Graduation Date

Spring 2021

Document Type



Master of Science degree with a major in Natural Resources, option Forestry, Watershed, & Wildland Sciences

Committee Chair Name

Dr. Harold Zald

Committee Chair Affiliation

HSU Faculty or Staff

Second Committee Member Name

Dr. Jim Graham

Second Committee Member Affiliation

HSU Faculty or Staff

Third Committee Member Name

Dr. Buddhika Madurapperuma

Third Committee Member Affiliation

HSU Faculty or Staff

Subject Categories



Science-based forest management requires quantitative information about forest attributes traditionally collected via sampled field plots in a forest inventory program. Remote sensing tools, such as active three-dimensional (3D) Light Detection and Ranging (lidar), are increasingly utilized to supplement and even replace field-based forest inventories. However, lidar remains cost prohibitive for smaller areas and repeat measurement, often limiting its use to single acquisitions of large contiguous areas. Recent advancements in unpiloted aerial systems (UAS), digital aerial photogrammetry (DAP) and high precision global positioning systems (HPGPS) have the potential to provide low-cost time and place flexible 3D data to support forest inventory and monitoring. The primary objective of this research was to assess the ability of low-cost commercial off the shelf UAS DAP and HPGPS to create accurate 3D data and predictions of key forest attributes, as compared to both lidar and field observations, in a wide range of forest conditions in California, USA. A secondary objective was to assess the accuracy of nadir vs. off-nadir UAS DAP, to determine if oblique imagery provides more accurate 3D data and forest attribute predictions. UAS DAP digital terrain models were comparable to lidar across sites and nadir vs. off-nadir imagery collection, although model accuracy using off-nadir imagery was very low in mature Douglas-fir forest. Surface and canopy height models were shown to have less agreement to lidar, with high canopy density sites captured with off-nadir imagery showing the lowest amounts of agreement. UAS DAP models accurately predicted key forest metrics when compared to field data and were comparable to predictions made by lidar. Although lidar provided more accurate estimates of forest attributes across a range of forest conditions, this study shows that UAS DAP models, when combined with low-cost HPGPS, can accurately predict key forest attributes across a range of forest types, canopies densities, and structural conditions throughout California.

Citation Style

Remote Sensing of Environment


Thesis/Project Location