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Mapping moderate-scale land-cover over very large geographic areas within a collaborative framework: A case study of the Southwest Regional Gap Analysis Project (SWReGAP) |
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| Authors: | J Lowry RD Ramsey K Thomas T Sajwaj E Waller S Falzarano G Manis K Schulz K Pohs C Velasquez W Kepner L O'Brien B Thompson |
| Affiliation: | a Remote Sensing/GIS Laboratory, College of Natural Resources, Utah State University, Logan, UT, USA b New Mexico Cooperative Fish and Wildlife Research Unit, New Mexico State University, Las Cruces, NM, USA c US EPA, National Exposure Laboratory - ESD/LEB, Las Vegas, NV, USA d NatureServe, Boulder, CO, USA e USGS Southwest Biological Science Center, Flagstaff, AZ, USA f Colorado Division of Wildlife, Habitat Resources Section, Denver, CO, USA g Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA h USGS/BRD Gap Analysis Program, Las Cruces, NM, USA |
| Abstract: | Land-cover mapping efforts within the USGS Gap Analysis Program have traditionally been state-centered; each state having the responsibility of implementing a project design for the geographic area within their state boundaries. The Southwest Regional Gap Analysis Project (SWReGAP) was the first formal GAP project designed at a regional, multi-state scale. The project area comprises the southwestern states of Arizona, Colorado, Nevada, New Mexico, and Utah. The land-cover map/dataset was generated using regionally consistent geospatial data (Landsat ETM+ imagery (1999-2001) and DEM derivatives), similar field data collection protocols, a standardized land-cover legend, and a common modeling approach (decision tree classifier). Partitioning of mapping responsibilities amongst the five collaborating states was organized around ecoregion-based “mapping zones”. Over the course of 21/2 field seasons approximately 93,000 reference samples were collected directly, or obtained from other contemporary projects, for the land-cover modeling effort. The final map was made public in 2004 and contains 125 land-cover classes. An internal validation of 85 of the classes, representing 91% of the land area was performed. Agreement between withheld samples and the validated dataset was 61% (KHAT = .60, n = 17,030). This paper presents an overview of the methodologies used to create the regional land-cover dataset and highlights issues associated with large-area mapping within a coordinated, multi-institutional management framework. |
| Keywords: | Large-area mapping Meso-scale mapping Moderate scale mapping Land-cover mapping Vegetation mapping Southwestern U S Collaborative projects Remote sensing Decision tree classifiers Geographic information systems Gap Analysis Program (GAP) |
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