Predicting tree structure from tree height using terrestrial laser scanning and quantitative structure models

Krooks, Anssi; Kaasalainen, Sanna; Kankare, Ville; Joensuu, Marianna; Raumonen, Pasi; Kaasalainen, M.

doi:10.14214/sf.1125

Cited by 31 publications

(32 citation statements)

References 28 publications

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“…TLS, in combination with TreeQSM, has proven to be an accurate method to estimate direct tree parameters such as tree height (Burt et al 2013;Krooks et al 2014), diameter at breast height (DBH), trunk and branch volumes (Burt et al 2013); and even indirect and complex parameters such as biomass (Calders et al 2015) and changes in tree biomass ). Tree structure modelling with TreeQSM was also successfully employed for automatic species recognition as in Åkerblom et al (2017).…”

Section: Introductionmentioning

confidence: 99%

“…However, most studies so far have focused on measuring total tree volume as the only validation method for this approach (Burt et al 2013;Calders et al 2015;Gonzalez de Tanago et al 2017). Moreover, previous studies using TLS have mostly focused on temperate trees in their leafless condition and with a comparatively low canopy height (Burt et al 2013;Dassot et al 2010;Hackenberg et al 2014;Holopainen et al 2011;Kaasalainen et al 2014;Krooks et al 2014;Pueschel et al 2013;Seidel et al 2012) (but see Wilkes et al 2017;Gonzalez de Tanago et al 2017;Momo Takoudjou et al 2017 for tropical forests). Scanning tropical trees is more difficult due to the complex forest layers with evergreen species which lead to occlusion in the under story, frequently changing weather conditions and logistical challenges (such as scanner settings, hardware requirements, distance to plot, plot area) (Wilkes et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling

Lau

Bentley

Martius

et al. 2018

Trees

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119

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Key message A method using terrestrial laser scanning and 3D quantitative structure models opens up new possibilities to reconstruct tree architecture from tropical rainforest trees. Abstract Tree architecture is the three-dimensional arrangement of above ground parts of a tree. Ecologists hypothesize that the topology of tree branches represents optimized adaptations to tree's environment. Thus, an accurate description of tree architecture leads to a better understanding of how form is driven by function. Terrestrial laser scanning (TLS) has demonstrated its potential to characterize woody tree structure. However, most current TLS methods do not describe tree architecture. Here, we examined nine trees from a Guyanese tropical rainforest to evaluate the utility of TLS for measuring tree architecture. First, we scanned the trees and extracted individual tree point clouds. TreeQSM was used to reconstruct woody structure through 3D quantitative structure models (QSMs). From these QSMs, we calculated: (1) length and diameter of branches > 10 cm diameter, (2) branching order and (3) tree volume. To validate our method, we destructively harvested the trees and manually measured all branches over 10 cm (279). TreeQSM found and reconstructed 95% of the branches thicker than 30 cm. Comparing field and QSM data, QSM overestimated branch lengths thicker than 50 cm by 1% and underestimated diameter of branches between 20 and 60 cm by 8%. TreeQSM assigned the correct branching order in 99% of all cases and reconstructed 87% of branch lengths and 97% of tree volume. Although these results are based on nine trees, they validate a method that is an important step forward towards using tree architectural traits based on TLS and open up new possibilities to use QSMs for tree architecture.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling

Lau

Bentley

Martius

et al. 2018

Trees

Self Cite

119

134

View full text Add to dashboard Cite

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“…Metaproperty analysis augments contemporary TLS object reconstruction methods for studying ecosystems. Object reconstruction uses lidar data from one or more scans to construct representations of objects, such as trees, whose spatial properties, such as volume, are then measured and treated as proxies for the true objects’ ecological properties, such as biomass (Calders et al., ; Kaasalainen et al., ; Krooks et al., ; Raumonen et al., ; Romanczyk et al., ; Wu, Cawse‐Nicholson, & van Aardt, ). In this way, object reconstruction techniques refine a subset of the TLS data in one or more scans to model particular attributes of ecosystem structure.…”

Section: Introductionmentioning

confidence: 99%

Classifying ecosystems with metaproperties from terrestrial laser scanner data

et al. 2017

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In this study, we introduce metaproperty analysis of terrestrial laser scanner (TLS) data, and demonstrate its application through several ecological classification problems. Metaproperty analysis considers pulse level and spatial metrics derived from the hundreds of thousands to millions of lidar pulses present in a single scan from a typical contemporary instrument. In such large aggregations, properties of the populations of lidar data reflect attributes of the underlying ecological conditions of the ecosystems.In this study, we provide the Metaproperty Classification Model to employ TLS metaproperty analysis for classification problems in ecology. We applied this to a proof‐of‐concept study, which classified 88 scans from rooms and forests with 100% accuracy, to serve as a template.We then applied the Metaproperty Classification Model in earnest, to separate scans from temperate and tropical forests with 97.09% accuracy (N = 224), and to classify scans from inland and coastal tropical rainforests with 84.07% accuracy (N = 270).The results demonstrate the potential for metaproperty analysis to identify subtle and important ecosystem conditions, including diseases and anthropogenic disturbances. Metaproperty analysis serves as an augmentation to contemporary object reconstruction applications of TLS in ecology, and can characterize regional heterogeneity.

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“…They demonstrated that in 58% of cases the differences in heights of branched knots between reference and TLS measurements were less than 1.0 cm. Raumonen et al (2013) presented a method for modeling tree stem and branches automatically from the TLS point cloud as Krooks et al (2014) estimated branch size distribution from TLS data and concluded that tree height could be used in predicting branch size distribution for trees with similar growing conditions and topography. Branch size distribution has an effect on wood quality, but it is also used as bioenergy when information on branch amount and size are required.…”

Section: Tls and Mls In Predicting Vegetation Characteristicsmentioning

confidence: 99%

Predicting vegetation characteristics in a changing environment by means of laser scanning

Saarinen¹

2016

Diss. For.

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Accurate and up-to-date information concerning vegetation characteristics is needed for decision-making from individual-tree-level management activities to the strategic planning of forest resources. Outdated information may lead to unbeneficial or even wrong decisions, at least when it comes to the timing of management activities. Airborne laser scanning (ALS) has so far been successfully used for applications involving detailed vegetation mapping because of its capability to simultaneously produce accurate information on vegetation and ground surfaces. The aim of this dissertation was to develop methods for characterizing vegetation and its changes in varying environments. A method called multisource single-tree inventory (MS-STI) was developed in substudy I to update urban tree attributes. In MS-STI stem map was produced with terrestrial laser scanning and by combining the stem map with predictors derived from ALS data it was possible to obtain improved estimates of diameter-at-breast height but also to produce new attributes such as height and crown size. Boat-based mobile laser scanning (MLS) data were employed in substudy II to map riverbank vegetation and identify changes. The overall classification accuracy of 73% was obtained, which is similar to accuracies found in other studies. With multi-temporal MLS data sets changes in vegetation were mapped year to year. In substudy III, open access ALS data were combined with multisource national forest inventory (NFI) data to investigate the drivers associated to wind damage. The special interest was in ALS-based predictors to map areas with wind disturbance and apply logistic regression to produce a continuous probability surface of wind predisposition to identify areas most likely to experience wind damage. The results demonstrated that a combination of ALS and multisource NFI in the modelling approach increased the prediction accuracy from 76% to 81%. The dissertation showed the capability of ALS and MLS for characterizing vegetation and mapping changes in varying environments. The developed applications could increase and expand the utilization of multi-temporal 3D data sets as well as increase data value. The results of this dissertation can be utilized in producing more accurate, diverse, and up-to-date information for decision-making related to natural resources.

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Predicting tree structure from tree height using terrestrial laser scanning and quantitative structure models

Cited by 31 publications

References 28 publications

Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling

Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling

Classifying ecosystems with metaproperties from terrestrial laser scanner data

Predicting vegetation characteristics in a changing environment by means of laser scanning

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