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Long Term Aquatic Vegetation Dynamics in Longgan Lake Using Landsat Time Series and Their Responses to Water Level Fluctuation

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成果类型:
期刊论文
作者:
Tan, Wenxia;Xing, Jindi;Yang, Shao*;Yu, Gongliang;Sun, Panpan;...
通讯作者:
Yang, Shao
作者机构:
[Xing, Jindi; Jiang, Yan; Tan, Wenxia] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.
[Yang, Shao; Sun, Panpan] Cent China Normal Univ, Sch Life Sci, Wuhan 430079, Peoples R China.
[Yu, Gongliang] Wuhan Univ, Inst Hydrobiol, Key Lab Algal Biol, Wuhan 430072, Peoples R China.
通讯机构:
[Yang, Shao] C
Cent China Normal Univ, Sch Life Sci, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
spatial-temporal dynamics;aquatic vegetation;water level fluctuation;Longgan lake;Google Earth Engine
期刊:
Water
ISSN:
2073-4441
年:
2020
卷:
12
期:
8
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [31670464, 41506208]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [CCNU19TD002, CCNU18ZDPY03, CCNU19TS001]
机构署名:
本校为第一且通讯机构
院系归属:
城市与环境科学学院
生命科学学院
摘要:
Aquatic vegetation in shallow freshwater lakes are severely degraded worldwide, even though they are essential for inland ecosystem services. Detailed information about the long term variability of aquatic plants can help investigate the potential driving mechanisms and help mitigate the degradation. In this paper, based on Google Earth Engine cloud-computing platform, we made use of a 33-year (1987-2019) retrospective archive of moderate resolution Landsat TM, ETM + and OLI satellite images to estimate the extent changes in aquatic vegetation ...

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