Empirical 3D and 4D structural tree models from TLS data

Pasi Raumonen

3D Tree Models for Forest Dynamics 9th - 10th Jan 2020

Helsinki, Finland

Outline

1. Data: Terrestrial laser-scanning

2. Empirical 3D models: Woody structure

3. Empirical 3D models: Leaves

4. Empirical 4D models

Terrestrial laser-scanning

3D point cloud

Terrestrial laser-scanning

• Accurate and comprehensive 3D data from tree’s surface

• Makes possible to measure (and model) trees • non-destructively• safely• fast• cheaply

• We can have accurate empirical models of trees• Branching structure• Volumetric and geometric data• Leaves

Photogrammetric point clouds

• Use a camera (even a smart phone camera) to take lot of photos of the trees and then produce 3D point cloud using the Structure from Motion (SfM) method.

• Marzulli et al. 2020: “Estimating tree stem diameters and volume from smartphone photogrammetric point clouds”. Forestry: An International Journal of Forest Research.

• This conference: Phil Wilkes: “A comparison of terrestrial LiDAR and photogrammetry for rapid characterisation of fine scale branch structure”.

2. Empirical 3D models: Woody structure

Empirical tree models

• No right or obvious way to model trees.

• No useful parametrisable surface presentations.

• Model that contains all the essential information of the data.

• Robust way to reconstruct the model.

• Solution: Building block or geometric primitive approach.

• Tree modelled as a hierarchical collection of cylinders or other primitives fitted to local details.

Photo and point cloud data provided by Eric Casella, UK Forest Research Agency

QSM - Quantitative Structure Model

• Hierarchical collection of cylinders fitted to local details of the tree.

• Compact model containing essential topological, geometrical and volumetric tree properties.

• Branching structure, branching order.

• Volumes, lengths, angles, diameters, etc.

Geometric primitives

• Other simple shapes as building blocks.

• Elliptical and polygonal cylinders, cones.

• Hybrid models with different building blocks possible.

• Cylinder is the most robust choice.

• Not yet implemented in TreeQSM, except the possibility for modelling the bottom of the stem with triangular mesh.

• Åkerblom et al. 2015: “Analysis of geometric primitives in quantitative structure models of tree stems”. Remote Sensing

Geometric primitives

• Simple model whose surface is discontinuous.

• May not be visually pleasing or realistic-looking.

• Still topologically and geometrically accurate and contains most of the structural information.

• More complicated modelling often only decreases the stability and accuracy without any essential or useful new information.

• Visualisation with nice-looking surfaces and textures possible from QSMs.

QSM papers and reconstruction methods• 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.

• Disney et al. 2018: “Weighing trees with lasers: advances, challenges and opportunities”. Interface Focus.

• TreeQSM - MATLAB implementation, freely available in GitHub.

• Hackenberg et al. 2015: “SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds”. Forests.

• Trochta et al. 2017: “3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR”. Plos One.

• Computree. GIP ECOFOR, ONF, Arts et Métiers Paristech, IGN, INRA and Université de Sherbrooke.

TreeQSM: How it works?

Input: point cloud, parameters —> Branch-segmented point cloud —> Cylindrical QSM

xyz-data Topology, branching structure Geometry, volumes

Point cloud data provided by Eric Casella, UK Forest Research Agency

TreeQSM: Basic assumptions

• Single tree in the point cloud. Can have some points from ground and understory.

• Only (x,y,z) -data needed, not using intensity, colour, etc.

• Whole tree is wood. Leaves/noise present in the data may be modelled as part of the wood.

• “Data-driven”: Only sufficiently visible tree parts can be accurately reconstructed.

• Cylinder is an acceptable building block.

• Optional: Branches taper and are smaller in diameter than their parents.

• Separate stem near the ground clearly visible in the data.

• No special assumptions about tree species or size other than the above ones.

Structural but not fully architectural

• TLS-derived QSMs have structure: stem and branches, diameters, lengths, volume, etc.

• But normally the TLS-derived QSMs do not contain full tree architecture.• For example, the primitives (cylinders) don’t correspond to yearly growth

(internodes), shoots, or other functional units, etc.• Still, a lot of useful structural information can be accessed from QSMs.• This conference: Hans Verbeeck: “Time for a Plant Structural Economics

Spectrum”.

Challenges and limitations• Need for automatic tree extraction.

• Raumonen et al. 2015: “Massive-scale tree modelling from TLS data”. ISPRS Annals.

• Andrew Burt 2017: “treeseg", “New 3D measurements of forest structure”. UCL.

• This conference: Di Wang: “Towards an automated processing chain for 3D tree reconstructions from large scale TLS data”.

• Leaves should be separated from the point clouds.• Vicari et al. 2018, Methods in Ecology and Evolution.• Moorthy et al. 2019, IEEE Transactions On Geoscience And Remote Sensing.• Wang et al. 2019, Methods in Ecology and Evolution.

• Occlusion, particularly the visibility of the canopy.• More scans, drones, dynamic scanning positioning, mobile scanning.

• Parameters need to be somehow optimise.• How to measure the fit of the model against the data?• How to decide the best model?

Above-ground volume and biomass• Lidar+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.• Kunz et al. (2017): Comparison of wood volume estimates of young trees from

terrestrial laser scan data. iForest.• Gonzalez de Tanago Menaca et al. 2018: ”Estimation of above-ground biomass of

large tropical trees with Terrestrial LiDAR”. Methods in Ecology and Evolution.

• Generally under 10% error in biomass

• Accuracy/error independent of tree size

• For big trees allometry can give large (30-50%) errors

• This conference: Eric Casella: “Sensitivity analysis of an automated processing chain and uncertainty in the prediction of tree above ground biomass from TLS data”.

• This conference: Alvaro Lau: “Tropical tree biomass equations from terrestrial LiDAR”.

Below-ground biomass and structure

• Stump-root systems of big trees were uprooted and cleaned, then scanned.

• Hybrid-QSM: mesh (stump) and cylinders (roots).

• Smith et al. (2014): “Root system characterization and volume estimation by terrestrial laser scanning”. Forests.

• Underestimated volume by 4.4%.

Feature spaces from QSMs• QSMs allow to access myriad (potentially thousands) tree

features, most manually unmeasurable.

• Increases the dimensionality of LiDAR data.

• Åkerblom et al. 2017: “Automatic tree species recognition with quantitative structure models”. Remote Sensing of Environment.

Species classification

• Åkerblom et al. 2017: “Automatic tree species recognition with quantitative structure models”. Remote Sensing of Environment.

• 3 species, 5 plots, over 1000 trees.

• 95% recognition accuracy.

Database of QSMs

• Save QSMs with proper metadata into a database with free access.

• Make queries to mine the data.

• Could be useful for validation and generation of many scientific hypothesis in ecology and forest research.

• This conference: Special discussion session hosted by Markku Åkerblom.

• This conference: Atticus Stovall: “Global Trends In Three-Dimensional Tree Structure”.

3. Empirical 3D models: Leaves

Wood-leaf segmentation

• Woody structure modelling: preferred to scan in leaf-off conditions.

• Usually the point cloud contains points both from leaves and wood.

• There is a need for accurate/rough separation of wood and leaf points:• Accurate QSM-reconstruction.• Ecological applications (e.g. total leaf-area).

Wood-leaf segmentation

• Classification methods:• Vicari et al. 2018: “Leaf and wood classification framework for terrestrial LiDAR point clouds”.

Methods in Ecology and Evolution.• Point-wise classification based on geometric features• Path-analysis

• Moorthy et al. 2019: “Improved Supervised Learning-Based Approach for Leaf and Wood Classification From LiDAR Point Clouds of Forests”. IEEE Transactions On Geoscience And Remote Sensing.• Point-wise classification based on geometric features

• Wang et al. 2019: “LeWoS: A Universal Leaf-wood Classification Method to Facilitate the 3D Modelling of Large Tropical Trees Using Terrestrial LiDAR”. Methods in Ecology and Evolution.• Point-wise geometric features• Recursive point cloud segmentation and regularisation

QSMs with leaves

• Hard to measure leaves, particularly individual leaves.

• It might be possible to measure leaf-distributions:

• location (leaf-area-density)

• leaf-size

• leaf-orientation

• This conference: Van-Tho Nguyen: “Validation of plant area density estimated from TLS data by using a voxel representation of 3D forests”.

• Distributions supported by QSMs.

• Sample those distributions to populate QSMs with leaves.

• Åkerblom et al 2018: “Non-Intersecting Leaf Insertion Algorithm for Tree Structure Models”. Interface Focus.

• Matlab code: https://github.com/InverseTampere/qsm-fanni-matlab

4. Empirical 4D models

4D empirical tree models

• Generate a time-series of empirical 3D models:

• Scan the trees repeatedly for many years.

• Reconstruct empirical 3D models (QSMs) from each repeated scan.

• This conference: • Eric Casella: “Sensing the growth of oak trees from an eight-year TLS survey period”.• Kim Calders: “Quantifying forest growth in a free-air CO2 enrichment experiment using

terrestrial laser scanning”.• Miro Demol: “TLS for long-term forest monitoring: experience from the ICOS flux tower

network”.• Eetu Puttonen: “Experiences in monitoring seasonal variation in vegetation with high

density spatial and temporal terrestrial laser scanning time series”.

How to model tree growth?

• Biology-based theoretical functional-structural plant models (FSPMs) such as Lignum.

• TLS-derived empirical 3D models can help for the development and validation of FSPMs.

Validation/development of FSPMs with empirical tree models

• Beyer et al. 2017: “Validation of a functional-structural tree model using terrestrial Lidar data”. Ecological Modelling.• Comparison of the simulated tree crown

(3D spatial leaf density) to empirical crowns obtained from TLS data.

• TLS data provides an unprecedented degree of information on tree geometry compared to traditional forest inventory measurements.

Validation/development of FSPMs with empirical tree models

• Sievänen et al. 2018: “A study of crown development mechanisms using a shoot-based tree model and segmented terrestrial laser scanning data”. Annals of Botany.• LIGNUM and pseudo-time-series of empirical QSMs.• Different formulations of crown development (flushing of

buds and length of growth of new internodes) in LIGNUM.• Optimized the parameter values of each formulation to

observe the best fit of LIGNUM simulations to the measured trees.

• Metric combined both tree-level characteristics and measures of crown shape.

• Pseudo time series in Sievänen et al. 2018:

• TLS-derived QSMs (left) and modified Lignum models (right).

How to model tree growth?

• Biology-based theoretical functional-structural plant models (FSPMs).

• More fully synthetic “4D-geometric” models that flexibly represent intuitive aspects of growth, resources, and structure without strict biological rules.

• Combine the two and add stochastic properties:    

• Stochastic Structure Model  - SSM.

Stochastic Structure Model - SSM

• A model with fixed parameters (deterministic ones and those of probability distributions) creates statistically similar trees:

• Morphological clones approximating the case:• same genotype • similar growth conditions• so the differences are due to random effects.

• Potapov et al 2016: “Bayes Forest: a data-intensive generator of morphological tree clones”, GigaScience.

• Matlab code: https://github.com/inuritdino/BayesForest

Tuning SSMs Using Empirical Tree Models (QSMs)

Potapov et al 2016: “Bayes Forest: a data-intensive generator of morphological tree clones”. GigaScience.

Clone-generation

Potapov etal.:Data-basedstochasticmodelingoftreegrowthandstructureformation, SilvaFennica,2016

Next steps• Fully automatic processing chain: Plot level point cloud —> QSMs of individual trees with leaves.

• Automatic tree extraction.• Automatic wood-leaf-segmentation.• Automatic QSM reconstruction with leaves.

• QSM and point cloud quality estimation/grading.

• Upscaling: from TLS to satellite data – large comprehensively analysed test plots for large-scale calibration.

• Use multi-channel or hyperspectral lidar information in QSM and leaf reconstruction and for 4D models.

• Large scale repeated TLS measurements and time series of QSMs.• Ecological research.• 4D tree model development and validation.

• Measuring and modelling accurately distributions of leaf area density, size, and orientation.

• Publicly accessible QSM database with tens of thousands of trees.