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Open AccessArticle

Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density

1
Chair of Forest Growth, University of Freiburg, Tennenbacher Str. 4, 79106 Freiburg, Germany
2
Experimental Center for Tropical Forestry, 532600 Pingxiang, China
*
Author to whom correspondence should be addressed.
Academic Editor: Xinlian Liang
Forests 2015, 6(4), 1274-1300; https://doi.org/10.3390/f6041274
Received: 1 December 2014 / Revised: 26 March 2015 / Accepted: 1 April 2015 / Published: 21 April 2015
(This article belongs to the Special Issue Forest Ground Observations through Terrestrial Point Clouds)
This paper presents a method for predicting the above ground leafless biomass of trees in a non destructive way. We utilize terrestrial laserscan data to predict the volume of the trees. Combining volume estimates with density measurements leads to biomass predictions. Thirty-six trees of three different species are analyzed: evergreen coniferous Pinus massoniana, evergreen broadleaved Erythrophleum fordii and leafless deciduous Quercus petraea. All scans include a large number of noise points; denoising procedures are presented in detail. Density values are considered to be a minor source of error in the method if applied to stem segments, as comparison to ground truth data reveals that prediction errors for the tree volumes are in accordance with biomass prediction errors. While tree compartments with a diameter larger than 10 cm can be modeled accurately, smaller ones, especially twigs with a diameter smaller than 4 cm, are often largely overestimated. Better prediction results could be achieved by applying a biomass expansion factor to the biomass of compartments with a diameter larger than 10 cm. With this second method the average prediction error for Q. petraea could be reduced from 33.84% overestimation to 3.56%. E. fordii results could also be improved reducing the average prediction error from View Full-Text
Keywords: biomass; density; volume; TLS; forestry; tree; stem; branch; point cloud biomass; density; volume; TLS; forestry; tree; stem; branch; point cloud
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MDPI and ACS Style

Hackenberg, J.; Wassenberg, M.; Spiecker, H.; Sun, D. Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density. Forests 2015, 6, 1274-1300.

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