Methods development using terrestrial LiDAR for the assessment of forest structures at the tree and stand levels

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Terrestrial LiDAR (Light Detection and Ranging) or TLiDAR is a sensor probing its surroundings with laser pulses following a hemispherical scan. The laser returns from the scan produce a cloud composed of several million points, each representing a distance and a reflectance value. The 3D point cloud results from probing the surrounding objects. Exact 3D rendition of objects is efficient for scenes composed of geometric forms, but it is strongly limited in complex environments such as natural forest. This research program aims to develop methods for the use TLiDAR in natural forest environments to estimate a large array of tree and stand structural attributes. The resulting algorithms are planned as a complement of current inventory methods or sometime beyond what in situ conventional measurements can provide. Four categories of scientific questions will be addressed, which are all related to the use of LiDAR sensors in natural forests: (1) develop algorithms overcoming the limitations of TLiDAR data, (2) develop new methods to measure tree/stand structure, (3) expand the methods towards application to different LiDAR sensors, and (4) use the LiDAR metrics for useful relationships in forest ecology (e.g. ecological services, habitat). This Discovery proposal is a follow-up of my previous Discovery grant on a similar topic. It consolidates some advances and it proposes new promising avenues to use TLiDAR in forestry. The four-component approach proposed here addresses what was found as the main potential advances and to deal with current and critical limitations for the use of TLiDAR data in forestry. The first category of contributions deals with data limitations common to all LiDAR sensors, more specifically for signal occlusion and variable sampling density. We propose an approach using voxel to reduce the impact of these two limitations with the use of a mathematical framework to derive surface density. Secondly, in the past years we developed several innovative algorithms to assess tree stems, tree crowns and model tree architecture. We propose (i) developing a new tree isolation algorithm adapted to T-LiDAR data, and (ii) pushing further our use of tree architecture and virtual plots. Thirdly, we plan to adapt several of our TLiDAR algorithms to other LiDAR sensors types, namely to mobile units and to airborne LiDAR; either on an Unmanned Airborne Vehicle or on an airplane. Each sensor having specific configurations, the adaptation of the algorithms will allow taking advantage of different platforms. Fourthly, we wish to expand the use of TLiDAR to broader issues in forest ecology. This will lead to studies dealing with ecological services, forest habitat and biodiversity. Overall, all the planned studies are tailored to an increased capacity to assess forest structure, taking advantage of the vast untapped potential of LiDAR data.