Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

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• 作者：Shezhou Luo^a ; ^b ; Cheng Wang^a ; ^{wangcheng@radi.ac.cn} ; Xiaohuan Xi^a ; Feifei Pan^c ; Dailiang Peng^a ; Jie Zou^d ; Sheng Nie^a ; Haiming Qin^a
• 关键词：Airborne LiDAR ; Aboveground biomass ; Belowground biomass ; Hyperspectral imagery ; Fusion ; Partial least squares regression
• 刊名：Ecological Indicators
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：73
• 期：Complete
• 页码：378-387
• 全文大小：2170 K
• 卷排序：73

文摘

BGB, AGB and TB were reliably estimated using fused LiDAR and hyperspectral data. LiDAR metrics had significantly better prediction power for biomass estimation than Hyperspectral metrics. Fusion of LiDAR and hyperspectral data improved biomass estimation accuracy. Hyperspectral metrics made a small contribution to improving forest biomass estimates. BGB, AGB and TB prediction models from fused data have high R2 (0.785, 0.893, 0.882) and low RMSEr (19.89%, 15.16%, 15.46%).