alexa Unbiasing a Stochastic Endmember Interpolator Using ENVI Object-BasedClassifiers, a Farquhar's Single Voxel Leaf Photosynthetic ResponseExplanatory Model and Boolean Time Series Statistics for ForecastingShade-Canopied Simulium damnosum s.l. Larval Habitats in Burkina Faso | Abstract
ISSN: 2469-4134

Journal of Remote Sensing & GIS
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Research Article

Unbiasing a Stochastic Endmember Interpolator Using ENVI Object-BasedClassifiers, a Farquhar's Single Voxel Leaf Photosynthetic ResponseExplanatory Model and Boolean Time Series Statistics for ForecastingShade-Canopied Simulium damnosum s.l. Larval Habitats in Burkina Faso

Benjamin Jacob1*, Robert J Novak1, Laurent Toe2, Moussa S Sanfo2, Semiha Caliskhan1, Alain Pare3, Mounkaila Noma4, Laurent Yameogo4 and Thomas Unnasch1
1Global Infectious Disease Research Program, Department of Public Health, College of Public Health, University of South Florida, Tampa, Florida, USA
2Onchocerciasis Control Programme (OCP), Ouagadougou, Burkina Faso, West Africa
3Multi Disease Surveillance Centre, Ouagadougou, Burkina Faso, West Africa
4African Program for Onchocerciasis Control (APOC), Epidemiology and Vector Elimination, Ouagadougou, Burkina Faso, West Africa
Corresponding Author : Benjamin Jacob
Global Infectious Disease Research Program
Department of Public Health, College of Public Health
University of South Florida, Tampa, Florida, USA
Tel: (813) 974-2311
Fax: (813) 974-4718
E-mail: [email protected]
Received April 04, 2013; Accepted June 21, 2013; Published July 15, 2013
Citation: Jacob B, Novak RJ, Toe L, Sanfo MS, Caliskhan S, et al. (2013) Unbiasing a Stochastic Endmember Interpolator Using ENVI Object-Based Classifiers and Boolean Statistics for Forecasting Canopied Simulium damnosum s.l. Larval Habitats in Burkina Faso. J Geophys Remote Sensing 2:109. doi:10.4172/2169-0049.1000109
Copyright: © 2013 Jacob B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Endmember spectra recovered from sub-meter resolution data [e.g., Quick Bird visible and near infra-red (NIR) 0.61m wavebands ratio] of an arthropod-related infectious disease aquatic larval habitat can act as a dependent variable within a least squares estimation algorithm. By so doing, seasonal endemic transmission -oriented risk variables can be accurately interpolated. Spectral mixing, however, is a problem inherent to multi-dimensional canopy-oriented arthropod-related infectious disease larval habitat feature attributes resulting in few image sub-pixel spectra representing "pure" targets. This can lead to a biased endmember target signature due to spectrally unquantitated mixed sub-pixel radiance originating from different canopy-oriented larval habitat object types. An erroneous endmember larval habitat signature will render inconsistent residual forecasts in a stochastic/deterministic interpolator. In this analyses we spectrally extracted and decomposed multiple canopied endmembers surface-oriented sub-meter resolution pixel reflectance values derived from a georeferenced QuickBird imaged canopied larval habitat of Similium damnosum s.l. (Figure 1), a black fly vector of onchocerciasis in an epidemiological riverine study site in Burkina Faso. We employed ENVI object-based classifiers, a 3-Dimensional radiative transfer equation and the Li-Strahler geometric-optical model to perform the pixel decomposition. Thereafter, the georeferenced larval habitat and the within canopy radiance values (e.g., Precambrian rock) were spectrally isolated and weighed using a robust Successive Progression Algorithm (SPA) within a Boolean domain. The decomposed endmember then rendered a robust spectral signature in ArcGIS which was subsequently kriged to identify unknown, unsampled productive S. damnosum s.l. larval habitats along a Burkina Faso river system using a blind study format. The validation model revealed a 100% correlation among the predicted georeferenced productive black fly habitat sites based on the seasonal-sampled larval density counts values.

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