Terrestrial laser scanning to quantify above-ground biomass of structurally complex coastal wetland vegetation

Abstract

Above-ground biomass represents a small yet significant contributor to carbon storage in coastal wetlands. Despite this, above-ground biomass is often poorly quantified, particularly in areas where vegetation structure is complex. Traditional methods for providing accurate estimates involve harvesting vegetation to develop mangrove allometric equations and quantify saltmarsh biomass in quadrats. However broad scale application of these methods may not capture structural variability in vegetation resulting in a loss of detail and estimates with considerable uncertainty. Terrestrial laser scanning (TLS) collects high resolution three-dimensional point clouds capable of providing detailed structural morphology of vegetation. This study demonstrates that TLS is a suitable non-destructive method for estimating biomass of structurally complex coastal wetland vegetation. We compare volumetric models, 3-D surface reconstruction and rasterised volume, and point cloud elevation histogram modelling techniques to estimate biomass. Our results show that current volumetric modelling approaches for estimating TLS-derived biomass are comparable to traditional mangrove allometrics and saltmarsh harvesting. However, volumetric modelling approaches oversimplify vegetation structure by underutilising the large amount of structural information provided by the point cloud. The point cloud elevation histogram model presented in this study, as an alternative to volumetric modelling, utilises all of the information within the point cloud, as opposed to sub-sampling based on specific criteria. This method is simple but highly effective for both mangrove (r 2 = 0.95) and saltmarsh (r 2 > 0.92) vegetation. Our results provide evidence that application of TLS in coastal wetlands is an effective non-destructive method to accurately quantify biomass for structurally complex vegetation.

Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling

“…The last four LiDAR metrics include two metrics describing the point cloud variation (HVAR and HSD), one density metric (D01), and one canopy cover index (CC 1.3 ). The sensitivity of the HVAR and HSD in this study are in accordance with what was observed by Owers et al [40] in a mangrove AGB study using terrestrial LiDAR. Consequently, we can infer that the canopy thickness is the most important LiDAR metric for mangrove AGB estimation, followed by upper canopy height metrics, bottom canopy height metrics, and point cloud variation metrics, in that order.…”

“…Few studies have used high-density LiDAR point clouds to estimate mangrove AGB, and few studies have reported the most important LiDAR metrics for predicting mangrove AGB [13,14]. A total of 53 LiDAR metrics were derived preliminarily (24 for height, 12 for density, and 17 for canopy volume) [14,37], as presented in Table A1 [14,[38][39][40]. A novel canopy thickness metric (CTHK) was created and used to describe the thickness of mangrove canopy based on the analysis of UAV-LiDAR point clouds [41].…”

Estimating Mangrove Tree Biomass and Carbon Content: A Comparison of Forest Inventory Techniques and Drone Imagery

“…Consequently, carbon financing and accounting methods recommend and reward the use of accurate, site-based measurements of mangrove above-and below-ground carbon pools that capture variability and improve confidence in carbon stock estimates (Gibbs et al, 2007;Kauffman and Donato, 2012;Howard et al, 2014;IPCC, 2014). However, the mangrove above-ground carbon pool is generally poorly quantified (Owers et al, 2018b).…”

Estimating Mangrove Forest Volume Using Terrestrial Laser Scanning and UAV-Derived Structure-from-Motion

“…A volumetric or surface differencing approach was used to estimate mangrove forest volume, which involves subtracting a DTM from a DSM to obtain a CHM, thereby normalizing object heights above ground [52]. Forest volume, which includes all pixels associated with aboveground vegetation, can then be estimated by multiplying the canopy height value of a raster cell by its resolution [53,54]. A DTM was created in ArcMap (version 10.6) by interpolating all 45 topographic points with the 'Topo to Raster' tool ( Figure 5).…”