Research Article Open Access Jacob et al., J Geophys Remote Sens 2013, 2:1 DOI:10.4172/2169-0049.1000109 Volume 2 • Issue 1 • 1000109 J Geophys Remote Sensing ISSN: 2169-0049 JGRS, an open access journal Keywords: Endmembers; Mixels; Successive progression algorithm; Quick bird; Similium damnsoum s.l.; Burkina Faso Introduction Onchocerciasis, or River Blindness, is a neglected tropical disease caused by the parasitic worm Onchocerca volvulus. Estimates project 37 million individuals worldwide as at risk for O. volvulus infection, with most residing in rural Africa. The disease is transmitted through repeated bites by black flies of the genus Simulium. It is called “River Blindness” because the black fly that transmits the infection lives near fast-flowing streams and rivers, and the infection can result in blindness. In addition to visual impairment or blindness, onchocerciasis causes various skin diseases, including nodules under the skin or debilitating itching. Worldwide onchocerciasis is second only to trachoma as an infectious cause of blindness [1]. Various control programs were employed to stop onchocerciasis from continuing as a public health problem. The first was the Onchocerciasis Control Programme (OCP), which launched in 1974. At its peak, it covered 30 million people in 11 countries through the use of larvicide spraying of fast-flowing rivers to control black fly populations. Treatment and control of onchocerciasis as a public health problem was revolutionized in 1988 by the discovery that Mectizan TM (ivermectin) had a potent effect on the larval stages of O. volvulus. It was initially believed that Mectizan TM distribution alone could not successfully eliminate onchocerciasis in Africa, due to the widespread distribution of the infection and the intensity of transmission. However, recent data suggests that this is not the case, and that long term community wide distribution of Mectizan TM may be capable of eliminating onchocerciasis in at least some foci in Africa. This discovery has resulted in a re-focusing of the international community from an emphasis on control of onchocerciasis in Africa towards an emphasis upon possible elimination. Unfortunately, endemic riverine communities are currently identified through ground-based epidemiological surveys. These can be difficult to conduct in remote and conflict-ridden regions of Africa, such as in Southern Sudan and the Democratic Republic of Congo. Methods to identify at-risk riverine communities from the spatial aggregation of georeferenced prolific habitats are based on field- sampled density count values for delineating seasonal endemic onchocerciasis transmission zones. Ground-based epidemiological surveys, that overcome the time consuming, labor intensive and costly characteristics of said surveys, are urgently needed. *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: bjacob1@health.usf.edu Received April 04, 2013; Accepted June 21, 2013; Published July 15, 2013 Citation: Jacob B, Novak RJ, Toe L, Sanfo MS, Caliskan 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., QuickBird visible and near infra-red (NIR) 0.61m waveband ratio] of an arthropod-related infectious disease aquatic larval habitat can act as a dependent variable within a least squares estimation algorithm. Consequently, 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 (i.e., mixel) radiance originating from different canopy-oriented larval habitat object types. An erroneous endmember sub-mixel larval habitat signature renders inconsistent residual forecasts in a stochastic/ deterministic interpolator. In these 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., 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 decomposition. Thereafter, the georeferenced larval habitat and the 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 endmembers rendered a robust spectral signature in ArcGIS which 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 count values. Unbiasing a Stochastic Endmember Interpolator Using ENVI Object-Based Classifiers, a Farquhar's Single Voxel Leaf Photosynthetic Response Explanatory Model and Boolean Time Series Statistics for Forecasting Shade-Canopied Simulium damnosum s.l. Larval Habitats in Burkina Faso Benjamin Jacob 1 *, Robert J Novak 1 , Laurent Toe 2 , Moussa S Sanfo 2 , Semiha Caliskan 1 , Alain Pare 3 , Mounkaila Noma 4 , Laurent Yameogo 4 and Thomas Unnasch 1 1 Global Infectious Disease Research Program, Department of Public Health, College of Public Health, University of South Florida, Tampa, Florida, USA 2 Onchocerciasis Control Programme (OCP), Ouagadougou, Burkina Faso, West Africa 3 Multi Disease Surveillance Centre, Ouagadougou, Burkina Faso, West Africa 4 African Program for Onchocerciasis Control (APOC), Epidemiology and Vector Elimination, Ouagadougou, Burkina Faso, West Africa J o u r n a l o f R e m e o t S e n s i n g & G I S ISSN: 2469-4134 Journal of Remote Sensing & GIS