研究目的
The purpose of this study was to estimate plot-level above-ground biomass (AGB) in different plot sizes of 20 m × 20 m and 30 m × 30 m, and to develop a regression model for AGB prediction.
研究成果
The voxel-based approach enables a greater understanding of complex forest structure and is expected to contribute to the advancement of forest carbon quantification techniques.
研究不足
This study is limited in that it is constrained to an intact MDF forest in a tropical region within a relatively small number of 24 sample plots.
1：Experimental Design and Method Selection:
The study used both point cloud-based (PCB) and voxel-based (VB) metrics to maximize the efficiency of low-density LiDAR data within a dense forest.
2：Sample Selection and Data Sources:
Field inventory data were obtained from the CTFS for the field study of carbon stock estimation and carbon sequestration.
3：List of Experimental Equipment and Materials:
Airborne LiDAR data were acquired at an average of 1400 m above ground level (AGL) with a sampling density of
4：13 pt·m?Experimental Procedures and Operational Workflow:
LiDAR point cloud data were processed to fall within each voxel space and these were used to build voxel-based (VB) metrics.
5：Data Analysis Methods:
Multiple regression analysis was applied separately to the 20 m × 20 m plot and the 30 m × 30 m plot.
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