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Classification of salt marsh vegetation based on pixel-level time series from Landsat images

  • role： First author第一作者
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

  • Email：nbuzhengjh@gmail.com
  • Introduction：郑嘉豪，研究方向为湿地遥感。E-mail：nbuzhengjh@gmail.com
ZHENG Jiahao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

    Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research at Ningbo University, Ningbo 315211, China

    Donghai Academy, Ningbo University, Ningbo 315211, China

  • Email：sunchao@nbu.edu.cn
  • Introduction：孙超，研究方向为海岸带资源环境遥感。E-mail：sunchao@nbu.edu.cn
SUN Chao123*,  
  • Affiliation：

    Ningbo Institute of Surveying, Mapping and Remote Sensing, Ningbo 315042, China

LIN Yun4,  
  • Affiliation：

    Ningbo Institute of Surveying, Mapping and Remote Sensing, Ningbo 315042, China

LI Lu4,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

    Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research at Ningbo University, Ningbo 315211, China

    Donghai Academy, Ningbo University, Ningbo 315211, China

LIU Yongchao123

résumé

Salt marshes are the world’s most valuable and vulnerable ecosystem. Thus, accurate and timely monitoring of the distribution of salt marsh vegetation is essential. With the accumulation of multi-source remote sensing imagery, the time-series method has increasingly become important for monitoring coastal areas. However, effectively constructing time series is still challenging as the number of available observations is relatively low owing to the frequent cloudy weather in the coastal areas. In this study, we coupled multi-sourced Landsat images and constructed a pixel-level time series with XGBoost. Based on this, the feasibility and stability of classifying salt marsh vegetation were tested using the three typical sites in the Yangtze River Delta. Results showed that (1) Inter-calibration for multi-sourced images was necessary for not only improving the availability of images but also reducing the spectral differences among sensors. (2) The performance of salt marsh vegetation classification based on the pixel-level time series was favorable, reflected by 81.50% as the mean overall accuracy and 0.755 as the Kappa coefficient. The classification results were excellent, particularly for the widely distributed Suaeda salsa and Spartina alterniflora in the Yangtze River Delta. (3) Compared with the single-phrase classifications, the pixel-level time series–based classifications were stable, evidenced by an inter-annual absolute mean error lower than 3.27%. Therefore, our proposed method is expected for dynamic monitoring of salt marsh vegetation, which facilitates managing coastal resources and implementing ecological conservation effectively of China’s coasts.

mots-clés

remote sensing classification;salt marsh vegetation;Landsat;pixel-level time-series;XGBoost;Yangtze River Delta;Red-crowned Crane Nature Reserve;Jiuduansha Wetland;Southern Coastal Wetland in Hangzhou Bay

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