Modelling vegetation understory cover using LiDAR metrics

Autoři: Lisa A. Venier aff001;  Tom Swystun aff001;  Marc J. Mazerolle aff002;  David P. Kreutzweiser aff001;  Kerrie L. Wainio-Keizer aff001;  Ken A. McIlwrick aff001;  Murray E. Woods aff003;  Xianli Wang aff001
Působiště autorů: Canadian Forest Service, Great Lakes Forestry Centre, Natural Resources Canada, Saul Ste Marie, ON, Canada aff001;  Department of Wood and Forest Sciences, Center for forest research, Université Laval, Quebec, QC, Canada aff002;  Ontario Ministry of Natural Resources and Forestry, North Bay, ON, Canada aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0220096


Forest understory vegetation is an important characteristic of the forest. Predicting and mapping understory is a critical need for forest management and conservation planning, but it has proved difficult with available methods to date. LiDAR has the potential to generate remotely sensed forest understory structure data, but this potential has yet to be fully validated. Our objective was to examine the capacity of LiDAR point cloud data to predict forest understory cover. We modeled ground-based observations of understory structure in three vertical strata (0.5 m to < 1.5 m, 1.5 m to < 2.5 m, 2.5 m to < 3.5 m) as a function of a variety of LiDAR metrics using both mixed-effects and Random Forest models. We compared four understory LiDAR metrics designed to control for the spatial heterogeneity of sampling density. The four metrics were highly correlated and they all produced high values of variance explained in mixed-effects models. The top-ranked model used a voxel-based understory metric along with vertical stratum (Akaike weight = 1, explained variance = 87%, cross-validation error = 15.6%). We found evidence of occlusion of LiDAR pulses in the lowest stratum but no evidence that the occlusion influenced the predictability of understory structure. The Random Forest model results were consistent with those of the mixed-effects models, in that all four understory LiDAR metrics were identified as important, along with vertical stratum. The Random Forest model explained 74.4% of the variance, but had a lower cross-validation error of 12.9%. We conclude that the best approach to predict understory structure is using the mixed-effects model with the voxel-based understory LiDAR metric along with vertical stratum, because it yielded the highest explained variance with the fewest number of variables. However, results show that other understory LiDAR metrics (fractional cover, normalized cover and leaf area density) would still be effective in mixed-effects and Random Forest modelling approaches.

Klíčová slova:

Ecosystems – Forest ecology – Forests – Machine learning – Pines – Shrubs – Wildlife – Lidar


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Článek vyšel v časopise


2019 Číslo 11