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Information Extraction and Prediction of Rocky Desertification Based on Remote Sensing Data

Author

Listed:
  • Jiaju Cao

    (Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Xingping Wen

    (Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Meimei Zhang

    (Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Dayou Luo

    (Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Yinlong Tan

    (Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China)

Abstract

Rock desertification has become the third most serious ecological problem in western China after desertification and soil erosion. It is also the primary environmental problem to be solved in the karst region of southwest China. Karst landscapes in China are mainly distributed in southwest China, and the area centered on the Guizhou plateau is the center of karst landscape development in southern China. It has a fragile ecological environment, and natural factors and human activities have influenced the development of stone desertification in the karst areas to different degrees. In this paper, Dafang County, Guizhou Province, was selected as the study area to analyze the effect of the decision tree and multiple linear regression model on stone desertification and to analyze the evolution characteristics of stone desertification in Dafang County from 2005 to 2020. The FLUS model was applied to predict and validate the stone desertification information. The results show that the overall accuracy of multiple linear regression extraction of stone desertification is 70%, and the Kappa coefficient is 0.69; the overall accuracy of decision tree extraction of stone desertification is 60%, and the Kappa coefficient is 0.521. The multiple linear regression stone desertification extraction model is more accurate than the traditional decision tree classification. The overlay analysis of stone desertification and slope, elevation, slope direction and vegetation cover showed that stone desertification was more distributed between 1300–1900 m in elevation; stone desertification decreased gradually with the increase in slope; each grade of stone desertification was mainly distributed in the range of 5 to 25° in slope, which might be related to human activities. The FLUS model was used to predict the accuracy of 2015 data in the region and project the changes in stone desertification area in 2035 under a conventional scenario and an ecological protection scenario in the region to provide a new reference for predicting stone desertification.

Suggested Citation

  • Jiaju Cao & Xingping Wen & Meimei Zhang & Dayou Luo & Yinlong Tan, 2022. "Information Extraction and Prediction of Rocky Desertification Based on Remote Sensing Data," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13385-:d:945079
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    References listed on IDEAS

    as
    1. Sipei Pan & Jiale Liang & Wanxu Chen & Jiangfeng Li & Ziqi Liu, 2021. "Gray Forecast of Ecosystem Services Value and Its Driving Forces in Karst Areas of China: A Case Study in Guizhou Province, China," IJERPH, MDPI, vol. 18(23), pages 1-20, November.
    2. Zhao Wang & Tinglong Zhang & Chenyang Pei & Xiaonan Zhao & Yingying Li & Shuai Hu & Chongfeng Bu & Qingfeng Zhang, 2022. "Multisource Remote Sensing Monitoring and Analysis of the Driving Forces of Vegetation Restoration in the Mu Us Sandy Land," Land, MDPI, vol. 11(9), pages 1-21, September.
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