REDD - Reducing Emissions from Deforestation and Degradation

Reducing Emissions from Deforestation and Forest Degradation (REDD) is a climate change mitigation strategy employed by conservation organizations, project developers and governments in developing countries. The intent of REDD is the conservation of forests to sequester carbon and reduce climate change impacts.

A key task of a REDD project is the determination of historical deforestation rates and patterns, as well the proximate causes and underlying forces of such deforestation. This baseline mapping is critical for the identification of future REDD project scenarios to conserve carbon.

Clark Labs provides purpose-built software as well as consulting and advisory services to REDD practitioners that address conservation strategies such as forest protection (e.g., protecting rainforest areas and infrastructure management) or sustainable forest production (e.g., agroforestry projects).

The IDRISI Taiga software includes a complete land analysis toolkit, compatible with international requirements, for mapping historical baselines and modeling future scenarios. Its integrated and comprehensive features allow you to process your satellite data for the land cover mapping component as well as manage and conserve forest carbon, biodiversity, and related ecosystem services. The participation of local stakeholders is also possible since IDRISI includes a unique suite of decision support tools. 

Application areas include:

  • Land cover mapping and forest monitoring
  • Business-as-usual baseline projection
  • Projection and validation of future deforestation scenarios
  • Leakage zone assessment
  • REDD forest carbon accounting
  • Modeling of biodiversity and habitat co-benefit analysis
  • Modeling ecosystem services impacts
  • Climate trend assessment

Learn more about how IDRISI Taiga and its Land Change Modeler application can be utilized to assess, measure and predict land cover change for REDD projects.

IDRISI Taiga GIS and Image Processing Brochure

REDD Project Services

  • Consulting and project planning
  • Training and capacity building
  • Leakage zone assessment
  • REDD rapid assessment
  • Custom software development

Contact Stefano Crema at screma@clarku.edu for more information on how Clark Labs can assist you.

 
REDD Land Change Analysis in Madagascar

The first stage of a REDD project is the understanding of the historical trends in land use change. The Change Analysis tab in Land Change Modeler provides tools for the rapid assessment of change, allowing one to generate a series of evaluations of gains and losses, net change, persistence and specific transitions both in map and graphical form. This figure shows the historical pattern of change in the Ankeniheny-Mantadia Corridor in Madagascar.
 

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REDD Transition Potential Modeling and Driver Variable Selection

An important element for predicting future scenarios is understanding the underlying driving forces of change. The transition potential modeling facility in Land Change Modeler allows users to derive transition potential maps based on a set of driver variables, such as: slope, distance from roads, distance from markets, elevation, etc... These variables can be static or dynamic.
 

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REDD Land Change Prediction and Scenario Modeling

Projecting future scenarios is crucial to understanding the potential impacts of proposed project interventions. Based on the historical patterns of change and identified driver variables, users can engage in scenario modeling of future outcomes and assess the impacts of these predictions as they pertain to REDD projects. These modeled impacts include not only forest and carbon assessments, but also impacts on biodiversity and ecosystem services. 

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GEOMOD Land Change Simulation with IDRISI Taiga

GEOMOD is a land change simulation modeling tool. It predicts the locations where land is likely to change from one category to another. In combination with the IDRISI statistical modules, such as ROC and VALIDATE, these tools allow users to measure the certainty of scenarios of future land change.

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