Linking pesticides and human health: A geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

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

Institution:	1. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 De Soto St, Pittsburgh, PA 15261, USA;2. Spatial Sciences Institute, University of Southern California, 3616 Trousdale Pkwy AHF B55, Los Angeles, CA 90089, USA;3. Division of Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute, 5150 Centre Ave, Pittsburgh, PA 15232, USA;4. Department of Medicine, University of Pittsburgh, 1218 Scaife Hall, 3550 Terrace St, Pittsburgh, PA 15261, USA;1. Faculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, B-9000 Ghent, Belgium;2. Department of Geography, Ghent University, Krijgslaan 281 (S8), B-9000 Ghent, Belgium;1. Institute of Geography, Universitätsstraße 65–67, 9020, Klagenfurt am Wörthersee, Austria;2. Institute of Statistics, Universitätsstraße 65–67, 9020, Klagenfurt am Wörthersee, Austria

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.

Keywords:	Pesticide exposure Geographic information system (GIS) Remote sensing Normalized Difference Vegetation Index (NDVI) Environmental epidemiology AI"} {"#name":"keyword" "$":{"id":"kwrd0040"} "$$":[{"#name":"text" "_":"active ingredient CA"} {"#name":"keyword" "$":{"id":"kwrd0050"} "$$":[{"#name":"text" "_":"California CCM"} {"#name":"keyword" "$":{"id":"kwrd0060"} "$$":[{"#name":"text" "_":"compressed county mosaic CDPR"} {"#name":"keyword" "$":{"id":"kwrd0070"} "$$":[{"#name":"text" "_":"California Department of Pesticide Regulation CDWR"} {"#name":"keyword" "$":{"id":"kwrd0080"} "$$":[{"#name":"text" "_":"California Department of Water Resources COST"} {"#name":"keyword" "$":{"id":"kwrd0090"} "$$":[{"#name":"text" "_":"cosine estimation of atmospheric transmittance DNR"} {"#name":"keyword" "$":{"id":"kwrd0100"} "$$":[{"#name":"text" "_":"Department of Natural Resources GIS"} {"#name":"keyword" "$":{"id":"kwrd0110"} "$$":[{"#name":"text" "_":"geographic information system LUS"} {"#name":"keyword" "$":{"id":"kwrd0120"} "$$":[{"#name":"text" "_":"land use survey MLC"} {"#name":"keyword" "$":{"id":"kwrd0130"} "$$":[{"#name":"text" "_":"maximum likelihood classification NAD83"} {"#name":"keyword" "$":{"id":"kwrd0140"} "$$":[{"#name":"text" "_":"North American Datum 1983 NAIP"} {"#name":"keyword" "$":{"id":"kwrd0150"} "$$":[{"#name":"text" "_":"National Agriculture Imagery Program NASA"} {"#name":"keyword" "$":{"id":"kwrd0160"} "$$":[{"#name":"text" "_":"National Aeronautics and Space Administration NDVI"} {"#name":"keyword" "$":{"id":"kwrd0170"} "$$":[{"#name":"text" "_":"Normalized Difference Vegetation Index NIR"} {"#name":"keyword" "$":{"id":"kwrd0180"} "$$":[{"#name":"text" "_":"near-infrared NPS"} {"#name":"keyword" "$":{"id":"kwrd0190"} "$$":[{"#name":"text" "_":"nonpoint source PLSS"} {"#name":"keyword" "$":{"id":"kwrd0200"} "$$":[{"#name":"text" "_":"Public Land Survey System PUR"} {"#name":"keyword" "$":{"id":"kwrd0210"} "$$":[{"#name":"text" "_":"Pesticide Use Report PUS"} {"#name":"keyword" "$":{"id":"kwrd0220"} "$$":[{"#name":"text" "_":"Pesticide Usage Survey R"} {"#name":"keyword" "$":{"id":"kwrd0230"} "$$":[{"#name":"text" "_":"red (spectral band) SPOT"} {"#name":"keyword" "$":{"id":"kwrd0240"} "$$":[{"#name":"text" "_":"Satellite Pour l'Observation de la Terre SRS"} {"#name":"keyword" "$":{"id":"kwrd0250"} "$$":[{"#name":"text" "_":"stratified random sampling TM"} {"#name":"keyword" "$":{"id":"kwrd0260"} "$$":[{"#name":"text" "_":"Thematic Mapper US"} {"#name":"keyword" "$":{"id":"kwrd0270"} "$$":[{"#name":"text" "_":"United States USDA"} {"#name":"keyword" "$":{"id":"kwrd0280"} "$$":[{"#name":"text" "_":"United States Department of Agriculture USGS"} {"#name":"keyword" "$":{"id":"kwrd0290"} "$$":[{"#name":"text" "_":"United States Geological Survey
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