UAV image analysis for forest ecology
Project description
PyForestScan: Airborne Point Cloud Analysis for Forest Structure
Overview
PyForestScan is a Python library designed for analyzing and visualizing forest structure using airborne 3D point cloud data. The library helps derive important forest metrics such as Canopy Height, Plant Area Index (PAI), Canopy Cover, Plant Area Density (PAD), and Foliage Height Diversity (FHD).
Features
- Forest Metrics: Calculate and visualize key metrics like Canopy Height, PAI, Canopy Cover, PAD, and FHD.
- Airborne Data Compatibility: Supports LiDAR and Structure from Motion (SfM) data from drones and UAVs.
- Visualization: Create 2D and 3D visualizations of forest structures.
- Extensibility: Easily add custom filters and visualization techniques to suit your needs.
Installation
Install PyForestScan using pip:
pip install pyforestscan
Developer Dependencies
- PDAL and Python PDAL bindings
- GDAL
- Python
- Python requirements (requirements.txt)
Quick Start
Derive Forest Metrics from Airborne Data
from pyforestscan.handlers import read_lidar
from pyforestscan.calculate import assign_voxels, calculate_pad, calculate_pai
arrays = read_lidar("path/to/lidar/file.las", "EPSG:32605", hag=True)
voxels = assign_voxels(arrays[0], (25, 25, 5))
pad = calculate_pad(voxels, 5)
pai = calculate_pai(pad)
2D Visualization of Forest Metrics
from pyforestscan.visualize import plot_pai
# Define the spatial extent of your data
extent = [0, 100, 0, 100]
# Plot Plant Area Index (PAI)
plot_pai(pai, extent=extent)
3D Visualization of Airborne Canopy Structure
from pyforestscan.visualize import plot_3d
plot_3d([array1, array2], z_dim='HeightAboveGround')
Documentation
For detailed instructions and examples, visit our documentation.
Contributing
We welcome contributions! Please check our Contributing Guidelines to get started.
License
This project is licensed under the MIT License. See the LICENSE.md file for details.
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