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Research on the Inversion Method of Dust Retention in Grassland Plant Canopies Based on UAV-Borne Hyperspectral Data

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Listed:
  • Yibo Zhao

    (Engineering Research Center of Ministry of Education for Mine Ecological Restoration, China University of Mining and Technology, Xuzhou 221116, China)

  • Shaogang Lei

    (Engineering Research Center of Ministry of Education for Mine Ecological Restoration, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Monitoring the dust retention content in grassland plants around open-pit coal mines is of significant importance for environmental pollution monitoring and the development of dust control strategies. This paper focuses on the HulunBuir grassland in the Inner Mongolia Autonomous Region, China. UAV-borne hyperspectral data and measured dust retention content in plant canopies are used as data sources. The spectral response characteristics of canopy dust retention are analyzed, and four types of optimized spectral indices are constructed, including the difference index ( DI ), ratio index ( RI ), normalized difference index ( NDI ), and inverse difference index ( IDI ). The spectral index with the highest absolute value of the correlation coefficient with the canopy dust retention is selected as the feature variable for each spectral index. In addition, machine learning methods such as the partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) methods are used to develop models for the inversion of canopy dust retention. The results show that as the dust retention content increases, the canopy reflectance in the visible wavelength initially increases and then decreases, while the reflectance in the near-infrared wavelength gradually decreases. The spectral reflectance values at different dust retention levels exhibit significant differences in the 400–420 nm, 579–698 nm, and 714–1000 nm ranges. The four types of spectral indices constructed exhibit high correlations with the canopy dust retention content, and the spectral index with the highest absolute value of the correlation coefficient is composed of near-infrared bands. The dust retention inversion model established using the RF method is more accurate than those established using the PLSR and SVM methods and yields a higher prediction accuracy. The high canopy dust retention areas are mainly distributed within 900 m of the mining area, and the dust retention gradually decreases with distance. In addition, with increasing dust retention, the fractional vegetation cover (FVC) decreases. The results of this study provide a theoretical basis and technical support for monitoring dust retention in grassland plant canopies and for dust control measures.

Suggested Citation

  • Yibo Zhao & Shaogang Lei, 2025. "Research on the Inversion Method of Dust Retention in Grassland Plant Canopies Based on UAV-Borne Hyperspectral Data," Land, MDPI, vol. 14(3), pages 1-16, February.
  • Handle: RePEc:gam:jlands:v:14:y:2025:i:3:p:458-:d:1597719
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    References listed on IDEAS

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    1. Hui Sun & Meichen Feng & Lujie Xiao & Wude Yang & Chao Wang & Xueqin Jia & Yu Zhao & Chunqi Zhao & Saleem Kubar Muhammad & Deying Li, 2019. "Assessment of plant water status in winter wheat (Triticum aestivum L.) based on canopy spectral indices," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-15, June.
    2. Cheng Han & Jilong Lu & Shengbo Chen & Xitong Xu & Zibo Wang & Zheng Pei & Yu Zhang & Fengxuan Li, 2021. "Estimation of Heavy Metal(Loid) Contents in Agricultural Soil of the Suzi River Basin Using Optimal Spectral Indices," Sustainability, MDPI, vol. 13(21), pages 1-21, November.
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