Mapping Invasive Spartina alterniflora Using Phenological Information and Red-Edge Bands of Sentinel-2 Time-Series Data
Year: 2024
Type: article
Abstract: The accurate mapping ofSpartina alterniflora(S. alterniflora) invasion is crucial for controlling its spread and reducing severe ecological problems. Satellite images have been extensively employed forS. alterniflorainvasion monitoring; however, there are still several issues that need to be addressed. The spectral similarities betweenS. alternifloraand surrounding ground objects make it challenging for traditional classifiers to achieve satisfactory extraction accuracy. Since the phenological information and red-edge spectral differences have been considered as informative features for identifyingS. alterniflora, current studies mainly used them separately as classification features and seldom considered the differences of red-edge information at different phenological periods. Therefore, we proposed a pixel-based phenological and red-edge feature composite method (PpRef-CM) forS. alternifloraextraction considering both phenological information and red-edge bands derived from Sentinel-2 time series based on the existing pixel-based phenological feature composite method (Ppf-CM). The proposed PpRef-CM and machine-learning algorithms were employed forS. alternifloraextraction in two typical mangrove forests along coastal China. Results indicated that red-edge information at different phenological periods is essential for detectingS. alterniflora.S. alternifloraextraction achieved the highest accuracy of 96.57% by using the eXtreme gradient boost algorithm when compared with other machine-learning algorithms. The PpRef-CM gave 2.72% and 2.61% more extraction accuracies ofS. alterniflorathan the Ppf-CM in two study sites, separately. These findings provide insights for selecting suitable classification features forS. alternifloraextraction studies and serve as an effective control and management ofS. alterniflora.
Authors: Yiwei Ma, Li Zhuo, Jingjing Cao
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $1,496
OpenAlex ID: W4404179425