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Wetlands mapping in typical regions of South America with multi-source and multi-feature integration

  • role： First author第一作者
  • Affiliation：

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

  • Email：18339161167@163.com
  • Introduction：黄玉玲，研究方向为湿地遥感信息提取。E-mail：18339161167@163.com
HUANG Yuling1,  
  • role： Corresponding author通信作者
  • Affiliation：

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

  • Email：yanggang@nbu.edu.cn
  • Introduction：杨刚，研究方向为遥感影像数据质量改善与信息提取理论和方法、遥感滨海健康监测技术与应用。E-mail： yanggang@nbu.edu.cn
YANG Gang1*,  
  • Affiliation：

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

SUN Weiwei1,  
  • Affiliation：

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

ZHU Lin1,  
  • Affiliation：

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

HUANG Ke1,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

MENG Xiangchao2

résumé

Wetlands play an important role in maintaining ecological balance, conserving water resources, recharging groundwater, and controlling soil erosion. They are often called the “kidneys of the earth” because they help purify water by filtering out pollutants and sediments. South America has a vast area of wetlands, as well as a variety of wetlands types. While most of these wetlands were conserved in a relatively good condition until a few decades ago, pressures brought about by land use and climate change have threatened their integrity in recent years. However, no complete and uniform wetland map has provided adequate information on the location, distribution, size, and changing status of wetlands in South America. Remote sensing has been an effective tool for characterizing, mapping, and monitoring the complexity and dynamics of large areas of wetlands. Although fine wetland mapping may be done by combining data from many sources, the following two issues persist. On the one hand, given the complicated temporal dynamics and spectral heterogeneity of wetlands, large-scale wetland mapping remains a challenging task. On the other hand, supervised classification is a widely used technique for multi-category wetland mapping. However, selecting training samples is time consuming and labor intensive. Moreover, finer and more precise wetland information is currently unavailable for reference. In the study, we selected four study areas of typical wetlands in South America. First, an effective wetland sample collection process was proposed by using the existing land cover dataset to ensure the sample quality. Second, a multi-source feature set was constructed by combining Sentinel-1, Sentinel-2, and SRTM data. Then, feature selection is carried out on the basis of the random forest recursive feature elimination method (RF_RFE). We constructed a multi-feature combination scheme to compare the influence of multi-source features on wetlands classification. Finally, the random forest algorithm is used to classify wetlands in the study area. The research results show that the process facilitates the sample collection and improves the sample quality. The combination of Sentinel-1 and Sentinel-2 data can improve the accuracy of land cover mapping, and terrain features help greatly improve the overall classification accuracy and the accuracy of various types of objects. For wetland categories, the addition of multi-source data features can improve the separability of wetland categories. The feature selection based on RF_RFE can reduce the feature redundancy and improve the classification accuracy. The feature optimization results show that SAR polarization features and derived texture features can be used as an effective supplement to optical features. However, the dominant features account for less. The overall classification accuracy of the study area was 85.62%, and the Kappa coefficient was 0.8333. The study proposed an effective classification process and sample collection scheme to large-scale wetlands in South America. This study integrated Sentinel-1 synthetic aperture radar data, Sentinel-2 optical data, and terrain data to explore their importance to the extraction of different wetlands at a large scale. This work also verified the feasibility of feature selection based on random forest recursive feature elimination method. The research results reveal that the sample collection process facilitates sample collection and improves sample quality. The combination of Sentinel-1 and Sentinel-2 data can improve the accuracy of land cover mapping, and terrain features help greatly improve the overall classification accuracy and the accuracy of various types of objects. For wetland categories, the addition of multi-source data features can improve the separability of wetland categories. The feature selection based on RF_RFE can reduce the feature redundancy and improve the classification accuracy.

mots-clés

remote sensing;Sentinel-1;Sentinel-2;Google Earth Engine;wetlands classification;feature selection;South America

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