A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data
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. Th...
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| Format: | Article |
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MDPI AG
2018-08-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/10/8/1279 |
| _version_ | 1828137720018370560 |
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| author | Qi Chen Ruihong Yu Yanling Hao Linhui Wu Wenxing Zhang Qi Zhang Xunan Bu |
| author_facet | Qi Chen Ruihong Yu Yanling Hao Linhui Wu Wenxing Zhang Qi Zhang Xunan Bu |
| author_sort | Qi Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:24:15Z |
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| id | doaj.art-7f00f5467ced490fa0ee88a302db183f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:24:15Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-7f00f5467ced490fa0ee88a302db183f2022-12-22T04:09:40ZengMDPI AGRemote Sensing2072-42922018-08-01108127910.3390/rs10081279rs10081279A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite DataQi Chen0Ruihong Yu1Yanling Hao2Linhui Wu3Wenxing Zhang4Qi Zhang5Xunan Bu6Inner Mongolia Key Laboratory of River and Lake Ecology & Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, ChinaInner Mongolia Key Laboratory of River and Lake Ecology & Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, ChinaInner Mongolia Key Laboratory of River and Lake Ecology & Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, ChinaInner Mongolia Key Laboratory of River and Lake Ecology & Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, ChinaInner Mongolia Key Laboratory of River and Lake Ecology & Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, ChinaInner Mongolia Key Laboratory of River and Lake Ecology & Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, ChinaInner Mongolia Key Laboratory of River and Lake Ecology & Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, ChinaIt 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.http://www.mdpi.com/2072-4292/10/8/1279aquatic vegetationconcave–convex decision functionremote sensing extractionGF-1 satelliteLake UlansuhaiChina |
| spellingShingle | Qi Chen Ruihong Yu Yanling Hao Linhui Wu Wenxing Zhang Qi Zhang Xunan Bu A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data Remote Sensing aquatic vegetation concave–convex decision function remote sensing extraction GF-1 satellite Lake Ulansuhai China |
| title | A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data |
| title_full | A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data |
| title_fullStr | A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data |
| title_full_unstemmed | A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data |
| title_short | A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data |
| title_sort | new method for mapping aquatic vegetation especially underwater vegetation in lake ulansuhai using gf 1 satellite data |
| topic | aquatic vegetation concave–convex decision function remote sensing extraction GF-1 satellite Lake Ulansuhai China |
| url | http://www.mdpi.com/2072-4292/10/8/1279 |
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