MODEL OF LAND COVER CHANGE PREDICTION USING COMBINATION OF BINARY LOGISTIC REGRESSION (BLR) AND CELLULAR AUTOMATA-MARKOV CHAIN (CA-MC) BASED ON ACCESSIBILITY (CASE STUDY: WEST JAVA)

摘要

In an effort to understand the phenomenon of land cover changes, can be approached through land cover change modeling. One model that was developed is Binary Logistic Regression (BLR). Based on the ability, this model can simulated the prediction of land cover changes that occur in a region by considering the parameters that represent the characteristics of the studied area, in this case represented by accessibility. BLR models limitation will be solved by doing a combination with Cellular Automata- Markov Chain (CA-MC). CA-MC is a model that used to determine the statistical change probabilistic for each of land cover type from land cover data at different time periods. CA-MC is able to provide the output of land cover type that should occurred. Results from a combination of BLR models and CA-MC in predicting land cover changes showed an accuracy rate of 95.42%.