Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data

Abstract

Individual tree crown (ITC) segmentation is an approach to isolate individual tree from the background vegetation and delineate precisely the crown boundaries for forest management and inventory purposes. ITC detection and delineation have been commonly generated from canopy height model (CHM) derived from light detection and ranging (LiDAR) data. Existing ITC segmentation methods, however, are limited in their efficiency for characterizing closed canopies, especially in tropical forests, due to the overlapping structure and irregular shape of tree crowns. Furthermore, the potential of 3-dimensional (3D) LiDAR data is not fully realized by existing CHM-based methods. Thus, the aim of this study was to develop an efficient framework for ITC segmentation in tropical forests using LiDAR-derived CHM and 3D point cloud data in order to accurately estimate tree attributes such as the tree height, mean crown width and aboveground biomass (AGB). The proposed framework entails five major steps: (1) automatically identifying dominant tree crowns by implementing semi-variogram statistics and morphological analysis; (2) generating initial tree segments using a watershed algorithm based on mathematical morphology; (3) identifying “problematic” segments based on predetermined set of rules; (4) tuning the problematic segments using a modified distance-based algorithm (DBA); and (5) segmenting and counting the number of individual trees based on the 3D LiDAR point clouds within each of the identified segment. This approach was developed in a way such that the 3D LiDAR points were only examined on problematic segments identified for further evaluations. 209 reference trees with diameter at breast height (DBH) ≥ 10 cm were selected in the field in two study areas in order to validate ITC detection and delineation results of the proposed framework. We computed tree crown metrics (e.g., maximum crown height and mean crown width) to estimate aboveground biomass (AGB) at tree level using previously published allometric equations. Accuracy assessment was performed to calculate percentage of correctly detected trees, omission and commission errors. Our method correctly identified individual tree crowns with detection accuracy exceeding 80 percent at both forest sites. Also, our results showed high agreement (R2 > 0.64) in terms of AGB estimates using 3D LiDAR metrics and variables measured in the field, for both sites. The findings from our study demonstrate the efficacy of the proposed framework in delineating tree crowns, even in high canopy density areas such as tropical rainforests, where, usually the traditional algorithms are limited in their performances. Moreover, the high tree delineation accuracy in the two study areas emphasizes the potential robustness and transferability of our approach to other densely forested areas across the globe.

Combined Impact of Sample Size and Modeling Approaches for Predicting Stem Volume in Eucalyptus spp. Forest Plantations Using Field and LiDAR Data

“…Although LiDAR has shown to be a powerful technology for forest inventory around the world, its application for monitoring Eucalyptus forest plantations in Brazil is relatively new [18,51]. On examination of the trends observed in previous studies, that have employed a wide range of modeling methods for forest attribute estimation and reported results representing varying accuracies, it is clear that appropriate selection of methods is paramount for attaining the best prediction results [20,37,44,52,53]. The novelty of this research is to investigate how the combined influence of sample size and different modeling techniques affect the overall prediction accuracy of forest plantation attributes and demonstrate the potential of reduced sample sizes to generate accurate prediction results.…”

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

“…Considerable work has been done to develop and improve automated ITCD techniques [10][11][12][13][14][15]. Light Detection and Ranging (LiDAR) crown delineation methods tend to be favored over spectral methods because they are not impaired by shadow and illumination artifacts [16], and because of their ability to directly measure crown architecture [17].…”

Remote sensing of american maple in alluvial forests: a case study in an island complex of the Loire valley (France)

“…An overview of the bibliography reveals that there are two main approaches to tree crown delineation from LiDAR data. The first one aims to model tree shape directly in the LiDAR cloud, but this process is quite complex (Monnet 2011, Jaafar et al 2018. The second approach involves reducing the LiDAR cloud from 3D to 2D.…”