Empirical 3D and 4D structural tree models from TLS data - Pasi Raumonen 3D Tree Models for Forest Dynamics 9th - 10th Jan 2020 Helsinki, Finland
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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, • Åkerblom et al. 2015: “Analysis
of geometric primitives in
except the possibility for modelling
quantitative structure models of
the bottom of the stem with
tree stems”. Remote Sensing
triangular mesh.
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 AgencyTreeQSM: 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 regularisationQSMs 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-matlab4. 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/BayesForestTuning SSMs Using Empirical Tree Models (QSMs) Potapov et al 2016: “Bayes Forest: a data-intensive generator of morphological tree clones”. GigaScience.
Clone-generation Potapov et al.: Data-based stochastic modeling of tree growth and structure formation, Silva Fennica, 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.You can also read
