干旱区生态环境知识资源中心(Arid): Assessment of the Declining Degree of Farmland Shelterbelts in a Desert Oasis Based on LiDAR and Hyperspectral Imagery

Arid

DOI	10.3390/rs15184508
	Assessment of the Declining Degree of Farmland Shelterbelts in a Desert Oasis Based on LiDAR and Hyperspectral Imagery
	Luo, Chengwei; Yang, Yuli; Xin, Zhiming; Li, Junran; Jia, Xiaoxiao; Fan, Guangpeng; Zhu, Junying; Song, Jindui; Wang, Zhou; Xiao, Huijie
通讯作者	Xiao, HJ
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:18
英文摘要	The deterioration of farmland shelterbelts in the Ulan Buh desert oases could weaken their protective functions. Therefore, an accurate method is essential to assess tree decline degree in order to guide the rejuvenation and transformation of these shelterbelts. This study selected three typical farmland shelterbelts in the Ulan Buh desert oases as the objects. Terrestrial laser scanning (TLS) and airborne hyperspectral imagery (AHI) were used to acquire point cloud data and detailed spectral information of trees. Point cloud and spectral characteristics of trees with varying decline levels were analyzed. Six models were constructed to identify decline degree of shelterbelts, and model accuracy was evaluated. The coefficient of determination between the structural parameters of trees extracted by TLS and field measurements ranged from 0.76 to 0.94. Healthy trees outperformed declining trees in structural parameters, particularly in tridimensional green biomass and crown projection area. Spectral reflectance changes in the 740-950 nm band were evident among the three tree types with different decline levels, decreasing significantly with increased decline level. Among the TLS-derived feature parameters, the canopy relief ratio of tree points and point cloud density strongly correlated with the degree of tree decline. The plant senescence reflectance index and normalized difference vegetation index exhibited the closest correlation with tree decline in AHI data. The average accuracy of the models constructed based on the feature parameters of LiDAR, AHI, and the combination of both of them were 0.77, 0.61, and 0.81, respectively. The light gradient-boosting machine model utilizing TLS-AHI comprehensive feature parameters accurately determined tree decline. This study highlights the efficacy of employing feature parameters derived from TLS alone to accurately identify tree decline. Combining feature parameters from the TLS and AHI enhances the precision of tree decline identification. This approach offers guidance for decisions regarding the renewal and transformation of declining farmland shelterbelts.
英文关键词	desert oases protective functions tree decline remote sensing laser scanning spectrum machine learning classification
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:001075859700001
WOS关键词	TREE SPECIES CLASSIFICATION ; LEAF-AREA INDEX ; ABOVEGROUND BIOMASS ; PINUS-YUNNANENSIS ; DAMAGE ; DEFOLIATION ; TEXTURE
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/2XILL650/398319
推荐引用方式 GB/T 7714	Luo, Chengwei,Yang, Yuli,Xin, Zhiming,et al. Assessment of the Declining Degree of Farmland Shelterbelts in a Desert Oasis Based on LiDAR and Hyperspectral Imagery[J],2023,15(18).
APA	Luo, Chengwei.,Yang, Yuli.,Xin, Zhiming.,Li, Junran.,Jia, Xiaoxiao.,...&Xiao, Huijie.(2023).Assessment of the Declining Degree of Farmland Shelterbelts in a Desert Oasis Based on LiDAR and Hyperspectral Imagery.REMOTE SENSING,15(18).
MLA	Luo, Chengwei,et al."Assessment of the Declining Degree of Farmland Shelterbelts in a Desert Oasis Based on LiDAR and Hyperspectral Imagery".REMOTE SENSING 15.18(2023).