研究目的
To examine the potential of TLS and SfM photogrammetry for rapid nondestructive estimation of grass AGB.
研究成果
The results demonstrate the potential of SfM photogrammetry and TLS for nondestructive estimation of grass AGB. SfM provided more accurate estimation than TLS or disc pasture meter. Further research is recommended to determine major influencing factors and establish optimal methodologies for SfM and TLS data acquisition in grassland ecosystems.
研究不足
The study was limited to one type of grass and may not be transferable to other species and types. Neither the SfM nor the TLS provided canopy penetration to the litter layer or to the ground surface due to the dense packing of the grass.
1:Experimental Design and Method Selection
The study compared the effectiveness of TLS, SfM photogrammetry, and disc pasture meter for estimating grass AGB. Volume metrics from TLS and SfM 3D point clouds and disc pasture meter settling heights were compared to destructively harvested AGB measurements.
2:Sample Selection and Data Sources
Eleven 1 × 1 m grass plots with a range of biomass in South Dakota, USA, were selected. The plots contained Smooth Brome grass and were on relatively flat sites.
3:List of Experimental Equipment and Materials
Compact biomass LiDAR (CBL), Canon EOS 6D digital single-lens reflex camera, disc pasture meter, wooden frame for destructive harvesting.
4:Experimental Procedures and Operational Workflow
TLS and SfM data were collected for each plot. Disc pasture meter measurements were taken after remote sensing data collection. Destructive harvesting was conducted to measure AGB.
5:Data Analysis Methods
Volume metrics from TLS and SfM point clouds were compared to AGB measurements using ordinary least squares linear regression. A boot-strapped 'leave-one-out' model cross-validation approach was used.
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