Remote Sensing Applications and Decision Support

Phenology-based Spartina alterniflora mapping in coastal wetland of the Yangtze Estuary using time series of GaoFen satellite no. 1 wide field of view imagery

[+] Author Affiliations
Jinquan Ai

East China Normal University, Key Laboratory of Geographic Information Science, Ministry of Education, Shanghai, China

East China Normal University, School of Geographic Sciences, Shanghai, China

Wei Gao, Runhe Shi

East China Normal University, Key Laboratory of Geographic Information Science, Ministry of Education, Shanghai, China

East China Normal University, School of Geographic Sciences, Shanghai, China

East China Normal University, Joint Laboratory for Environmental Remote Sensing and Data Assimilation, Shanghai, China

East China Normal University and Colorado State University, Joint Research Institute for New Energy and the Environment, Shanghai, China

Zhiqiang Gao

Chinese Academy of Sciences, Yantai Institute of Coastal Zone Research, Yantai, China

Chao Zhang

East China Normal University, Key Laboratory of Geographic Information Science, Ministry of Education, Shanghai, China

East China Normal University, School of Geographic Sciences, Shanghai, China

East China Normal University, Joint Laboratory for Environmental Remote Sensing and Data Assimilation, Shanghai, China

J. Appl. Remote Sens. 11(2), 026020 (May 24, 2017). doi:10.1117/1.JRS.11.026020
History: Received November 10, 2016; Accepted May 3, 2017
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Abstract.  Spartina alterniflora is an aggressive invasive plant species that replaces native species, changes the structure and function of the ecosystem across coastal wetlands in China, and is thus a major conservation concern. Mapping the spread of its invasion is a necessary first step for the implementation of effective ecological management strategies. The performance of a phenology-based approach for S. alterniflora mapping is explored in the coastal wetland of the Yangtze Estuary using a time series of GaoFen satellite no. 1 wide field of view camera (GF-1 WFV) imagery. First, a time series of the normalized difference vegetation index (NDVI) was constructed to evaluate the phenology of S. alterniflora. Two phenological stages (the senescence stage from November to mid-December and the green-up stage from late April to May) were determined as important for S. alterniflora detection in the study area based on NDVI temporal profiles, spectral reflectance curves of S. alterniflora and its coexistent species, and field surveys. Three phenology feature sets representing three major phenology-based detection strategies were then compared to map S. alterniflora: (1) the single-date imagery acquired within the optimal phenological window, (2) the multitemporal imagery, including four images from the two important phenological windows, and (3) the monthly NDVI time series imagery. Support vector machines and maximum likelihood classifiers were applied on each phenology feature set at different training sample sizes. For all phenology feature sets, the overall results were produced consistently with high mapping accuracies under sufficient training samples sizes, although significantly improved classification accuracies (10%) were obtained when the monthly NDVI time series imagery was employed. The optimal single-date imagery had the lowest accuracies of all detection strategies. The multitemporal analysis demonstrated little reduction in the overall accuracy compared with the use of monthly NDVI time series imagery. These results show the importance of considering the phenological stage for image selection for mapping S. alterniflora using GF-1 WFV imagery. Furthermore, in light of the better tradeoff between the number of images and classification accuracy when using multitemporal GF-1 WFV imagery, we suggest using multitemporal imagery acquired at appropriate phenological windows for S. alterniflora mapping at regional scales.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Jinquan Ai ; Wei Gao ; Zhiqiang Gao ; Runhe Shi and Chao Zhang
"Phenology-based Spartina alterniflora mapping in coastal wetland of the Yangtze Estuary using time series of GaoFen satellite no. 1 wide field of view imagery", J. Appl. Remote Sens. 11(2), 026020 (May 24, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.026020


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