S
Shezhou Luo
Researcher at Chinese Academy of Sciences
Publications - 29
Citations - 985
Shezhou Luo is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Lidar & Leaf area index. The author has an hindex of 16, co-authored 28 publications receiving 681 citations. Previous affiliations of Shezhou Luo include Fujian Agriculture and Forestry University & University of Toronto.
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Estimation of wetland vegetation height and leaf area index using airborne laser scanning data
TL;DR: In this paper, the authors explored the potential of estimating vegetation structural parameters such as vegetation height and leaf area index (LAI) for short wetland vegetation using airborne discrete-return LiDAR data.
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Spring green-up phenology products derived from MODIS NDVI and EVI: Intercomparison, interpretation and validation using National Phenology Network and AmeriFlux observations
Dailiang Peng,Chaoyang Wu,Cunjun Li,Xiaoyang Zhang,Zhengjia Liu,Huichun Ye,Shezhou Luo,Xinjie Liu,Yong Hu,Bin Fang +9 more
TL;DR: In this article, a comprehensive intercomparison and evaluation of these two spring green-up datasets using intensive ground observations have not been conducted, limiting their applications in regional interpretation of land surface phenology.
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Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation
Shezhou Luo,Shezhou Luo,Cheng Wang,Xiaohuan Xi,Feifei Pan,Dailiang Peng,Jie Zou,Sheng Nie,Haiming Qin +8 more
TL;DR: In this article, the potential of fused LiDAR and hyperspectral data for biomass estimation was tested in the middle Heihe River Basin, northwest China, and the best estimation model was using a fusion of LIDAR and HSI metrics (R 2 ǫ = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively).
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Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification
TL;DR: This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR and CASI hyperspectral data, and finds that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers.
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Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data.
TL;DR: The objective of this paper is to develop and validate an automated approach for better estimating vegetation canopy height and results indicated that the estimated vegetation canopy heights have a relatively strong correlation with the reference vegetation heights derived from airborne discrete-return LiDAR data.