气候变化领域集成服务门户(CCPortal): A novel method for robust marine habitat mapping using a kernelised aquatic vegetation index

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DOI	10.1016/j.isprsjprs.2024.02.015
	A novel method for robust marine habitat mapping using a kernelised aquatic vegetation index
	Mastrantonis, Stanley; Radford, Ben; Langlois, Tim; Spencer, Claude; de Lestang, Simon; Hickey, Sharyn
发表日期	2024
ISSN	0924-2716
EISSN	1872-8235
起始页码	209
卷号	209
英文摘要	Efficient and timely mapping and monitoring of marine vegetation are becoming increasingly important as coastal habits have experienced significant declines in spatial extent due to climate change. Recent advances in machine learning and cloud computing, such as Google Earth Engine, have demonstrated that online analysis platforms make global-scale habitat mapping and monitoring possible. However, the mapping and monitoring of marine ecosystems with remote sensing is challenging, and we lack reliable, generalisable and scalable indices such as NDVI to assess spatiotemporal change in marine habitats. Here, we present a novel method for mapping coastal marine habitats using a kernelised aquatic vegetation index (kNDAVI) with spatially balanced in -water ground truthing and compare it to existing indices and mapping approaches. The kernelised vegetation index provides a simple, consistent, scalable and accurate method for mapping shallow marine vegetation across similar to 400 km of coastline along mid-west Australia (31.58(degrees)S - 29.56(degrees)S). This region has significant coastal macroalgae cover that provides critical recruitment habitat for many invertebrates, including commercially valuable fisheries, and has experienced significant loss due to climate-induced heatwaves. We extensively validate kNDAVI and satellite-derived covariates for their utility in mapping SAV using three approaches 1] cross -validation, 2] block cross -validation and 3] site validation. Habitat models that included the kernelised vegetation index achieved excellent agreement (Accuracy > 0.90 and Cohen's kappa > 0.80) for classifying submerged vegetation. We demonstrate that the kNDAVI index has considerable potential for large-scale vegetation monitoring and provides an applicable metric to map spatiotemporal dynamics and more effectively manage these changing coastal habitats.
英文关键词	Cloud Computing; Remote sensing; Marine habitat; Vegetation indices; Kernel methods; Submerged vegetation
语种	英语
WOS研究方向	Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001207623600001
来源期刊	ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299114
作者单位	University of Western Australia; University of Western Australia; University of Western Australia; Australian Institute of Marine Science; University of Western Australia; Western Australian Fisheries & Marine Research Laboratories
推荐引用方式 GB/T 7714	Mastrantonis, Stanley,Radford, Ben,Langlois, Tim,et al. A novel method for robust marine habitat mapping using a kernelised aquatic vegetation index[J],2024,209.
APA	Mastrantonis, Stanley,Radford, Ben,Langlois, Tim,Spencer, Claude,de Lestang, Simon,&Hickey, Sharyn.(2024).A novel method for robust marine habitat mapping using a kernelised aquatic vegetation index.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,209.
MLA	Mastrantonis, Stanley,et al."A novel method for robust marine habitat mapping using a kernelised aquatic vegetation index".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 209(2024).