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.
Highlights
IntroductionEcological restoration refers to the scientific and technological methods used to enhance the resilience of an ecosystem and, supplemented by artificial measures, to gradually restore a damaged ecosystem or allow an ecosystem to develop more naturally [1]
The HP95 reflects the distribution of the true height of vegetation, while the standard deviation of height reflects the difference in height distribution of various vegetation species compositions, and the difference in height distribution of trees is relatively large [31]
The height variation coefficient can reflect the degree of dispersion in the height distribution of different vegetation species compositions, with Populus spp. having the highest dispersion degree, followed by Caragana microphylla + Hippophae sp., while Robinia pseudoacacia and Amorpha fruticosa have the most concentrated height distribution
Summary
Ecological restoration refers to the scientific and technological methods used to enhance the resilience of an ecosystem and, supplemented by artificial measures, to gradually restore a damaged ecosystem or allow an ecosystem to develop more naturally [1]. Monitoring vegetation species composition is very important for assessing the effectiveness of ecological restoration and biodiversity management after restoration. Ecological restoration monitoring depends on obtaining timely and accurate statistics. Vegetation restoration is affected by factors including the soil matrix, plant growth environment, and Remote Sens.
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