Tree Species Traits Determine the Success of LiDAR-Based Crown Mapping in a Mixed Temperate Forest

Abstract

The ability to automatically delineate individual tree crowns using remote sensing data opens the possibility to collect detailed tree information over large geographic regions. While individual tree crown delineation (ITCD) methods have proven successful in conifer-dominated forests using Light Detection and Ranging (LiDAR) data, it remains unclear how well these methods can be applied in deciduous broadleaf-dominated forests. We applied five automated LiDAR-based ITCD methods across fifteen plots ranging from conifer- to broadleaf-dominated forest stands at Harvard Forest in Petersham, MA, USA, and assessed accuracy against manual delineation of crowns from unmanned aerial vehicle (UAV) imagery. We then identified tree- and plot-level factors influencing the success of automated delineation techniques. There was relatively little difference in accuracy between automated crown delineation methods (51–59% aggregated plot accuracy) and, despite parameter tuning, none of the methods produced high accuracy across all plots (27—90% range in plot-level accuracy). The accuracy of all methods was significantly higher with increased plot conifer fraction, and individual conifer trees were identified with higher accuracy (mean 64%) than broadleaf trees (42%) across methods. Further, while tree-level factors (e.g., diameter at breast height, height and crown area) strongly influenced the success of crown delineations, the influence of plot-level factors varied. The most important plot-level factor was species evenness, a metric of relative species abundance that is related to both conifer fraction and the degree to which trees can fill canopy space. As species evenness decreased (e.g., high conifer fraction and less efficient filling of canopy space), the probability of successful delineation increased. Overall, our work suggests that the tested LiDAR-based ITCD methods perform equally well in a mixed temperate forest, but that delineation success is driven by forest characteristics like functional group, tree size, diversity, and crown architecture. While LiDAR-based ITCD methods are well suited for stands with distinct canopy structure, we suggest that future work explore the integration of phenology and spectral characteristics with existing LiDAR as an approach to improve crown delineation in broadleaf-dominated stands.

Individual tree crown detection and delineation across a woodland using leaf-on and leaf-off imagery from a UAV consumer-grade camera

“…Across a boreal forest, Nevalainen et al 19 found the accuracy of individual tree identification from the UAV photogrammetric point clouds varying between 26% and 96%, depending on the characteristics of the different test sites. These results confirm that heterogeneous forest structures (such as the Oak and Mix plots in this study) are challenging targets for automated individual tree crown detection and delineation using remotely sensed data 21,23 . On the other hand, an accuracy of >90% in delineated tree crows from UAV-derived 3D point clouds data was reported by Sanchez et al 28 , which is likely due to the simpler structure of the study area (an almond tree plantation).…”

“…The validation/reference data consisted of manually delineated crowns, a common approach when high or very high resolution imagery is available 21,22,41 . Considering the very high spatial resolution of the UAV orthomosaics (10 cm), tree crowns were extracted by a manual delineation approach in…”

“…There are many approaches available to detect and delineate tree crowns from a CHM, with no single method performing best for all applications 8,21 . The accuracy of single-tree-level attributes estimation can be affected by the delineation method and data chosen but also, it can be highly dependent on the forest structure under investigation 10,12,22 .…”

Modelling the Diameter Distribution of Savanna Trees with Drone-Based LiDAR

“…Automatically segmented crown polygons were imported into ArcGIS Pro 2.5 (ESRI, Redlands, CA, USA), where the overlap between automatically and manually segmented crowns was classified as either over-segmentation, under-segmentation, false positive or true positive, following Hastings et al [40]. As the mITCs from the calibration plots were used to parametrize the segmentation model, the accuracy assessment was also applied to the mITCs from the validation plots to allow for independent validation.…”

A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

“…Crown delineation is critical for remote sensing of individual trees, as well as improving broad scale studies of forest ecology, silviculture and ecosystem services [1]. There have been dozens of proposed crown delineation algorithms, but these algorithms are designed for and evaluated using a range of different data inputs [2–4], sensor resolutions [5], forest structures [6,7], and evaluation protocols [8,9]. This diversity of approaches makes it difficult to track algorithmic progress and prevents practitioners from weighing tradeoffs in proposed pipelines.…”