The Open Remote Sensing Journal


(Discontinued)

ISSN: 1875-4139 ― Volume 6, 2015

A Taxonomy of Unmixing Algorithms Using Li-Strahler Geometric-Optical Model and other Spectral Endmember Extraction Techniques for Decomposing a QuickBird Visible and Near Infra-Red Pixel of an Anopheles arabiensis Habitat


The Open Remote Sensing Journal, 2011, 4: 1-25

Benjamin G. Jacob, Joseph M. Mwangangi, Charles M. Mbogo, Robert J. Novak

School of Medicine, Division of Infectious Diseases, University of Alabama at Birmingham, 845 19th Street South, Birmingham, AL 35294-2170, USA

Electronic publication date 29/8/2011
[DOI: 10.2174/1875413901004010001]

Abstract:

Spectral unmixing algorithms have proliferated in a variety of ecological disciplines by exploiting remotelysensed data. However, in East African rice field agro-ecosystems, aquatic habitats of Anopheles arabiensis, a major vector of malaria in Sub-Saharan Africa (SSA), utilize semi-permanent to temporary habitats (e.g., floodwater areas, vernal pools, hoof prints) which pose a special problem in landscape studies, basically one of spatial scale. For example, low spatial resolution pixel sizes from satellite sensors are often too large for identification of productive riceland An. arabiensis habitats. In this research we spectrally decomposed a sub-meter spatial resolution (i.e., QuickBird) riceland An. arabiensis habitat pixel for predicting productive habitats in a riceland environment. Initially, we constructed a regression model which revealed that paddy preparation An. arabiensis habitats were the most productive based on spatiotemporal field-sampled count data. Individual pixel spectral reflectance estimates from a QuickBird visible and near-infra-red (NIR) at 0.61m spatial resolution data of a paddy preparation An. arabiensis habitat were then extracted by using a Li-Strahler geometric-optical model. The model used three scene components: sunlit canopy (C), sunlit background (G) and shadow (T) generated from the riceland image. The G, C, T components’ classes were estimated using ENVI, an objectbased classification algorithm. In ENVI®, the Digital Number (DN) of the pixel in every QuickBird band was viewed using the z-profile from a spectral library. After making an atmospheric correction from the image for the study site, the DN was converted into ground reflectance. A convex geometrical model was also used for endmember validation of the spectrally decomposed paddy preparation habitat. An ordinary kriged-based interpolation was performed in ArcGIS® Geostatistical Analyst using the reference signature generated from the unmixing models. Linear unbiased predictors and variance estimates were derived of all productive An. arabiensis habitats in the study site based on the extracted pixel endmember reflectance estimates. Spectral unmixing tools may be used to decompose QuickBird visible and NIR pixel reflectance of a productive An. arabiensis habitat. Thereafter, an ordinary interpolator can use the sub-pixel data along with other spatially continuous explanatory variables sampled from productive habitats for targeting other high density foci habitat sites which can help implement larval control strategies in a riceland environment.


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