Individual tree biomass estimation using terrestrial laser scanning

Kankare, Ville; Holopainen, Markus; Vastaranta, Mikko; Puttonen, Eetu; Yu, Xiaowei; Hyyppä, Juha; Vaaja, Matti; Hyyppä, Hannu; Alho, Petteri

doi:10.1016/j.isprsjprs.2012.10.003

Cited by 242 publications

(176 citation statements)

References 22 publications

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“…Kankare et al [20] estimated single crown biomass from multiple-scan TLS data and used variable selection to build predictive models from a range of geometrically and statistically TLS-derived features. Field reference biomass was measured trough destructive sampling, and Norway spruce was one of two species considered.…”

Section: Discussionmentioning

confidence: 99%

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Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data

Hauglin

Gobakken

Astrup³

et al. 2014

Forests

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Several methods to conduct single-tree inventories using airborne laser scanning (ALS) have been proposed, and even terrestrial laser scanning (TLS) has recently emerged as a possible tool for the collection of forest inventory data. In the present study, a novel methodological framework for a combined use of ALS and TLS in an inventory was tested and compared to a method without the use of TLS. Single-tree Norway spruce crown biomass was predicted using an ALS-model with training data obtained by TLS. ALS and TLS data were collected for sets of sample trees, including 68 trees with both ALS and TLS data. In total, 29 destructively sampled trees were used to fit a TLS crown biomass model, which then was used to predict crown biomass in a separate set of 68 trees. This dataset was subsequently used to fit an ALS crown biomass model. When validating the model, using a separate dataset with accurately measured crown biomass obtained through destructive sampling, the mean error was 32% of the observed mean biomass. Corresponding crown biomass predictions derived with ALS-predicted diameters and the use of conventional and existing allometric models resulted in a mean error of 35%. Thus, in the present study, a slight improvement, in terms of prediction accuracy, was found when using training data with ground reference values obtained by TLS. OPEN ACCESSForests 2014, 5 385

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Section: Discussionmentioning

confidence: 99%

“…There are also studies presenting procedures to estimate, e.g., total above-ground biomass from TLS data [17,18]. Hauglin et al [19] and Kankare et al [20] have described methods to estimate crown biomass using TLS.…”

Section: Introductionmentioning

confidence: 99%

Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data

Hauglin

Gobakken

Astrup³

et al. 2014

Forests

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“…The TLS measurements of the stand and sample trees were collected in multi-scan mode (e.g., [26]). The TLS data were collected in four scan groups consisting of 10 or more scans per group.…”

Section: Terrestrial Laser Scanningmentioning

confidence: 99%

“…Previously, TLS has been capable of measuring tree characteristics, such as dbh, h, location and biomass [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Timber Quality of Scots Pine with Terrestrial Laser Scanning

Kankare

Joensuu

Holopainen

et al. 2014

Forests

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Preharvest information on the quality of Scots pine (Pinus sylvestris) timber is required by the forest industry in Nordic countries, due to the strong association between the technical quality and product recovery of this species in particular. The objective of this study was to assess the accuracy of estimating external quality attributes and classifying the quality of mature Scots pine trees by terrestrial laser scanning (TLS). The tree quality was estimated using a random forest approach, based on both field and TLS measurements of stem diameters, tree height and branch heights. The relative root mean squared errors of the TLS measurements for tree height, diameter, diameter at 6 m and the lowest living and OPEN ACCESSForests 2014, 5 1880 dead branch height were 7.1%, 5.9%, 8.9%, 9.6% and 42.9%, respectively. The highest errors of the branch heights were caused by the shadowing effect in the point cloud data. The quality classes were estimated accurately, based on both (field and TLS measured) tree attributes. Trees were classified with 95.0% and 83.6% accuracy into three operationally-important quality classes and with 87.1% and 76.4% accuracy into five classes using, field or TLS measurements, respectively. The obtained quality classification results were promising. The enhanced tree quality information could have a significant effect on planning forest management procedures, wood supply chains and optimizing the flow of raw materials. To fully integrate tree quality measurements in operational forestry, the methods used should be fully automated.

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