干旱区生态环境知识资源中心(Arid): Stream Boundary Detection of a Hyper-Arid, Polar Region Using a U-Net Architecture: Taylor Valley, Antarctica

Arid

DOI	10.3390/rs14010234
	Stream Boundary Detection of a Hyper-Arid, Polar Region Using a U-Net Architecture: Taylor Valley, Antarctica
	Barlow, Mary C.; Zhu, Xinxiang; Glennie, Craig L.
通讯作者	Barlow, MC (corresponding author)，Univ Houston, Natl Ctr Airborne Laser Mapping, 5000 Gulf Freeway,Bldg 4,Room 216, Houston, TX 77204 USA.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:1
英文摘要	Convolutional neural networks (CNNs) are becoming an increasingly popular approach for classification mapping of large complex regions where manual data collection is too time consuming. Stream boundaries in hyper-arid polar regions such as the McMurdo Dry Valleys (MDVs) in Antarctica are difficult to locate because they have little hydraulic flow throughout the short summer months. This paper utilizes a U-Net CNN to map stream boundaries from lidar derived rasters in Taylor Valley located within the MDVs, covering & SIM;770 km(2). The training dataset consists of 217 (300 x 300 m(2)) well-distributed tiles of manually classified stream boundaries with diverse geometries (straight, sinuous, meandering, and braided) throughout the valley. The U-Net CNN is trained on elevation, slope, lidar intensity returns, and flow accumulation rasters. These features were used for detection of stream boundaries by providing potential topographic cues such as inflection points at stream boundaries and reflective properties of streams such as linear patterns of wetted soil, water, or ice. Various combinations of these features were analyzed based on performance. The test set performance revealed that elevation and slope had the highest performance of the feature combinations. The test set performance analysis revealed that the CNN model trained with elevation independently received a precision, recall, and F1 score of 0.94 & PLUSMN;0.05, 0.95 & PLUSMN;0.04, and 0.94 & PLUSMN;0.04 respectively, while slope received 0.96 & PLUSMN;0.03, 0.93 & PLUSMN;0.04, and 0.94 & PLUSMN;0.04, respectively. The performance of the test set revealed higher stream boundary prediction accuracies along the coast, while inland performance varied. Meandering streams had the highest stream boundary prediction performance on the test set compared to the other stream geometries tested here because meandering streams are further evolved and have more distinguishable breaks in slope, indicating stream boundaries. These methods provide a novel approach for mapping stream boundaries semi-automatically in complex regions such as hyper-arid environments over larger scales than is possible for current methods.
英文关键词	lidar fluvial geomorphology stream width remote sensing deep learning
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:000749848700001
WOS关键词	MCMURDO DRY VALLEYS ; BANKFULL WIDTH ; EXTRACTION ; CHANNELS
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/2XILL650/376735
作者单位	[Barlow, Mary C.; Zhu, Xinxiang; Glennie, Craig L.] Univ Houston, Natl Ctr Airborne Laser Mapping, 5000 Gulf Freeway,Bldg 4,Room 216, Houston, TX 77204 USA
推荐引用方式 GB/T 7714	Barlow, Mary C.,Zhu, Xinxiang,Glennie, Craig L.. Stream Boundary Detection of a Hyper-Arid, Polar Region Using a U-Net Architecture: Taylor Valley, Antarctica[J],2022,14(1).
APA	Barlow, Mary C.,Zhu, Xinxiang,&Glennie, Craig L..(2022).Stream Boundary Detection of a Hyper-Arid, Polar Region Using a U-Net Architecture: Taylor Valley, Antarctica.REMOTE SENSING,14(1).
MLA	Barlow, Mary C.,et al."Stream Boundary Detection of a Hyper-Arid, Polar Region Using a U-Net Architecture: Taylor Valley, Antarctica".REMOTE SENSING 14.1(2022).