Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information

By J. Kalliovirta, Timo Tokola. 2005

164 citations recorded by the source corpus.

The aim was to investigate the relations between diameter at breast height and maximum crown diameter, tree height and other possible independent variables available in stand databases. Altogether 76 models for estimating stem diameter at breast height and 60 models for tree age were formulated using height and maximum crown diameter as independent variables. These types of models can be utilized in modern remote sensing applications where tree crown dimensions and tree height are measured automatically. Data from Finnish national forest inventory sample plots located throughout the country were used to develop the models, and a separate test site was used to evaluate them. The RMSEs of the diameter models for the entire country varied between 7.3% and 14.9% from the mean diameter depending on the combination of independent variables and species. The RMSEs of the age models for entire country ranged from 9.2% to 12.8% from the mean age. The regional models were formulated from a data set in which the country was divided into four geographical areas. These regional models reduced local error and gave better results than the general models. The standard deviation of the dbh estimate for the separate test site was almost 5 cm when maximum crown width alone was the independent variable. The deviation was smallest for birch. When tree height was the only independent variable, the standard deviation was about 3 cm, and when both height and maximum crown width were included it was under 3 cm. In the latter case, the deviation was equally small (11%) for birch and Norway spruce and greatest (13%) for Scots pine.

Subfields: Environmental Engineering, Insect Science, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/10574ff760e88e52e7111a79393fb226b4044afe

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Citations in the corpus

Obtaining plotwise mean height and volume growth in boreal forests using multi‐temporal laser surveys and various change detection techniques (2008, methodology): “The DBH of the tree was derived from tree height using a model (Kalliovirta and Tokola 2005 ):where d is the DBH in mm and H the tree height in dm.”
The use of airborne laser scanning to estimate sawlog volumes (2008, methodology): “The estimation of tree diameter from tree height Downloaded from https://academic.oup.com/forestry/article-abstract/81/4/499/526904 by guest on 27 July 2018 and crown diameter (e.g. Kalliovirta and Tokola, 2005 ) is usually problematic, and the result can be inaccurate.”
Effects of pulse density on predicting characteristics of individual trees of Scandinavian commercial species using alpha shape metrics based on airborne laser scanning data (2008, background): “Although the use of tree crown dimensions improves the diameter estimates (Kalliovirta and Tokola, 2005; Villikka et al., 2007), and variables derived from the height and density distributions of ALS point clouds could further improve the result (Villikka et al., 2007), it is obvious that…”
Predicting Tree Attributes and Quality Characteristics of Scots Pine Using Airborne Laser Scanning Data (2009, methodology): “The accuracy of the DBH estimates was similar to that reported in earlier studies in the case of the SUR models (Kalliovirta and Tokola 2005, Korpela et al. 2007).”
Estimating Pinus palustris tree diameter and stem volume from tree height, crown area and stand-level parameters (2014, methodology): “The stand parameters included in the models reported by Kalliovirta and Tokola (2005) for P. sylvestris showed only small improvements in model performance.”
Comparative testing of single-tree detection algorithms under different types of forest (2011, background): “…would place challenges for an international comparison due to requirements for a local understanding of tree morphology to differentiate species (Korpela et al., 2010) and a highly extensive field reference data to construct allometric relationships (Kalliovirta and Tokola, 2005), for example.”