Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty.

Spectral unmixing is a common task in hyperspectral data analysis. In order to sufficiently spectrally unmix the data, three key steps must be accomplished: Estimate the number of endmembers (EMs), identify the EMs, and then unmix the data. Several different statistical and geometrical approaches have been developed for all steps of the unmixing process. However, many of these methods rely on using the full image to estimate the number and extract the EMs from the background data. In this paper, spectral unmixing is accomplished using a spatially adaptive approach. Linear unmixing is performed per pixel with EMs identified at the local level, but global abundance maps are created by clustering the locally determined EMs into common groups. Results show that the unmixing residual error of each pixel's spectrum from real data, estimated from the spatially adaptive methodology, is reduced when compared to a global scale EM estimation and linear unmixing methodology. The component algorithms of the new spatially adaptive approach, which complete the three key unmixing steps, can be interchanged while maintaining spatial information, making this new methodology modular. A final advantage of the spatially adaptive spectral unmixing methodology is the user-defined spatial scale size.

Spectral image complexity is an ill-defined term that has been addressed previously in terms of dimensionality, multivariate normality, and other approaches. Here, we apply the concept of the linear mixture model to the question of spectral image complexity at spatially local scales. Essentially, the "complexity" of an image region is related to the volume of a convex set enclosing the data in the spectral space. The volume is estimated as a function of increasing dimensionality (through the use of a set of endmembers describing the data cloud) using the Gram Matrix approach. It is hypothesized that more complex regions of the image are composed of multiple, diverse materials and will thus occupy a larger volume in the hyperspace. The ultimate application here is large area image search without a priori information regarding the target signature. Instead, image cues will be provided based on local, relative estimates of the image complexity. The technique used to estimate the spectral image complexity is described and results are shown for representative image chips and a large area flightline of reflective hyperspectral imagery. The extension to the problem of large area search will then be described and results are shown for a 4-band multispectral image.

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