Spatio-temporal analysis and prediction of landscape patterns and change processes in the Central Zagros region, Iran

Abstract

Unfortunately in recent decades Central Zagros region has been changed adversely due to human activities. To analyze changes land cover maps produced using multi-temporal classification of Landsat satellite images of TM, ETM+ and OLI sensors, respectively for 1989, 2000 and 2013 years. Then derived maps used as input data in Land Change Modeler (LCM) to predict land cover changes on 2020, 2030, 2040 and 2050 based on a historical scenario. Results of classification and prediction were used to analyze landscape patterns and change processes. Five landscape pattern metrics as Normalized Entropy, Edge Density, Patch Area, Patch Compactness and Relative Richness were used to quantify landscape pattern and occurrence of ten landscape change processes as Attrition, Aggregation, Dissection, Creation, Shrinkage, Perforation, Shift, Deformation, Enlargement and Fragmentation were studied. Land cover maps showed great changes in central Zagros region in Lorestan province (Iran) whereas residential areas have destroyed about 5166.7 km2 of Zagros forests from 1989 to 2013. In this period residential areas are increased about 47.71 km2. Based on a “historical” scenario between 2013 up to 2050 about 4304.66 km2 of Zagros forests will lose and residential areas will increase about 244.51 km2 from. Results of landscape pattern analysis show increase of all studied landscape pattern metrics values; Normalized entropy from 0.001895 to 0.45236, Relative richness 11.12 to 22.89 percent, Edge density 0.002–0.44 m/ha, Patch area 92.04–193171.7 ha and Patch compactness 0.006 to 0.568 patches/100ha from 1989 to 2050. More over occurred change processes in different periods include: Shift, Enlargement, Attrition, Aggregation, Creation and Dissection. Results of this study present a clear perspective of future for managers and planners and can be used in natural resources management and environmental policy making, as well as creating mitigation measures in vulnerable areas.