Abstract
Abstract. Forests have complex vertical structure and spatial mosaic pattern. Subtropical forest ecosystem consists of vast vegetation species and these species are always in a dynamic succession stages. It is very challenging to characterize the complexity of subtropical forest ecosystem. In this paper, CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System was used to collect waveform Lidar and hyperspectral data in Puer forest region, Yunnan province in the Southwest of China. The study site contains typical subtropical species of coniferous forest, evergreen broadleaf forest, and some other mixed forests. The hypersectral images were orthorectified and corrected into surface reflectance with support of Lidar DTM product. The fusion of Lidar and hyperspectral can classify dominate forest types. The lidar metrics improved the classification accuracy. Then forest biomass estimation was carried out for each dominate forest types using waveform Lidar data, which get improved than single Lidar data source.
Highlights
Forests play an irreplaceable role in maintaining regional ecological environment, global carbon balance and mitigating global climate change
The performances of biomass estimation using airborne LiDAR and Hyperspectral data will be explored in a sub-tropical forest of Southwest China
The LiDARCCD-Hyperspectral (LiCHy) airborne LiDAR system was developed by the Institute of Forest Resource Information Techniques, Chinese Academic of forestry (CAF) (Pang et al, 2013)
Summary
Forests play an irreplaceable role in maintaining regional ecological environment, global carbon balance and mitigating global climate change. Forest aboveground biomass (AGB) is an important indicator of forest carbon stock. Estimating forest aboveground biomass accurately could significantly reduce the uncertainties in terrestrial ecosystem carbon cycle. Airborne lidar provides accurate information on the vertical structure of forests (Wulder et al, 2008; Naesset et al, 2008). As simulated by Tompalski et al (2014), species impacted stand volume estimation dramatically. Anderson et al (2008) found the integration of waveform lidar with hyperspectral data improved about 9% of AGB estimation.
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