Land use/cover (LCLU) is considered as one of the most serious environmental challenges that threatens developed and less developed countries. LCLU changes' monitoring using the integration of remote sensing (RS) and geographical information systems (GIS) and their predicting using an artificial neural network (ANN) in the western part of the Tarim River Basin (Aksu), north-western Xinjiang-China, from 1990 to 2030 have been investigated first time through satellite imageries available. The imageries of 1990, 2000, 2005, 2010, and 2015 were downloaded from GLCF and USGS websites. After digital image processing, the object-oriented image classification approach was applied. The ANN method with MOLUSCE Plugin was used to simulate the LCLU changes in 2020, 2025, and 2030. GIS has also been used to calculate the distance from the road and water and etc. The simulation results of 2010 and 2015 were validated using classification data with Kappa coefficient. The results showed high accuracy of the classification and prediction as the validation of simulated 2010 and 2015 maps to the referenced maps have high accuracy of Kappa 84 and 88%, respectively. The results revealed that the land cover classes forest-, grass-, wet-, and barren land have been decreased from 50.01, 13.06, 8.24, and 1.06% in 1990 to 32.03, 3.06, 6.26, and 0.97% in 2015, respectively, while the land use classes, crop or farm land, and urban land have been increased almost double from 25.5 and 2.13% in 1990 to 53.71 and 3.86% from the total area in 2015, respectively. For the prediction, forest- and wetlands will loss more than half of their areas by 2030, the grass land will be cleared completely to be only 1.3% from the total study area, while the urban land will be increased to be 4.4% or the double of 1990. These results are attributed to population growth and expanding of agriculture land on the grass land, but the effect of climate was weak as the rainfall increased during the study period. Causes and effects of the LCLU changes were briefly discussed. The output of the study serves as useful tools for policy and decision makers combatting natural resources misused in arid lands.
Keywords: Aksu; Artificial neural network (ANN); Land use/cover change; Reconnaissance Drought Index; Tarim River Basin; Xinjiang.