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.
Highlights
The synoptic, multi-spectral and multi-temporal coverage provided by terrestrial remote sensing programs has been a boon to a range of environmental science applications, including terrestrial ecology [1], ecosystem characterization [2], disturbance [3,4,5], land use and land cover change [6,7,8], and biogeochemical cycling and ecosystem functioning [9,10,11], to name a few important avenues of research
It is illuminating to discuss the contributions of remote sensing to the field of public health in light of two recent parallel trends in remote sensing: the first is the resurgence in production and use of field-based products best exemplified by normalized difference vegetation index (NDVI) [23,24], and by recent additions such as continuous percentage of tree cover (e.g., [25]) or continuous percentage of impervious surface cover [26,27]
We covered the use of continuous products such as NDVI in modeling risk and spread of vector-borne diseases around the world and obesity in urban environments
Summary
The synoptic, multi-spectral and multi-temporal coverage provided by terrestrial remote sensing programs has been a boon to a range of environmental science applications, including terrestrial ecology [1], ecosystem characterization [2], disturbance [3,4,5], land use and land cover change [6,7,8], and biogeochemical cycling and ecosystem functioning [9,10,11], to name a few important avenues of research. These field-based, continuous data products derived from remotely sensed imagery are very useful in ecology: they capture inherent spatial gradients in the target being mapped (vegetation vigor, soil moisture, etc.) 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]. This review positions OBIA for public health applications in a wider technological context than do other recent review papers on public health and OBIA (e.g., [22])
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