Assessing land degradation with predicting land cover and climatic parameters changes in coastal areas of southern Iran using Ca-Markov model

Land use changes are identified as main driver for global changes with considerable effects on the ecosystems, climate and earth planet. At local to global scales, land resources have effective role on the humanity subsistence and ecosystem performance and its health. Changing in land use, degradation of vegetation and habitats are main causes for biodiversity destruction. Such changes are created by natural factors and anthropogenic activities (deforestation, agricultural unsuitable activities, overgrazing, etc.) resulting in land degradation. Rainfall and temperature are the two most important climatic variables which they use to climatic zonation and classification, and these variables are effective factors on the human, natural, economic and agricultural activities. Regarding to great area of the coastal region in Iran, in order to assessing land degradation and desertification, monitoring effective cover, land use and climatic variables is vital, and therefore understanding relationship between land use changes and driver factors are essential.

In this research, we studied trend of land use changes in the coastal regions of southern Iran involving three provinces (Hormozgan, Sistan and Baluchestan and Busher) during period 1998-2019 using Landsat images (OLI and TM sensors). Likelihood maximum method applied to classifying satellite images and then SAVI, as a vegetation cover index, used to show vegetation dynamic and exploring land degradation and desertification trend. In order to map temperature and precipitation, we applied inverse distance weighting (IDW). For predicting land use changes, SAVI, temperature and precipitation, we used CA-Markov automat cell.

Studying land use change trend in the coastal regions of the southern Iran during 1998-2019 indicates Salt land and man-made classes were increased 8.6 and 1.2%, and among all classes these two classes show the most changes, whereas a decreasing trend observed in the vegetation cover (with – 3.5%) and sand dunes (with -6%) classes, and also, a decreasing trend observed in water bodies. Based on the results, an increasing trend (with 0.8%) observed in the class 0-0.2% for SAVI, whereas we found a decreasing trend in the 0.2-0.4% class. According to results, a decreasing trend -0.7 and -8.6% were observed in the < 25.5 C and > 28.5 C classes, whereas 27-28.5% class shows an increasing trend with 9.7%. Studying precipitation changes showed that < 150 mm and 250-300 mm classes show an increasing and decreasing trend, respectively. Based on the predictions for 2039, three classes including salt land, man-made region and vegetation cover land uses show an increasing trend with 0.4%, 1.5% and 0.99% respectively. We observed a decreasing trend with -6.8% in the sand dune land use whereas water bodies have constant trend. Based on the forecasting results for SAVI, we observed an increasing trend 1.8% for 0.2-0.4% class and observed a decreasing trend (-1.7%) in the 0.4-0.6% class. Results showed that 25.5-27 C and 27-28.5 C classes experience an increasing trend (17%) and a decreasing trend (-18%) in 2039, respectively. According to precipitation predictions for 2039, we observed an increasing trend in the < 150 mm and 150-200mm classes, whereas we found a decreasing trend in the 250-300mm and 200-250mm classes, respectively.

Land use changing has an important role in instability ecosystem and its services, and it is an effective factor in direction of land degradation. According to results provided for effective climatic variables (with an increasing trend in temperature and a decreasing trend in the precipitation) changes vegetation cover and result in low SAVI. We can conclude that climatic variables and SAVI are related factors which play important roles in land degradation, desertification and combating desertification in the coastal area. Results of this study are consistent with Salehi et al., 2019; Halebian & Soltanian, 2017; Maher et al., 2018; Alijani et al., 2012.