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Classification and change detection of vegetation in the Ruoergai Wetland using optical and SAR remote sensing data

  • role： First author第一作者
  • Affiliation：

    Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi'an 710127, China

  • Email：mys@stumail.nwu.edu.cn
  • Introduction：明義森，研究方向为遥感与GIS应用。E-mail： mys@stumail.nwu.edu.cn
MING Yisen,  
  • Affiliation：

    Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi'an 710127, China

LIU Qihang,  
  • Affiliation：

    Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi'an 710127, China

BAI He,  
  • role： Corresponding author通信作者
  • Affiliation：

    Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi'an 710127, China

  • Email：changh@nwu.edu.cn
  • Introduction：黄昌，研究方向为遥感和GIS应用。E-mail： changh@nwu.edu.cn
HUANG Chang*

Resümee

Wetland vegetation plays an important role in the process of carbon sequestration. As a typical alpine wetland ecosystem, the Ruoergai wetland has been attracting increasing attention due to its carbon sink function, which makes the classification and change detection of its vegetation coverage crucial.This study aims to present a method for mapping the vegetation of the Ruoergai wetland and monitor its change by integrating Sentinel-2 optical data and Sentinel-1 Synthetic Aperture Radar (SAR) data, taking advantage of their respective advantages.We utilize the spectral characteristics of Sentinel-2 MSI data and adopt the dynamic time warping algorithm to extract the time-series phenological characteristics of Sentinel-1 SAR data; in this manner, different wetland vegetations can be differentiated easily. The random forest algorithm was used to combine both data for classifying wetland vegetation types. In-site samples obtained by UAV in 2020 are used and migrated to 2017 to train and validate the classification results.The classification results have an overall accuracy of 97.43% and a Kappa coefficient of 0.96. Vegetation in the Ruoerge wetlands remains overall unchanged from 2017 to 2020, with the changed area exhibiting a recovery trend (~7% of the total area).On the basis of the principle of sample migration, this study solved the problem in which the field samples were not available in the historical period. By combining Sentinel-1 and Sentinel-2 data, their respective multitemporal and multispectral advantages were fully exploited to obtain reliable classification results.

Schlüsselwort

remote sensing;Change Detection;training sample migration;SAR;wetland vegetation classification;Zoige wetland

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