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

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.

Plant phenotyping relevance

UAVハイパースペクトルデータから植物キャノピーのダスト保持量を推定するスペクトル指標と機械学習モデルを開発・比較しており、植物状態の取得・推定手法が中心である。

Code and data availability

The supplied blocks describe UAV-borne hyperspectral data collection, dust retention measurements, spectral index construction, and PLSR/SVM/RF modeling, but contain no public dataset deposit, no author analysis code or model availability statement, and no supplementary asset links. Only generic software (MATLAB R2018b

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