气候变化领域集成服务门户(CCPortal): Automatically quantifying evolution of retrogressive thaw slumps in Beiluhe (Tibetan Plateau) from multi-temporal CubeSat images

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DOI	10.1016/j.jag.2021.102399
	Automatically quantifying evolution of retrogressive thaw slumps in Beiluhe (Tibetan Plateau) from multi-temporal CubeSat images
	Huang, Lingcao; Liu, Lin; Luo, Jing; Lin, Zhanju; Niu, Fujun
通讯作者	Huang, LC (通讯作者)，Chinese Univ Hong Kong, Fac Sci, Earth Syst Sci Programme, Hong Kong, Peoples R China. ; Huang, LC (通讯作者)，Univ Colorado, Earth Sci & Observat Ctr, Cooperat Inst Res Environm Sci, Boulder, CO 80309 USA.
发表日期	2021
ISSN	1569-8432
EISSN	1872-826X
卷号	102
英文摘要	Retrogressive thaw slumps (RTSs) are among the most dynamic landforms resulting from the thawing of ice-rich permafrost. However, RTS distribution and evolution are poorly quantified because most of them occur in remote and inaccessible areas. In this study, we propose a method that integrates deep learning, change detection, and medial axis transform, aiming to automatically quantify the RTS development on multi-temporal images in the Beiluhe region on the Tibetan Plateau from 2017 to 2019. The images are taken by the Planet CubeSat constellation with high spatial and temporal resolution. The experiments show that automatic delineation based on deep learning can produce similar results to manual delineation, providing the potential of using these results to quantify the changes of RTS boundaries in different years. Our method reveals that among manuallydelineated 342 RTSs in the Beiluhe region, 83% and 76% of them expanded from 2017 to 2018 and 2018 to 2019, respectively. For the expansion from 2017 to 2018, the average and maximum expanding areas are 0.20 ha and 1.47 ha, while the average and maximum retreat distances are 21.3 m and 91 m, respectively. For 2018 to 2019 the average and maximum expansion areas and retreat distances are 0.22 ha, 2.53 ha, 25.0 m, and 212 m, respectively. The results show that the method can quantify RTS development automatically on multi-temporal images but may miss some small and subtle RTSs. Moreover, this study provides the very first quantitative report on RTS development on the Tibetan Plateau, which helps to advance the understanding of permafrost degradation.
关键词	CLIMATE-CHANGE ACTIVE-LAYER PERMAFROST THERMOKARST ECOSYSTEMS BASIN
英文关键词	Change Detection; Deep Learning; Medial Axis Transform; Permafrost; Retrogressive Thaw Slumps
语种	英语
WOS研究方向	Remote Sensing
WOS类目	Remote Sensing
WOS记录号	WOS:000700850000003
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254328
作者单位	[Huang, Lingcao; Liu, Lin] Chinese Univ Hong Kong, Fac Sci, Earth Syst Sci Programme, Hong Kong, Peoples R China; [Luo, Jing; Lin, Zhanju; Niu, Fujun] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou, Peoples R China; [Luo, Jing; Lin, Zhanju; Niu, Fujun] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Frozen Soil Engn, Lanzhou, Peoples R China; [Huang, Lingcao] Univ Colorado, Earth Sci & Observat Ctr, Cooperat Inst Res Environm Sci, Boulder, CO 80309 USA
推荐引用方式 GB/T 7714	Huang, Lingcao,Liu, Lin,Luo, Jing,et al. Automatically quantifying evolution of retrogressive thaw slumps in Beiluhe (Tibetan Plateau) from multi-temporal CubeSat images[J]. 中国科学院西北生态环境资源研究院,2021,102.
APA	Huang, Lingcao,Liu, Lin,Luo, Jing,Lin, Zhanju,&Niu, Fujun.(2021).Automatically quantifying evolution of retrogressive thaw slumps in Beiluhe (Tibetan Plateau) from multi-temporal CubeSat images.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,102.
MLA	Huang, Lingcao,et al."Automatically quantifying evolution of retrogressive thaw slumps in Beiluhe (Tibetan Plateau) from multi-temporal CubeSat images".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 102(2021).