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)

Autor:	W. Rieth, Bruce C. Thompson, J. Prior-Magee, C. Velasquez, R.D. Ramsey, B. Wolk, Eric K. Waller, D. Schrupp, S. Falzarano, Kenneth G. Boykin, Gerald S. Manis, Jessica Kirby, S. Schrader, Kathryn A. Thomas, Cynthia S.A. Wallace, William G. Kepner, K. Pohs, L. O'Brien, David F. Bradford, L. Langs, Keith A. Schulz, Patrick J. Comer, John H. Lowry, T. Sajwaj
Rok vydání:	2007
Předmět:	Geospatial analysis Data collection Geographic information system business.industry Decision tree learning Decision tree Soil Science Geology Land cover computer.software_genre Ecoregion Geography Gap Analysis Program Computers in Earth Sciences business computer Remote sensing
Zdroj:	Remote Sensing of Environment. 108:59-73
ISSN:	0034-4257
DOI:	10.1016/j.rse.2006.11.008
Popis:	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 2 1 /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 landcover 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::857ef955ce5c47f4db756be94d5b73a3 https://doi.org/10.1016/j.rse.2006.11.008 Zobrazit plný text záznamu Full Text from ScienceDirect