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Silva Fennica 1926-1997
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Acta Forestalia Fennica
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Articles by Jari Vauhkonen

Category: Research article

article id 10006, category Research article
Matti Maltamo, Tomi Karjalainen, Jaakko Repola, Jari Vauhkonen. (2018). Incorporating tree- and stand-level information on crown base height into multivariate forest management inventories based on airborne laser scanning. Silva Fennica vol. 52 no. 3 article id 10006. https://doi.org/10.14214/sf.10006
Highlights: The most accurate tree-level alternative is to include crown base height (CBH) to nearest neighbour imputation; Also mixed-effects models can be applied to predict CBH using tree attributes and airborne laser scanning (ALS) metrics; CBH prediction can be included with an accuracy of 1–1.5 m to forest management inventory applications.

This study examines the alternatives to include crown base height (CBH) predictions in operational forest inventories based on airborne laser scanning (ALS) data. We studied 265 field sample plots in a strongly pine-dominated area in northeastern Finland. The CBH prediction alternatives used area-based metrics of sparse ALS data to produce this attribute by means of: 1) Tree-level imputation based on the k-nearest neighbor (k-nn) method and full field-measured tree lists including CBH observations as reference data; 2) Tree-level mixed-effects model (LME) prediction based on tree diameter (DBH) and height and ALS metrics as predictors of the models; 3) Plot-level prediction based on analyzing the computational geometry and topology of the ALS point clouds; and 4) Plot-level regression analysis using average CBH observations of the plots for model fitting. The results showed that all of the methods predicted CBH with an accuracy of 1–1.5 m. The plot-level regression model was the most accurate alternative, although alternatives producing tree-level information may be more interesting for inventories aiming at forest management planning. For this purpose, k-nn approach is promising and it only requires that field measurements of CBH is added to the tree lists used as reference data. Alternatively, the LME-approach produced good results especially in the case of dominant trees.

  • Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail: matti.maltamo@uef.fi (email)
  • Karjalainen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail: tomimkarjalainen@gmail.com
  • Repola, Natural Resources Institute of Finland (Luke), Natural resources, Eteläranta 55, FI-96300 Rovaniemi, Finland ORCID ID:E-mail: jaakko.repola@luke.fi
  • Vauhkonen, Natural Resources Institute of Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, 80100 Joensuu, Finland ORCID ID:E-mail: jari.vauhkonen@luke.fi
article id 203, category Research article
Matti Maltamo, Jussi Peuhkurinen, Jukka Malinen, Jari Vauhkonen, Petteri Packalén, Timo Tokola. (2009). Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data. Silva Fennica vol. 43 no. 3 article id 203. https://doi.org/10.14214/sf.203
The development of airborne laser scanning (ALS) during last ten years has provided new possibilities for accurate description of the living tree stock. The forest inventory applications of ALS data include both tree and area-based plot level approaches. The main goal of such applications has usually been to estimate accurate information on timber quantities. Prediction of timber quality has not been focused to the same extent. Thus, in this study we consider here the prediction of both basic tree attributes (tree diameter, height and volume) and characteristics describing tree quality more closely (crown height, height of the lowest dead branch and sawlog proportion of tree volume) by means of high resolution ALS data. The tree species considered is Scots pine (Pinus sylvestris), and the field data originate from 14 sample plots located in the Koli National Park in North Karelia, eastern Finland. The material comprises 133 trees, and size and quality variables of these trees were modeled using a large number of potential independent variables calculated from the ALS data. These variables included both individual tree recognition and area-based characteristics. Models for the dependent tree characteristics to be considered were then constructed using either the non-parametric k-MSN method or a parametric set of models constructed simultaneously by the Seemingly Unrelated Regression (SUR) approach. The results indicate that the k-MSN method can provide more accurate tree-level estimates than SUR models. The k-MSN estimates were in fact highly accurate in general, the RMSE being less than 10% except in the case of tree volume and height of the lowest dead branch.
  • Maltamo, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland ORCID ID:E-mail: matti.maltamo@joensuu.fi (email)
  • Peuhkurinen, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Malinen, Finnish Forest Research Institute, Joensuu Research Unit, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Vauhkonen, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Packalén, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Tokola, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland ORCID ID:E-mail:

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