No abstract

Iran is mainly located in the arid and semiarid climate zone and seriously affected by desertification. This is a severe environmental problem, which results in a persistent loss of ecosystem services that are fundamental to sustaining life. Process understanding of this phenomenon through the evaluation of important drivers is, however, a challenging work. The main purpose of this study was to perform a quantitative evaluation of the current desertification status in the Segzi Plain, Isfahan Province, Iran, through the modified Mediterranean Desertification and Land Use (MEDALUS) model and GIS. In this regard, five main indicators including soil, groundwater, vegetation cover, climate, and erosion were selected for estimating the environmental sensitivity to desertification. Each of these qualitative indicators is driven by human interference and climate. After statistical analysis and a normality test for each indicator data, spatial distribution maps were established. Then, the maps were scored in the MEDALUS approach, and the current desertification status in the study area from the geometric mean of all five quality indicators was created. Based on the results of the modified MEDALUS model, about 23.5% of the total area can be classified as high risk to desertification and 76.5% classified as very high risk to desertification. The results indicate that climate, vegetation, and groundwater quality are the most important drivers for desertification in the study area. Erosion (wind and water) and soil indices have minimal importance.

Detection and prediction of changes in landscape, is necessary for the maintenance of an ecosystem, especially in developing countries with rapid changes and without planning. The object of this research, is monitoring landscape changes in past and it's simulation for future using Markov chain Consolidated and automated cells (CA-Markov) in arid and semi-arid region of Meymeh Dehloran, Ilam. Landsat satellite images of (TM) 1985, Landsat (TM) 2000 and Landsat (ETM +) 2016 were used. Change detection maps were prepared in seven classes of agriculture, Forest, fair range, poor range, rocky protrusions, residential land and salt land using supervised classification ARTMAP FUZZY neural network. Accuracy of the classification landscape maps for 1985, 2000 and 2016, are 93, 95 and 93 percent, respectively. Changes in landscape were predicted for 2030, using Markov chain model and automated cells. Predicted matrix results based on 2001 and 2016 maps showed that in span of 2016-2030, it is likely that 13% of agricultural land, 54% of Forest, 48% of the fair range, 82% of poor range, 55% of rocky protrusions, 52% of the residential land, 93% of salt lands and marsh land converted to other land uses. To validating the model, simulated landscape map of 2016, were compared with satellite image classification of the same year. Kappa coefficient was 87%, which shows the high capabilities of CA-Markov model to simulate landscape changes in arid and semi-arid region of Meymeh Dehloran.