Mapping multi-decadal wetland loss: Comparative analysis of linear and nonlinear spatiotemporal characterization
Mapping multi-decadal wetland loss: Comparative analysis of linear and nonlinear spatiotemporal characterization
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DOI：
10.1016/j.rse.2023.113969
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发表时间：
2024-03
影响因子：
13.5
通讯作者：
Margot Mattson;Daniel Sousa;Amy Quandt;Paul Ganster;Trent Biggs
中科院分区：
文献类型：
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作者：
Margot Mattson;Daniel Sousa;Amy Quandt;Paul Ganster;Trent Biggs
湿地为鸟类和濒危物种提供了重要的栖息地，并可能受到水供应和土地使用变化的影响。陆地卫星图像档案可以帮助记录历史湿地变化和监测当前动态，但多年回顾性研究面临两个重要挑战：1）许多重要的生态水文过程发生在比30米Landsat像素视场更精细的尺度上，2）具有生态意义的湿地响应单元在植被和地表水中经常表现出时空复杂性，这可能不是很好-由简单的研究区域空间平均值表示。在这里，我们探讨了一种新的方法，这些挑战与应用程序的迅速变化的安德拉德梅萨湿地沿着美国-墨西哥边境。使用26年（1995-2021年）的Landsat图像时间序列，我们跟踪地表水和植被的变化，使用绿色植被（V）和黑暗（D）的光谱混合分析（SMA）估计的分数。V和D分数的时空动态特征使用两种技术：线性主成分分析（PCA）和非线性流形学习（通过均匀流形近似和投影; UMAP）。界定时间端元从PCA确定，并用于构建一个时间混合模型，量化地表水和植被损失的总面积，包括不确定性估计。大气监测评价方案明确区分了使用五成分分析法无法识别的植被和水流失开始时间不同的地区。然后，将变化图与不同的机制和结果联系起来，包括可能的机械清除植被和意外的木本植被绿化。地表水和植被下降的总体时间如下的所有美洲运河的衬里，这表明在墨西哥的湿地和水的效率措施在美国实施的损失之间的潜在的跨境水文关联。
Wetlands provide critical habitat for birds and endangered species, and can be influenced by shifts in water availability and land use. The Landsat image archive can help both document historical wetland change and monitor current dynamics, but multi-decadal retrospective studies face two important challenges: 1) many important ecohydrological processes occur at scales finer than the field of view of a 30 m Landsat pixel, and 2) ecologically meaningful wetland response units frequently exhibit spatiotemporal complexity in both vegetation and surface water which may not be well-represented by a simple study area spatial mean. Here we explore a novel method for contending with these challenges with application to the rapidly changing Andrade Mesa wetlands along the US-Mexico border. Using a 26-year (1995–2021) Landsat image time series, we track changes in surface water and vegetation using green vegetation (V) and dark (D) fractions estimated from spectral mixture analysis (SMA). Spatiotemporal dynamics in both V and D fractions were characterized using two techniques: linear Principal Component Analysis (PCA) and nonlinear manifold learning (via Uniform Manifold Approximation and Projection; UMAP). Bounding temporal endmembers were identified from PCA and used to construct a temporal mixture model which quantified total area of surface water and vegetation loss, including an uncertainty estimate. UMAP clearly distinguished areas with differing onset of vegetation and water loss which were not identifiable using PCA. Change maps were then associated with different mechanisms and outcomes, including possible mechanical clearing of vegetation and unexpected greening of woody vegetation. The overall timing of surface water and vegetation decline follows the lining of the All-American Canal, suggesting a potential cross-border hydrologic association between loss of a wetland in Mexico and water efficiency measures implemented in the United States.