Importing, Preprocessing, and Writing Data

To use pyforestscan, first import it in your Python project:

import pyforestscan

Then, you can use it to load point cloud data and extract forest structure metrics.

The following sections will provide an overview of usage of the major functions of pyforestscan. For a complete reference of all functions in pyforestscan, please check the API documentation. For comprehensive examples of these functions, please see the example jupyter notebooks.

Importing Point Cloud Data

pyforestscan supports reading from the following point cloud data formats:

• las
• laz
• copc
• ept

and reading point clouds is done using the read_lidar function:

from pyforestscan.handlers import read_lidar

file_path = "../example_data/20191210_5QKB020880.laz"
arrays = read_lidar(file_path, "EPSG:32605", hag=True)
pointcloud = arrays[0]

Preprocessing Point Cloud Data

pyforestscan provides some basic functionality to help preprocess point cloud data. Many of these functions are wrapped PDAL routines. For example, to remove outliers and classify ground points:

from pyforestscan.filters import remove_outliers_and_clean, classify_ground_points

cleaned_arrays = remove_outliers_and_clean(arrays, mean_k=8, multiplier=3.0)
classified_arrays = classify_ground_points(cleaned_arrays)

Exporting Point Clouds

pyforestscan supports exporting processed point clouds to las and laz formats. To export a point cloud as a LAZ file:

from pyforestscan.handlers import write_las

write_las(classified_arrays, "/path/to/exported_file.las", srs="EPSG:32605", compress=True)