Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.3390/rs10081279 |
A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data | |
Chen, Qi; Yu, Ruihong1; Hao, Yanling1; Wu, Linhui; Zhang, Wenxing; Zhang, Qi; Bu, Xunan | |
通讯作者 | Yu, Ruihong ; Hao, Yanling |
来源期刊 | REMOTE SENSING
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ISSN | 2072-4292 |
出版年 | 2018 |
卷号 | 10期号:8 |
英文摘要 | It is difficult to accurately identify and extract bodies of water and underwater vegetation from satellite images using conventional vegetation indices, as the strong absorption of water weakens the spectral feature of high near-infrared (NIR) reflected by underwater vegetation in shallow lakes. This study used the shallow Lake Ulansuhai in the semi-arid region of China as a research site, and proposes a new concave-convex decision function to detect submerged aquatic vegetation (SAV) and identify bodies of water using Gao Fen 1 (GF-1) multi-spectral satellite images with a resolution of 16 meters acquired in July and August 2015. At the same time, emergent vegetation, Huangtai algae bloom, and SAV were classified simultaneously by a decision tree method. Through investigation and verification by field samples, classification accuracy in July and August was 92.17% and 91.79%, respectively, demonstrating that GF-1 data with four-day short revisit period and high spatial resolution can meet the standards of accuracy required by aquatic vegetation extraction. The results indicated that the concave-convex decision function is superior to traditional classification methods in distinguishing water and SAV, thus significantly improving SAV classification accuracy. The concave-convex decision function can be applied to waters with SAV coverage greater than 40% above 0.3 m and SAV coverage 40% above 0.1 m under 1.5 m transparency, which can provide new methods for the accurate extraction of SAV in other regions. |
英文关键词 | aquatic vegetation concave-convex decision function remote sensing extraction GF-1 satellite Lake Ulansuhai China |
类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
收录类别 | SCI-E |
WOS记录号 | WOS:000443618100115 |
WOS关键词 | REMOTE-SENSING DATA ; ALGAE-BLOOM ; WATER-DEPTH ; TAIHU LAKE ; IMAGERY ; MACROPHYTES ; CHINA ; IDENTIFICATION ; AGREEMENT ; CANOPIES |
WOS类目 | Remote Sensing |
WOS研究方向 | Remote Sensing |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/212641 |
作者单位 | 1.Inner Mongolia Univ, Inner Mongolia Key Lab River & Lake Ecol, Sch Ecol & Environm, Hohhot 010021, Peoples R China; 2.Inner Mongolia Univ, Minist Educ, Sch Ecol & Environm, Key Lab Ecol & Resource Use Mongolian Plateau, Hohhot 010021, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Qi,Yu, Ruihong,Hao, Yanling,et al. A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data[J],2018,10(8). |
APA | Chen, Qi.,Yu, Ruihong.,Hao, Yanling.,Wu, Linhui.,Zhang, Wenxing.,...&Bu, Xunan.(2018).A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data.REMOTE SENSING,10(8). |
MLA | Chen, Qi,et al."A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data".REMOTE SENSING 10.8(2018). |
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