A phenology- and trend-based approach for accurate mapping of sea-level driven coastal forest retreat
A phenology- and trend-based approach for accurate mapping of sea-level driven coastal forest retreat
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DOI：
10.1016/j.rse.2022.113229
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发表时间：
2022-11
影响因子：
13.5
通讯作者：
Yaping Chen;M. Kirwan
中科院分区：
文献类型：
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作者：
Yaping Chen;M. Kirwan
高地森林迅速被不断侵蚀的沼泽地取代是全球海平面上升的一个显著表现。及时和高分辨率的信息的位置和范围的过渡森林（高地森林和沼泽之间的过渡带，树木死亡，由于海水入侵开始）是根本的了解的过程和模式的SLR驱动的景观重组。尽管其重要性，盐影响过渡森林的准确表征仍然具有挑战性，由于沿海环境的复杂性，缺乏地面实况数据，以及缺乏有效的映射算法。在这里，我们使用1984年至2021年的Landsat图像的完整档案来调查过渡森林的光谱，时间和物候特征，并开发一个强大的框架来监测美国大西洋中部的沿海植被变化，全球单反热点我们发现，过渡森林表现出强烈的负NDVI趋势和陆地表面物候的偏差，从沼泽和高地森林，区别于周围的植被。通过整合时间趋势和地表物候，我们的研究结果表明，上级区别沼泽和沿海森林现有的地图产品（如NOAA海岸变化分析程序，国家土地覆盖数据库），允许可靠的识别沿海树线。我们应用该方法绘制了1985年、2000年和2020年的区域土地覆盖图（总体分类精度>92%），发现沿海森林面积从1985年到2020年减少了22.0%，其中大部分过渡到沼泽地（92.3%，5.3 × 103公顷）。基于海岸海侵的精细尺度模式，我们创建了一个实用的工作流程，在空间上明确量化森林退缩率。随着海平面上升，沿海森林从0.63（± 0.27）m上升到2020年的0.78（± 0.32）m，水平森林退缩速率从1985-2000年的3.1（0-36）m/a加速到2001-2020年的4.7（0-55）m/a。随着SLR的不断加速，我们的研究可以作为一个可扩展的解决方案，用于持续跟踪森林和湿地可持续管理迫切需要的沿海景观演变。
The rapid replacement of upland forest by encroaching marshland is a striking manifestation of global sea-level rise (SLR). Timely and high-resolution information on the location and extent of transition forest (the ecotone between upland forest and marsh where tree mortality due to seawater intrusion begins) is fundamental to understanding the processes and patterns of SLR-driven landscape reorganization. Despite its significance, accurate characterization of salt-impacted transition forest remains challenging due to the complexity of coastal environments, scarcity of ground-truth data, and the lack of effective mapping algorithms. Here we use the full archive of Landsat images between 1984 and 2021 to investigate the spectral, temporal, and phenological characteristics of transition forest, and develop a robust framework for monitoring coastal vegetation shifts in the mid-Atlantic U.S., a global SLR hotspot. We found that transition forest exhibits strong negative NDVI trends and a deviation of land surface phenology from marsh and upland forest that distinguishes itself from surrounding vegetation. By integrating temporal trends and land surface phenology, our results demonstrate superior discrimination between marsh and coastal forests to existing map products (e.g. NOAA Coastal Change Analysis Program, National Land Cover Database) that allows a reliable identification of the coastal treeline. We applied the approach to map regional land cover in 1985, 2000 and 2020 (overall classification accuracy >92%) and found that the area of coastal forest decreased by 22.0% from 1985 to 2020, the majority of which transitioned to marshland (92.3%, 5.3 × 103ha). Based upon fine-scale patterns of coastal transgression, we created a practical workflow for spatially explicit quantification of forest retreat rates. Concurrent with rising sea level, coastal forests migrated upslope from 0.63 (± 0.27) m above sea level in 1985 to 0.78 (± 0.32) m above sea level in 2020, and horizontal forest retreat rates accelerated from 3.1 (range of 0–36) m yr−1during 1985–2000 to 4.7 (0–55) m yr−1during 2001–2020. As SLR continues to accelerate, our study may serve as a scalable solution for consistent tracking of coastal landscape evolution that is urgently needed for sustainable forest and wetland management.