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Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV

Author

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  • Xue Xu

    (Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China)

  • Luyao Liu

    (Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China)

  • Peng Han

    (Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China)

  • Xiaoqian Gong

    (Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China)

  • Qing Zhang

    (Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
    Collaborative Innovation Center for Grassland Ecological Security (Jointly Supported by the Ministry of Education of China and Inner Mongolia Autonomous Region), Hohhot 010021, China)

Abstract

Grassland desertification has become one of the most serious environmental problems in the world. Grasslands are the focus of desertification research because of their ecological vulnerability. Their application on different grassland desertification grades remains limited. Therefore, in this study, 19 vegetation indices were calculated for 30 unmanned aerial vehicle (UAV) visible light images at five grades of grassland desertification in the Mu Us Sandy. Fractional Vegetation Coverage (FVC) with high accuracy was obtained through Support Vector Machine (SVM) classification, and the results were used as the reference values. Based on the FVC, the grassland desertification grades were divided into five grades: severe (FVC < 5%), high (FVC: 5–20%), moderate (FVC: 21–50%), slight (FVC: 51–70%), and non-desertification (FVC: 71–100%). The accuracy of the vegetation indices was assessed by the overall accuracy (OA), the kappa coefficient ( k ), and the relative error (RE). Our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. Excess Green Red Blue Difference Index (EGRBDI), Visible Band Modified Soil Adjusted Vegetation Index (V-MSAVI), Green Leaf Index (GLI), Color Index of Vegetation Vegetative (CIVE), Red Green Blue Vegetation Index (RGBVI), and Excess Green (EXG) accurately assessed grassland desertification at severe, high, moderate, and slight grades. In addition, the Red Green Ratio Index (RGRI) and Combined 2 (COM 2 ) were accurate in assessing severe desertification. The assessment of the 19 indices of the non-desertification grade had low accuracy. Moreover, our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. This study emphasizes that the applicability of the vegetation indices varies with the degree of grassland desertification and hopes to provide scientific guidance for a more accurate grassland desertification assessment.

Suggested Citation

  • Xue Xu & Luyao Liu & Peng Han & Xiaoqian Gong & Qing Zhang, 2022. "Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV," IJERPH, MDPI, vol. 19(24), pages 1-15, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16793-:d:1003337
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    References listed on IDEAS

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    1. Lei Zhang & Guangyu Hong & Zhuofan Li & Xiaowei Gao & Yongzhi Wu & Xiaojiang Wang & Pingping Wang & Jie Yang, 2018. "Assessment of the Ecosystem Service Function of Sandy Lands at Different Times Following Aerial Seeding of an Endemic Species," Sustainability, MDPI, vol. 10(4), pages 1-14, March.
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    Cited by:

    1. Huilin Jiang & Rigeng Wu & Yongan Zhang & Meian Li & Hao Lian & Yikun Fan & Wenqian Yang & Peng Zhou, 2024. "Classification Model of Grassland Desertification Based on Deep Learning," Sustainability, MDPI, vol. 16(19), pages 1-16, September.

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