Tree Modelling TLS data, QSMs, applications Pasi Raumonen, Markku Åkerblom, Mikko Kaasalainen Department of Mathematics, Tampere University of Technology Tree data and modelling workshop 7 th - 9 th June 2016, Tampere, Finland math.tut.fi/workshop2016

Terrestrial Laser Scanning (TLS) TLS offers comprehensive measurement of the tree surface seen from few scan positions Fast, nondestructive, (cheap) Produces 3D xyz-point clouds Presentations by Alan, Kim and Mark Use TLS data for comprehensive tree modelling - QSMs

QSM - Quantitative Structure Model Tree modelled as a hierarchical collection of cylinders – Raumonen et al. 2013: “Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data”. Remote Sensing – Calders et al 2015: “Nondestructive estimates of above-ground biomass using terrestrial laser scanning”. Methods in Ecology and Evolution – Raumonen et al. 2015: “Massive-Scale Tree Modelling From TLS Data”. ISPRS Annals Compact tree architecture model containing essential topological and geometrical tree properties – Branching structure, branching order – Volumes, lengths, angles, taper, etc. Other building blocks and “hybrid” models possible – Åkerblom et al. 2015: “Analysis of geometric primitives in quantitative structure models of tree stems”. Remote Sensing

QSM - Quantitative Structure Model Segmentation – Use small patches of the point cloud to hierarchical segment it into branches without sub-branches – Segments have the branching order and parent-child relation Cylinder fitting – Divide each segment into smaller sub-segments for least squares cylinder fitting Our method has been implemented in MATLAB – Modeling time few minutes Lots of applications

Above-ground biomass TLS+QSM gives volume + wood density = biomass – Calders et al. 2015: “Non-destructive estimates of above- ground biomass using terrestrial laser scanning”. Methods in Ecology and Evolution – Raumonen et al. 2015: “Massive-Scale Tree Modelling From TLS Data”. ISPRS Annals – Hackenberg et al. 2015: “SimpleTree - an efficient open source tool to build tree models from TLS clouds”. Forests About 10% error in biomass Relative error does not correlate with DBH/size

Below-ground biomass and structure Smith et al. 2014: “Root system characterization and volume estimation by terrestrial laser scanning”. Forests Stump-root systems of big Norway spruce were uprooted, cleaned, and scanned Hybrid QSM – triangulation for the stump – cylinders for the roots Volume underestimated by 4.4% Other topological and volumetric root variables possible

Change detection and quantification Nondestructive TLS measurements can be repeated in time QSM can detect and quantify changes Kaasalainen et al. 2014: “Change Detection of Tree Biomass with Terrestrial Laser Scanning and Quantitative Structure Modelling”. Remote Sensing

Species recognition Compute large number of features for species classification Over 95 % classification accuracy possible Åkerblom et al. 2016: “Automatic tree species recognition with quantitative structure models”. submitted to Remote Sensing of Environment

Tree database QSM: Compact model containing topological and geometrical tree architecture information – Branching structure, branching order – Volumes, lengths, angles, taper, etc. Make a web-based database of all QSMs – Thousands of trees, different plots and species – Available to researchers Structure of the database – Plot, tree, QSM, branch, cylinder?

Massive scale tree modelling Multi-hectare, thousands of trees, TLS points Raumonen et al. 2015: “Massive-scale tree modelling from TLS data”. ISPRS Annals – Basic computer is enough – Tree extraction in hours Still lot of challenges in automatic tree extraction

Other applications Mechanical response to wind forcing Forest fires and fuel structure modelling Plant scaling-laws estimation QSMs as supports for eco-physiological data – leaves, water, chlorophyll, etc. 3D forest inventory with accurate measurements of individual trees Better pre-harvest planning Determination of phenotypes FSPMs (Ilya’s presentation)

Open question, challenges, visions QSM reconstruction optimization: – How to choose parameters? How to measure the quality of QSMs against the data? – Robust tree extraction (A priori information, iterative and probabilistic methods) – Leaves/needless: how to filter out and how to bring back? (where and in which format) Testing QSM reconstruction robustness and errors – Effects of wind, leaves, occlusion and measurement setup (scan positions, resolution)? Tree database: – What is stored and in what format? Who maintains? How to make the structure dynamic? Any new applications for QSMs? Future measuring technology – How to measure tree crowns? (mobile/flying TLS, multi-wavelength, crane, airborne) Vision: Fast (mobile) TLS + Robust and accurate tree extraction + QSM reconstruction in massive scale + Database