In this study, the Land Use/Land Cover (LULC) change has been observed in wetlands comprises of Manchar Lake, Keenjhar Lake, and Chotiari Reservoir in Pakistan over the last four decades from 1972 to 2020. Each wetland has been categorized into four LULC classes; water, natural vegetation, agriculture land, and dry land. Multitemporal Landsat satellite data including; Multi-Spectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images were used for LULC changes evaluation. The Supervised Maximum-likelihood classifier method is used to acquire satellite imagery for detecting the LULC changes during the whole study period. Soil adjusted vegetation index technique (SAVI) was also used to reduce the effects of soil brightness values for estimating the actual vegetation cover of each study site. Results have shown the significant impact of human activities on freshwater resources by changing the natural ecosystem of wetlands. Change detection analysis showed that the impacts on the land cover affect the landscape of the study area by about 40% from 1972 to 2020. The vegetation cover of Manchar Lake and Keenjhar Lake has been decreased by 6,337.17 and 558.18 ha, respectively. SAVI analysis showed that soil profile is continuously degrading which vigorously affects vegetation cover within the study area. The overall classification accuracy and Kappa statistics showed an accuracy of >90% for all LULC mapping studies. This work demonstrates the LULC changes as a critical monitoring basis for ongoing analyses of changes in land management to enable decision-makers to establish strategies for effectively using land resources.

在这项研究中，观察了 1972 年至 2020 年过去四十年中巴基斯坦 Manchar 湖、Keenjhar 湖和 Chotiari 水库等湿地的土地利用/土地覆盖 (LULC) 变化。每个湿地被分为四个 LULC课程；水、自然植被、农业用地和旱地。多时相 Landsat 卫星数据包括；多光谱扫描仪 (MSS)、专题制图仪 (TM) 和操作土地成像仪 (OLI) 图像用于土地利用覆盖率变化评估。使用监督最大似然分类器方法获取卫星图像，以检测整个研究期间的 LULC 变化。土壤调整植被指数技术（SAVI）也被用来减少土壤亮度值的影响，以估计每个研究地点的实际植被覆盖。结果表明，人类活动通过改变湿地自然生态系统对淡水资源产生重大影响。变化检测分析表明，1972年至2020年，土地覆盖影响对研究区景观影响约40%。Manchar湖和Keenjhar湖的植被覆盖度分别减少了6,337.17和558.18 ha。SAVI 分析表明，土壤剖面持续退化，严重影响研究区域内的植被覆盖。总体分类精度和 Kappa 统计显示所有 LULC 制图研究的精度均 > 90%。这项工作表明，土地利用和土地利用变化是持续分析土地管理变化的关键监测基础，使决策者能够制定有效利用土地资源的战略。