Land use and land cover change (LULCC) is a main driver of global environmental change and has destructive effects on the structure and function of the ecosystem. This study attempts to detect temporal and spatial changes in LULC patterns of the Chalus watershed during the last two decades using multitemporal Landsat images and predict the future LULC changes and patterns of the Chalus watershed for the year 2040. A hybrid method between segment-based and pixel-based classification was applied for each Landsat image 2001, 2014 and 2021 to produce LULC maps of the Chalus watershed. In this study, the transition potential maps and the transition probability matrices between LULC types were provided by the Support Vector Machine (SVM) algorithm and the Markov Chain model, respectively, to project the 2021 and 2040 LULC maps. The achieved K-index values that compared the simulated LULC map with the actual LULC map of the year 2021 resulted in a Kstandard = 0.9160, Kno = 0.9379, Klocation = 0.9318 and KlocationStrata = 0.9320, showing a good agreement between the actual and simulated LULC map. Analysis of the historical LULC changes depicted that during 2001-2021, the significant increase of Agricultural land (14317 ha) and Barren area (9063 ha), and the sharp decline of Grassland (26215 ha) and Forest cover (5989 ha) were the major LULC changes in the Chalus watershed. The model predicted that Forest cover will continue to decrease from 29.46% (50720.2667 ha) in 2021 to 25.67% of area (44207.78694 ha) in 2040, as well as, unceasing expansion of Barren area, Agricultural land and Built-up area will be expected by 2040. Therefore, understanding the spatiotemporal dynamics of LULC change is extremely important to implement essential measures and minimize the destructive consequences of these changes.

Drought has been identified as one of the significant complicated natural disasters exacerbated by land degradation and climate change. Hence, monitoring drought and evaluating its spatiotemporal dynamics are essential to manage regional drought conditions and protecting the natural environment. In this study, various single remote sensing-based drought indices including soil moisture condition index (SMCI), precipitation condition index (PCI), temperature condition index (TCI), and vegetation condition index (VCI) and combined RS-based drought Indices including optimized meteorological drought index (OMDI) and synthesized drought index (SDI) have been used to investigate the spatiotemporal variations of meteorological and agricultural droughts between 2000 and 2021 in Iran. The in situ drought indices, including the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) series of 1, 3, 6, 12, and 24 months were utilized to verify remote sensing-based drought indices and evaluate their applicability for analyzing drought conditions. The results indicated that the correlation coefficients of the in situ drought indices with the combined drought indices are higher than the RS-based single drought indexes. Generally, single-factor drought indexes, including VCI, TCI, PCI, and SMCI, have specific characteristics. The PCI and SMCI have an acceptable correlation with the short-term SPI and SPEI and are more applicable to monitoring short-term drought conditions. Further, the TCI has better performance in monitoring long-term drought conditions in Iran. This research concluded that the central, eastern, and southeastern parts of Iran mainly were experiencing exceptional and extreme drought conditions as the worst agricultural and meteorological drought conditions observed in the years 2008 and 2021 in the region during the last 20 years. The results also showed that, in 2019 and 2020, most areas of Iran had higher OMDI and