Mangroves of North-western Australia mapped with multi-dimensional space–time remote sensing (ICoAST)

Mangroves are a globally important ecosystem subject to significant anthropogenic and climate impacts. Tidally submerged forests and those that occur in arid and semi-arid regions are particularly susceptible to sea level rise or are growing at the margins of their their ecophysiological limits. The spatial extent of these types of mangroves over broad scales are typically poorly documented as their structural and environmental characteristics make them difficult to detect using remote sensing models.

This study utilised the entire Landsat 8 satellite collection between January 2014 and June 2021. A new cloud-based time-series method was used that accounts for tidal variance in detecting mangrove areas that are periodically inundated and have historically been difficult to detect with traditional remote sensing methods. A habitat area model was derived for remote North-western Australia and detected an additional 32% (76,048 hectares) of mangroves that were previously undocumented. The accuracy of the model was assessed within the distinct geomorphic zones of the region through visual validation from high-resolution imagery.

See accompanying report for full methodology:
Hickey, S.M.; Radford, B. Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space–Time Remote Sensing. Remote Sens. 2022, 14, x. https://doi.org/10.3390/rs14143365

红树林（Mangroves）是全球重要的生态系统，正遭受显著的人为活动与气候变化影响。潮浸森林以及分布于干旱、半干旱区域的红树林，尤其易受海平面上升威胁，或正处于其自身生态生理极限的边缘。此类红树林在大尺度范围内的空间分布范围通常鲜有记录，因其结构与环境特征，传统遥感模型难以对其进行有效识别。

本研究采用了2014年1月至2021年6月期间的全部陆地卫星8号（Landsat 8）卫星影像数据集。本研究使用了一种新型基于云平台的时间序列方法，该方法可在检测过程中考量潮汐变化差异，用于识别那些周期性被淹没、且长期以来难以通过传统遥感手段检测的红树林区域。针对澳大利亚西北部偏远区域，本研究构建了生境面积模型，额外检测出占此前已记录红树林面积32%的区域，合76048公顷。本研究通过高分辨率影像的目视验证，对该区域不同地貌分区内的模型精度开展了评估。

完整研究方法详见随附报告：
希基，S.M.；雷德福，B. 《依托多维时空遥感技术破解边缘红树林测绘难题》，《遥感（Remote Sens.）》2022，14，x. https://doi.org/10.3390/rs14143365