UAV-derived estimates of forest structure to inform ponderosa pine forest restoration

Authors: BELMONTE, A. - Northern Arizona University; SANKEY, T. - Northern Arizona University; Biederman, Joel; BRADFORD, J. - Us Geological Survey (USGS); GOETZ, S. - Northern Arizona University; KOLB, T. - Northern Arizona University; WOOLLEY, T. - The Nature Conservancy

Submitted to: Remote Sensing in Ecology and Conservation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/12/2019
Publication Date: 11/25/2019
Citation: Belmonte, A., Sankey, T., Biederman, J.A., Bradford, J., Goetz, S., Kolb, T., Woolley, T. 2019. UAV-derived estimates of forest structure to inform ponderosa pine forest restoration. Remote Sensing in Ecology and Conservation. 6(2):181-197. https://doi.org/10.1002/rse2.137.
DOI: https://doi.org/10.1002/rse2.137

Interpretive Summary: After 120 years of fire suppression, many forests on public lands of the American West have become excessively dense, increasing the risk of catastrophic wildfire. Therefore, the US Forest Service and other stakeholders have planned forest thinning on millions of acres of public lands. Meeting the forest restoration goals requires time-consuming and expensive measurement of forest structure using ground-based or airplane based methods. In this paper, we use an unmanned aerial vehicle (UAV) to make forest structure measurements before and after thinning of a ponderosa forest in Arizona, US. We find the UAV imagery works well for low- and medium-density forests, but like other methods, UAV-based metrics are less accurate for very dense forests. Our results demonstrate a method of forest structure measurement which is more flexible and less expensive than existing methods.

Technical Abstract: Restoring forest ecosystems has become an increasingly high priority for land managers across the American West. Millions of hectares of forest are in need of drastic yet strategic reductions in their density. Meeting the restoration and management goals requires quantifying metrics of vertical and horizontal forest structure, which has relied upon either field-based measurements, manned airborne, or satellite remote sensing datasets. We use unmanned aerial vehicle (UAV) image-derived structure from motion (SfM) models and high resolution multispectral orthoimagery in this study to quantify vertical and horizontal forest structure at both the fine- (< 4 ha) and mid-scales (4-400 ha), as well as across a forest density gradient. We then use these forest structure estimates to assess specific objectives of a forest restoration treatment. At the fine-scale, we find that estimates of individual tree height and canopy diameter are most accurate in low-density conditions, with accuracies degrading significantly in high-density conditions. Mid-scale estimates of canopy cover and forest density follow a similar pattern across the density gradient, demonstrating the effectiveness of UAV image-derived estimates in low to medium-density conditions as well as the challenges associated with high-density conditions. We find that post-treatment conditions are mostly in line with the prescription objectives and demonstrate the UAV image application in quantifying changes from a mechanical thinning treatment. We provide a novel approach to forest restoration monitoring using UAV-derived data, one that considers varying density conditions and spatial scales. Future research should consider a more spatially extensive sampling design, including different restoration treatments, as well as experimenting with different combinations of equipment, flight parameters, and data processing workflows.