Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6538504 | Applied Geography | 2015 | 11 Pages |
Abstract
Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500Â m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.
Keywords
PLSsNIRMaximum likelihood classificationCDPRPUSDNRMLCUSDAUSGSSRSSPOTCCmNPsLUSEnvironmental epidemiologyUnited StatespurUnited States Department of AgricultureUnited States Geological Surveynational aeronautics and space administrationGISRemote sensingGeographic information systemGeographic information system (GIS)normalized difference vegetation indexNormalized Difference Vegetation Index (NDVI)NDVIPesticide exposureactive ingredientNonpoint sourceNASANAIPNear-infraredThematic MapperStratified random samplingCostCalifornia
Related Topics
Life Sciences
Agricultural and Biological Sciences
Forestry
Authors
Trang VoPham, John P. Wilson, Darren Ruddell, Tarek Rashed, Maria M. Brooks, Jian-Min Yuan, Evelyn O. Talbott, Chung-Chou H. Chang, Joel L. Weissfeld,