Forest ecosystems contain more biomass than any other ecosystem. However, estimating biomass without cutting down trees is difficult. Traditional methods of estimating aboveground biomass (ABG) are based on correlations between destructive estimates of volume and diameter and/or height, which can be measured more easily in the field.
However, harvesting trees is expensive, often impractical and undesirable. 3D-FOREST, a three-year project funded by the Belgian Federal Science Policy Office led by Dr. Kim Calders and Prof. Hans Verbeeck from Ghent University, partnering with Dr. Harm Bartholomeus and Prof. Martin Herold from Wageningen University, aims at providing novel LiDAR in-situ 3D forest structure and biomass estimates to validate large-scale air/spaceborne biomass products. Co-incident LiDAR data from different platforms was collected to quantify aboveground biomass and forest structure in five tropical sites in Australia during a two-month fieldwork campaign in the dry season.
Fusion of Terrestrial and UAV LiDAR
The concept of the project is to capture data to create “virtual forests” with a high level of detail by using terrestrial laser scanning (TLS), realizing that we are limited from exploring the ecological uncertainties that happen at larger scale by the relatively small coverage of TLS. The combined, bottom-up TLS and top-down UAV LiDAR data is expected to improve biomass estimates and knowledge on how we can upscale plot-based measurements to landscape level.
Dr. Calders collected ground-based LiDAR (TLS) with the RIEGL VZ-400, scanning from 121 different locations per plot (typically about 1 hectare). This 10 m by 10 m regular grid pattern minimises the data-occlusion, a key requirement for biomass estimates through digital tree reconstruction.
In collaboration with Wageningen University, co-incident UAV LiDAR was collected using the state-of-the-art RIEGL RiCOPTER with VUX-SYS, operated by Dr. Harm Bartholomeus. Each site was covered by an approximate 9 ha area of high density UAV LiDAR (1000+ pts/m2) and a larger 15-20 ha area of lower density UAV LiDAR (a few 100s pts/m2).
Tropical forests are challenging sites to launch the UAV as we require a canopy gap of reasonable size, as well as line-of-sight during the flight. Our selected sites were part of Australian Terrestrial Ecosystem Research Network (TERN, www.tern.org.au). These TERN sites offered great infrastructure such as canopy towers or canopy cranes to successfully fly the planned missions.
Traceable Biomass Estimates from LiDAR Data
3D forests created from LiDAR data can be used to essentially “virtually harvest” the trees and weigh them digitally through calculating their volume and converting this to mass. Over the last couple of years, algorithms were developed to convert 3D TLS point clouds of single trees to actual tree models, called quantitative structure models (QSMs), which allow straightforward calculation of volume. It still requires testing of how the current QSM models will perform on UAV data.
Spatial analysis of these 3D structural metrics and biomass estimates will allow us to understand the spatial patterns of tree structure and evenness at larger scale than the typical 1 hectare inventory plots. This information will be crucial for more efficient forest management but also for better understanding of the spatial variation of forest structure in ecosystem models.
The Bigger Picture
Remote sensing data is seen as a key data source to fill global forest-monitoring gaps because these data offer a synoptic view over large areas. Recently published pantropical biomass maps showed substantial differences in spatial patterns. Our current understanding of the global magnitude and distribution of terrestrial carbon sinks and sources is highly uncertain. Constraining the inaccuracy of these estimates is essential to support effective forest management and climate mitigation action.
This novel lidar driven approach supports the need for accurate, effective and nondestructive methods to assess AGB. The techniques, methods, applications and knowledge developed in this project will provide new insights into the assessment, upscaling and dynamics of AGB. Better information about the current and future state of forests will improve the decisions made by natural resource managers or policy makers.
Furthermore, more accurate estimates of AGB will be essential for the validation and calibration of three upcoming spaceborne satellites (ESA BIOMASS, NASA GEDI, NISAR). These missions are designed to estimate the contribution of forest biomass to the global carbon budget and monitor ecosystem disturbances. However, the success of these future missions will be limited if we cannot provide accurate ground reference measurements.
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