Monitoring and modeling changes of forest area using logistic regression-markov and geomod
Detecting land use, land cover changes and recognizing effective factors is necessary to prevent land use changes and better management. The aim of this study was detecting changes of Arasbaran forest cover in two periods of 12 years, modeling and predicting forest cover destruction in this region. At first, the multi temporal Landsat 5 images in 1990, ETM+ Landsat 7 in 2002 and OLI Landsat 8 in 2014 were provided and were classified in two categories including high dense forest, low dense forest. Forest changes were detected in three periods, 1990-2002, 2002-2014, and 1990-2014, also changes in forest cover were estimated in different classes of variables influencing changes. Forest area changes in the study period were modeled by logistic regression models and Geomod. In order to compare the performance of these two models in predicting land uses status by preparing maps in 2014 and validating by real map of that year. Results showed that in the period of 24 years, 992 and 1592 hectares of high and low dense forests were degraded during 1990-2014, respectively. The results of decreasing forest cover modeling showed that variables such as distances from roads and residential, elevation and slope has a direct relation with forest degradation. However, there is an inverse relation between forest degradation and distance from forest variables. The validation result of forest cover maps which is predicted in 2014 show total accuracy and kappa coefficient is 96.8 and 0.9342, for logistic regression map and 96.4 and 0.9269 for Geomod map respectively. These results indicated that model had a good performance in predicting of land use changes. Finally, using the logistic regression and Geomod, forest cover changes predicted for 2025. The result of predicting showed that the forest cover will degradeted 3.9% in the next 10 years.
Geographic Space, Volume:19 Issue:65, 2019
171 - 189
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