This study provides the first comprehensive mangrove cover change assessment from 1990 to 2020, at five-year intervals, across all five mangrove areas in Pakistan, i.e. Indus Delta, Sandspit, Sonmiani, Kalmat Khor, and Jiwani. Using the Google Earth Engine (GEE) geospatial cloud computing platform, Random Forest (RF) classifier was applied on Landsat 30 m spatial resolution satellite images to classify three major land cover classes: ‘mangrove’, ‘water’ and ‘other’. High temporal and spectral resolutions of Landsat images, with a low saturation level of spectral bands with the integration of indices, are the main factors that ensured >90% overall accuracy of land cover maps. Over the last three decades (1990–2020), the annual rate of change calculation, cross-tabulated method, and fragmentation analysis were carried out to identify the changes in mangrove cover. Overall, the findings of this paper revealed that, at the national scale, an estimated 477.22 km² was covered with mangrove in 1990, which increased to 1463.59 km² in 2020, a 3.74% annual rate of change. Mangrove fragmentation mapping results have also showed enhancement in mangrove tree canopy density. Due to planting and effective conservation practices, the current study shows positive changes in mangrove cover across all five study sites. The findings of this study will prove useful for design and implementation of mangrove ecosystem management plans, initiatives for adaptation to extreme weather events, carbon budgeting, and others.

这项研究提供了从1990年到2020年的每五年一次的巴基斯坦红树林三角洲，桑德斯皮特，桑米亚尼，卡尔马特·霍尔和吉瓦尼等所有五个红树林地区的综合红树林覆盖率变化评估。使用Google Earth Engine（GEE）地理空间云计算平台，将随机森林（RF）分类器应用于Landsat 30 m空间分辨率卫星图像，以对三个主要的土地覆盖类别进行分类：“红树林”，“水”和“其他”。Landsat图像的高时间分辨率和光谱分辨率，以及光谱带的饱和度较低，以及索引的集成，是确保这些土地覆盖图的整体准确性> 90％的主要因素。年变化率计算，交叉表方法，进行了碎片化分析，以确定过去三十年来红树林覆盖率的变化。总体而言，本文的调查结果表明，在全国范围内，估计为477.22 km1990年有^2个被红树林覆盖，到2020年增加到1463.59 km ²，年变化率为3.74％。红树林碎片测绘结果也显示出红树林树冠密度的提高。由于种植和有效的保护措施，本研究显示了所有研究地点的红树林覆盖率均发生了积极变化。这项研究的结果将被证明对红树林生态系统管理计划的设计和实施，适应极端天气事件的倡议，碳预算等有用。