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Precise categorization of mangrove forests with medium spatial resolution satellite data is challenging and occasionally yields mixed outcomes. The available methods to estimate mangrove vegetation cover using moderately high-resolution images lack differentiation between mangrove and homestead vegetation. Mangrove vegetation displays a range of responses across the phenological cycle at different wavelengths of an optical sensor. Taking advantage of this principle, this study utilized some mangrove and non-mangrove vegetation indices (VIs) as predictor variables sourced from monthly Sentinel-2 data into the random forest algorithm to derive a phenology-based classification outcome. It also ascertained a suitable month for thresholding mangroves across different VIs. Results indicated that phenologybased classification with three classes was more accurate (95% overall accuracy) than threshold-based or WorldCover v100 classifications. MI and MVI layers from December image performed better in discerning mangroves. Findings have important implications in separating mangroves from other coastal vegetations. [ABSTRACT FROM AUTHOR]

Seasonal and annual water supplies of the rivers originating in the Hindukush-Karakoram-Himalaya (HKH) region of Pakistan are important to manage the Indus basin irrigation system for better agricultural production and its dependent agrarian economy. In this study, we simulated the current and future snowmelt runoff in a poorly gauged river basin of the Hindukush region under Representative Concentration Pathways (RCP) climate change scenarios. Snowmelt Runoff Model (SRM) furnished with satellite snow cover maps and hydro-meteorological data were used to simulate the daily river discharge for the period 2000‒2005. The results indicated that SRM has effectually simulated the runoff in Chitral River with Nash-Sutcliffe model efficiency coefficient of 0.85 (0.84) and 0.88 (0.83) in the basin-wide (zone-wise) application during the calibration and validation periods, respectively. The results obtained under future climate change scenario showed ∼14‒19% increase in mean summer discharge under three mid-21st century RCP (2.6, 4.5 and 8.5) scenarios. While an increase of ∼13‒37% is expected under late-21st century RCP scenarios. This study can help water resource managers to plan and manage peak discharges from the Chitral River Basin in the future and can thus prevent major losses due to floods in the area. [ABSTRACT FROM AUTHOR]