研究目的
To exploit wheel-based LiDAR data for plant height and canopy cover estimation to aid in non-invasive biomass prediction.
研究成果
Wheel-based LiDAR data effectively estimates plant height and canopy cover, with higher canopy cover values than RGB-based methods due to better canopy penetration. These traits can be integrated into biomass prediction models, but future work is needed to optimize grid size and account for destructive sampling.
研究不足
The grid size used for canopy cover estimation may be too small, leading to binary results. Destructive sampling between data collection days introduces bias. Areas with no LiDAR points due to vehicle trajectory or viewing angles are not fully captured.
1：Experimental Design and Method Selection:
The study uses a mobile mapping system with LiDAR to collect 3D point cloud data for generating Crop Surface Models (CSMs) and canopy cover estimates. Methods include percentile-based height mapping and point density analysis.
2：Sample Selection and Data Sources:
Data is collected from agricultural plots (Plots 4101 to 4172) at the Agronomy Center for Research and Education (ACRE), Purdue University, on specific dates (6th, 19th, and 26th July 2017).
3：7).
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: PhenoRover-based mobile mapping system, Velodyne HDL-32E laser scanners, Applanix POSLV-125 unit, and associated georeferencing equipment.
4：Experimental Procedures and Operational Workflow:
LiDAR data is acquired by driving the PhenoRover over the field. CSMs are generated using 30th, 60th, and 90th percentile heights for grid cells. Canopy cover is estimated by comparing points above bare earth height to total points, using a Digital Terrain Model (DTM).
5：Data Analysis Methods:
Height maps and canopy cover are analyzed using rasterization, nearest neighbor interpolation, and averaging over plots. Results are compared with RGB orthophoto-based estimates.
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