Dynamic monitoring of invasive Spartina alterniflora clearance via fusion of Sentinel-2 and GF-1 time series images

被引：0
|
作者：
Min Y. [1 ,2 ,3 ]
Ke Y. [1 ,2 ,3 ]
Han Y. [1 ,2 ,3 ]
Yin X. [1 ,2 ,3 ]
Zhou D. [1 ,2 ,3 ]
机构：
[1] State Key Laboratory Cultivation Base of Urban Environment Process and Simulation, Beijing
[2] Beijing Laboratory of Water Resources Security, Beijing
[3] College of Resource Environment and Tourism, Capital Normal University, Beijing
来源：
National Remote Sensing Bulletin | 2023年 / 27卷 / 06期
基金：
中国国家自然科学基金;
关键词：
coastal wetland; GF-1; invasive species; Sentinel-2; spartina alterniflora management; time series;
D O I：
10.11834/jrs.20232279
中图分类号：
学科分类号：
摘要：
Invasion of Spartina alterniflora poses a serious threat to the biodiversity and ecosystem health of coastal wetlands in China. Many coastal provinces in China have initiated projects for clearance and treatment of S. alterniflora in recent years. The timely and accurate understanding of S. alterniflora clearance dynamics is crucial in coastal wetland management and decision making. The objective of this study was to propose a new method for monitoring S. alterniflora clearance dynamics on the basis of dense time series remote sensing images. The Yellow River Estuary wetland was taken as the study area in this work. First, Sentinel-2 MSI, GF-1 PMS, and GF-1 WFV images were fused to construct time-series Normalized Difference Vegetation Index (NDVI) dataset. Second, temporal variations of NDVI were analyzed, and the potential clearance periods were detected. Finally, tidal inundation was examined and S. alterniflora clearance date was identified by eliminating the influence of tidal inundation on NDVI time series. The map of S. alterniflora clearance dates with a spatial resolution of 10 m was obtained for the Yellow River Estuary. The overall accuracy of clearance dates was 88.24%, and the Kappa coefficient was 0.87. Results showed that the fusion of Sentinel-2 and GF-1 data can effectively improve the identification accuracy of clearance dates compared with the single Sentinel-2 date source. The cleared area of S. alterniflora from September to December 2021 was 4816.35 ha, which accounts for 92.81% of the total S. alterniflora region in the study area. Uncleared areas are mainly distributed in the coastal areas of the north shore with complex hydrology and interlaced tidal creeks. The project was completed in two stages because of the early October flood peak in the lower reaches of the Yellow River. The first stage was finished between early September and early October, and the second stage was concluded between mid-October and mid-December, with the majority of S. alterniflora being cleared in early December. The rapid and accurate observation of the dynamics of S. alterniflora clearance through the proposed method is crucial in the monitoring and evaluation of S. alterniflora treatment and wetland restoration projects in coastal wetlands across the country. This method is expected to be applied to the dynamic monitoring and comprehensive evaluation of the effectiveness of large-scale treatment projects. © 2023 National Remote Sensing Bulletin. All rights reserved.
引用
收藏
页码：5 / 17
页数：12
相关论文
共 31 条
  • [1] Chen J, Wang S Y, Mao Z P, Monitoring wetland changes in Yellow River Delta by remote sensing during 1976- 2008, Progress in Geography, 30, 5, pp. 585-592, (2011)
  • [2] Chen Y N, Gao S, Jia J J, Wang A J, Tidalflat ecological changes by transplanting Spartina anglica and Spartina alterniflorea, northern Jiangsu coast, Oceanologia Et Limnologia Sinica, 36, 5, pp. 394-403, (2005)
  • [3] The Yellow River flood no. 3 in 2021, the Ministry of Water Resources maintained level III emergency response for flood and drought disaster prevention, (2021)
  • [4] Chung C H, Forty years of ecological engineering with Spartina plantations in China, Ecological Engineering, 27, 1, pp. 49-57, (2006)
  • [5] Fernandez-Guisuraga J M, Calvo L, Suarez-Seoane S, Comparison of pixel unmixing models in the evaluation of post-fire forest resilience based on temporal series of satellite imagery at moderate and very high spatial resolution, ISPRS Journal of Photogrammetry and Remote Sensing, 164, pp. 217-228, (2020)
  • [6] Gao F, Anderson M C, Zhang X Y, Yang Z W, Alfieri J G, Kustas W P, Mueller R, Johnson D M, Prueger J H, Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery, Remote Sensing of Environment, 188, pp. 9-25, (2017)
  • [7] Han Y, Ke Y H, Zhu L J, Feng H, Zhang Q, Sun Z, Zhu L, Tracking vegetation degradation and recovery in multiple mining areas in Beijing, China, based on time-series Landsat imagery, GIScience and Remote Sensing, 58, 8, pp. 1477-1496, (2021)
  • [8] Lei K, Zhang M X, The wetland resources in china and the conservation advices, Wetland Science, 3, 2, pp. 81-86, (2005)
  • [9] Li F F, Ecological management of Spartina alterniflora, Farmers' Daily
  • [10] Li Y R, Wu H T, Zhang S, Lu X, Lu K L, Morphological characteristics and changes of tidal creeks in coastal wetlands of the Yellow River Delta under Spartina alterniflora invasion and continuous expansion, Wetland Science, 19, 1, pp. 88-97, (2021)
1234