Examining Forest Structure With Terrestrial Lidar: Suggestions and Novel Techniques Based on Comparisons Between Scanners and Forest Treatments

Donager, Jonathon; Sankey, Temuulen Tsagaan; Sankey, Joel B.; Meador, Andrew J. Sánchez; Springer, Abraham E.; Bailey, John D.

doi:10.1029/2018ea000417

Cited by 17 publications

(15 citation statements)

References 106 publications

(132 reference statements)

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“…A statistical comparison between the measured and estimated diameters showed a significant difference ( p -value < 0.05). The estimated DBHs were consistent with the reference measurements, but DBHs derived from TLS were slightly overestimated [ 61 ] in both plots ( Figure 9 ). In plot 1, the average of the measured diameters was 329.46 mm compared to the estimated value of 339.72 mm.…”

Section: Resultssupporting

confidence: 78%

Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application

Panagiotidis

Abdollahnejad

Slavík

2021

Sensors

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Timber volume is an important asset, not only as an ecological component, but also as a key source of present and future revenues, which requires precise estimates. We used the Trimble TX8 survey-grade terrestrial laser scanner (TLS) to create a detailed 3D point cloud for extracting total tree height and diameter at breast height (1.3 m; DBH). We compared two different methods to accurately estimate total tree heights: the first method was based on a modified version of the local maxima algorithm for treetop detection, “HTTD”, and for the second method we used the centers of stem cross-sections at stump height (30 cm), “HTSP”. DBH was estimated by a computationally robust algebraic circle-fitting algorithm through hierarchical cluster analysis (HCA). This study aimed to assess the accuracy of these descriptors for evaluating total stem volume by comparing the results with the reference tree measurements. The difference between the estimated total stem volume from HTTD and measured stems was 2.732 m3 for European oak and 2.971 m3 for Norway spruce; differences between the estimated volume from HTSP and measured stems was 1.228 m3 and 2.006 m3 for European oak and Norway spruce, respectively. The coefficient of determination indicated a strong relationship between the measured and estimated total stem volumes from both height estimation methods with an R2 = 0.89 for HTTD and R2 = 0.87 for HTSP for European oak, and R2 = 0.98 for both HTTD and HTSP for Norway spruce. Our study has demonstrated the feasibility of finer-resolution remote sensing data for semi-automatic stem volumetric modeling of small-scale studies with high accuracy as a potential advancement in precision forestry.

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Section: Resultssupporting

confidence: 78%

Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application

Panagiotidis

Abdollahnejad

Slavík

2021

Sensors

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“…For this study, we focused on a pure ponderosa pine (Pinus ponderosa) forest near Flagstaff, AZ where we could make use of previously collected ALS and TLS lidar datasets (Figure 2) [18]. These sites were managed by the US Forest Service as part of the "Fire and Fire Surrogate" study network [29] with original treatment objectives including fuels reductions [30].…”

Section: Study Sitementioning

confidence: 99%

“…Mobile laser scanner systems offer a powerful tool to ease the time and labor demands of traditional plot-based sampling while potentially providing accurate and highly precise estimates of basic forest structural conditions [12,13]. While fixed-wing airborne laser scanning (ALS) platforms have been commonplace for decades, more recently, terrestrial and mobile lidar systems have been investigated for characterizing forest structure, as their higher point densities allow for more detailed and robust extraction of structural attributes, subcanopy occlusion of smaller trees and other ecologically important species can be reduced, and their data can be collected more frequently than airborne systems [13,[16][17][18][19][20][21]. While the absolute positional error of points collected by MLS was typically less precise than TLS due to the propagation of positioning errors accumulated during scan registrations (e.g., see Figure 1), MLS made use of localization algorithms (e.g., SLAM; Simultaneous Localization and Mapping), which enabled accurate positioning of the scanner in vegetated environments where GNSS and GPS signals were typically hindered by dense canopies and the movement of MLS may have reduced overall occlusion [13,15,22].…”

Section: Introductionmentioning

confidence: 99%

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

2021

Self Cite

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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.

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“…In our study there was a similar placement of plots from different forest types in multivariate space based on ALS and TLS structural diversity metrics. However, there were both similarities and differences in the hierarchical agglomerative clustering by TLS (5 clusters) and ALS (3 clusters is desirable to resolve fine-scale structural variation and its role in forest ecosystem function; for example, to understand differences in local disturbance on the same forest type [24] or to assess the effects of forest management actions [36]. However, previous studies have demonstrated that at larger scales greater variation in structural diversity occurs across forest types and sites rather than within sites [2,18,37].…”

Section: Discussionmentioning

confidence: 99%

Compatibility of Aerial and Terrestrial LiDAR for Quantifying Forest Structural Diversity

LaRue¹,

Wagner²,

Fei³

et al. 2020

Preprint

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Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that describe heterogeneity of the outer most layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicate that aerial LiDAR can be of use in quantifying broad-scale variation in structural diversity across macroscales.

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Examining Forest Structure With Terrestrial Lidar: Suggestions and Novel Techniques Based on Comparisons Between Scanners and Forest Treatments

Cited by 17 publications

References 106 publications

Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application

Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application

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

Compatibility of Aerial and Terrestrial LiDAR for Quantifying Forest Structural Diversity

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