Comparison of Low-Cost Commercial Unpiloted Digital Aerial Photogrammetry to Airborne Laser Scanning across Multiple Forest Types in California, USA

Abstract

Science-based forest management requires quantitative estimation of forest attributes traditionally collected via sampled field plots in a forest inventory program. Three-dimensional (3D) remotely sensed data such as 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 measurements, 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 study 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 (DTMs) were comparable to lidar DTMS across most sites and nadir vs. off-nadir imagery collection (R2 = 0.74–0.99), although model accuracy using off-nadir imagery was very low in mature Douglas-fir forest (R2 = 0.17) due to high canopy density occluding the ground from the image sensor. Surface and canopy height models were shown to have less agreement to lidar (R2 = 0.17–0.69), with off-nadir imagery surface models at high canopy density sites having the lowest agreement with lidar. UAS DAP models predicted key forest metrics with varying accuracy compared to field data (R2 = 0.53–0.85), and were comparable to predictions made using 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.

Optimizing aerial imagery collection and processing parameters for drone‐based individual tree mapping in structurally complex conifer forests

“…Our tests of camera pitch revealed that oblique (25°) and oblique‐nadir composite imagery, regardless of flight altitude, yielded ITD accuracy worse than nadir imagery collected at 120 m. This finding is surprising because oblique imagery is known to yield more accurate terrain models (Nesbit & Hugenholtz, 2019) and increase understorey point cloud density (Díaz et al, 2020), especially in stands without a closed canopy (Lamping et al, 2021). However, our findings corroborate existing evidence that for ITD specifically, greater accuracy is achieved with nadir imagery (Swayze et al, 2021).…”

“…Remote sensing‐based forest mapping has traditionally taken an ‘area‐based’ approach in which remote sensing data (e.g. spectral reflectance data from satellite or aerial imagery) are used to estimate forest summary statistics such as tree density, mean tree height and above‐ground biomass (De Luca et al, 2019; Jayathunga et al, 2018; Lamping et al, 2021; Puliti et al, 2019; Rodman et al, 2019). However, the increasing quality of remote sensing data and processing workflows has recently enabled remote forest mapping more analogous to field‐based approaches that involve detecting and characterizing individual trees (Jeronimo et al, 2018; Koontz et al, 2021; Swayze et al, 2021).…”

“…Despite the promise of drone‐based tree mapping using SfM, relatively little work has quantitatively evaluated the influence of different imagery collection, imagery processing and tree detection methods on the accuracy of the resulting tree maps. Using an oblique (as opposed to directly downward, or ‘nadir’) camera pitch can increase the accuracy of digital terrain models derived from drone images in areas with low vegetation cover (Nesbit & Hugenholtz, 2019) and in forests can increase the point cloud density in the lower canopy and understorey (Díaz et al, 2020; Lamping et al, 2021). However, the only published evaluation of camera pitch specifically in the context of ITD found that tree detection accuracy was greater with a nadir versus oblique camera pitch (Swayze et al, 2021).…”

“…SfM‐derived point cloud data share many characteristics with point clouds derived from aerial light detection and ranging (lidar; also known as aerial laser scanning, ALS), which can also be used for ITD (Jeronimo et al, 2018; Zaforemska et al, 2019) and may better capture subcanopy structure because some laser pulses penetrate the canopy (Jayathunga et al, 2018; Lamping et al, 2021; Lisein et al, 2013). Because ALS‐derived point clouds have historically been collected using crewed aircraft flying higher than a typical drone operation, they generally cover more ground area per mission but have substantially reduced resolution and point density (e.g.…”

Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity

“…In addition, our previous research found multispectral UAS-SfM to be useful for monitoring changes in upper-canopy structure and greenness after wildfires (Reilly et al, 2021). We also note that multi-angle views can improve detection of sub-canopy structure with UAS-SfM (Lamping et al, 2021), a factor that we could not explore as we were constrained to a nadir view by our UAS and sensor equipment.…”