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Land Cover Classification by Gaofen Satellite Images Based on CART Algorithm in Yuli County, Xinjiang, China

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  • Chunyu Li

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Aeronautics and Astronautics, University of Chinese Academy of Sciences, Beijing 100049, China
    Investigation College of People’s Public Security University of China, Beijing 100038, China)

  • Rong Cai

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    School of Aeronautics and Astronautics, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Wei Tian

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Junna Yuan

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Xiaofei Mi

    (Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

Abstract

High-resolution remote-sensing images can be used in human activity analysis and criminal activity monitoring, especially in sparsely populated zones. In this paper, we explore the applicability of China’s Gaofen satellite images in the land cover classification of Xinjiang, China. First of all, the features of spectral reflectance and a normalized radar cross section (NRCS) for different types of land covers were analyzed. Moreover, the seasonal variation of the NRCS in SAR (Synthetic Aperture Radar) images for the study area, Dunkuotan Village of Yuli County, China, was demonstrated by the GEE (Google Earth Engine) platform accordingly. Finally, the CART (classification and regression trees) algorithm of a DT (decision tree) was applied to investigate the classification of land cover in the western area of China when both optical and SAR images were employed. An overall classification accuracy of 83.15% with a kappa coefficient of 0.803 was observed by using GF-2/GF-3 images (2017–2021) in the study area. The DT-based classification procedure proposed in this investigation proved that Gaofen series remote-sensing images can be engaged to effectively promote the routine workflow of the administrative department.

Suggested Citation

  • Chunyu Li & Rong Cai & Wei Tian & Junna Yuan & Xiaofei Mi, 2023. "Land Cover Classification by Gaofen Satellite Images Based on CART Algorithm in Yuli County, Xinjiang, China," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2535-:d:1052444
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    References listed on IDEAS

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    1. Jianbin Tao & Wenbin Wu & Wenbin Liu & Meng Xu, 2020. "Exploring the Spatio-Temporal Dynamics of Winter Rape on the Middle Reaches of Yangtze River Valley Using Time-Series MODIS Data," Sustainability, MDPI, vol. 12(2), pages 1-12, January.
    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.
    3. Jiawen Hou & Mao Ye, 2022. "Effects of Dynamic Changes of Soil Moisture and Salinity on Plant Community in the Bosten Lake Basin," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
    4. Bo Tie & Fang Huang & Jian Tao & Jun Lu & Dongwei Qiu, 2018. "A Parallel and Optimization Approach for Land-Surface Temperature Retrieval on a Windows-Based PC Cluster," Sustainability, MDPI, vol. 10(3), pages 1-17, February.
    5. Xuchao Yang & Lin Lin & Yizhe Zhang & Tingting Ye & Qian Chen & Cheng Jin & Guanqiong Ye, 2019. "Spatially Explicit Assessment of Social Vulnerability in Coastal China," Sustainability, MDPI, vol. 11(18), pages 1-20, September.
    6. Kotapati Narayana Loukika & Venkata Reddy Keesara & Venkataramana Sridhar, 2021. "Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    7. Ana Andries & Richard J. Murphy & Stephen Morse & Jim Lynch, 2021. "Earth Observation for Monitoring, Reporting, and Verification within Environmental Land Management Policy," Sustainability, MDPI, vol. 13(16), pages 1-34, August.
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