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COMPARATIVE EVALUATION OF LAND COVER DATA SOURCES FOR EROSION PREDICTION 1
Author(s) -
Fraser Robert H.,
Warren Maureen V.,
Barten Paul K
Publication year - 1995
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1995.tb03415.x
Subject(s) - watershed , land cover , environmental science , nonpoint source pollution , land use , geographic information system , aerial photography , tributary , remote sensing , hydrology (agriculture) , universal soil loss equation , rangeland , erosion , surface runoff , geography , cartography , computer science , geology , agroforestry , soil loss , ecology , paleontology , geotechnical engineering , machine learning , biology
A fundamental problem in protecting surface drinking water supplies is the identification of sites highly susceptible to soil erosion and other forms of nonpoint source (NPS) pollution. The New York City Department of Environmental Protection is trying to identify erodible sites as part of a program aimed at avoiding costly filtration. New York City's 2,000 square mile watershed system is well suited for analysis with geographic information systems (GIS); an increasingly important tool to determine the spatial distribution of sensitive NPS pollution areas. This study used a GIS to compare three land cover sources for input into the Modified Universal Soil Loss Equation (MUSLE), a model estimating soil loss from rangeland and forests, for a tributary watershed within New York City's water supply system. Sources included both conventional data (aerial photography) and Landsat data (MSS and TM images). Although land cover classifications varied significantly across these sources, location‐specific and aggregate watershed predictions of the MUSLE were very similar. We conclude that using Landsat TM imagery with a hybrid classification algorithm provides a rapid, objective means of developing large area land cover databases for use in the MUSLE, thus presenting an attractive alternative to photo interpretation.