Evaluation of UAS flight parameters for rapid monitoring of forest characteristics
Date
2020
Authors
Swayze, Neal, author
Tinkham, Wade T., advisor
Vogeler, Jody, committee member
Hudak, Andrew, committee member
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Abstract
Forest managers are increasingly turning to finer spatial and temporal resolution data for monitoring forest structure in a rapidly changing world. Traditionally utilized networks of field plots for inventorying forest resources require significant time and financial investments; in response to this, remote sensing techniques have been investigated for providing inventory data across large extents. These methods, including light detection and ranging (LiDAR), require significant financial investment that limits the frequency of repeated surveys. Unmanned Aerial Systems (UAS) have emerged as potential alternatives for generating fine spatial and temporal resolution 2D and 3D data for modeling forest structure. The use of Structure from Motion (SfM) photogrammetry has made it possible to use UAS to collect aerial images and generate point clouds that can be used to model vertical forest structure information in a cost-effective way. Recent research has indicated that UAS-derived SfM point clouds are comparable to LiDAR point clouds for forest structure characterization through both area-based and individual tree observations. However, substantial knowledge gaps exist regarding the influence of UAS flight parameters on SfM-derived forest attributes. This thesis presents two studies to address these knowledge gaps. Specifically, Chapter 1 investigates the influence of UAS altitude and flight speed on modeling aboveground forest biomass through an area-based approach and Chapter 2 evaluates the influence of UAS altitude, camera angle, and flight pattern on extracted tree level and summarized plot and stand level attributes. Results show a strong positive relationship between flight altitude and plot-based aboveground biomass modeling, with UAS predictions increasingly outperforming (2-24% increased variance explained) contemporary LiDAR strategies as acquisition altitude increased from 80-120 m. When monitored at the individual tree level, UAS acquisitions conducted using a combination of crosshatch flight paths and off-nadir camera angles (20-30°) maximized tree detection rates (F-score of 0.77), correlations between stem mapped and extracted tree heights and DBHs (0.995 and 0.910, respectively), and estimates of stand and plot level basal area per hectare and TPH. These results indicate that UAS can be utilized to accurately summarize tree, plot, and stand level forest structure to assist in monitoring and planning of management prescriptions.
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Subject
biometrics
natural resources
UAV
forestry
biomass
UAS