Focus Paper: Land Change Modeler for Ecological Sustainability
Land Change Modeler is a software solution that provides tools for the assessment and prediction of land change, as well as the impacts for habitat and biodiversity. This Focus Paper describes the modeling logic of these tools and presents the typical workflow.
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Focus Paper: Classification Tree Analysis
Classification Tree Analysis is a type of machine learning algorithm used for classifying remotely sensed and ancillary data in support of land cover mapping and analysis. This Focus Paper offers an explanation of this procedure and its implementation within IDRISI.
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Focus Paper: Segmentation and Segment-based Classification
Unlike traditional pixel-based classification methods, segment-based classification is an approach that classifies a remotely-sensed image based on image segments. Segmentation is the process of defining homogeneous pixels into these spectrally similar segments. This Focus Paper explores how this functionality is incorporated within IDRISI and outlines the workflow.
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GIS Helps to Improve the Quality of Brazilian Wine Development of GIS-Based Pest Detection Strategies and Mapping Subsequent Risk Clark Labs Assists Local Town in the Siting of Cell Towers with IDRISI Watershed Mapping and Land Cover Classification in Sedimentation Study Native Communities Use GIS for Nuclear Risk Management Sustainability Research in the Yucatan Peninsula Using Multi-Criteria Evaluation Tools for Sustainable Forest Management Canadian Wheat Board Monitors Crops with IDRISI Hyperspectral Imagery Studied for Indications of Emerald Ash Borer Infestation Application of Spatial Priors in the Maximum Likelihood Classification of Tropical Dry Forest Classes Analyzing Motion with Trend Surface Analysis 
