干旱区生态环境知识资源中心(Arid): Revealing the Structure and Composition of the Restored Vegetation Cover in Semi-Arid Mine Dumps Based on LiDAR and Hyperspectral Images

Arid

DOI	10.3390/rs14040978
	Revealing the Structure and Composition of the Restored Vegetation Cover in Semi-Arid Mine Dumps Based on LiDAR and Hyperspectral Images
	Tang, Jiajia; Liang, Jie; Yang, Yongjun; Zhang, Shaoliang; Hou, Huping; Zhu, Xiaoxiao
通讯作者	Yang, YJ (corresponding author)，China Univ Min & Technol, Engn Res Ctr, Minist Educ Mine Ecol Restorat, Xuzhou 221008, Jiangsu, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:4
英文摘要	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.
英文关键词	ecological restoration hierarchical classification vegetation structure LiDAR vegetation species
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000765064500001
WOS关键词	TREE SPECIES CLASSIFICATION ; PHOTOCHEMICAL REFLECTANCE INDEX ; CHLOROPHYLL CONTENT ; REMOTE ; LEAF ; ALGORITHMS ; VARIABLES ; RECOVERY ; FORESTS ; MODEL
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376361
作者单位	[Tang, Jiajia; Yang, Yongjun; Zhang, Shaoliang; Hou, Huping; Zhu, Xiaoxiao] China Univ Min & Technol, Engn Res Ctr, Minist Educ Mine Ecol Restorat, Xuzhou 221008, Jiangsu, Peoples R China; [Tang, Jiajia] China Univ Min & Technol, Sch Environm & Spatial Informat, Xuzhou 221008, Jiangsu, Peoples R China; [Liang, Jie] China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China; [Liang, Jie] Inst Territorial & Spatial Planning Inner Mongoli, Hohhot 010070, Peoples R China
推荐引用方式 GB/T 7714	Tang, Jiajia,Liang, Jie,Yang, Yongjun,et al. Revealing the Structure and Composition of the Restored Vegetation Cover in Semi-Arid Mine Dumps Based on LiDAR and Hyperspectral Images[J],2022,14(4).
APA	Tang, Jiajia,Liang, Jie,Yang, Yongjun,Zhang, Shaoliang,Hou, Huping,&Zhu, Xiaoxiao.(2022).Revealing the Structure and Composition of the Restored Vegetation Cover in Semi-Arid Mine Dumps Based on LiDAR and Hyperspectral Images.REMOTE SENSING,14(4).
MLA	Tang, Jiajia,et al."Revealing the Structure and Composition of the Restored Vegetation Cover in Semi-Arid Mine Dumps Based on LiDAR and Hyperspectral Images".REMOTE SENSING 14.4(2022).