Land Change Modeler Product Features

Land Change Modeler is an innovative land planning and decision support software tool. Widely used for the prioritization of conservation and planning efforts, Land Change Modeler allows you to rapidly analyze land cover change, simulate future land change scenarios, model REDD emission scenarios, and model species impacts and biodiversity.

With an automated, user-friendly workflow, Land Change Modeler simplifies the complexities of change analysis, resource management and habitat assessment. Fully integrated within the IDRISI GIS and Image Processing system, Land Change Modeler provides a start-to-finish solution for your land change analysis needs. Land Change Modeler is also available separately as an extension to ESRI's ArcGIS.

Learn more about Land Change Modeler 


Land Change Modeler provides a set of tools for the rapid assessment and mapping of change, allowing for one-click evaluations of land cover gains and losses, net change and persistence, both in map and graphical form.

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Land Change Modeler Key Features

Land Change Analysis

  • Quickly generate graphs and maps of land change, including gains and losses, net change, persistence and specific transitions. 
  • Uncover underlying trends of complex land change with a change abstraction tool.

Land Transition Potential Modeling

  • Model land cover transition potentials that express the likelihood that land will transition in the future using one of three methodologies—a multi-layer perceptron neural network with full reporting on the explanatory power of driver variables, logistic regression, and SimWeight, a modified machine-learning procedure.
  • Incorporate into the prediction model dynamic or static environmental variable maps that might drive or explain change.

Change Prediction

  • Incorporate planning interventions, incentives and constraints, such as reserve areas and infrastructural changes that may alter the course of development in the change prediction process. 
  • Conduct scenario mapping by creating either a hard prediction map based on a multi-objective land competition model with a single realization or a soft prediction map that is a continuous map of vulnerability to change.
  • Validate the quality of the prediction land cover map in relation to a map of reality. Through a 3-way crosstabulation, hits, misses, and false alarms are reported.

REDD Analysis

  • Evaluate REDD related forest conservation strategies and carbon impact scenarios with full GHG emission impact accounting.
  • Assess additionality of REDD projects and business-as-usual projection scenarios.

Impact Analysis

  • Assess the effect of land cover change on habitat including habitat status and assessment, habitat change analysis, gap analysis, and landscape pattern analysis.
  • Perform species distribution modeling and the refinement of species range polygon maps. An interface to Maxent is available.
  • Calculate biodiversity through mapping of alpha diversity, gamma diversity, beta diversity, Sorensen's dissimilarity, and range restriction.

Land Planning

  • Generate biological corridor designs that are optimized for habitat suitability, ecological significance and protection status.
  • Develop reserve selection and design scenarios using an interface to MARXAN, a conservation planning tool.

Analytical Examples:

Habitat Assessment Land Change Modeler

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.

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An example of the Maxent interface in LCM and its output. In this example, the range of Dromiciops gliroides (Monito del Monte) is modeled based on a collection of observation points (shown as points) and a set of nine environmental variables, such as annual mean temperature and precipitation. This new Maxent option extends the existing group of species distribution modeling tools such as the Multi-Layer Perceptron neural network, Mahalanobis Typicalities and Logistic Regression.

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The REDD tab in LCM provides a full accounting of CO2 and non-CO2 baseline emissions for a REDD project area and the reductions that would be expected as a result of REDD project activities. A set of 19 tables are generated following the logic of the World Bank’s BioCarbon Fund methodology.

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