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Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images

  • role： First author第一作者
  • Email：zhangleigis@outlook.com
  • Introduction：张磊，1993年生，男，硕士研究生，研究方向为遥感技术及地学分析应用。E-mail：zhangleigis@outlook.com
Lei ZHANG,  
  • role： Corresponding author通信作者
  • Email：gongzhn@163.com
  • Introduction：宫兆宁，1976年生，女，副教授，研究方向为遥感技术及地学分析应用。E-mail：gongzhn@163.com
Zhaoning GONG*,  
  • No information about the author is available
Qiwei WANG,  
  • No information about the author is available
Diandian JIN,  
  • No information about the author is available
Xing WANG

Resümee

Estuary wetland is a special wetland type, and the extraction of estuary wetland information plays an important role in wetland conservation and scientific research. In this study, Yellow River Delta wetlands, as a typical estuary wetland in the north part of China, are considered the study area. The random forest method, which has evident advantages in feature selection and classification, was chosen to extract wetland information from the study area. First, five different characteristic variables, namely, spectral features, vegetation index, water index, red edge index, and texture features, were generated based on Sentinel-2 data with rich multi-temporal and spectral information. Then, six different classification schemes were constructed based on the preceding characteristic information. Finally, random forest classifier was used to extract the wetland information of the Yellow River Delta and verify the extraction accuracy of different results. The purpose is to select the best plan to improve the effect of wetland information extraction. Results are as follows: (1) The effective use of multiple feature variables is the key to improving the extraction of wetland information. The contribution of different characteristics to the wetland information extraction is described as follows: the red edge index > vegetation index and water index > spectral feature > texture feature. (2) The preferred features based on the random forest algorithm are crucial to extraction accuracy, with an overall accuracy of up to 90.93%, and Kappa coefficient of 0.90. This result shows that the random forest algorithm can effectively process feature selection. In feature variable data mining, the accuracy of the wetland information extraction can be guaranteed, and the operation efficiency can be improved. This study also provides a new idea, method, and technology for the selection of data sources and feature and method selections for wetland information extraction.

Schlüsselwort

estuarine wetland;information extraction;Sentinel-2;Random Forest;feature selection;red edge index;multi-temporal data

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