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Silva Fennica 1926-1997
Acta Forestalia Fennica

Articles containing the keyword 'crown height'

Category : Article

article id 7519, category Article
Jori Uusitalo. (1997). Pre-harvest measurement of pine stands for sawing production planning. Acta Forestalia Fennica no. 259 article id 7519.
Keywords: Pinus sylvestris; diameter distribution; height models; crown height estimation; forest sampling; lumber quality prediction
Abstract | View details | Full text in PDF | Author Info

To enhance the utilization of the wood, the sawmills are forced to place more emphasis on planning to master the whole production chain from the forest to the end product. One significant obstacle to integrating the forest-sawmill-market production chain is the lack of appropriate information about forest stands. Since the wood procurement point of view in forest planning systems has been almost totally disregarded there has been a great need to develop an easy and efficient pre-harvest measurement method, allowing separate measurement of stands prior to harvesting. The main purpose of this study was to develop a measurement method for Scots pine (Pinus sylvestris L.) stands which forest managers could use in describing the properties of the standing trees for sawing production planning.

Study materials were collected from ten Scots pine stands located in North Häme and South Pohjanmaa, in Southern Finland. The data comprise test sawing data on 314 pine stems, diameter at breast height (dbh) and height measures of all trees and measures of the quality parameters of pine sawlog stems in all ten study stands as well as the locations of all trees in six stands. The study was divided into four sub-studies which deal with pine quality prediction, construction of diameter and dead branch height distributions, sampling designs and applying height and crown height models. The final proposal for the pre-harvest measurement method is a synthesis of the individual sub-studies.

Quality analysis resulted in choosing dbh, distance from stump height to the first dead branch, crown height and tree height as the most appropriate quality characteristics of Scots pine. Dbh and dead branch height are measured from each pine sample tree while height and crown height are derived from dbh measures by aid of mixed height and crown height models. Pine and spruce diameter distribution as well as dead branch height distribution are most effectively predicted by the kernel function. Roughly 25 sample trees seem to be appropriate in pure pine stands. In mixed stands the number of sample trees needs to be increased in proportion to the intensity of pines in order to attain the same level of accuracy.

  • Uusitalo, E-mail: ju@mm.unknown (email)
article id 7685, category Article
Risto Ojansuu. (1993). Prediction of Scots pine increment using a multivariate variance component model. Acta Forestalia Fennica no. 239 article id 7685.
Keywords: Pinus sylvestris; crown height; volume increment; bark volume; variance component models; multivariate models; stem curve models; tree form change
Abstract | View details | Full text in PDF | Author Info

Diameter and volume increment as well as change in stem form of Scots pine (Pinus sylvestris L.) were analysed to predict tree increment variables. A stem curve set model is presented, based on prediction of the diameters at fixed angles in a polar coordinate system. This model consists of three elementary stem curves: 1) with bark, 2) without bark, and 3) without bark five years earlier. The differences between the elementary stem curves are the bark curve and the increment curve. The error variances at fixed angles and covariances between the fixed angles are divided into between-stand and within-stand components. Using principal components, the between-stand and within-stand covariance matrices are condensed separately for stem curve with bark, bark curve and increment curve. The two first principal components of the bark curve describe the vertical change in Scots pine bark type and the first principal component of the increment curve describes the increment rate. The elementary stem curves, bark curve and increment curve as well as corresponding stem volumes, bark volume and volume increment can be predicted for all trees in the stand with free choice of sample tree measurements. When only a few sample trees are measured, the stem curve set model gives significantly more accurate predictions of bark volume and volume increment for tally trees than does the volume method, which is based on the differences between two independent predictions of volume. The volume increment of tally trees can be predicted as reliably with as without measurement of sample tree height increment.

The PDF includes a summary in English.

  • Ojansuu, E-mail: ro@mm.unknown (email)

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.
Keywords: forest inventory; LIDAR; alpha shape; crown height; nearest neighbor; mixed-effects model
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.
Abstract | Full text in HTML | Full text in PDF | Author Info

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 E-mail: (email)
  • Karjalainen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail:
  • Repola, Natural Resources Institute of Finland (Luke), Natural resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail:
  • Vauhkonen, Natural Resources Institute of Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, 80100 Joensuu, Finland E-mail:
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.
Keywords: LIDAR; alpha shape; crown height; height metrics; k-MSN; timber quality
Abstract | View details | Full text in PDF | Author Info
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 E-mail: (email)
  • Peuhkurinen, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail:
  • Malinen, Finnish Forest Research Institute, Joensuu Research Unit, FI-80101 Joensuu, Finland E-mail:
  • Vauhkonen, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail:
  • Packalén, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail:
  • Tokola, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail:

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