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Extraction of Cropland Spatial Distribution Information Using Multi-Seasonal Fractal Features: A Case Study of Black Soil in Lishu County, China

Author

Listed:
  • Qi Wang

    (College of Information Science and Engineering, Shandong Agricultural University, Jinan 271018, China
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    These authors contributed equally to this work.)

  • Peng Guo

    (College of Information Science and Engineering, Shandong Agricultural University, Jinan 271018, China
    These authors contributed equally to this work.)

  • Shiwei Dong

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources, Nanjing 210013, China)

  • Yu Liu

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources, Nanjing 210013, China)

  • Yuchun Pan

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Cunjun Li

    (Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

Abstract

Accurate extraction of cropland distribution information using remote sensing technology is a key step in the monitoring, protection, and sustainable development of black soil. To obtain precise spatial distribution of cropland, an information extraction method is developed based on a fractal algorithm integrating temporal and spatial features. The method extracts multi-seasonal fractal features from the Landsat 8 OLI remote sensing data. Its efficiency is demonstrated using black soil in Lishu County, Northeast China. First, each pixel’s upper and lower fractal signals are calculated using a blanket covering method based on the Landsat 8 OLI remote sensing data in the spring, summer, and autumn seasons. The fractal characteristics of the cropland and other land-cover types are analyzed and compared. Second, the ninth lower fractal scale is selected as the feature scale to extract the spatial distribution of cropland in Lishu County. The cropland vector data, the European Space Agency (ESA) WorldCover data, and the statistical yearbook from the same period are used to assess accuracy. Finally, a comparative analysis of this study and existing products at different scales is carried out, and the point matching degree and area matching degree are evaluated. The results show that the point matching degree and the area matching degree of cropland extraction using the multi-seasonal fractal features are 90.66% and 96.21%, and 95.33% and 83.52%, respectively, which are highly consistent with the statistical data provided by the local government. The extracted accuracy of cropland is much better than that of existing products at different scales due to the contribution of the multi-seasonal fractal features. This method can be used to accurately extract cropland information to monitor changes in black soil, and it can be used to support the conservation and development of black soil in China.

Suggested Citation

  • Qi Wang & Peng Guo & Shiwei Dong & Yu Liu & Yuchun Pan & Cunjun Li, 2023. "Extraction of Cropland Spatial Distribution Information Using Multi-Seasonal Fractal Features: A Case Study of Black Soil in Lishu County, China," Agriculture, MDPI, vol. 13(2), pages 1-19, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:486-:d:1072797
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    References listed on IDEAS

    as
    1. Shiwei Dong & Hong Li & Danfeng Sun, 2017. "Fractal Feature Analysis and Information Extraction of Woodlands Based on MODIS NDVI Time Series," Sustainability, MDPI, vol. 9(7), pages 1-17, July.
    2. Le’an Qu & Zhenjie Chen & Manchun Li, 2019. "CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China," Sustainability, MDPI, vol. 11(20), pages 1-23, October.
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    Cited by:

    1. Jibo Yue & Chengquan Zhou & Haikuan Feng & Yanjun Yang & Ning Zhang, 2023. "Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring," Agriculture, MDPI, vol. 13(10), pages 1-4, October.

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