Facultad de Ingeniería, Universidad Nacional Autónoma de México, A.P. 70-305, Av. Universidad 3000, Ciudad Universitaria, Coyoacán, Cd., México, 04510, Mexico.
Instituto de Ciencias del Mar y Limnología, Unidad Académica Procesos Oceánicos y Costeros, Universidad Nacional Autónoma de México, A.P. 70-305, Av. Universidad 3000, Ciudad Universitaria, Coyoacán, Cd., México, 04510, Mexico.
J Environ Manage. 2022 Oct 15;320:115830. doi: 10.1016/j.jenvman.2022.115830. Epub 2022 Aug 6.
Due to their location in tropical latitudes, mangrove forests are susceptible to the impact of hurricanes and can be vastly damaged by their high-speed winds. Given the logistic difficulties regarding field surveys in mangroves, remote sensing approaches have been considered a reliable alternative. We quantified trends in damage and early signs of canopy recovery in a fringe Rhizophora mangle area of Marismas Nacionales, Mexico, following the landfall of Hurricane Willa in October 2018. We monitored (2016-2021) broad canopy defoliation using 21 vegetation indices (VI) from the Google Earth Engine tool (GEE). We also mapped a detailed canopy fragmentation and developed digital surface models (DSM) during five study periods (2018-2021) with a consumer-grade unmanned aerial vehicle (UAV) over an area of 100 ha. Based on optical data from the GEE time series, results indicated an abrupt decline in the overall mangrove canopy. The VARI index was the most reliable VI for the mangrove canopy classification from a standard RGB sensor. The impact of the hurricane caused an overall canopy defoliation of 79%. The series of UAV orthomosaics indicate a gradual recovery in the mangrove canopy, while the linear model predicts at least 8.5 years to reach pre-impact mangrove cover conditions. However, the sequence of DSM estimates that the vertical canopy configuration will require a longer time to achieve its original structure.
由于位于热带纬度，红树林容易受到飓风的影响，其高速风可能会对其造成巨大破坏。考虑到在红树林进行实地调查的后勤困难，遥感方法已被认为是一种可靠的替代方法。我们在 2018 年 10 月飓风薇拉登陆后，对墨西哥国家湿地边缘的红树 mangrove 地区的受损情况和树冠恢复的早期迹象进行了量化。我们使用 Google Earth Engine 工具（GEE）中的 21 种植被指数（VI）监测了（2016-2021 年）树冠的广泛落叶情况。我们还在五个研究期间（2018-2021 年）利用消费级无人机（UAV）绘制了详细的树冠破碎化图，并开发了数字表面模型（DSM），总面积为 100 公顷。基于 GEE 时间序列的光学数据，结果表明红树林树冠整体急剧下降。VARI 指数是从标准 RGB 传感器对红树林树冠分类最可靠的 VI。飓风的影响导致树冠整体落叶 79%。一系列的 UAV 正射镶嵌图表明红树林树冠正在逐渐恢复，而线性模型预测至少需要 8.5 年才能达到飓风前的红树林覆盖条件。然而，DSM 的序列估计树冠的垂直配置需要更长的时间才能恢复到原始结构。
