Linking pesticides and human health: A geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure. (August 2015)

• Record Type: Journal Article
• Authors: VoPham, Trang; Wilson, John P.; Ruddell, Darren; Rashed, Tarek; Brooks, Maria M.; Yuan, Jian-Min; Talbott, Evelyn O.; Chang, Chung-Chou H.; Weissfeld, Joel L.
• Is Part Of: Applied geography. Volume 62(2015:Aug.)
• Page Start: 171
• Page End: 181
• Publication Date: 2015-08
• Subjects: Pesticide exposure -- Geographic information system (GIS) -- Remote sensing -- Normalized Difference Vegetation Index (NDVI) -- Environmental epidemiology; AI active ingredient -- CA California -- CCM compressed county mosaic -- CDPR California Department of Pesticide Regulation -- CDWR California Department of Water Resources -- COST cosine estimation of atmospheric transmittance -- DNR Department of Natural Resources -- GIS geographic information system -- LUS land use survey -- MLC maximum likelihood classification -- NAD83 North American Datum 1983 -- NAIP National Agriculture Imagery Program -- NASA National Aeronautics and Space Administration -- NDVI Normalized Difference Vegetation Index -- NIR near-infrared -- NPS nonpoint source -- PLSS Public Land Survey System -- PUR Pesticide Use Report -- PUS Pesticide Usage Survey -- R red (spectral band) -- SPOT Satellite Pour l'Observation de la Terre -- SRS stratified random sampling -- TM Thematic Mapper -- US United States -- USDA United States Department of Agriculture -- USGS United States Geological Survey; Geography -- Periodicals; Human geography -- Periodicals; Human ecology -- Periodicals; 910
• DOI: 10.1016/j.apgeog.2015.04.009
• Languages: English
• ISSNs: 0143-6228

Abstract

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 500 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. Highlights: The Landsat method is a GIS and remote sensing pesticide exposure estimation method. Landsat images are classified into crop fields and matched to pesticide applications. Pesticide exposure is estimated using 500 m buffers around geocoded locations. More pesticide applications were matched to Landsat crops vs. standard method crops. The Landsat method can reconstruct past pesticide exposure for epidemiologic use.