Abstract:The advancement of LiDAR technology has enabled more detailed evaluations of forest structures. The so-called "Volumetric pixel (voxel)" has emerged as a new comprehensive approach. The purpose of this study was to estimate plot-level above-ground biomass (AGB) in different plot sizes of 20 m × 20 m and 30 m × 30 m, and to develop a regression model for AGB prediction. Both point cloud-based (PCB) and voxel-based (VB) metrics were used to maximize the efficiency of low-density LiDAR data within a dense forest. Multiple regression model AGB prediction performance was found to be greatest in the 30 m × 30 m plots, with R 2 , adjusted R 2 , and standard deviation values of 0.92, 0.87, and 35.13 Mg·ha −1 , respectively. Five out of the eight selected independent variables were derived from VB metrics and the other three were derived from PCB metrics. Validation of accuracy yielded RMSE and NRMSE values of 27.8 Mg·ha −1 and 9.2%, respectively, which is a reasonable estimate for this structurally complex intact forest that has shown high NRMSE values in previous studies. This voxel-based approach enables a greater understanding of complex forest structure and is expected to contribute to the advancement of forest carbon quantification techniques.
This study aims to quantify the stand-level above ground biomass in intact tropical rain forest of Brunei using airborne LiDAR data. Twenty four sub-plots with the size of 0.09ha (30 m×30 m) were located in the 25ha study area along the altitudinal gradients. Field investigated data (Diameter at Breast Height (DBH) and individual tree position data) in sub-plots were used. Digital Surface Model (DSM), Digital Terrain Model (DTM) and Canopy Height Model (CHM) were constructed using airborne LiDAR data. CHM was divided into 24 sub-plots and 12 LiDAR height metrics were built. Multiple regression equation between the variables extracted from the LiDAR data and biomass calculated by using a allometric equation was derived. Stand-level biomass estimated from LiDAR data were distributed from 155.81 Mg/ha to 597.21 Mg/ha with the mean value of 366.48 Mg/ha. R-square value of the verification analysis was 0.84.
Tropical forests play a critical role in mitigating climate change, and therefore, an accurate and precise estimation of tropical forest carbon (C) is needed. However, there are many uncertainties associated with C stock estimation in a tropical forest, mainly due to its large variations in biomass. Hence, we quantified C stocks in an intact lowland mixed dipterocarp forest (MDF) in Brunei, and investigated variations in biomass and topography. Tree, deadwood, and soil C stocks were estimated by using the allometric equation method, the line intersect method, and the sampling method, respectively. Understory vegetation and litter were also sampled. We then analyzed spatial variations in tree and deadwood biomass in relation to topography. The total C stock was 321.4 Mg C ha -1 , and living biomass, dead organic matter, and soil C stocks accounted for 67%, 11%, and 23%, respectively, of the total. The results reveal that there was a relatively high C stock, even compared to other tropical forests, and that there was no significant relationship between biomass and topography. Our results provide useful reference data and a greater understanding of biomass variations in lowland MDFs, which could be used for greenhouse gas emission-reduction projects.
Forest carbon is accurately quantified by observing individual tree positions and heights. This paper proposes a novel algorithm for individual tree detection using Light Detection and Ranging (LiDAR) data in the Chollipo arboretum, South Korea. The proposed algorithm does not need to specify a proper window size for operation, taking advantage over the mostly used local maxima (LM) filtering for forest analysis. Four hundred twenty-nine treetops were detected and the average height and standard errors were 12.74 ± 0.24 m. Reference data were collected from two sources for verifying accuracy: field survey and visual interpretation. Overall, the result was overestimated but showed relatively high accuracy. The field survey detected 87% of the trees with a coefficient of determination (R 2 ) and root mean square error (RMSE) of 0.77 and 1.57 m, respectively. The accuracy index (AI), which examines the correspondence between LiDAR detected and visually interpreted trees, was 91%. The average tree height error between on-site and LiDAR derived data was -1.42 ± 0.64 m and between visually interpreted and LiDAR derived data was -0.84 ± 0.10 m. This study emphasized the choice of algorithm and its parameters depending on forest conditions may influence the individual tree detection result. By comparing our work against previous studies, we found the tree location and height identification accuracy could be improved if different algorithms were used for different types of forests, as well as the LiDAR point density with each algorithm. This study suggests that more accurate individual tree detection could be obtained with different applications based on forest conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.