Downscaling Pesticide Use Data to the Crop Field Level in California Using Landsat Satellite Imagery: Paraquat Case Study

Maxwell, Susan

doi:10.3390/rs3091805

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…The ultimate goal of this study was to incorporate the abundance results for deriving models along with human vulnerability data to quantify the risk of disease transmission from mosquitoes. By identifying high-risk areas in advance, environmental and public health officials can improve the efficacy of disease prevention measures and also minimize pesticide usage by integrated management using satellite data such as Landsat [26]. Specifically, public health and emergency managers can target where to implement surveillance, early-warning systems, abatement, and educational programs.…”

Section: Discussionmentioning

confidence: 99%

Remote Sensing and Modeling of Mosquito Abundance and Habitats in Coastal Virginia, USA

Cleckner

Allen

Bellows³

2011

Remote Sensing

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Section: Discussionmentioning

confidence: 99%

Remote Sensing and Modeling of Mosquito Abundance and Habitats in Coastal Virginia, USA

Cleckner

Allen

Bellows³

2011

Remote Sensing

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“…That positional uncertainty can have an impact on exposure assignment has been demonstrated for air pollutants associated with proximity to roads, where distance of place-of-residence to road centerlines determines exposure assignment (Whitsel, Quilbrera et al 2006; Zandbergen 2007); for exposure to agricultural herbicides and pesticides, where exposures are aggregated to spatially coarse resolutions such as the section level (1 square mile) and exposure is imputed at the place-of-residence (Ward, Nuckols et al 2005; Maxwell 2011); and for drinking water contaminated by perfluorooctanoate (PFOA) as supplied to homes (Vieira, Howard et al 2010), among others.…”

Section: Impacts Of Geocoding Positional Error In Health Analysismentioning

confidence: 99%

A research agenda: Does geocoding positional error matter in health GIS studies?

Jacquez

2012

Spatial and Spatio-temporal Epidemiology

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Until recently, little attention has been paid to geocoding positional accuracy and its impacts on accessibility measures; estimates of disease rates; findings of disease clustering; spatial prediction and modeling of health outcomes; and estimates of individual exposures based on geographic proximity to pollutant and pathogen sources. It is now clear that positional errors can result in flawed findings and poor public health decisions. Yet the current state-of-practice is to ignore geocoding positional uncertainty, primarily because of a lack of theory, methods and tools for quantifying, modeling, and adjusting for geocoding positional errors in health analysis. This paper proposes a research agenda to address this need. It summarizes the basics of the geocoding process, its assumptions, and empirical evidence describing the magnitude of geocoding positional error. An overview of the impacts of positional error in health analysis, including accessibility, disease clustering, exposure reconstruction, and spatial weights estimation is presented. The proposed research agenda addresses five key needs: 1) A lack of standardized, open-access geocoding resources for use in health research; 2)A lack of geocoding validation datasets that will allow the evaluation of alternative geocoding engines and procedures; 3) A lack of spatially explicit geocoding positional error models; 4)A lack of resources for assessing the sensitivity of spatial analysis results to geocoding positional error; 5)A lack of demonstration studies that illustrate the sensitivity of health policy decisions to geocoding positional error.

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“…Maxwell, Airola, and Nuckols (2010) demonstrated how Landsat imagery of California could be used to downscale the identification of PUR pesticide-treated crop fields below the LUS level (minimum mapping unit 0.008 km 2 ) (Nuckols et al, 2007). Normalized Difference Vegetation Index (NDVI) values, a measure of vegetative density, were used to classify imagery into crop fields via a minimum distance method, and when used in conjunction with PLSS sections, can identify probable crops treated with pesticides (Maxwell, 2011). …”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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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.

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Downscaling Pesticide Use Data to the Crop Field Level in California Using Landsat Satellite Imagery: Paraquat Case Study

Cited by 8 publications

References 23 publications

Remote Sensing and Modeling of Mosquito Abundance and Habitats in Coastal Virginia, USA

Remote Sensing and Modeling of Mosquito Abundance and Habitats in Coastal Virginia, USA

A research agenda: Does geocoding positional error matter in health GIS studies?

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

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