Post-Disaster Recovery Assessment of Mangrove Forests in Leyte Island, Philippines using Sentinel-2 Imagery

The research investigated the synergy of different vegetation indices, biophysical variables, and landscape metrics in assessing the post-disaster recovery of the mangrove forests in Maasin City and Matalom in Leyte Island, Philippines after Super Typhoon Odette (International Name: Rai). These data show the monthly mean vegetation indices, monthly mean biophysical variables, and four-month landscape metrics values in the study area from 2019 to 2022, which spans three years before and one year after Super Typhoon Odette hit the area. The data were gathered using Google Earth Engine, SNAP Biophysical Processor, and QGIS Landscape Ecology Statistics Plug-In. Time series analysis was used to interpret the data, which includes the Theil-Sen Estimator and Mann-Kendall Test. Notable finding includes decline across all post-disaster parameters which reveal substantial damage to the functional, structural, and landscape configuration characteristics of the mangrove forest. Contrasting recovery and resiliency profiles were observed between vegetation indices and biophysical variables, which indicate that previous post-disaster studies that only employed NDVI may have reported underestimd recovery values.本研究探讨了不同植被指数（Vegetation Indices）、生物物理变量（Biophysical Variables）与景观格局指数（Landscape Metrics）在评估菲律宾莱特岛马斯辛市和马塔隆市红树林于超级台风奥黛特（国际名称：雷伊（Rai））过境后的灾后恢复状况中的协同效应。本数据集包含2019至2022年研究区域内的月均植被指数、月均生物物理变量以及为期四个月的景观格局指数数值，时间跨度覆盖超级台风奥黛特过境前三年与过境后一年。数据通过谷歌地球引擎（Google Earth Engine）、SNAP生物物理处理器（SNAP Biophysical Processor）以及QGIS景观生态统计插件（QGIS Landscape Ecology Statistics Plug-In）采集得到。研究采用时间序列分析法对数据进行解读，所涉方法包括Theil-Sen估计量（Theil-Sen Estimator）与曼-肯德尔检验（Mann-Kendall Test）。核心研究结果显示，所有灾后相关参数均出现下降，揭示出红树林在功能、结构与景观配置特征上遭受了严重破坏。此外，研究观测到植被指数与生物物理变量呈现出截然不同的恢复与韧性特征，这表明此前仅采用归一化差异植被指数（NDVI）开展的灾后研究，可能得出了偏低的恢复评估值。