Characterizing heterogeneous forest structure in ponderosa pine forests via UAS-derived structure from motion.

Area of Science:

• Forestry and Remote Sensing
• Ecological Restoration
• Geospatial Analysis

Background:

• Dry conifer forest restoration aims to reestablish historical complexity and ecological functions.
• Traditional forest inventory methods lack the spatial resolution for detailed structural analysis.
• Uncrewed Aerial System (UAS) Structure from Motion (SfM) offers potential for high-resolution, spatially explicit forest data.

Purpose of the Study:

• To evaluate the accuracy of UAS-SfM for estimating tree, clump, and stand structural attributes in restored ponderosa pine forests.
• To compare UAS-derived metrics with traditional forest survey data across various silvicultural prescriptions.

Main Methods:

• UAS-SfM remote sensing was employed to collect high-resolution imagery.
• Tree detection, height, diameter at breast height (DBH), canopy cover, and density were estimated.
• UAS-derived metrics were compared against ground-based forest survey data.

Main Results:

• High tree detection success (F-scores 0.64-0.89), particularly for trees >5.0 m tall.
• Average errors for tree height (0.34 m) and DBH (-0.04 cm) were minimal.
• UAS accurately described forest structure, with minor overestimation of stand density and crown area in some cases.

Conclusions:

• UAS-SfM is a reliable tool for generating spatially explicit forest inventory data in ponderosa pine ecosystems.
• The technology effectively informs management objectives by detailing forest structure at multiple scales.
• UAS-based assessments support the goals of dry conifer forest restoration.