Comparisons between field- and LiDAR-based measures of stand structural complexity

Abstract

Forest structure, as measured by the physical arrangement of trees and their crowns, is a fundamental attribute of forest ecosystems that changes as forests progress through suc;cessional stages. We examined whether LiDAR data could be used to directly assess the successional stage of forests by determining the degree to which the LiDAR data would show the same relative ranking of structural development among sites as would traditional field measurements. We sampled 94 primary and secondary sites , and 600 years old) from three conifer forest zones in western Washington state, USA, in the field and with small-footprint, discrete return LiDAR. Seven sets of LiDAR metrics were tested to measure canopy structure. Ordinations using the of LiDAR 95th percentile height, rumple, and canopy density metrics had the strongest correlations with ordinations using two sets of field metrics (Procrustes R = 0.72 and 0.78) and a combined set of LiDAR and field metrics (Procrustes R = 0.95). These results suggest that LiDAR can accurately characterize forest successional stage where field measurements are not available. This has important implications for enabling basic and applied studies of forest structure at stand to landscape scales.Résumé : La structure de la forêt, telle qu'elle est mesurée par la disposition physique des arbres et de leur cime, est un attribut fondamental des écosystèmes forestiers qui change à mesure que les forêts passent à travers les stades de succession. Nous avons examiné si les données obtenus avec le LiDAR pouvaient être utilisées pour évaluer directement le stade de succession en déterminant dans quelle mesure les données LiDAR produisent le même classement relatif du développe-ment structural de différentes stations que les mesures traditionnelles prises sur le terrain. Nous avons échantillonné 94 stations dans des forêts vierges et de seconde venue (âgées de 19 à 93, 223 à 350 et 600 ans) sur le terrain et avec un LiDAR à impulsions discrètes et petite empreinte. L'étude a été réalisée dans trois régions couvertes de forêt résineuse dans l'ouest de l'É tat de Washington, aux É tats-Unis. Sept séries de mesures obtenues avec le LiDAR ont été testées pour mesurer la structure du couvert. Les ordinations utilisant les mesures du 95 ième percentile de la hauteur, du plissement et de la densité du couvert provenant du LiDAR avaient les plus fortes corrélations avec les ordinations utilisant deux séries de mesures prises sur le terrain (Procrustes R = 0,72 et 0,78) et une série de mesures provenant du LiDAR et de mesures prises sur le terrain combinées (Procrustes R = 0,95). Ces résultats indiquent que le LiDAR peut caractériser avec exactitude le stade de succession de la forêt lorsque les données terrain ne sont pas disponibles. Cela a d'importantes répercus-sions pour permettre la réalisation de travaux de recherche fondamentale et appliquée sur la structure de la forêt en allant de l'échelle du peuplement à l'échelle du paysage.[Traduit par la Rédaction]

Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

“…Earlier studies have suggested that the information in the ALS data may be condensed to a few metrics [72,73], the partitioning of which will provide a stratification corresponding closely to the structural complexity observed in the field [59,74,75]. In this study, a similar partitioning was carried out using the textural features, and the applicability of the obtained information was demonstrated by prioritizing the field plots to be measured for predicting plot V using other features.…”

Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer–Broadleaf Forest: Comparison with Airborne Laser Scanning

“…Previous forestry studies have assessed the accuracy of remote sensing techniques by extracting various structural metrics that play major roles in forest management and other ecological applications [9,14,23,24,39,43,55]. To assess the accuracy of the UAV-SfM point cloud in detail and to thoroughly evaluate the differences between the UAV-SfM and LiDAR point clouds, we used common plot-level structural metrics that can be easily generated from point cloud data.…”

“…LiDAR structural metrics are strongly correlated with variation in vertical and horizontal forest structure at the stand level and hence explain the structural complexity of the overall forest canopy [9,65]. Therefore, we selected several plot-level LiDAR structural metrics described in the previous section, including mean height, canopy cover greater than 2 m, and surface area ratio, to determine the influence of stand structure on the RMSD of canopy height.…”

“…In addition, the forest canopy plays an important role as a biotic habitat [3], an area of high photosynthetic capacity [4], an indicator of biodiversity [2,5,6], and a gauge of forest health [7]. However, our understanding of forest canopy structure may be constrained in some important ways (e.g., structural and spatial complexity of the canopy at the landscape level) because most existing studies of the forest canopy are based on data collected through field surveys within a small set of sample plots that are often selected subjectively [8,9]. In addition, characterisation of the three-dimensional (3D) structure and fixed-wing UAVs [54].…”

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

“…Numerous studies have demonstrated that forest inventory variables can be measured and modeled accurately (and precisely) from LiDAR height and density metrics [1][2][3]. These include critical parameters, such as species identification [4], mean diameter at breast height (DBH) [5,6], stand and canopy structural complexity [7,8], forest succession [8], fractional cover [9], leaf area index (LAI) [9,10], crown closure [11], timber volume [6,12,13] and biomass [14][15][16][17]. Estimation of many forest inventory variables using LiDAR data is now moving beyond the research realm and into the operational forum [18][19][20][21][22].…”