REDD Deforestation Modeling Video

Clark Labs Releases New Video Demonstrating REDD Deforestation Modeling with Land Change Modeler in IDRISI Selva

This latest video, entitled REDD Deforestation Modeling with Land Change Modeler in IDRISI, introduces the new REDD modeling feature tool included in the latest release of IDRISI, the Selva Edition. This video demonstrates how to model baseline land cover change and to predict future scenarios, as well as how these scenarios will impact projected GHG emissions through the implementation of a REDD project.

A REDD project proposal, or the Project Design Document submitted to the Verified Carbon Standard (VCS) Organization, requires maps and statistics of GHG emissions, based on various scenarios and standards. The new REDD analysis tool within Land Change Modeler facilitates the creation of these VCS outputs at the specified reporting intervals.

This video, utilizing Madagascar data from Conservation International, highlights how to model land use change from forest to non-forest and create baseline prediction maps based on business-as-usual scenarios and calculate the amount of potential GHG emissions from such predicted forest loss. The Land Change Modeler software also allows you to quantify reductions in GHG emissions in both the project and leakage areas if a REDD project were to be implemented.

This video is one of several videos directly related to the widely-used Land Change Modeler application. IDRISI’s Land Change Modeler is becoming the de facto modeling software for developing REDD projects. This particular video follows on from the REDD Baseline Modeling video. It is recommended that you watch the other videos as well to gain a better appreciation of the complexities involved in modeling land use change.

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Land Change Modeler Video

Introduction to Land Change Modeler in IDRISI

Clark Labs has recently published a video to provide an introduction to the change analysis tools in the Land Change Modeler application within IDRISI. The Land Change Modeler allows the user to analyze, measure, and project the impacts of land change on habitat and biodiversity. This video explores tools for the rapid assessment of land change in both map and graphical forms.

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Clark Labs Publishes New Focus Paper on Modeling REDD Baselines using IDRISI's Land Change Modeler

Clark Labs has recently published a Focus Paper on modeling REDD baselines with the Land Change Modeler application in their IDRISI GIS and Image Processing software. Reducing Emissions from Deforestation and Forest Degradation (REDD) is a climate change mitigation strategy for the protection and maintenance of forests that has gained momentum with conservation organizations, project developers and governments in developing countries. As forests play a major role in the sequestration of carbon, especially tropical forests, REDD offers great potential for reducing greenhouse gas emissions. Carbon offset payments, based on an assigned financial value for the likely carbon storage capacity of protected or maintained forests, provide incentives for adopting such a strategy.

A REDD project is comprised of a number of significant components and requires the expertise and input of multiple parties. This paper discusses one of the required elements, the modeling of a baseline, a scenario of the likely greenhouse gas emissions if the project were not to be implemented. The modeling relies on land cover and land use change maps as its foundation. Baseline mapping includes determining historical deforestation rates and patterns, identifying the unique drivers and underlying factors of the area’s deforestation, and modeling potential scenarios.

The new Focus Paper provides a general overview, featuring a case study of a REDD project developed by the government of Madagascar, with assistance from Conservation International and the BioCarbon Fund, for the area along the Ankeniheny-Mantadia Corridor. The overall objectives for this REDD project were to reduce greenhouse gas emissions, increase protected areas, and reduce the loss of biodiversity by reconnecting forest fragments and reducing future forest fragmentation.

Download the Modeling REDD Baselines using IDRISI's Land Change Modeler Focus Paper.

Modeling Deforestation in the Bolivian Lowlands Species and Carbon Impacts - Carbon Impact

Carbon Impact

Projecting future land cover scenarios is critical also for assessing the impact on carbon mass. The traditional method has been to use a uniform carbon figure across a region (Figure 8). In our case, we have a uniform carbon figure for all forested areas per hectare and we can simply calculate the loss of carbon mass due to projected deforestation. However, using a proxy of forest land cover, we can refine this process by redistributing the gross carbon mass associated with all forested areas based on the forest’s NDVI (Figure 9) so that the intensity of the NDVI will be our spatial proxy, i.e., the higher the NDVI, the more carbon. Using the projected land cover map to 2015, we identified only the deforested areas and redistributed the gross carbon mass using the NDVI (Figure 10). Assuming business as usual, the table shows the potential additional loss of carbon mass using a non-uniform carbon mass surface (Figure 11). Using modeling tools such as GIS, we can present various scenarios with differing potential impacts for designing carbon offset projects while simultaneously managing biodiversity.

Modeling Deforestation in the Bolivian Lowlands Species and Carbon Impacts - Model Development

Model Development

Change modeling in LCM is empirically based (the empirical method chosen for this study is the multi-layer perceptron neural network), using areas of known transition from land cover maps along with user-specified explanatory variable maps that express the state of driving forces or the inherent suitability of land to undergo a specific transition. In this illustration, a total of 26 variables were used (Figure 1). The result is a transition potential map for each modeled land cover transition -- an expression of the readiness of land to undergo a transition in the next time step. These transition potential maps then serve as primary inputs to the prediction process, along with predicted quantities of change derived by Markov Chain Analysis. The specific allocation of change is then achieved by means of a multi-objective land competition process with intermediate recalculation of dynamic variables (such as proximity to disturbance) and associated transition potential maps.

In our case, forest change was modeled based on land cover maps from 1992 to 2001 (Figure 2). To assess the quality of the model, a prediction was made to 2004 for comparison with a known validation map. The resulting model attained a Pierce Skill Score of .61. Two types of maps were produced: a hard prediction map that expresses one change scenario (Figure 3) and a soft prediction map (Figure 4) which provides a comprehensive assessment of change potential, i.e., a map of vulnerability to change for the selected set of transitions. The soft prediction result therefore shows the potential that any given area may change at some future date and is very important for identifying areas of high risk: an important planning concern.

Once the model was calibrated, additional future scenarios were then produced. One such scenario was a prediction of the Bolivian Lowlands to the year 2015 (Figure 3) and the assessment of this scenario on endemic species habitat. Using collections of species range polygons, initially supplied by NatureServe and subsequently recalibrated using LCM, a species richness map was developed for all birds, mammals, and amphibians. The Bolivian Lowlands is home to 73 endemic species, 36 amphibians, 16 mammals, and 21 birds. This map was combined with the 2015 soft prediction map to derive a risk of biodiversity loss (Figure 5). We then applied the same process to all the endemic vertebrate species to derive a risk of endemic loss (Figure 6).

Assuming business as usual, the potential loss could be devastating to biodiversity in Bolivia. For the period we do know, from 2001 to 2004, 4% of current mammal habitat was lost (Figure 7). Tools such as these provide an important means to assist those engaged in important conservation practices and to prioritize threatened environments.

Modeling Deforestation in the Bolivian Lowlands Species and Carbon Impacts - Introduction

Introduction

With the importance of tropical forests for climate system function and biodiversity, land cover change in the Amazon has been the focus of intensive research. In Bolivia, the rate of deforestation is second only to that of Brazil. Due in part to a tremendous amount of economic growth in recent decades, the Bolivian Lowlands have seen forest loss of almost 3 million hectares between the years of 1992 and 2004, with two-thirds of this loss coming between 2001 and 2004 alone. With forests dominating over 50% of the land cover in lowland Bolivia, special planning tools are essential to manage, monitor and prioritize the risks associated with economic development and to assist the conservation community better understand the changes and impacts that are taking place. This poster demonstrates a set of techniques to assess land cover change, predict future scenarios, and assess the potential impacts of these scenarios on biodiversity and carbon loss. These techniques will be demonstrated using the tool Land Change Modeler (LCM) as found in the IDRISI GIS and Image Processing system.

Comparison of Two GIS-based Land Change Modules for Constructing REDD Baselines in Bolivia

AAG - 2010 Annual Meeting, Washington, DC

Comparison of Two GIS-based Land Change Modules for Constructing REDD Baselines in Bolivia

is part of the Paper Session:
Land Use and Spatial Analysis

scheduled on Sunday, 4/18/10 at 8:00 AM.

Author(s):
Oh Seok Kim* - Department of Geography, University of Southern California

Abstract:
With the increasing concerns in developing methodologies for Reducing Emissions from Deforestation and Degradation (REDD) projects, there is a need to understand the characteristics of existing Land-Use/Cover Change (LUCC) modules. This research presents a comparison of two existing approaches: GEOMOD Modeling (GM) and Land Change Modeler (LCM). The comparison uses data from a case study in Chiquitanía, Bolivia. Data from 1986 and 1994 are used to simulate land-cover of 2000; the resulting maps are compared to an observed land-cover map of 2000. GM and LCM simulate baseline deforestation at the pixel-level. The model structures of linear extrapolation and Markov chain are compared to review quantity of LUCC; and the model structures of empirical frequency, logistic regression and multilayer perceptron are compared to review allocation of LUCC. Relative operating characteristics, figure of merit and multiple resolution analysis are employed to assess predictive accuracy of multiple transition modeling. By design, GM lacks the potential to model multiple transitions, while LCM may produce different results for each simulation. Based on the model structure and predictive accuracy comparisons, the LCM's logistic regression seems the most suitable LUCC module to construct a REDD baseline in this case. However, it is crucial to emphasize that these results are strictly limited to this particular case. Thus, there is no obvious method that is most accurate, so if a REDD project employs predictive GIS-based LUCC modeling for its spatially-explicit baseline construction, it should include the framework employed in this research to establish the baseline in a scientific manner.