Skip to content

PyForestScan Documentation

Binder PyPI Docker Pulls Contributors Tests Coverage

Calculate Forest Structural Metrics from lidar point clouds in Python

Height Above Ground visualization of lidar point cloud data

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, PAD, and FHD.
  • Large Point Cloud Support: Utilizes efficient data formats such as EPT for large point cloud processing.
  • Visualization: Create 2D and 3D visualizations of forest structure and structural metrics
  • Extensibility: Easily add custom filters and visualization techniques to suit your needs.

Examples

The examples below are jupyter notebooks and can help you get started!

To install jupyter, you can use conda or pip, with either:

1
conda install jupyter
or 

1
pip install jupyter

Attribution

This library makes heavy use of PDAL (Butler et al. 2024; Butler et al. 2021) for its IO operations. PDAL and the PDAL Python Bindings provide excellent functional support for conducting standard operations on point clouds. Our work to calculate forest structural metrics would have been a lot harder without PDAL.

Butler, H., Bell, A., Gerlek, M. P., chambbj, Gadomski, P., Manning, C., Łoskot, M., Couwenberg, B., Barker, N., Ramsey, P., Dark, J., Mann, K., Chaulet, N., Rouault, E., Villemin, G., Foster, C., Moore, O., Rosen, M., Lewis, S., ... Brookes, D. (2024). PDAL/PDAL: 2.8.1 (Version 2.8.1). Zenodo. https://doi.org/10.5281/zenodo.13993879

Butler, H., Chambers, B., Hartzell, P., & Glennie, C. (2021). PDAL: An open source library for the processing and analysis of point clouds. Computers & Geosciences, 148, 104680. https://doi.org/https://doi.org/10.1016/j.cageo.2020.104680