Modeling REDD-baselines using IDRISI’s LCM - Step 4

Modeling Future Emissions

Clark Labs is designing a REDD tool within the Land Change Modeler module of IDRISI. This tool will guide the user through the steps of the baseline development and directly produce tables and graphics necessary for reporting. This will result in a tool that can greatly reduce the time and cost in the development of a deforestation baseline, which currently is delaying the finalization of project development for many prospective REDD projects. The project will also include case studies, testing of different REDD scenarios and production of user guidelines. The tool will be co-designed by the Center for Applied Biodiversity Science (CABS) at Conservation International and Clark Labs. CABS will provide all case study data for testing the tool.

Figure 4. The REDD tab facility for the extraction and calculation of future carbon emission for the project area.

The following parameters are required; the reference area, the project area and the leakage area, the starting and ending date of the project and the number of reporting intervals. The carbon pools as well as the sources of green house gases to be included in the project also must be specified. Average carbon density can be entered either as a constant for each landuse or as a continuous surface image of carbon.

This information combined with the predicted landuse for the REDD project generates a series of tables of future emissions. These emissions estimates must be included in the final project to be submitted to VCS for approval.

Modeling REDD-baselines using IDRISI’s LCM - Step 3

Predicting Change

The Change Prediction tab provides the controls for a dynamic landcover change prediction process. Using the transition potential maps created in the Transition Potentials tab and after specifying the end date, the quantity of change in each transition can be modeled. Both hard and soft outputs are provided.

Figure 3. Hard and soft prediction images output from the model with the transition probability matrix of change.

This step allows one to determine the amount of change that will occur to some point in the future using the Markov Chain prediction process or a user-specified model. The amount of change can be determined by the default procedure: Markov Chain. Using the earlier and later landcover maps along with the date specified, it determines exactly how much land would be expected to transition from the later date to the prediction date based on a projection of the transition potentials into the future and creates a transition probabilities file. The transition probabilities file is a matrix that records the probability that each landcover category will change to every other category. Alternately, you can specify a transition probability file from an external model.

The final stage is the allocation process in which the parameters for the prediction are set and run the process. Both hard and soft prediction maps can be produced. The hard prediction is based on a multi-objective land competition model. The soft prediction output is a continuous map of vulnerability to change for a selected set of transitions. The soft prediction model is generally preferred for habitat and biodiversity assessment since it provides a comprehensive assessment of change potential.

Modeling REDD-baselines using IDRISI’s LCM - Step 2

Modeling the Potential for Change

In the Land Change Modeler, change analysis and prediction are organized around a series of empirically evaluated transition sub-models. A transition sub-model can consist of a single landcover transition or a group of transitions that are thought to have the same underlying driver variables. All selected transition sub-models must be modeled before change prediction can be undertaken. The Transition Potentials tab allows one to group transitions between two landcover maps into a set of sub-models resulting in a transition potential map for each transition—an expression of time-specific potential for change. Transitions are modeled using either Logistic Regression or a Multi-Layer Perceptron (MLP) neural network.

Figure 2. Transition potential image derived from a set of driver variables such as slope, distance from roads, distance from towns, elevation, etc...

Driver variables can be added to the model either as static or dynamic components. Static variables express aspects of basic suitability for the transition under consideration, and are unchanging over time. Dynamic variables are time-dependent drivers such as proximity to existing development or infrastructure (roads) and are recalculated over time during the course of a prediction.

Once model variables have been selected here, each transition is modeled in the Run Transition Sub-Model panel using either Logistic Regression or the Multi-Layer Perceptron (MLP) neural network. The MLP neural network offers an automatic mode that requires no user intervention. At this stage samples are extracted from the two landcover maps of areas that underwent the transitions being modeled as well as the areas that were eligible to change, but did not. The result in either case is a transition potential map for each transition.

Modeling REDD-baselines using IDRISI’s LCM- Step 1

Analyzing Change

The first stage of a REDD project is the understanding of the historical trends in landuse change. The Change Analysis tab provides a set of 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. For the change and prediction analyses, a minimum requirement is the specification of two landcover maps that can be used as the basis of understanding the nature of change in the study region and the means of establishing samples of transitions that should be modeled. Other essential files associated with the study area are also specified: reference area, basis roads, and elevation.

Figure 1. Illustrates the historical pattern of change between three dates in time of the Ankeniheny-Mantadia Corridor in Madagascar.

The Change Analysis panel provides a series of very useful graphs of change between the two maps specified in the Project Parameters panel. One can view these graphs in a variety of units (cells, hectares, square kilometers, acres, square miles, % of change and % of area). This rapid assessment allows for an understanding of the dynamic of change among the different landuse categories of the two time periods.

With a greater understanding of the historical changes, the Change Maps panel provides the ability to create a spatial representation of the historical change, including maps of persistence, gains and losses, transitions and exchanges.

Modeling REDD-baselines using IDRISI's LCM - Introduction

Introduction

Initiatives to implement REDD at the site level require the development of a reference scenario of greenhouse gas emissions. This is an estimate of the probable emissions rate throughout a REDD project period if the project were not to be implemented. This must be calculated for the project area as well as the surrounding leakage area. During project implementation, the actual emissions are monitored in both areas and compared to the reference scenario emissions to calculate the creditable emissions reductions.

Several methods for estimating reference emissions levels have been proposed to the Voluntary Carbon Standards (VCS) group, and all require modeling future emissions based on historical trends in rates and relationships between deforestation patterns and drivers of deforestation. One of the first methodologies submitted to VCS for review is the Mosaic methodology, submitted by the World Bank BioCarbon Fund. As part of this submission, the application of this methodology for the Mantadia REDD site in Madagascar was provided by Conservation International (CI) and Clark Labs at Clark University.

The steps include:

1) Definition of the spatial and temporal boundaries of the project, including the project site and leakage area.

2) Estimation of historical deforestation rates and patterns over at least three time periods covering at least 10 years, the first period used to calibrate the spatial model and the second period to validate it.

3) Projection of the future deforestation rate for the study area, which may be different from the historical rate, given assumptions of population change and infrastructure development.

4) Spatial modeling to determine the patterns of the potential for deforestation, based on the relationships between historical deforestation patterns and spatial variables that characterize land access and suitability.

5) Spatial modeling of deforestation patterns for the validation period, based on (3) and (4).

6) A comparison of the estimated and actual deforestation rates inside vs outside of the project area, in order to validate the model’s ability to estimate the amount of deforestation that will occur inside the project site.

7) Spatial modeling of deforestation patterns for each reporting period throughout the project duration.

8) Combination of the deforestation projection and data on biomass to estimate GHG emissions over each reporting period, for comparison with actual emissions estimates to be produced during the project-monitoring phase of the project.