Articles containing the keyword 'crown height'

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

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

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: matti.maltamo@uef.fi
• Karjalainen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tomimkarjalainen@gmail.com
• Repola, Natural Resources Institute of Finland (Luke), Natural resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: jaakko.repola@luke.fi
• Vauhkonen, Natural Resources Institute of Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, 80100 Joensuu, Finland E-mail: jari.vauhkonen@luke.fi

• Maltamo, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: matti.maltamo@joensuu.fi
• Peuhkurinen, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: jp@nn.fi
• Malinen, Finnish Forest Research Institute, Joensuu Research Unit, FI-80101 Joensuu, Finland E-mail: jm@nn.fi
• Vauhkonen, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: jv@nn.fi
• Packalén, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: pp@nn.fi
• Tokola, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: tt@nn.fi