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.
Highlights
Mean absolute error (MAE) for diameter at breast height (DBH) was lowest for mobile laser scanning (MLS) (4.8 cm) and only slightly smaller than terrestrial laser scanning (TLS) (5.0 cm; Table 2)
ALS predictions of DBH, which had to be derived from tree height, were found to be significantly higher with mean absolute error (MAE) of 9.1 cm (Table 2)
Tree locations from MLS exhibited the smallest prediction errors (Figure 4c), followed by those obtained by TLS and ALS, and all were significantly different with means under 1.27 m (Figure 4c)
Summary
Successful forest conservation, management, and restoration require detailed information on forest structure and composition, which are vital decision-making components and modeling inputs This information has been commonly used to assess current condition, compare management outcomes, and in the case of forest restoration, monitor the success or failure of moving a degraded ecosystem towards recovery. Efforts to quantify ecological patterns and processes typically necessitate precise data on the amount and distribution of biotic and abiotic resources, estimates of how resources may change over time, and assessments of disturbance [1]. These activities often require costly and resource-intensive
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