Mangroves are important ecosystems for coastal biodiversity, resilience and carbon dynamics that are being threatened globally by human pressures and the impacts of climate change. Yet, at several geographic range limits in tropical–temperate transition zones, mangrove ecosystems are expanding poleward in response to changing macroclimatic drivers. Mangroves near range limits often grow to smaller statures and form dynamic, patchy distributions with other coastal habitats, which are difficult to map using moderate‐resolution (30‐m) satellite imagery. As a result, many of these mangrove areas are missing in global distribution maps. To better map small, scrub mangroves, we tested Landsat (30‐m) and Sentinel (10‐m) against very high resolution (VHR) Planet (3‐m) and WorldView (1.8‐m) imagery and assessed the accuracy of machine learning classification approaches in discerning current (2022) mangrove and saltmarsh from other coastal habitats in a rapidly changing ecotone along the east coast of Florida, USA. Our aim is to (1) quantify the mappable differences in landscape composition and complexity, class dominance and spatial properties of mangrove and saltmarsh patches due to image resolution; and (2) to resolve mapping uncertainties in the region. We found that the ability of Landsat to map mangrove distributions at the leading range edge was hampered by the size and extent of mangrove stands being too small for detection (50% accuracy). WorldView was the most successful in discerning mangroves from other wetland habitats (84% accuracy), closely followed by Planet (82%) and Sentinel (81%). With WorldView, we detected 800 ha of mangroves within the Florida range‐limit study area, 35% more mangroves than were detected with Planet, 114% more than Sentinel and 537% more than Landsat. Higher‐resolution imagery helped reveal additional variability in landscape metrics quantifying diversity, spatial configuration and connectedness among mangrove and saltmarsh habitats at the landscape, class and patch scales. Overall, VHR satellite imagery improved our ability to map mangroves at range limits and can help supplement moderate‐resolution global distributions and outdated regional maps.

中文翻译：

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使用超高分辨率卫星图像揭示红树林范围限制，以探测动态沿海生态交错带中的小规模红树林和盐沼栖息地


红树林是沿海生物多样性、恢复力和碳动态的重要生态系统，但在全球范围内正受到人类压力和气候变化影响的威胁。然而，在热带-温带过渡区的几个地理范围限制内，红树林生态系统正在向极地扩展，以应对不断变化的宏观气候驱动因素。接近范围限制的红树林通常会长得较小，并与其他沿海栖息地形成动态、斑驳的分布，而使用中等分辨率（30米）卫星图像很难绘制地图。因此，许多红树林地区在全球分布地图上都缺失了。为了更好地绘制小型灌木丛红树林地图，我们针对超高分辨率 (VHR) Planet (3-m) 和 WorldView (1.8-m) 图像测试了 Landsat (30-m) 和 Sentinel (10-m)，并评估了机器的准确性学习分类方法，以区分美国佛罗里达州东海岸快速变化的生态交错带中的当前（2022）红树林和盐沼与其他沿海栖息地。我们的目标是（1）量化由于图像分辨率而导致的红树林和盐沼斑块在景观组成和复杂性、类优势和空间特性方面的可制图差异； (2) 解决该地区测绘的不确定性。我们发现，由于红树林的大小和范围太小而无法检测（准确度为 50%），Landsat 绘制前沿边缘红树林分布图的能力受到阻碍。 WorldView 在区分红树林和其他湿地栖息地方面最成功（准确率 84%），紧随其后的是 Planet（82%）和 Sentinel（81%）。 通过 WorldView，我们在佛罗里达范围限制研究区域内检测到了 800 公顷的红树林，比 Planet 检测到的红树林多了 35%，比 Sentinel 多了 114%，比 Landsat 多了 537%。更高分辨率的图像有助于揭示景观指标的额外变化，量化红树林和盐沼栖息地在景观、类别和斑块尺度上的多样性、空间配置和连通性。总体而言，VHR 卫星图像提高了我们在范围限制内绘制红树林地图的能力，并有助于补充中等分辨率的全球分布和过时的区域地图。