Canopy Height Model

A Canopy Height Model (CHM) was derived from LiDAR point clouds to aid in species population surveys.

Client
The Conservation Fund

Date Completed
Spring 2021

Role
Geospatial Analyst

Task objective

Identify and process LiDAR point clouds and produce a LiDAR-derived canopy height model for The Conservation Fund’s North Coast Forest to aid in Northern Spotted Owl surveys.

Background

Due to intensive logging throughout southwestern British Columbia, western Washington and Oregon, and northwestern California, Northern Spotted Owls (NSOs) have lost much of their habitat. NSOs were labeled as threatened under the Endangered Species Act almost 30 years ago. The populations of these small, brown-feathered birds continue to decline as they continue to lose what little is left of their habitat to logging and encroachment of larger, non-native owl species. Ideal NSO habitat is characterized by dense canopy closures of mature and old-growth forests with multi-layered canopies. Typically, a forest with these characteristics takes around 150 to 200 years to form. Preserving NSO populations and habitat requires protecting high-quality and occupied NSO habitat, actively managing forests in a way that balances timber harvesting and NSO health, and managing competition from the encroaching barred owl.

The Conservation Fund performs surveys to identify NSO locations and existing habitat. Though surveying these owls has become increasingly difficult with the increased population of Barred Owls, native to the Eastern U.S. These owls are more aggressive and have a broader diet than NSOs which makes them more resilient to the degraded habitat. Barred Owl presence also deters spotted owls from calling which makes locating them near impossible if NSOs feel threatened.

Simplified flow chart of process for calculating the canopy height model raster from lidar point clouds.

Combining existing knowledge of NSO habitat locations and a high-resolution canopy height model can be used to identify suitable habitat without surveying. Knowing where there is a higher chance of finding spotted owls can make surveying more efficient, and can be used to monitor changes in these areas.

  • Lidar point cloud tiles were downloaded from the USGS TNM Download portal for the areas of interest
  • LASzip was used to decompress the zipped tiles from .laz files to .las files
  • ArcGIS Pro Model Builder was used for Steps 2 to 5
A subset of the CHM product.