Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

Abstract

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

The shape of trees: Reimagining forest ecology in three dimensions with remote sensing

“…However, issues of data interpretation persist; Hastings et al (2020) show that tree size and plot diversity can determine the success of LiDAR-based crown mapping in mixed temperate forests, affecting its applicability in drawing widespread ecological conclusions. Nevertheless, ALS has the major strength in that it can be used to collect data at very large spatial scales, and though it fails to capture smaller trees occluded in the understorey (Donager et al, 2021), this can be addressed with integration of small-scale TLS data within larger ALS surveys to better monitor understorey volume (Liu et al, 2017) and improve biomass calculations (Stovall & Shugart, 2018).…”

3D LiDAR Scanning of Urban Forest Structure Using a Consumer Tablet

“…For example, crowdsourced tree density and DBH data could be combined with remote sensed tree height data (e.g., aerial LiDAR or photogrammetry) to better estimate tree carbon storage in urban forest and other forest areas. One of the main limitations of aerial LiDAR scanning is the inability to measure tree DBH and accurate distance between tree heights compared with mobile LiDAR scanning systems [30]. There are two distinct parts of forest data capture and metric determination with the iPad data.…”

The Potential of Low-Cost 3D Imaging Technologies for Forestry Applications: Setting a Research Agenda for Low-Cost Remote Sensing Inventory Tasks

“…Therefore, these technologies form the benchmark that other prospective sensors are compared to. These benchmark approaches are not without limitations, however, with both techniques subject to incomplete characterisations of forest structure due to occlusion and point spacing when capturing a forest structure from different angles [18,19].…”