Species distribution modeling has become an increasingly important technique in conservation planning. A model generates a representation of the relationship between the presence of a species and a set of environmental factors. It then extrapolates this information to create maps of habitat suitability or species' geographic ranges. This paper provides a general overview of this technique within IDRISI and features a case study of modeling the Brown-Throated Sloth.
Download the Species Distribution Modeling Focus Paper.
Reducing Emissions from Deforestation and Forest Degradation (REDD) is an incentive-driven climate change mitigation strategy for the protection and maintenance of forests, the conservation of which yields great potential for reducing greenhouse gas emissions. This paper provides an overview of the modeling of a REDD baseline--determining the historical deforestation rates and patterns as well as identifying the unique causes and agents of deforestation--utilizing a case study of an actual REDD project developed in Madagascar.
Download the Modeling REDD Baselines using IDRISI's Land Change Modeler Focus Paper.
Environmental image series provide a critically important resource for understanding both the dynamics and evolution of environmental phenomena. Earth Trends Modeler, a vertical application within the IDRISI software system, provides a wealth of tools for the analysis of trends and relationships evident in time series images. This paper explores several of the many data mining techniques, using for our example, monthly sea surface temperature data from 1982 to 2007.
Download the Image Time Series with Earth Trends Modeler Focus Paper.
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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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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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.
Download the Segmentation and Segment-Based Classification Focus Paper.
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