Generating land cover boundaries from remotely sensed data using object-based image analysis: Overview and epidemiological application

Maxwell, Susan

doi:10.1016/j.sste.2010.09.005

Cited by 14 publications

(18 citation statements)

References 34 publications

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“…In general, the availability of highresolution satellite images offer a good potential to derive appropriate land use predictors when readily available GIS data are lacking. The ease of use of GIS and remote sensing tools, as has been shown by other researchers (Maxwell, 2010;Mao et al, 2012;Allen et al, 2013), further facilitates the exploitation of satellite imagery in air pollution modelling and exposure assessments in low income countries.…”

Section: Generating Geographic Information System Datamentioning

confidence: 89%

Spatial air pollution modelling for a West-African town

Gebreab¹,

Vienneau²,

Feigenwinter³

et al. 2015

Geospat Health

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Land use regression (LUR) modelling is a common approach used in European and Northern American epidemiological studies to assess urban and traffic related air pollution exposures. Studies applying LUR in Africa are lacking. A need exists to understand if this approach holds for an African setting, where urban features, pollutant exposures and data availability differ considerably from other continents. We developed a parsimonious regression model based on 48-hour nitrogen dioxide (NO2) concentrations measured at 40 sites in Kaédi, a medium sized West-African town, and variables generated in a geographic information system (GIS). Road variables and settlement land use characteristics were found to be important predictors of 48-hour NO2 concentration in the model. About 68% of concentration variability in the town was explained by the model. The model was internally validated by leave-one-out cross-validation and it was found to perform moderately well. Furthermore, its parameters were robust to sampling variation. We applied the model at 100 m pixels to create a map describing the broad spatial pattern of NO2 across Kaédi. In this research, we demonstrated the potential for LUR as a valid, cost-effective approach for air pollution modelling and mapping in an African town. If the methodology were to be adopted by environmental and public health authorities in these regions, it could provide a quick assessment of the local air pollution burden and potentially support air pollution policies and guidelines.

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Section: Generating Geographic Information System Datamentioning

confidence: 89%

Spatial air pollution modelling for a West-African town

Gebreab¹,

Vienneau²,

Feigenwinter³

et al. 2015

Geospat Health

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“…A range of segmentation parameters were evaluated in an attempt to automate the generation of field boundaries. The process resulted in either too many boundaries (sub-field polygons) or not enough boundaries (multiple fields within one polygon) which has been found in other studies [29,30]. All of the case studies required manual editing to add or delete vector lines which, although was a fairly quick process for this limited number of Sections, the process could take a significant amount of time in a large study spanning hundreds of Sections and multiple years.…”

Section: Discussionmentioning

confidence: 90%

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

Maxwell

2011

Remote Sensing

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Exposure to pesticides has been associated with increased risk of many adverse health effects. To understand the relationships between pesticide exposure and health outcomes, epidemiologists need information on where pesticides are applied in the environment. California maintains one of the most comprehensive pesticide use reporting systems in the world, yet the data are only recorded at a coarse geographic scale of approximately 2.6 km 2 area. A method is presented that uses Landsat image time series to downscale California pesticide use data to the crop field-level. The approach is demonstrated using paraquat applied to vineyard and cotton fields.

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“…Despite some skepticism (e.g., [14]) this is in large a positive trend, allowing for the examination of broad scale patterns in landscapes that may lead to or prevent spread of disease [15], delineation of habitat patches and refugia for disease vectors or their hosts [16][17][18], and the measurement of environmental and biophysical variables (e.g., temperature, amount and health of vegetation) for use in process-based models that capture human-animal disease dynamics and predict disease risk [18][19][20]. Remote sensing has been used to target sampling efforts and health interventions [21], and in the analysis of chemical and other exposures [16,22].…”

Section: Introductionmentioning

confidence: 99%

“…which vary over space, and they are commonly used in computational spatial models that are raster based and require control over cell size [28]. They are large-scale, economical, and anonymous [22], and they are in widespread use across environmental science, and are often included in epidemiological models [19,29].…”

Section: Introductionmentioning

confidence: 99%

“…Yet compared to the environmental sciences, there are relatively few examples of research in public health that use these OBIA approaches. One of the more recent exceptions is Maxwell [22], who provides a brief review of the use of remote sensing to map discrete landscape patches that can indirectly influence human health-either through the provision of habitat for a disease vector, or through a harmful land use practice-and a demonstration of OBIA to more accurately map land cover features at multiple scales over time to understand pesticide drift from agricultural fields. There are other examples (discussed below), but their relative paucity is interesting, since some of the key algorithms commonly used in geographic OBIA were developed for medical and health related applications.…”

Section: Introductionmentioning

confidence: 99%

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Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis

et al. 2011

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Abstract:The benefits of terrestrial remote sensing in the environmental sciences are clear across a range of applications, and increasingly remote sensing analyses are being integrated into public health research. This integration has largely been in two areas: first, through the inclusion of continuous remote sensing products such as normalized difference vegetation index (NDVI) or moisture indices to answer large-area questions associated with the epidemiology of vector-borne diseases or other health exposures; and second, through image classification to map discrete landscape patches that provide habitat to disease-vectors or that promote poor health. In this second arena, new improvements in object-based image analysis (or "OBIA") can provide advantages for public health research. Rather than classifying each pixel based on its spectral content alone, the OBIA approach first segments an image into objects, or segments, based on spatially connected pixels with similar spectral properties, and then these objects are classified based on their spectral, spatial and contextual attributes as well as by their interrelations across scales. The approach can lead to increases in classification accuracy, and it can also develop multi-scale topologies between objects that can be utilized to help understand human-disease-health systems. This paper provides a brief review of what has been done in the public health literature with continuous and discrete mapping, and then highlights the key concepts in OBIA that could be more of use to public health researchers interested in integrating remote sensing into their work.

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Generating land cover boundaries from remotely sensed data using object-based image analysis: Overview and epidemiological application

Cited by 14 publications

References 34 publications

Spatial air pollution modelling for a West-African town

Spatial air pollution modelling for a West-African town

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

Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis

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