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Article

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

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Ecological Restoration Institute, Northern Arizona University, Flagstaff, AZ 86011, USA
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Ecological Restoration Institute and School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA
3
School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Peter Krzystek
Remote Sens. 2021, 13(12), 2297; https://doi.org/10.3390/rs13122297
Received: 22 April 2021 / Revised: 3 June 2021 / Accepted: 9 June 2021 / Published: 11 June 2021
(This article belongs to the Special Issue Feature Paper Special Issue on Forest Remote Sensing)
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. View Full-Text
Keywords: Arizona; graph theory; individual-tree segmentation; monitoring; ponderosa pine; rapid assessment; tree bole detection Arizona; graph theory; individual-tree segmentation; monitoring; ponderosa pine; rapid assessment; tree bole detection
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MDPI and ACS Style

Donager, J.J.; Sánchez Meador, A.J.; Blackburn, R.C. Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare? Remote Sens. 2021, 13, 2297. https://doi.org/10.3390/rs13122297

AMA Style

Donager JJ, Sánchez Meador AJ, Blackburn RC. Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare? Remote Sensing. 2021; 13(12):2297. https://doi.org/10.3390/rs13122297

Chicago/Turabian Style

Donager, Jonathon J., Andrew J. Sánchez Meador, and Ryan C. Blackburn. 2021. "Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?" Remote Sensing 13, no. 12: 2297. https://doi.org/10.3390/rs13122297

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