Accuracy of LiDAR-based tree height estimation and crown recognition in a subtropical evergreen broad-leaved forest in Okinawa, Japan

Abstract

Aim of study: To present an approach for estimating tree heights, stand density and crown patches using LiDAR data in a subtropical broad-leaved forest.Area of study: The study was conducted within the Yambaru subtropical evergreen broad-leaved forest, Okinawa main island, Japan.Materials and methods: A digital canopy height model (CHM) was extracted from the LiDAR data for tree height estimation and a watershed segmentation method was applied for the individual crown delineation. Dominant tree canopy layers were estimated using multi-scale filtering and local maxima detection. The LiDAR estimation results were then compared to the ground inventory data and a high resolution orthophoto image for accuracy assessment. Main results: A Wilcoxon matched pair test suggests that LiDAR data is highly capable of estimating tree height in a subtropical forest (z = 4.0, p = 0.345), but has limitation to detect small understory trees and a single tree delineation. The results show that there is a statistically significant different type of crown detection from LiDAR data over forest inventory (z = 0, p = 0.043). We also found that LiDAR computation results underestimated the stand density and overestimated the crown size.Research highlights: Most studies involving crown detection and tree height estimation have focused on the analysis of plantations, boreal forests and temperate forests, and less was conducted on tropical and/or subtropical forests. Our study tested the capability of LiDAR as an effective application for analyzing a highly dense forest.Key words: Broad-leaved; inventory; LiDAR; subtropical; tree height.Abbreviations: DBH: Diameter at Breast Height, CHM: Canopy Height Model, DEM: Digital Elevation Model, DSM: Digital Surface Model, LiDAR: Light Detection and Ranging, YFA: Yambaru Forest Area.

Individual tree detection and characterization using 3D remote sensing

“…It can be seen from Figure 7 that most of the tree detection methods are based on rasters. In this raster-based single tree detection and crown segmentation, Zawawi et al (2015) used ALS-data with 5 pulses per m 2 (Figure 8). They created a digital terrain model (DTM), digital surface model (DSM) and CHM with a resolution of 0.5 m by subtracting DTM from DSM.…”

“…Left: example workflow for tree detection and crown segmentation with rasters(Zawawi et al, 2015). (a) Grayscale image of the filtered canopy height model (CHM) marked by local maxima seeds identified as tree tops (b) Segmented image interpreted as tree crowns derived from the watershed segmentation method.…”

The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data

“…This might help to explain why the individual tree detection could not be employed when the canopy cover was larger than 70% in the study by Peuhkurinen et al [36]. Tree density is a vegetation characteristic that has indisputably a great influence on individual tree segmentation [15,21,61]. In conifer and broadleaf forests, the F-score of the four segmentation methods showed a downward trend overall, and the CHM, PFCHM, and LSS decreased when the tree density was greater than 200 trees/ha, and PCS was affected when the tree density was greater than 325 trees/ha.…”

Mobile 3D Mapping with a Low-Cost UAV-Based LIDAR System

“…UAV can be used as an effective platform for developing the system. A UAV-based laser scanner system is considered as a useful tool to quickly collect 3D point cloud data of the crop or monitoring the forest such as estimating canopy height (Ehlert et al, 2010, Zawawi et al, 2015and Sibona et al, 2017, canopy structure (Rice et al, 2005), carbon stork (Maan et al, 2015) and vertical plant density profile Osama 2006, 2009). Most of low-cost UAV limits the weight of payload.…”