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DOI | 10.1016/j.rse.2020.111916 |
Quantifying expansion and removal of Spartina alterniflora on Chongming island, China, using time series Landsat images during 1995–2018 | |
Zhang X.; Xiao X.; Wang X.; Xu X.; Chen B.; Wang J.; Ma J.; Zhao B.; Li B. | |
发表日期 | 2020 |
ISSN | 00344257 |
卷号 | 247 |
英文摘要 | The rampant encroachment of Spartina alterniflora into coastal wetlands of China over the past decades has adversely affected both coastal ecosystems and socio-economic systems. However, there are no annual or multi-year epoch maps of Spartina saltmarsh in China, which hinders our understanding and management of Spartina invasion. In this study, we selected Chongming island, China, where Spartina saltmarsh had expanded rapidly since its introduction in the 1990s. We investigated phenology of Spartina, Phragmites and Scirpus saltmarshes, and the time series vegetation indices derived from Landsat images showed that Spartina saltmarsh did not green-up in April–May and stayed green in December–January, which differed from the phenology of Phragmites and Scirpus saltmarshes. We developed a pixel- and phenology-based algorithm that used time series Landsat data to identify and map Spartina saltmarsh, and we applied it to quantify the temporal dynamics (expansion and removal) of Spartina saltmarsh on Chongming island during 1995–2018. The resultant maps showed that Spartina saltmarsh area on Chongming island increased from ~4 ha in 1995 to ~2067 ha in 2012 but dropped substantially to ~729 ha in 2016 after a large-scale ecological engineering project (US$ 186 million) was started to remove Spartina during 2013–2016. Chongming island still had ~1315 ha Spartina saltmarsh in 2018, and majority of it was distributed outside the Chongming Dongtan National Nature Reserve, which could serve as the sources for reinvasion in the near future. This study demonstrates the feasibility of using time series Landsat images, pixel- and phenology-based algorithm, and GEE platform to identify and map Spartina saltmarsh over years in the region, which is useful to the management of invasive plants in coastal wetlands. © 2020 Elsevier Inc. |
英文关键词 | Chongming island; Phenology; Spartina alterniflora; Time series Landsat images |
语种 | 英语 |
scopus关键词 | Biology; Ecosystems; Environmental management; Expansion; Forestry; Pixels; Wetlands; Chongming islands; Coastal ecosystems; Coastal wetlands; Ecological engineering; Socio-economic systems; Spartina alterniflora; Temporal dynamics; Vegetation index; Time series; algorithm; coastal wetland; conservation management; ecological engineering; grass; introduced species; Landsat; phenology; pixel; pollutant removal; quantitative analysis; saltmarsh; satellite altimetry; satellite data; satellite imagery; socioeconomic conditions; time series analysis; vegetation index; China; Chongming Island; Shanghai; Phragmites; Scirpus; Spartina; Spartina alterniflora |
来源期刊 | Remote Sensing of Environment
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文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/179227 |
作者单位 | Ministry of Education Key Laboratory of Biodiversity Science and Ecological Engineering, Coastal Ecosystems Research Station of the Yangtze River Estuary, Institute of Biodiversity Science, School of Life Sciences, Fudan University, Shanghai, 200438, China; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK 73019, United States; Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS)Hainan Province 571737, China |
推荐引用方式 GB/T 7714 | Zhang X.,Xiao X.,Wang X.,等. Quantifying expansion and removal of Spartina alterniflora on Chongming island, China, using time series Landsat images during 1995–2018[J],2020,247. |
APA | Zhang X..,Xiao X..,Wang X..,Xu X..,Chen B..,...&Li B..(2020).Quantifying expansion and removal of Spartina alterniflora on Chongming island, China, using time series Landsat images during 1995–2018.Remote Sensing of Environment,247. |
MLA | Zhang X.,et al."Quantifying expansion and removal of Spartina alterniflora on Chongming island, China, using time series Landsat images during 1995–2018".Remote Sensing of Environment 247(2020). |
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