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

编号 030033304

推送时间 20220307

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

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

来源期刊 REMOTE SENSING

期 第333期

发表时间 20220217

关键词 ecological restoration;  hierarchical classification;  vegetation structure;  LiDAR;  vegetation species; 

摘要 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.

服务人员 付贺龙

服务院士 唐守正

PDF文件 浏览全文