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DOI	10.3390/rs12233986
	Driven by Drones: Improving Mangrove Extent Maps Using High-Resolution Remote Sensing
	Hsu, Astrid J.; Kumagai, Joy; Favoretto, Fabio; Dorian, John; Martinez, Benigno Guerrero; Aburto-Oropeza, Octavio
通讯作者	Hsu, AJ (corresponding author), Univ Calif San Diego, Scripps Inst Oceanog, San Diego, CA 92093 USA.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:23
英文摘要	This study investigated how different remote sensing techniques can be combined to accurately monitor mangroves. In this paper, we present a framework to use drone imagery to calculate correction factors which can improve the accuracy of satellite-based mangrove extent. We focus on semi-arid dwarf mangroves of Baja California Sur, Mexico, where the mangroves tend to be stunted in height and found in small patches, as well as larger forests. Using a DJI Phantom 4 Pro, we imaged mangroves and labeled the extent by manual classification in QGIS. Using ArcGIS, we compared satellite-based mangrove extent maps from Global Mangrove Watch (GMW) in 2016 and Mexico's national government agency (National Commission for the Knowledge and Use of Biodiversity, CONABIO) in 2015, with extent maps generated from in situ drone studies in 2018 and 2019. We found that satellite-based extent maps generally overestimated mangrove coverage compared to that of drone-based maps. To correct this overestimation, we developed a method to derive correction factors for GMW mangrove extent. These correction factors correspond to specific pixel patterns generated from a convolution analysis and mangrove coverage defined from drone imagery. We validated our model by using repeated k-fold cross-validation, producing an accuracy of 98.3% +/- 2.1%. Overall, drones and satellites are complementary tools, and the rise of machine learning can help stakeholders further leverage the strengths of the two tools, to better monitor mangroves for local, national, and international management.
英文关键词	unmanned aerial vehicles monitoring geographic information systems convolution area correction international commitments
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000597530800001
WOS关键词	CLIMATE-CHANGE ; FORESTS ; UAV ; BIOMASS ; IMAGERY ; INDEX
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/369230
作者单位	[Hsu, Astrid J.; Kumagai, Joy; Dorian, John; Aburto-Oropeza, Octavio] Univ Calif San Diego, Scripps Inst Oceanog, San Diego, CA 92093 USA; [Kumagai, Joy] Senckenberg Climate & Biodivers Res Ctr, D-60325 Frankfurt, Germany; [Favoretto, Fabio; Martinez, Benigno Guerrero] Ctr Biodiversidad Marina & Conservac, La Paz 23090, Mexico; [Favoretto, Fabio] Univ Autonoma Baja California, Dept Ingn Pesquerias, La Paz 23080, Mexico; [Dorian, John] Univ Calif San Diego, Scripps Inst Oceanog & Comp Sci & Engn, San Diego, CA 92093 USA
推荐引用方式 GB/T 7714	Hsu, Astrid J.,Kumagai, Joy,Favoretto, Fabio,et al. Driven by Drones: Improving Mangrove Extent Maps Using High-Resolution Remote Sensing[J],2020,12(23).
APA	Hsu, Astrid J.,Kumagai, Joy,Favoretto, Fabio,Dorian, John,Martinez, Benigno Guerrero,&Aburto-Oropeza, Octavio.(2020).Driven by Drones: Improving Mangrove Extent Maps Using High-Resolution Remote Sensing.REMOTE SENSING,12(23).
MLA	Hsu, Astrid J.,et al."Driven by Drones: Improving Mangrove Extent Maps Using High-Resolution Remote Sensing".REMOTE SENSING 12.23(2020).