文献类型：期刊文献

英文题名：Assessment of the Declining Degree of Farmland Shelterbelts in a Desert Oasis Based on LiDAR and Hyperspectral Imagery

作者：Luo, Chengwei[1,2,3] Yang, Yuli[1,2] Xin, Zhiming[3,4] Li, Junran[5] Jia, Xiaoxiao[1,2,3] Fan, Guangpeng[6] Zhu, Junying[1,2,3] Song, Jindui[1,2,3] Wang, Zhou[1,2,3] Xiao, Huijie[1,2,3]

第一作者：Luo, Chengwei

通信作者：Xiao, HJ[1];Xiao, HJ[2];Xiao, HJ[3]

机构：[1]Beijing Forestry Univ, Coll Soil & Water Conservat, Beijing 100083, Peoples R China;[2]Beijing Forestry Univ, State Key Lab Efficient Prod Forest Resources, Beijing 100083, Peoples R China;[3]Natl Forestry & Grassland Adm, Inner Mongolia Dengkou Desert Ecosyst Natl Observ, Dengkou 015200, Peoples R China;[4]Chinese Acad Forestry, Expt Ctr Desert Forestry, Dengkou 015200, Peoples R China;[5]Univ Hong Kong, Dept Geog, Hong Kong 999077, Peoples R China;[6]Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China

年份：2023

卷号：15

期号：18

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20234014836242);Scopus(收录号：2-s2.0-85173062054);WOS:【SCI-EXPANDED(收录号:WOS:001075859700001)】；

基金：The authors would like to thank Huaiyuan Liu and Litao Wang for their efforts in collecting and preprocessing the airborne imagery.

语种：英文

外文关键词：desert oases; protective functions; tree decline; remote sensing; laser scanning; spectrum; machine learning; classification

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.