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Ecological AnalysisIf we are to appreciate the diversity and interdependencies of our ecological systems and plan for a sustainable future, we need robust tools that allow us to inquire deeply into our environment. IDRISI includes a unique GIS modeling environment for evaluating the impacts of land use change on species habitat and biodiversity. The Land Change Modeler (LCM) is a vertical application that includes an integrated suite of tools for species habitat suitability and gap analysis, species distribution modeling, and biodiversity analysis. Land Change Modeler includes an interface to Marxan, a utility for reserve planning (available from the University of Queensland). Land Change Modeler is an excellent tool to incorporate in REDD (Reducing Emissions from Deforestation and Forest Degradation) and REDD+ projects. REDD+ expands the concept of REDD to include co-benefits such as conservation and the sustainable management of forests. Although fully integrated into IDRISI, Land Change Modeler is available also as an extension to ArcGIS. Application areas include:
Analytical Examples: [click images to enlarge] 
Land Change Modeler provides tools for habitat assessment. Areas are mapped into areas of primary and secondary habitat, primary and secondary corridor, and unsuitable lands based on land cover and habitat suitability. Parameters such as home range, size, buffer widths, and gap crossing distances are also used.
Land Change Modeler can model habitat suitability and species distribution using both expert knowledge and empirical data. Utilizing existing species range polygons, the distribution can be refined based on modeling from environmental variables.
Land Change Modeler has the facility to conduct biodiversity analysis. Five different measures of biodiversity can be mapped: alpha, gamma and beta diversity, Sorensen's dissimilarity index and an index of relative range restriction.
IDRISI has a variety of statistical tools for ecological modeling. Neural network analysis is used to model the presence of the gypsy moth pest in the uninfested portions of the United States using environmental driving variables.
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"Practical combination of integrated GIS, RS, and GPS technology at an affordable price" - David Green, Geography & Environment, University of Aberdeen
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