Revealing the Structure and Composition of the Restored Vegetation Cover in Semi-Arid Mine Dumps Based on LiDAR and Hyperspectral Images

Abstract

Remotely sensed images with low resolution can be effectively used for the large-area monitoring of vegetation restoration, but are unsuitable for accurate small-area monitoring. This limits researchers’ ability to study the composition of vegetation species and the biodiversity and ecosystem functions after ecological restoration. Therefore, this study uses LiDAR and hyperspectral data, develops a hierarchical classification method for classifying vegetation based on LiDAR technology, decision tree and a random forest classifier, and applies it to the eastern waste dump of the Heidaigou mining area in Inner Mongolia, China, which has been restored for around 15 years, to verify the effectiveness of the method. The results were as follows. (1) The intensity, height, and echo characteristics of LiDAR point cloud data and the spectral, vegetation indices, and texture features of hyperspectral image data effectively reflected the differences in vegetation species composition. (2) Vegetation indices had the highest contribution rate to the classification of vegetation species composition types, followed by height, while spectral data alone had a lower contribution rate. Therefore, it was necessary to screen the features of LiDAR and hyperspectral data before classifying vegetation. (3) The hierarchical classification method effectively distinguished the differences between trees (Populus spp., Pinus tabuliformis, Hippophae sp. (arbor), and Robinia pseudoacacia), shrubs (Amorpha fruticosa, Caragana microphylla + Hippophae sp. (shrub)), and grass species, with classification accuracy of 87.45% and a Kappa coefficient of 0.79, which was nearly 43% higher than an unsupervised classification and 10.7–22.7% higher than other supervised classification methods. In conclusion, the fusion of LiDAR and hyperspectral data can accurately and reliably estimate and classify vegetation structural parameters, and reveal the type, quantity, and diversity of vegetation, thus providing a sufficient basis for the assessment and improvement of vegetation after restoration.

Using unmanned aerial vehicle multispectral data for monitoring the outcomes of ecological restoration in mining areas

“…The applications of UAVs in vegetation assessments in mining areas are wide‐ranging, including monitoring vegetation growth conditions such as soil temperature (Ruan et al, 2022); mapping vegetation communities, vegetation structure and height (Banerjee & Raval, 2022; Tang et al, 2022); and assessing biomass (Ren et al, 2022). Multispectral sensors can be used to monitor ecological indicators such as vegetation coverage, aboveground biomass, and tree crown coverage (Fernandez‐Guisuraga et al, 2022; Villoslada et al, 2020), and can provide an alternative to high‐cost airborne hyperspectral and LiDAR approaches.…”

“…The applications of UAVs in vegetation assessments in mining areas are wide-ranging, including monitoring vegetation growth conditions such as soil temperature (Ruan et al, 2022); mapping vegetation communities, vegetation structure and height (Banerjee & Raval, 2022;Tang et al, 2022); and assessing biomass (Ren et al, 2022).…”

Microtopographic reconstruction improves soil erosion resistance and vegetation characteristics on the slopes of large dump sites in semi-arid areas

“…Owing to an increasing emphasis on ecological conservation in China, the number of studies on soil erosion control and vegetation restoration techniques associated with mine dumps has increased. Previous studies have shown that the biodiversity of mine dumps depends on vegetation restoration efforts (Feng et al, 2019;Tang et al, 2022). Suitable vegetation restoration techniques improve the nutrient contents and activity in the surface soil of mine dumps (Yuan et al, 2018;Raghunathan et al, 2021).…”