applications required to analyze satellite imagery or the GIS software with which to interrogate digital data sources. As a result, students are now. likely to consider such high-tech forms of analysis the only way to study relatively recent forms of land cover change (see Baumann et al. 1994). What is often forgotten-by students and their instructors alike-is that satellite imagery is not the only, nor necessarily the best tool for teaching or researching land cover change. Among its shortcomings, high-quality remotely sensed data can be expensive, and the comparison of time-series data can be impeded by the irreconcilability of changing data qualities through time. Further, it is often difficult to quickly and inexpensively compare land cover criteria across large spaces2 (e.g., states) because of the incompatibilities in scale, dates, and other factors. These shortcomings make satellite-based analysis or GIS-reliant interrogation of digital map sources relatively inaccessible for undergraduate or graduate students interested in deriving quick but reliable estimates of land cover change. Yet such techniques can enrich a student research paper, help to focus a Master's thesis project early on through exploratory data analysis, or provide an attractive in-class assignment that introduces students to the challenge of primary research. Without incorporating accessible techniques into our pedagogy, geography undergraduates are effectively being trained to perceive the derivation of land-cover change data as a research option that can only be engaged with late in their coursework, after prior preparation in GIS and satellite imagery analysis. In this paper, we suggest that this need not be so. We describe an alternative and relatively inexpensive method for estimating land tover change using United States Geological Survey (USGS 2005) topographic maps. The method is designed to be sufficiently accessible, inexpensive, and time-efficient to be taught in the classroom or undertaken by students working on individual projects. Our method is grounded in the understanding that USGS topographic maps represent the standard for accurate, detailed land cover information on which many digital sources are based. As Monmonier (1996, 43) points out, "large-scale base maps have surprisingly few errors. A costly but efficient bureaucratic structure at government mapping agencies usually guarantees a highly accurate product." We argue that these high quality topographic maps

Journalof Geography 104: 161-178 ©2005 National Council for Geographic Edu cation

161

Ron McChesney and Kendra McSweeney

represent an overlooked source of relatively high-resolution data for analysis of U.S. land cover change over multiple time periods. In many cases, these map series offer temporal depths of four to five decades. They can provide information of several kinds of land cover change and their unusually standardized format facilitates comparison of change at a range of scales. Adding to their attractiveness, these maps are widely available in university libraries. Further, the method we propose requires no specific skills or equipment yet can yield estimates that are comparable to those produced by more sophisticated analytical products (see Appendix A). We are not the first to draw attention to the utility of USGS topographic maps as a tool for teaching and student research into historical land cover change research (see, for example, Reithmaier 2001; Vuorela et al. 2002; Kerski 2005). We do argue, however, that their pedagogic potential in this regard is too often eclipsed, rather than complemented, by more "high-tech" and less accessible land cover change approaches. In this regard, our method complements the type of student-friendly aerial photograph interpretation recently advocated by M!

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TopographicMaps: Rediscovering an Accessible Data Sourcefor Land Cover Change Research

these revisions occurred in 1994. Therefore the comparison of change from 1961 to 1994 is not an exact absolute statement, but a method to estimate the temporal impact that occurred during a thirty-year time frame. Tables 1 and 2 provide summary results for urban impact. Tables 4 provide results for surface mining in the same format as table 2, except shown as a relative percentage impact (the data outputs can either be in total estimated area or total estimated percent coverage of the study area). Table 5 shows the combined impact of urban areas and surface mining. Researchers and/or students record the land cover impact according to a selected ordinal category, which in this study was low, medium or high impact. Each category has a selected range, where low impact is zero to the low range threshold, high impact is a selected coverage perspective to one hundred percent, and medium impact goes from the low range threshold to the high range threshold. The selection is cognitive, in that a subject student looks at the map object, and identifies a pattern for the variable that is being estimated. This pattern can either be compact or dispersed, contiguous or scattered. A categorical determination is rendered, and recorded. The output data indicated a range of possibilities. For example, for low impact, the subjects could have either (1) all determined there was no or zero of the variable on the map object, or (2) all determined that there was low impact at the threshold category range. If low was 0-2 square miles, then a group of subjects could have 'seen' a range from 0 to 2 for a group of evaluated quad maps. Or, perhaps more likely, they might have seen the data differently which would result in a distribution of results for each ordinal impact category. For simplicity, it is assumed that this distribution has a mid-point between the lowest and highest value in the impact range. For 0-2 square Table 4. Surface mining in Ohio, ca. 1961 and ca. 1994, showing the range in coverage as a share of the state's total land area.

In Percentages Range

Low 0-1

1961 Medium 1-5

High 5-55

Total

Minimum Mid-point Maximum

0.0% 0.8% 1.6%

0.1% 0.3% 0.4%

0.7% 4.1% 7.4%

0.8% 5.1% 9.4%

In Percentages Range

Low 0-1

1994 Medium 1-5

High 5-55

Total

Minimum Mid-point Maximum

0.0% 0.7% 1.4%

0.2% 0.6% 0.9%

0.7% 4.4% 8.1%

0.9% 5.6% 10.4%

Table 5. A comparison of urban areas and surface area mining impact in Ohio, ca. 1961 and ca. 1994, expressed as a share of the state's total land area. 1961 area percent Minimum 1,012 Mid-point 5,000 Maximum 8,987

2.3% 11.5% 20.6%

change 1994 area percent in percent 1,668 6,990 12,311

3.8% 16.0% 28.2%

1.5% 4.6% 7.6%

miles, the midpoint would be 1 square mile, and if the corresponding percentage of the quad was 0 to 4 percent (rounding up from 3.6363 percent), then the mid-point impact is 2 percent. To obtain summary output for the total set of topographic quads in the study, a minimum estimate is provided assuming that either one researcher always saw zero impact when they recorded 'low', or that a group of students likewise always saw zero impact for an evaluated quad. A mid-point estimate is provided assuming that either one researcher or a group of students always saw a mid-point between category ranges. Finally, a maximum estimate assumes that one researcher or a group of students always saw 100 percent coverage when they recorded a 'high' impact for the variable land cover being studied. For a study involving 787 topographic maps over two time periods, a large volume of output is provided, and for brevity purposes most of that is not depicted here (but can be provided by request to the authors). This output data can be subject to further statistical evaluation. Also, the study area can be subdivided into smaller components. Table 6 shows the combined impact of urban and mining, expressed in minimum, mid-point, and maximum ranges, for Ohio that is divided into four quadrants. The results indicate that Northeast Ohio experienced the largest change (6.9% of the land area) in urban and mining impact. Also, the relatively large impact of surface mining is reflected in Southeast Ohio, with a ca. 1994 total impact of 19.2%, which is significant given the rural nature of that part of the state. Finally, Figure 5 shows that a subset of the data can be selected in the form of a metropolitan area. In this case, the Columbus, Ohio Metropolitan Area as defined in 2000 is enveloped using 84 quads, or slightly more than ten percent of the study area (or 4,620 square miles have some overlap of coverage from the 4,000 square mile county boundary delineated metropolitan area). Table 7 shows the results for urban impact (mining impact is low in this part of the state). The mid-point value for ca. 1994 is 379 square miles, or 8.2% of the metropolitan areas. As a comparative, the year 2000 combined urban land in the Columbus metropolitan area was about 515 square miles (Census 2000). 175

Ron McChesney and KendraMcSweeney Table 6. Summary table for Ohio land cover study, estimated urban + surface mining area disturbed. F 11111111

I FTI

1-H

Ohio Location

ca. 1961 area

ca. 1961 percent

ca. 1994 area

ca. 1994 change percent in percent

0.8% 3.9% 2.6% 1.8% 2.3%

139 697 421 411 1,668

1.4% 6.3% 3.8% 3.5% 3.8%

0.7% 2.4% 1.1% 1.8% 1.5%

4.9% 17.2% 15.0% 8.0% 11.5%

641 2,676 2,152 1,522 6,990

6.6% 24.1% 19.2% 13.1% 16.0%

1.6% 6.9% 4.2% 5.1% 4.6%

9.1% 30.5% 27.3% 14.2% 20.6%

1,143 4,654 3,882 2,632 12,311

11.8% 41.9% 34.6% 22.7% 28.2%

2.6% 11.4% 7.3% 8.4% 7.6%

Minimum Northwest 74 Northeast 435 Southeast 296 Southwest 207 Total 1,012

-l Hill

Mid-point H

I I I i I I 11111 I I I

L II I I

III

u-I_

II I

I

II'

Northwest 481 Northeast 1,910 Southeast 1,679 Southwest 930 Total 5,000 Maximum

Notes: 84 topos envelope the Columbus Ohio metropolitan area (as defines in the 2000 Census) Map represents 787 topographic map quadrangles.

Figure 5. Map of Ohio represented as 787 topographic quads, depicting the spatial location of 84 quads that envelope the Columbus, Ohio Metropolitan Area.

Adjusting for likely urban develoipment between 1994 and 2000, the mid-point value in this case is probably 10-15% below the US Census Bureau estimate. The data can be further evalt iated for many different delineations, including water stheds, congressional districts, and many other physical a nd social subdivisions,

Table Z Estimated urban + surfa ce mining area disturbed, Columbus, Ohio Metropolitan Area. Ohio

ca. 1961

ca. 1961

ca.

Location

area

percent

1994 area

ca. 1994 percent

change in percent

Minimum

50

1.1%

102

2.2%

1.1%

Mid-point

235

5.1%

379

8.2%

3.1%

Maximum

420

9.1%

655

14.2%

5.1%

Note: 84 topographic quads envelope the 8 county Columbus, Metropolitan Area (as defined in the 2000SU.S. Census)

176

Ohio

Northwest Northeast Southeast Southwest Total

888 3,385 3,061 1,653 8,987

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TopographicMaps: Rediscovering an Accessible Data Sourcefor Land Cover Change Research

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