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.