UAS Photogrammetry of Forest Structural Characteristics across California's Diverse Forest Types.
Abstract
As demand grows for accurate and timely inventory and monitoring of forest ecosystems, remotely sensed data has become a common way to capture forest structural characteristics. High resolution images captured from an unmanned aerial system (UAS) can be used with Structure-from-Motion (SfM) algorithms to generate three-dimensional point cloud data that represents both ground and vegetation, which can then be used to derive key forest metrics. Off-nadir imagery has the potential to improve SfM point clouds and forest structural characterization, but empirical comparisons of off-nadir SfM imagery to other data sources such as field measurements and LiDAR is lacking across diverse forest types. For this study we collected field data and UAS imagery across six forest types with different combinations of forest composition and structural complexity. All study sites also had recently acquired airborne discrete return LiDAR data. Standard forest metrics were calculated for both UAS SfM imagery and LiDAR data, and UAS SfM metrics compared to lidar-deirved metrics and field data. Off-nadir UAS SfM improved predictions of forest structural metrics over nadir-only images, but both nadir and off-nadir UAS SfM had reduced accuracy compared to lidar in forests with higher canopy cover and complexity.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMB081.0017L
- Keywords:
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- 0414 Biogeochemical cycles;
- processes;
- and modeling;
- BIOGEOSCIENCES;
- 0416 Biogeophysics;
- BIOGEOSCIENCES;
- 0426 Biosphere/atmosphere interactions;
- BIOGEOSCIENCES;
- 0476 Plant ecology;
- BIOGEOSCIENCES