Saif Ullah

Climate change, rapid urbanization, and population growth are increasingly influencing the quality and quantity of surface water resources, especially in vulnerable reservoir systems. This study investigates the spatiotemporal changes in water features and quality of three key drinking water source lakes‐Rawal, Simly, and Khanpur (RSK), located in and around Islamabad, Pakistan. Using Level 2 Landsat 5, 7 and 8 satellite data from 1991 to 2020, changes in lake surface area were assessed through the Google Earth Engine (GEE) platform. Thresholding and geospatial analysis in ArcGIS 10.8 were used to extract and visualize water bodies and surface feature changes. The study found that lake surface areas were directly linked to rainfall levels and decreased with rising temperatures especially during 1991, 2000 2010, and 2020. Water quality was assessed using standard laboratory procedures. Notably, higher bacterial counts were recorded during the wet season, indicating increased microbial contamination likely due to surface runoff. Among the heavy metals analyzed (Fe, F, As, Cu, Zn, Mn, Cr, Pb, Ni, B, Cd, P, Hg), only boron (B), nickel (Ni), and chromium (Cr) were detected above background levels, though within permissible limits. The study highlights the significant influence of climatic variables on both the physical extent and microbial quality of drinking water lakes. These findings offer critical insights for policymakers and water resource managers, providing a replicable framework for monitoring and managing similar reservoirs in other climate‐sensitive regions.

Accurate, cost-efficient vegetation mapping is critical for managing afforestation projects, particularly in resource-limited areas. This study used a consumer-grade RGB unmanned aerial vehicle (UAV) to evaluate the optimal spatial and temporal resolutions (leaf-off and leaf-on) for precise, economically viable tree species mapping. This study conducted in 2024 in Kasho, Bannu district, Pakistan, using UAV missions at multiple altitudes captured high-resolution RGB imagery (2, 4, and 6 cm) across three sampling plots. A Support Vector Machine (SVM) classifier with 5-fold cross-validation was assessed using accuracy, Shannon entropy, and cost–benefit analyses. The results showed that the 6 cm resolution achieved a reliable accuracy (R² = 0.92–0.98) with broader coverage (12.3–22.2 hectares), while the 2 cm and 4 cm resolutions offered higher accuracy (R² = 0.96–0.99) but limited coverage (4.8–14.2 hectares). The 6 cm resolution also yielded the highest benefit–cost ratio (BCR: 0.011–0.015), balancing cost-efficiency and accuracy. This study demonstrates the potential of consumer-grade UAVs for affordable, high-precision tree species mapping, while also accounting for other land cover types such as bare earth and water, supporting budget-constrained afforestation efforts.

Litterfall load is crucial in maintaining ecosystem health, controlling wildfires, and estimating carbon stock in arid regions. However, there is a lack of spatiotemporal analysis of litterfall in arid riparian forests. This study aims to estimate Litterfall load using a BP neural network based on vegetation indices from Landsat 5 and 8 satellite images, litterfall inventory data, slope, and distance to major river tributaries. It also aims to analyze the spatiotemporal distribution pattern of litter in the research area by estimating and analyzing the spatiotemporal pattern of litterfall along the desert riparian forests of the lower Qarqan and Tarim Rivers from 2001 to 2021. The results show that the initiation of the ecological water transfer project has facilitated the decomposition of litterfall, leading to an initial decline. Subsequently, the vegetation gradually recovered, leading to an increase in leaf litter input. Since 2001, litterfall initially decreased until reaching its lowest value of 4.39 × 10⁹ kg in 2005, followed by a subsequent increase, reaching its highest value of 12.5 × 10⁹ kg in 2021. The study concludes that ecological water conveyance promotes both the decomposition and increase of litterfall. Initially, it accelerates litterfall decomposition, while later stages foster an increase in Litterfall load. Meanwhile, due to the ecological water transfer project and the higher vegetation cover along the Tarim River compared to the Qarqan River, the Tarim River basin experiences higher average Litterfall load and variation.