Abstract

The objective of this study was to assess the impacts of land-use change by monitoring actual evapotranspiration in future periods in the Varamin Plain. Evapotranspiration was investigated from May to August during 2007–2009 using four single-source remote-sensing algorithms: the Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC), the Surface Energy Balance System (SEBS), and the Simplified Surface Energy Balance (SSEB), together with MODIS sensor imagery. To evaluate the effects of land-use change, Landsat images from 1999, 2009, and 2019 were classified using the maximum likelihood algorithm. The corresponding Kappa coefficients were 0.807, 0.837, and 0.801, respectively, with classification accuracies of 87.12%, 90.49%, and 99.88%. The land-use classes included water bodies, vegetation cover, rocky areas, built-up areas, barren lands, and agricultural lands. Using the Land Change Modeler (LCM) and the integrated Cellular Automata–Markov Chain (CA–Markov) model, land-use maps for 2030 and 2059 were simulated. The results showed a declining trend in agricultural land area, from 44.8 thousand hectares in 1999 to 39.3 thousand hectares in 2009 and 38.6 thousand hectares in 2019. Urban, rural, and industrial areas exhibited an increasing trend: these land uses covered 6.57% of the total study area in 1999, increasing to 18.48% by 2019. Rangeland vegetations cover also declined, from 14.9 thousand hectares in 1999 to 11.6 thousand hectares in 2009 and 6.4 thousand hectares in 2019. Based on the data results, an increasing trend is predicted for land use change and actual evapotranspiration in future periods.