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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Main Authors: Xiaohan Wang, Yu Zhang, Yanhong Yu, Yunmei Li, Heng Lyu, Junda Li, Xiaolan Cai, Xianzhang Dong, Gaolun Wang, Jianzhong Li, Mengmeng Song, Lanlan Chen
Format: Article
Language:English
Published: Elsevier 2025-02-01
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.
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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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