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Farmland shelterbelt information extraction based on multispectral image of the ZY1-02E satellite

  • role： First author第一作者
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

  • Email：liyifu@caf.ac.cn
  • Introduction：李毅夫，研究方向为林业遥感技术与应用。E-mail： liyifu@caf.ac.cn
LI Yifu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

  • Email：sunbin@ifrit.ac.cn
  • Introduction：孙斌，研究方向为草原和荒漠化遥感。E-mail： sunbin@ifrit.ac.cn
SUN Bin12*,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

GAO Zhihai12,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

WANG Bengyu12,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

YAN Ziyu12,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

    Department of Geography, Yunnan Normal University, Kunming 650500, China

SU Wensen123,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

GAO Ting12,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

    Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

YUE Wei12

Resümee

The successful launch and in-a-bit operation of the 5 m Optical Satellite 02 (ZY1-02E) have provided a wealth of remote sensing data for various main businesses within the forestry and grass industry, providing reliable information support for forestry management and ecological services. This study aims to test the application capability of ZY1-02E multispectral data in farmland shelterbelt monitoring, a primary forestry business. Zhangbei County, Hebei Province, serves as the study area. Spectral, vegetation index, and texture feature sets are constructed based on the ZY1-02E multispectral data, and four classification information extraction schemes are designed: (1) spectral features, (2) spectral features + vegetation index, (3) spectral features + texture features, and (4) spectral features + vegetation index + texture features. Random forest algorithm was employed for feature selection, classification information extraction, and validation to evaluate the application potential and effectiveness of the ZY1-02E multispectral data in farmland shelterbelt information extraction. The results show that (1) ZY1-02E multispectral data allow for the accurate extraction of farmland shelterbelt information in the study area, reflecting the actual distribution of farmland shelterbelts to a high degree. Among them, the overall accuracy and Kappa coefficient of Scheme 1 are 0.8371 and 0.7760, respectively; the overall accuracy and Kappa coefficient of Scheme 2 are 0.8440 and 0.7855, respectively; the overall accuracy and Kappa coefficient of Scheme 3 reach 0.8839 and 0.8403, respectively; and Scheme 4 has the highest accuracy, with its overall accuracy and Kappa coefficient being 0.8908 and 0.8499, respectively. (2) The effective use of multiple feature variables can significantly improve the accuracy of farmland shelterbelt information extraction. Regarding the contribution of different features to farmland shelterbelt information extraction, in terms of their contribution to farmland shelterbelt information extraction, the spectral features are the most significant, followed by texture features and vegetation indices. (3) ZY1-02E multispectral data exhibit high accuracy and reliable results for farmland shelterbelt information extraction, which can better meet the needs of protection forest monitoring operations and has considerable potential for application in forest surveys and monitoring thematic operations. In conclusion, this study demonstrates the potential and effectiveness of ZY1-02E multispectral data for extracting farmland shelterbelt information. Using multiple feature variables and the random forest algorithm enables the accurate extraction and validation of farmland shelterbelt information, providing valuable insights for future forest monitoring and management. As more data become available and the application capabilities of the ZY1-02E are further explored, future work can consider integrating multispectral data from different periods and linear features of farmland shelterbelt to enhance the accuracy of information extraction, ultimately achieving more efficient and precise extraction of farmland shelterbelt information.

Schlüsselwort

remote sensing;forestry and grass industry;farmland shelterbelt;feature extraction;application testing;ZY1-02E satellite

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