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

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.

[1]  G. S. Dunbar.  Applied Geography , 1978, Nature.

[2]  Mustafa Turker,et al.  Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study , 2011 .

[3]  B. Alewu,et al.  Pesticides and Human , 2022 .

[4]  Patrick Hostert,et al.  Differences in Landsat-based trend analyses in drylands due to the choice of vegetation estimate , 2011 .

[5]  Susan K Maxwell,et al.  Using Landsat satellite data to support pesticide exposure assessment in California , 2010, International journal of health geographics.

[6]  Prostitution In Nevada,et al.  ANNALS of the Association of American Geographers , 1974 .

[7]  J. Paruelo,et al.  Land cover classification in the Argentine Pampas using multi-temporal Landsat TM data , 2003 .

[8]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[9]  Tiziana Simoniello,et al.  Landscape-scale characterization of vegetation phenology using AVHRR-NDVI and Landsat-TM data , 2004, SPIE Remote Sensing.

[10]  Stanley A. Greene,et al.  Sittig's Handbook of Pesticides and Agricultural Chemicals , 2007 .

[11]  Rudolph P. Rull,et al.  Validation of self-reported proximity to agricultural crops in a case–control study of neural tube defects , 2006, Journal of Exposure Science and Environmental Epidemiology.

[12]  Nina S. N. Lam,et al.  Geospatial Methods for Reducing Uncertainties in Environmental Health Risk Assessment: Challenges and Opportunities , 2012 .

[13]  P. Gong,et al.  Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .

[14]  N. M. Idaikkadar,et al.  CHAPTER 10 – Census of Agriculture , 1979 .

[15]  J. Behren,et al.  Agricultural pesticide use in California: pesticide prioritization, use densities, and population distributions for a childhood cancer study. , 2001, Environmental health perspectives.

[16]  Ryan S. Miller,et al.  Linkage of the California Pesticide Use Reporting Database with Spatial Land Use Data for Exposure Assessment , 2007, Environmental health perspectives.

[17]  H. Adami,et al.  Pesticides and cancer , 1997, Cancer Causes & Control.

[18]  Guillermo R. Giannico,et al.  Remote sensing classification of grass seed cropping practices in western Oregon , 2011 .

[19]  Neng Wan.  Pesticides exposure modeling based on GIS and remote sensing land use data , 2015 .

[20]  Rikie Suzuki,et al.  Spatial distribution and its seasonality of satellite-derived vegetation index (NDVI) and climate in Siberia , 2001 .

[21]  Jonathan D. Phillips,et al.  Nonpoint Source Pollution and Spatial Aspects of Risk Assessment , 1988 .

[22]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[23]  Jan Verbesselt,et al.  Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology , 2013, Remote. Sens..

[24]  Beate Ritz,et al.  Pesticides and human health , 2014, Occupational and Environmental Medicine.

[25]  J. Hoppin,et al.  Health effects of chronic pesticide exposure: cancer and neurotoxicity. , 2004, Annual review of public health.

[26]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[27]  N. Pettorelli,et al.  Using the satellite-derived NDVI to assess ecological responses to environmental change. , 2005, Trends in ecology & evolution.

[28]  Susan K. Maxwell,et al.  Downscaling Pesticide Use Data to the Crop Field Level in California Using Landsat Satellite Imagery: Paraquat Case Study , 2011, Remote. Sens..

[29]  M. Liebman,et al.  Crop Rotation and Intercropping Strategies for Weed Management. , 1993, Ecological applications : a publication of the Ecological Society of America.

[30]  J. Campbell.  Introduction to remote sensing , 1987 .

[31]  Ryan S. Miller,et al.  Identifying populations potentially exposed to agricultural pesticides using remote sensing and a Geographic Information System. , 1999, Environmental health perspectives.

[32]  P. Chavez.  Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[33]  Peggy Reynolds,et al.  Residential proximity to agricultural pesticide applications and childhood acute lymphoblastic leukemia. , 2009, Environmental research.

[34]  A. J. W. De Wit,et al.  Efficiency and accuracy of per-field classification for operational crop mapping , 2004 .

[35]  A. A. Vassiliou,et al.  On the application of averaging median filters in remote sensing , 1988 .

[36]  Beate Ritz,et al.  Historical pesticide exposure in California using pesticide use reports and land-use surveys: an assessment of misclassification error and bias. , 2003, Environmental health perspectives.

[37]  Bai-lian Li,et al.  Characterizing urbanization, and agricultural and conservation land‐use change in Riverside County, California, USA , 2010, Annals of the New York Academy of Sciences.

[38]  Claire A. Franklin,et al.  Occupational and residential exposure assessment for pesticides , 2005 .

[39]  Pierre Goovaerts,et al.  Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research , 2010, Journal of Exposure Science and Environmental Epidemiology.

[40]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[41]  Rudolph P. Rull,et al.  Determinants of Agricultural Pesticide Concentrations in Carpet Dust , 2011, Environmental health perspectives.

[42]  R. Hill.  Image segmentation for humid tropical forest classification in Landsat TM data , 1999 .

[43]  Tim R. McVicar,et al.  Determining temporal windows for crop discrimination with remote sensing: a case study in south-eastern Australia , 2004 .

[44]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[45]  J. Harrison.  ‘Accidents’ and invisibilities: Scaled discourse and the naturalization of regulatory neglect in California's pesticide drift conflict , 2006 .

[46]  D. C. Watts.  LAND COVER MAPPING: , 2001 .

[47]  Rudolph P. Rull,et al.  Neural tube defects and maternal residential proximity to agricultural pesticide applications. , 2004, American journal of epidemiology.

[48]  J. Rosenheim,et al.  Cotton aphid emerges as major pest in SJV cotton. , 2000 .