This project presents a LiDAR-based workflow in R for individual tree detection, crown segmentation, structural metrics extraction, and above-ground biomass (AGB) estimation.
The analysis demonstrates how point cloud data can be transformed into practical outputs for forestry analysis, vegetation assessment, and spatial decision support.
- Individual tree detection from LiDAR point cloud data
- Crown segmentation using established algorithms
- Extraction of structural forest metrics
- Above-ground biomass (AGB) estimation
- Visualization of LiDAR-derived outputs
This project applies:
- Dalponte (2016) crown segmentation approach
- Li (2012) tree detection and segmentation workflow
- Black (2004) allometric equation for AGB estimation
- R
- LiDAR data processing workflow
- Spatial analysis and visualization tools
- LiDAR point cloud analysis
- Individual tree detection
- Crown delineation and segmentation
- Forest structural metrics extraction
- Above-ground biomass estimation
- Geospatial visualization and interpretation
| AGB Visualization | Pit-Free vs Point Cloud Comparison |
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miniproject LIDAR.R
plot_06.las
This repository demonstrates my capability to perform LiDAR-based geospatial analysis in R for forestry, biomass estimation, and vegetation structure assessment.
- LiDAR-based vegetation and forest analysis
- Tree detection and crown segmentation
- Biomass estimation and structural metrics extraction
- Spatial data analysis and geospatial visualization
- Research and technical reporting support
I am open to freelance, research, and project-based opportunities related to LiDAR, GIS, remote sensing, and spatial analysis.
Dedy Lesmana