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


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


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1

Department of Environmental Science, Policy and Management, University of California, Berkeley, 130 Mulford Hall #3114, Berkeley, CA 94720, USA

2

Geospatial Innovation Facility, College of Natural Resources, University of California, Berkeley, Berkeley, CA 94720, USA





*

Author to whom correspondence should be addressed.



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. View Full-Text

Keywords: object-based image analysis OBIA; vector-borne diseases; health exposures; image classification; fine spatial resolution imagery; topology object-based image analysis OBIA; vector-borne diseases; health exposures; image classification; fine spatial resolution imagery; topology





Author: Maggi Kelly 1,2,* , Samuel D. Blanchard 1, Ellen Kersten 1 and Kevin Koy 2

Source: http://mdpi.com/



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