Scale dependency of lidar‐derived forest structural diversity

Abstract

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.

Towards complex applications of active remote sensing for ecology and conservation

“…However, adding spectral metrics from colour infrared photography did not systematically enhance the overall accuracies of classifying living conifers, living broadleaves or dead trees, indicating that the sole use of LiDAR metrics may suffice for separating retention trees from other trees. In a more general context for operational LiDAR‐assisted forest inventories, Atkins et al (2023) discussed the crucial issue of spatial resolution (grain and extent) over which structural metrics are derived from airborne LiDAR and how spatial scale might affect their derivation across a wide range of forest ecosystem types. The study suggested, however, that multiple spatial grain sizes may suffice for capturing the optimal scale (shown as the representative elementary area) of specific groups of forest metrics.…”

“…1. Stand-, canopy-and tree-level structural analysis by active RS data (Atkins et al, 2023;Blanchard et al, 2023;Coverdale & Davies, 2023;Hardenbol et al, 2022;Schlund et al, 2022;Singh et al, 2023;Tatsumi et al, 2022;Zhang & Liu, 2023).…”

“…Of the accepted papers, four are published in Journal of Animal Ecology , two in Journal of Applied Ecology , four in Journal of Ecology and eight in Methods in Ecology and Evolution . When assessing the general trends being covered by the contributions, a few general (and partly overlapping) trends can be distinguished, including: Stand‐, canopy‐ and tree‐level structural analysis by active RS data (Atkins et al, 2023; Blanchard et al, 2023; Boucher et al, 2023; Coverdale & Davies, 2023; Hardenbol et al, 2022; Schlund et al, 2022; Singh et al, 2023; Tatsumi et al, 2022; Zhang & Liu, 2023). Climate‐mediated response of animal populations (from insects to ungulates) as investigated by active RS data (Brlík et al, 2022; Hockridge et al, 2022).…”

Integrating forest structural diversity measurement into ecological research

“…There is also no consensus about the spatial scale or grain size at which many FSD measurements should be taken, nor is there a robust statistical treatment or analysis of the role of scale (Garabedian et al, 2014). FSD metrics are often calculated at the scale of interest of a given function (Beland et al, 2019) without explicit regard given to the stability or scale‐dependency of those FSD metrics (Atkins et al, 2023). There is a clear need to understand how spatial grain and extent factor into FSD measurement.…”

“…The remote sensing platforms available for measuring FSD range from small handheld devices (e.g., personal mobile device) to spaceborne platforms (Figure 2). The spatial extent and grain of a given sensor inherently influences the types of structural features it can quantify and determines the accuracy and precision of which it is capable (Atkins et al, 2023;Saatchi et al, 2011). Broadly there are two types of remote sensing, passive and active.…”