Novel Lidar Data Improves Understanding of Tropical Forests

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Dr. Kim Calders and his team have just completed 2 months of field work in Australian tropical forests collecting both terrestrial and UAS lidar data to support a novel lidar data fusion methodology.

Example of Tree Segmentation using novel lidar approach

Example of Tree Segmentation

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.

To read the full article click here.

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