Uncovering mangrove range limits using very high resolution satellite imagery to detect fine‐scale mangrove and saltmarsh habitats in dynamic coastal ecotones

IF 3.9 2区 环境科学与生态学 Q1 ECOLOGY Remote Sensing in Ecology and Conservation Pub Date : 2024-05-24 DOI:10.1002/rse2.394
Cheryl L. Doughty, Kyle C. Cavanaugh, Samantha Chapman, Lola Fatoyinbo
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Abstract

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.
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利用甚高分辨率卫星图像探测动态沿海生态带中红树林和盐沼生境的精细尺度,揭示红树林的范围极限
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,在全球范围内正受到人类压力和气候变化的威胁。然而,在热带-温带过渡带的几个地理范围极限,红树林生态系统正随着宏观气候驱动因素的变化向极地扩展。靠近分布范围极限的红树林通常生长得较小,并与其他沿海栖息地形成动态的斑块分布,这很难用中等分辨率(30 米)的卫星图像进行测绘。因此,全球分布图中缺少许多这样的红树林区域。为了更好地绘制小型灌丛红树林地图,我们将 Landsat(30 米)和 Sentinel(10 米)与甚高分辨率(VHR)Planet(3 米)和 WorldView(1.8 米)图像进行了对比测试,并评估了机器学习分类方法在美国佛罗里达州东海岸快速变化的生态区中将当前(2022 年)红树林和盐沼与其他沿海生境区分开来的准确性。我们的目标是:(1) 量化红树林和盐沼斑块的景观组成和复杂性、类别优势和空间属性因图像分辨率而产生的可测绘差异;(2) 解决该地区测绘的不确定性。我们发现,由于红树林的面积和范围太小,无法进行探测(准确率为 50%),因此大地遥感卫星绘制红树林分布图的能力受到了影响。WorldView 在区分红树林和其他湿地生境方面最为成功(准确率 84%),紧随其后的是 Planet(82%)和 Sentinel(81%)。利用 WorldView,我们在佛罗里达州范围限制研究区域内发现了 800 公顷的红树林,比利用 Planet 发现的红树林多 35%,比 Sentinel 多 114%,比 Landsat 多 537%。更高分辨率的图像有助于揭示景观指标的更多变化,这些指标量化了红树林和盐沼栖息地在景观、等级和斑块尺度上的多样性、空间配置和连接性。总体而言,VHR 卫星图像提高了我们绘制红树林分布范围界限图的能力,有助于补充中等分辨率的全球分布图和过时的区域图。
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来源期刊
Remote Sensing in Ecology and Conservation
Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
9.80
自引率
5.50%
发文量
69
审稿时长
18 weeks
期刊介绍: emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.
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