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Google Earth Engine (GEE) is presently the most innovative international open-source platform for the advanced-level analysis of geospatial big data. In this study, we used three machine learning algorithms to apply this cloud platform for Land Use Land Cover (LULC) research in the Mardan, Pakistan. The machine learning algorithm in GEE is the most advanced technique to generate reliable and informative LULC maps from various satellite data to present reliable results. The primary goal of the present study is to compare the performance of various machine learning models (i.e., classification and regression trees [CART], support vector machine [SVM], and random forest [RF]) in GEE for the reliable four classes LULC maps using the Sentinel-2 imageries of 2022. In the current study, three satellite indices like the Normalized Difference Vegetation Index, Modified Normalized Difference Water Index, and Normalized Difference Built Index were applied to detect the features (i.e., vegetation, built, barren land, and water bodies in the study area). The performance of all three models was evaluated by validation and accuracy assessments. The Kappa coefficients of CART, SVM, and RF for Sentinel-2 images were 94%, 95%, and 97%, while the average overall accuracy is 96.25%, 97%, and 98.68%, respectively. The present study illustates that in this classification and comparison, RF performed better than SVM and CART. The current research study revealed that GEE has speedily processed the satellite imageries to develop the four classes of reliable LULC maps of the study area with the best accuracy results and deliver excellent support for further analysis.

Wetlands are important in many ways, including hydrological cycles, ecosystem diversity, climate change, and economic activity. Despite the Ramsar Convention's awareness programmes, the importance of wetlands is frequently disregarded in underdeveloped countries. The Ramsar Convention recognises 2491 wetlands worldwide, 19 of which are in Pakistan. The goal of this study is to use satellite sensor technology to identify neglected wetlands in Pakistan. The key goals of this research are to analyse water quality, monitor ecological changes, and comprehend the impact of climate change on the aforementioned wetlands. We used approaches like supervised classification and TCW to identify wetlands. To detect climate-induced changes, a change detection index was used to Quick Bird imagery. TCG and the NDTI were also employed to examine the water quality and ecological changes in these wetlands. Sentinel-2 data between 2016 and 2019 were used in the analysis. Furthermore, watershed analysis was carried out using ASTER DEM data. Modis data was used to calculate the LST (°C) of the selected wetlands, while rainfall (mm) data was collected from ANN databases. According to the study's findings, in 2016, Borith, Phander, Upper Kachura, Satpara, and Rama Lake held 22.73%, 20.79%, 23.01%, 24.63%, and 23.03% water, respectively. In 2019, the water ratios for these lakes were 23.40%, 22.10%, 22.43%, 25.01%, and 24.56%. These findings emphasise the need of taking preventative actions to protect these wetlands in order to improve ecosystem dynamics in the future. As a result, it is critical that the relevant authorities implement the necessary conservation measures.

Rangelands globally face escalating threats from overgrazing, land conversion, and climate change. This study investigates spatio-temporal rangeland degradation patterns in Pakistan's Bhakkar District, a semiarid region dependent on fragile pastoral ecosystems, over the past four decades at 10-yr intervals (1990, 2000, 2010, 2020). Remote sensing offers a valuable tool for monitoring these vast yet understudied dryland environments. We employed Landsat satellite data and machine learning algorithms to map land cover change and analyze vegetation health indicators. The random forest classifier achieved high accuracy (94%) in delineating six land cover categories–water, built-up, forest, cropland, rangeland, and barren land. Classified rangeland area declined by over 25%, largely due to agricultural expansion. Vegetation indices showed mixed trends, with decreases in enhanced vegetation index but marginal improvement in normalized difference vegetation index. Meanwhile, rising land surface temperatures pointed to increased aridity. These concerning changes underscore the urgent need for conservation policies tailored to community needs through participatory engagement. Rangeland degradation threatens the livelihoods and welfare of pastoral communities reliant on these ecosystems. Integrated solutions centered on adaptation and resilience can promote sustainability in Bhakkar's marginal dryland environments. This study demonstrates the power of satellite monitoring coupled with social research for implementing holistic strategies to address the globally prevalent threat of rangeland disruption.