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

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.

A Negative Binomial with a Non-Homogenous Gamma Distributed Mean for Robustifying Pseudo R2 Regression Values of Immature Vector and Nuisance Mosquito Count Data for Optimally Discerning Un-Geosampled Waste Tires in a Subtropical Oviposition Site in SAS®/GIS Utsing Worldview-3 Visble and Near Infra-Red Data in Hillsborough County, Florida

“…In areas where vegetative cover is low (i.e., <40%) and the soil surface is exposed, the reflectance of light in the red and nearinfrared spectra can influence vegetation index values [6]. In previous research, Jacob et al [12].…”

“…End member signature estimates, which are subpixel spectral surface radiance generated from a georeferenced unit habitat [5], were geosampled following interactive supervised image classification in ArcGIS, whereby homogenous waste tire spectral training samples were polygonised, merged, and their Red, Green and Blue band wavelength estimates generated ( Figure 5). Prior to this probabilistic outcome, the remote sensor data was subdivided into two major phases: calibration, in which the algorithm identified a classification scheme based on signatures of different bands obtained from known training sites with known class labels; and prediction, in which the classification algorithm based on a priori probability file in ASCII format or training samples was applied to find other imaged sites with unknown signature classification membership based on known, sampled signatures [6].…”

“…The reference biosignatures of these habitats generated from the unmixing algorithmic geomteri-optical models were then used to perform an ordinary krig-based interpolation in ArcGIS ® [5]. Likewise, Jacob et al [6] geo-predicted seasonal trailing vegation, discontinuous, infrequently canopied, turbid water, seasonal black -fly vector of onchocercisasis, Simulium damnosum s. l. (Diptera: Simuliidae) by extracting spectral end members of canopy shaded riverine sites featuring black Precambrian rock from QuickBird imagery [6]. The end members were decomposed to orthogonal eigenevctors render a specified graphical…”

Ecogeographically and Non-Ecogeographically Forecasting Discontinuous Canopied Simulium damnosum s.l. Habitats by Interpolating Metrizable Sub-Mixel Mean Solar Exoatmospheric Quantum Scalar Irradiance where θi is a Zenith Angle and Diatomically Etiolated Xanthophylls with Azimutually Isotropic Sources of Chloroplastic Carotenoid Zeaxanthins Spectrally Extracted from a Decomposed RapidEye™ Red Edge Normalized Difference Vegetation Index Reference Targeted Biosignature: A Case Study in Burkina Faso and Ugand

“…Geospectrally decomposable, QuickBird, visible and near infrared(NIR) (www.digitalglobe.com), sub-meter (m) resolution (i.e., 0.61m), sub-mixel (i.e., mixed pixel), endmember (i.e., reference biosignature) fractions of incident radiation reflected, transmitted and absorbed by prolific, georeferenced, canopied, larval habitats of Similium damnosum s.l., a black fly vector of onchocerciasis, is crucial in implementing control strategies in African riverine environments [1]. Onchocerciasis is a parasitic disease caused by the filarial worm Onchocerca volvulus which is transmitted through the bites of infected blackflies of Simulium species (http://www.who.int/topics/ onchocerciasis).…”

“…Remote sensing LULC imagery for vector entomological investigations (e.g., vulnearbility, mapping hyperendemic and mesoendemic transmission zones) necessitates the captured wavelenght, emissivity data to be converted into tangible georeferncable field explanatorial information material, the optical spectrum is a plot of fractionalized reflectance, emissivity transmittance, log-transformed as a function of the incident wavelength which makes it possible to identify different canopied, land use land cover (LULC geo-classified), classes and separate them by their endmember, spectral curves (http://fas.org/irp/imint/docs/rst/). Disturbances in empirically, decomposable, ecogeographical, processes or, time series dependent, vegetated canopied, LULC-oriented, biophysical attributes, for example of imaged, sub-meter resolution, shaded and sparsely-shaded regressively quantitatively, delineated, georeferncable, seasonally geosampled, S. damnosum s.l., riverine, larval habitat, covariate, parameter estimators (e.g., refractive fraction of leaf weight, leaf-and plant constituent spectra) can alter radiative interactions with the surface and, thus the amount of radiation-related, wavelenght, emissivities and transmittance received by a remote sensing detector [1,2]. The ability to unambiguously interpret time series, probabilistically regressed, geoclassified, vegetation-related, LULC, canopied, endmember, emissivity wavelenght derivative spectra and unmixed, decomposable, biogeochemical, photosynthetic, radiance estimates (e.g., foliar lignin) for a geosampled, georeferencable, shaded or sparsely-shaded, prolific, S. damnosum s.l., larval habitat may hinge directly on the ability to resolve the multitude of remotely sensed, riverine, ecohydrological, erroneous, wavelenght, transmittance cofactors (e.g atmospheric correction of at-sensor radiances and the consequent, uncertainties in a retrieved turbidity-related, reflectance, emissivity, parameterized estimator) in an ArcGIS cyberenvironment, simulids constitute important components of riverine ecosystems and breed in fast flowing highly oxygenated water [1].…”