Evaluating the sensitivity of forest structural diversity characterization to <scp>LiDAR</scp> point density

LaRue, Elizabeth A.; Fahey, Robert T.; Fuson, T.; Foster, Jane R.; Matthes, Jaclyn Hatala; Krause, Keith; Hardiman, Brady S.

doi:10.1002/ecs2.4209

Cited by 13 publications

(21 citation statements)

References 48 publications

(77 reference statements)

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“…For each site, we analysed between 900 and 2000 ha of lidar acquisition data including the airshed footprint of the eddy covariance tower at each site and the forest inventory plots adjacent to that eddy covariance tower. All NEON AOP data were collected during the peak growing season as determined based on long‐term analysis of periods within 90% of peak greenness (Kampe et al, 2010), collected between 2016 and 2018, with a point density range of 4–32 points m −2 —all above the range of acceptable point density for FSD metric calculation (LaRue et al, 2022). Lidar data were processed in R 4.1.2 (R Core Team, 2022) using the lidR package (Roussel et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

Scale dependency of lidar‐derived forest structural diversity

et al. 2023

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Lidar‐derived forest structural diversity (FSD) metrics—including measures of forest canopy height, vegetation arrangement, canopy cover (CC), structural complexity and leaf area and density—are increasingly used to describe forest structural characteristics and can be used to infer many ecosystem functions. Despite broad adoption, the importance of spatial resolution (grain and extent) over which these structural metrics are calculated remains largely unconsidered. Often researchers will quantify FSD at the spatial grain size of the process of interest without considering the scale dependency or statistical behaviour of the FSD metric employed. We investigated the appropriate scale of inference for eight lidar‐derived spatial metrics—CC, canopy relief ratio, foliar height diversity, leaf area index, mean and median canopy height, mean outer canopy height, and rugosity (RT)‐‐representing five FSD categories—canopy arrangement, CC, canopy height, leaf area and density, and canopy complexity. Optimal scale was determined using the representative elementary area (REA) concept whereby the REA is the smallest grain size representative of the extent. Structural metrics were calculated at increasing canopy spatial grain (from 5 to 1000 m) from aerial lidar data collected at nine different forested ecosystems including sub‐boreal, broadleaf temperate, needleleaf temperate, dry tropical, woodland and savanna systems, all sites are part of the National Ecological Observatory Network within the conterminous United States. To identify the REA of each FSD metric, we used changepoint analysis via segmented or piecewise regression which identifies significant changepoints for both the magnitude and variance of each metric. We find that using a spatial grain size between 25 and 75 m sufficiently captures the REA of CC, canopy arrangement, canopy leaf area and canopy complexity metrics across multiple forest types and a grain size of 30–150 m captures the REA of canopy height metrics. However, differences were evident among forest types with higher REA necessary to characterize CC in evergreen needleleaf forests, and canopy height in deciduous broadleaved forests. These findings indicate the appropriate range of spatial grain sizes from which inferences can be drawn from this set of FSD metrics, informing the use of lidar‐derived structural metrics for research and management applications.

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Section: Methodsmentioning

confidence: 99%

Scale dependency of lidar‐derived forest structural diversity

et al. 2023

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“…Forest canopy volume and four structural arrangement metrics that describe the 3D internal (variation in vegetation height inside the canopy) and external heterogeneity (variation in height of vegetation at canopy surface) of forest canopy vegetation were calculated (Supporting information). We point out that our measurement of volume and structural arrangement metrics are both measured using lidar data, however lidar provides reliable measurements of different structural dimensions of forest canopies, including metrics used here (Gough et al 2020, LaRue et al 2020, 2022, Atkins et al 2022). Plots had a minimum canopy height of 3 m or taller.…”

Section: Methodsmentioning

confidence: 99%

Diversity – volume relationships: adding structural arrangement and volume to species – area relationships across forest macrosystems

LaRue,

Downing,

Saucedo

et al. 2023

Ecography

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The species – area relationship (SAR) is a common pattern in which diversity increases with the area sampled, but ecosystems are three‐dimensional (3D) and diversity – volume relationships (DVRs) may exist in ecosystems that vary substantially in their vegetation volume. We tested whether forest vegetation volume, as a 3D extension of area in SARs, was a significant predictor of taxonomic (species) and structural (arrangement) diversity in five groups of organisms across the National Ecological Observatory Network (NEON). Vegetation volume and four structural arrangement metrics within the area of NEON plots were measured using NEON's discrete return lidar. Species richness was measured as the number of species within the respective NEON plot sampling area for understory plants, trees, breeding land birds, small mammals, and ground beetles. We found that volume negatively predicted understory plants and positively predicted tree and beetle species richness across the USA forest macrosystem, but not bird and small mammal species richness. Furthermore, volume was a significant predictor of several metrics that describe the internal and external heterogeneity of vegetation in forests (structural arrangement) within the ecosystem across the USA forest macrosystem. There were several significant within site‐level relationships, but not at all sites, between volume and species richness or structural arrangement in organism groups. Our study indicates that previous work that has focused on a 2D conceptualization of habitat can be expanded to 3D habitat space, but that the strength and the positive or negative direction of DVRs may vary taxonomically or geographically.

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“…We quantified forest structure over time using NEON's annual AOP airborne LiDAR (Table S2). We calculated mean of maximum canopy height (MOMCH), leaf area index (LAI), subcanopy leaf area index (LAIsub), deep gap fraction (DGF), top rugosity (TR) and Gini index (Gini) which are robust for low to medium point density of discrete return LiDAR (LaRue et al, 2019, 2022) (Table S2). Since the LiDAR densities are different among years, we standardized the density to 4 points/m 2 by randomly selecting points from raw LiDAR point clouds in all plots and sites (Figure S1).…”

Section: Methodsmentioning

confidence: 99%

“…LaRue et al, 2019LaRue et al, , 2022 (TableS2). Since the LiDAR densities are different among years, we standardized the density to 4 points/m 2 by randomly selecting points from raw LiDAR point clouds in all plots and sites (Figure…”

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Short‐term effects of moderate severity disturbances on forest canopy structure

et al. 2023

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Moderate severity disturbances, those that do not result in stand replacement, play an essential role in ecosystem dynamics. Despite the prevalence of moderate severity disturbances and the significant impacts they impose on forest functioning, little is known about their effects on forest canopy structure and how these effects differ over time across a range of disturbance severities and disturbance types. Using longitudinal data from the National Ecological Observatory Network project, we assessed the effects of three moderate severity press disturbances (beech bark disease, hemlock woolly adelgid and emerald ash borer, which are characterized by continuous disturbance and sustained mortality) and three moderate severity pulse disturbances (spring cankerworm moth, spongy moth and ground fire, which are associated with discrete and relatively short mortalities) on temperate forest canopy structure in eastern US. We studied (1) how light detection and ranging (LiDAR)‐derived metrics of canopy structure change in response to disturbance and (2) whether initial canopy complexity offsets impact of disturbances on canopy structure over time. We used a mixed‐effects modelling framework which included a non‐linear term for time to represent changes in canopy structure caused by disturbance, and interactions between time and both disturbance intensity and initial canopy complexity. We discovered that high intensity of both press and pulse disturbances inhibited canopy height growth while low intensity pulse disturbances facilitated it. In addition, high intensity pulse disturbances facilitated increases in the complexity of the canopy over time. Concerning the impact of initial canopy complexity, we found that the initial canopy complexity of disturbed plots altered the effects of moderate disturbances, indicating potential resilience effects. Synthesis. This study used repeated measurements of LiDAR data to examine the effects of moderate disturbances on various dimensions of forest canopy structure, including height, openness, density and complexity. Our study indicates that both press and pulse disturbances can inhibit canopy height growth over time. However, while the impact of press disturbances on other dimensions of canopy structure could not be clearly detected, likely because of compensatory growth, the impact of pulse disturbances over time was more readily apparent using multi‐temporal LiDAR data. Furthermore, our findings suggest that canopy complexity might help to mitigate the impact of moderate disturbances on canopy structures over time. Overall, our research highlights the usefulness of multi‐temporal LiDAR data for assessing the structural changes in forest canopies caused by moderate severity disturbances.

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Evaluating the sensitivity of forest structural diversity characterization to LiDAR point density

Cited by 13 publications

References 48 publications

Scale dependency of lidar‐derived forest structural diversity

Scale dependency of lidar‐derived forest structural diversity

Diversity – volume relationships: adding structural arrangement and volume to species – area relationships across forest macrosystems

Short‐term effects of moderate severity disturbances on forest canopy structure

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