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

Forests

Jan Hackenberg¹  Marc Wassenberg¹  Heinrich Spiecker¹  Dongjing Sun² 

[1] Chair of Forest Growth, University of Freiburg, Tennenbacher Str. 4, 79106 Freiburg, Germany; E-Mails:;Experimental Center for Tropical Forestry, 532600 Pingxiang, China; E-Mail:

关键词: biomass;    density;    volume;    TLS;    forestry;    tree;    stem;    branch;    point cloud;   

DOI  :  10.3390/f6041274

来源: mdpi

【 摘 要 】

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 −17.24% to −7.30%. Only P. massoniana results had a low prediction error of 2.75% utilizing the total TLS-estimated volume, which was not improved by the biomass expansion method (3.82%).

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.