Identification of dominant species of submerged vegetation based on Sentinel-2 red-edge band: A case study of Lake Erhai, China
Aquatic vegetation plays a crucial role as a primary producer and ecological regulator, serving as an indispensable purifier in inland lakes. However, weakened optical information caused by water absorption and reflection presents challenges in remote sensing identification and classification of sub...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25000974 |
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| author | Xiaohan Wang Yu Zhang Yanhong Yu Yunmei Li Heng Lyu Junda Li Xiaolan Cai Xianzhang Dong Gaolun Wang Jianzhong Li Mengmeng Song Lanlan Chen |
| author_facet | Xiaohan Wang Yu Zhang Yanhong Yu Yunmei Li Heng Lyu Junda Li Xiaolan Cai Xianzhang Dong Gaolun Wang Jianzhong Li Mengmeng Song Lanlan Chen |
| author_sort | Xiaohan Wang |
| collection | DOAJ |
| description | Aquatic vegetation plays a crucial role as a primary producer and ecological regulator, serving as an indispensable purifier in inland lakes. However, weakened optical information caused by water absorption and reflection presents challenges in remote sensing identification and classification of submerged vegetation (SAV). In this study, Sentinel-2 satellite data was employed to examine and compare the spectral attributes of three preeminent species, Potamogeton maackianus, Vallisneria natans, and Ceratophyllum demersum, within the confines of the Lake Erhai study area. Using the disparities in the red-edge band, red-edge chlorophyll index (CIedge), and red-edge normalized vegetation index (NDVIedge), a carefully designed decision tree model was instituted for remote sensing-driven identification of SAV. The ensuing species identification exhibited a commendable overall accuracy rate of 96.8%, underscored by a Kappa coefficient of 0.95. Geospatial distribution revealed the prevalence of P. maackianus in the northern lake bay, counterbalanced by the dominance of V. natans in the southern lake bay. Furthermore, an incisive inquiry into the nexus between diverse water quality variables and aquatic vegetation was conducted. Notably, water temperature (WT) emerged as a the most influential factor, exhibiting a highly significant positive correlation with SAV. The correlation coefficient reached 0.98, indicating a pronounced influence, with WT contributing substantively to the observed variance in SAV, accounting for 63.3%. The proposed method highlights the potential of Sentinel-2 red-edge bands for ecological monitoring and management of SAV, with implications for broader environmental applications. |
| format | Article |
| id | doaj-art-d7b86f50d8f740b0baca5387f5207575 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-d7b86f50d8f740b0baca5387f52075752025-02-04T04:10:21ZengElsevierEcological Indicators1470-160X2025-02-01171113168Identification of dominant species of submerged vegetation based on Sentinel-2 red-edge band: A case study of Lake Erhai, ChinaXiaohan Wang0Yu Zhang1Yanhong Yu2Yunmei Li3Heng Lyu4Junda Li5Xiaolan Cai6Xianzhang Dong7Gaolun Wang8Jianzhong Li9Mengmeng Song10Lanlan Chen11School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing 210023, ChinaSchool of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing 210023, ChinaYunnan Academy of Ecology & Environment Science, Kunming 650034, ChinaSchool of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing 210023, China; Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, China; Corresponding author.School of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing 210023, China; Jiangsu Center for Collaboration Invocation in Geographical Information Resource Development and Application, Nanjing 210023, ChinaSchool of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing 210023, ChinaSchool of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing 210023, ChinaSchool of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing 210023, ChinaSchool of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing 210023, ChinaSchool of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing 210023, ChinaSchool of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing 210023, ChinaSchool of Geography, Nanjing Normal University, Key Laboratory of Virtual Geographic Environment of Education Ministry, Nanjing 210023, ChinaAquatic vegetation plays a crucial role as a primary producer and ecological regulator, serving as an indispensable purifier in inland lakes. However, weakened optical information caused by water absorption and reflection presents challenges in remote sensing identification and classification of submerged vegetation (SAV). In this study, Sentinel-2 satellite data was employed to examine and compare the spectral attributes of three preeminent species, Potamogeton maackianus, Vallisneria natans, and Ceratophyllum demersum, within the confines of the Lake Erhai study area. Using the disparities in the red-edge band, red-edge chlorophyll index (CIedge), and red-edge normalized vegetation index (NDVIedge), a carefully designed decision tree model was instituted for remote sensing-driven identification of SAV. The ensuing species identification exhibited a commendable overall accuracy rate of 96.8%, underscored by a Kappa coefficient of 0.95. Geospatial distribution revealed the prevalence of P. maackianus in the northern lake bay, counterbalanced by the dominance of V. natans in the southern lake bay. Furthermore, an incisive inquiry into the nexus between diverse water quality variables and aquatic vegetation was conducted. Notably, water temperature (WT) emerged as a the most influential factor, exhibiting a highly significant positive correlation with SAV. The correlation coefficient reached 0.98, indicating a pronounced influence, with WT contributing substantively to the observed variance in SAV, accounting for 63.3%. The proposed method highlights the potential of Sentinel-2 red-edge bands for ecological monitoring and management of SAV, with implications for broader environmental applications.http://www.sciencedirect.com/science/article/pii/S1470160X25000974SAVRed edgeDominant species identification modelSentinel-2Lake Erhai |
| spellingShingle | Xiaohan Wang Yu Zhang Yanhong Yu Yunmei Li Heng Lyu Junda Li Xiaolan Cai Xianzhang Dong Gaolun Wang Jianzhong Li Mengmeng Song Lanlan Chen Identification of dominant species of submerged vegetation based on Sentinel-2 red-edge band: A case study of Lake Erhai, China Ecological Indicators SAV Red edge Dominant species identification model Sentinel-2 Lake Erhai |
| title | Identification of dominant species of submerged vegetation based on Sentinel-2 red-edge band: A case study of Lake Erhai, China |
| title_full | Identification of dominant species of submerged vegetation based on Sentinel-2 red-edge band: A case study of Lake Erhai, China |
| title_fullStr | Identification of dominant species of submerged vegetation based on Sentinel-2 red-edge band: A case study of Lake Erhai, China |
| title_full_unstemmed | Identification of dominant species of submerged vegetation based on Sentinel-2 red-edge band: A case study of Lake Erhai, China |
| title_short | Identification of dominant species of submerged vegetation based on Sentinel-2 red-edge band: A case study of Lake Erhai, China |
| title_sort | identification of dominant species of submerged vegetation based on sentinel 2 red edge band a case study of lake erhai china |
| topic | SAV Red edge Dominant species identification model Sentinel-2 Lake Erhai |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25000974 |
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