Articles by Jaakko Repola

We developed tree level biomass (dry weight) models for Norway spruce (Picea abies [L.] H. Karst.), silver birch (Betula pendula Roth), rowan (Sorbus aucuparia L.) and aspen (Populus tremula L.) growing in young spruce dominated seedling stands with high mixture of broadleaves. The study material was collected from three planted Norway spruce seedling stands located on mineral soil in southern Finland. Biomass models were estimated by individual tree component (stem, living branches, foliage, stump, and roots with diameter of 2 mm) by using a multi-response approach (seemingly unrelated regression), which estimated the parameters of the sub-models (tree component) simultaneously. Even though the application and generalization of the developed models can be restricted by the limited material, they provide new information of seedling biomass allocation and more reliable biomass predictions for spruce and birch growing in young seedling stand compared with those of the commonly applied biomass models (Repola 2008, 2009) in Finland. Repola’s models (2008, 2009) tended to produce biased predictions for crown and below-ground biomasses of seedlings by allocating too much biomass to roots and too little to needle and branches. In addition, this study provides biomass models for aspen and rowan, which were not previously available.

• Repola, Natural Resources Institute Finland (Luke), Ounasjoentie 6, FI-96200 Rovaniemi, Finland https://orcid.org/0000-0001-7086-0549 E-mail: jaakko.repola@luke.fi
• Luoranen, Natural Resources Institute Finland (Luke), Juntintie 154, FI-77600 Suonenjoki, Finland https://orcid.org/0000-0002-6970-2030 E-mail: jaana.luoranen@luke.fi
• Huuskonen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0001-8630-3982 E-mail: saija.huuskonen@luke.fi
• Peltoniemi, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0003-2028-6969 E-mail: mikko.peltoniemi@luke.fi
• Väänänen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: paivi.vaananen@luke.fi
• Uotila, Natural Resources Institute Finland (Luke), Juntintie 154, FI-77600 Suonenjoki, Finland E-mail: karri.uotila@luke.fi

Pulpwood arriving at the mills is mainly measured by weighing. In the loading phase of forwarding and trucking, timber is weighed using scales mounted in the grapple loader. The measured weight of timber is converted into volume using a conversion factor defined as green density (kg m^–3). At the mill, the green density factor is determined by sampling measurements, while in connection with weighing with grapple-mounted scales during transportation, fixed green density factors are used. In this study, we developed predictive regression models for the green density of pulpwood. The models were constructed separately by pulpwood assortments: pine (contains mainly Pinus sylvestris L); spruce (mainly Picea abies (L.) Karst.); decayed spruce; birch (mainly Betula pubescens Ehrh. and Betula pendula Roth); and aspen (mainly Populus tremula L.). Study material was composed of the sampling-based measurements at the mills between 2013–2019. The models were specified as linear mixed models with both fixed and random parameters. The fixed effect produced the expected value of green density as a function of delivery week, storage time, and meteorological conditions during storage. The random effects allowed the model calibration by utilizing the previous sampling weight measurements. The model validation showed that the model predictions faithfully reproduced the observed seasonal variation in green density. They were more reliable than those obtained with the current practices. Even the uncalibrated (fixed) predictions had lower relative root mean squared prediction errors than those obtained with the current practices.

• Repola, Natural Resources Institute Finland (Luke), Natural resources, Ounasjoentie 6, 96200 Rovaniemi, Finland E-mail: jaakko.repola@luke.fi
• Heikkinen, Natural Resources Institute Finland (Luke), Production systems, Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: juha.heikkinen@luke.fi
• Lindblad, Natural Resources Institute Finland (Luke), Natural resources, Latokartanonkaari 9, 00790 Helsinki, Finland E-mail: jari.lindblad@luke.fi

Airborne laser scanning (ALS) data is nowadays often available for forest inventory purposes, but adequate field data for constructing new forest attribute models for each area may be lacking. Thus there is a need to study the transferability of existing ALS-based models among different inventory areas. The objective of our study was to apply ALS-based mixed models to estimate the diameter, height and crown base height of individual sawlog sized Scots pines (Pinus sylvestris L.) at three different inventory sites in eastern Finland. Different ALS sensors and acquisition parameters were used at each site. Multivariate mixed-effects models were fitted at one site and the models were validated at two independent test sites. Validation was carried out by applying the fixed parts of the mixed models as such, and by calibrating them using 1–3 sample trees per plot. The results showed that the relative RMSEs of the predictions were 1.2–6.5 percent points larger at the test sites compared to the training site. Systematic errors of 2.4–6.2 percent points also emerged at the test sites. However, both the RMSEs and the systematic errors decreased with calibration. The results showed that mixed-effects models of individual tree attributes can be successfully transferred and calibrated to other ALS inventory areas in a level of accuracy that appears suitable for practical applications.

• Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland http://orcid.org/0000-0002-9352-0114 E-mail: lauri.korhonen@uef.fi
• Repola, Natural Resources Institute of Finland (Luke), Natural resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: jaakko.repola@luke.fi
• Karjalainen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tomikar@uef.fi
• Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
• Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi

The aim of this study was to develop individual-tree diameter and height growth models for Scots pine, Norway spruce, and pubescent birch growing in drained peatlands in Finland. Trees growing in peatland sites have growth patterns that deviate from that of trees growing in mineral soil sites. Five-year growth was explained by tree diameter, different tree and stand level competition measures, management operations and site characteristics. The drainage status of the site was influencing growth directly or in interaction with other variables. Site quality had a direct impact but was also commonly related to current site drainage status (need for ditch maintenance). Recent thinning increased growth of all species and former PK fertilization increased growth of pine and birch. Temperature sum was a significant predictor in all models and altitude for spruce and birch. The data were a subsample of the 7th National Forest Inventory (NFI) sample plots representing northern and southern Finland and followed by repeated measurements for 15–20 yrs. Growth levels predicted by the models were calibrated using NFI11 data to remove bias originating from the sample of the modelling data. The mixed linear models technique was used in model estimation. The models will be incorporated into the MOTTI stand simulator to replace the current peatlands growth models.

• Repola, Natural Resources Institute Finland (Luke), Natural resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: jaakko.repola@luke.fi
• Hökkä, Natural Resources Institute Finland (Luke), Natural resources, Paavo Havaksen tie 3, FI-90014 University OF Oulu, Finland E-mail: hannu.hokka@luke.fi
• Salminen, Natural Resources Institute Finland (Luke), Natural resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: hannu.salminen@luke.fi

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

This study was aimed at determining the maximum cost level of artificial drying required for cost-efficient operation. This was done using a system analysis approach, in which the harvesting potential and procurement cost of alternative fuel chip production systems were compared at the stand and regional level. The accumulation and procurement cost of chipped delimbed stems from young forests were estimated within a 100 km transport distance from a hypothetical end use facility located in northern Finland. Logging and transportation costs, stumpage prices, tied up capital, dry matter losses and moisture content of harvested timber were considered in the study. Moisture content of artificially dried fuel chips made of fresh timber (55%) was set to 20%, 30% and 40% in the comparisons. Moisture content of fuel chips based on natural drying during storing was 40%. Transporting costs were calculated according to new higher permissible dimensions and weight limits for truck-trailers. The procurement cost calculations indicated that with artificial drying and by avoiding dry material losses of timber, it could be possible to reduce current costs of the prevailing procurement system based on natural drying of timber at roadside landings. The maximum cost level of artificial drying ranged between 1.2–3.2 € MWh^–1 depending on the supply chain, moisture content and procurement volume of fuel chips. This cost margin corresponds to, e.g., organization, forwarding and transportation costs or stumpage price of delimbed stems.

• Laitila, Natural Resources Institute Finland (Luke), Bio-based business and industry, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: juha.laitila@luke.fi
• Ahtikoski, Natural Resources Institute Finland (Luke), Management and Production of Renewable Resources, Paavo Havaksen tie 3, FI-90570 Oulu, Finland E-mail: anssi.ahtikoski@luke.fi
• Repola, Natural Resources Institute Finland (Luke), Management and Production of Renewable Resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: jaakko.repola@luke.fi
• Routa, Natural Resources Institute Finland (Luke), Bio-based business and industry, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: johanna.routa@luke.fi

• Repola, Finnish Forest Research Institute, Rovaniemi Research Unit, P.O. Box 16, FI-96301 Rovaniemi, Finland E-mail: jaakko.repola@metla.fi
• Ahnlund Ulvcrona, SLU, Forest Biomaterials and Technology, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: kristina.ulvcrona@slu.se

• Hökkä, Finnish Forest Research Institute, Rovaniemi Unit, P.O. Box 16, FI-96301 Rovaniemi, Finland E-mail: hannu.hokka@metla.fi
• Repola, Finnish Forest Research Institute, Rovaniemi Unit, P.O. Box 16, FI-96301 Rovaniemi, Finland E-mail: jaakko.repola@metla.fi
• Moilanen, Finnish Forest Research Institute, Oulu Unit, P.O. Box 413, FI-90014 University of Oulu, Finland E-mail: mikko.moilanen@metla.fi
• Saarinen, Finnish Forest Research Institute, Parkano Unit, Kaironiementie 15, FI-39700 Parkano, Finland E-mail: markku.saarinen@metla.fi

• Hökkä, Finnish Forest Research Institute, Rovaniemi Research Unit, FI-96301 Rovaniemi, Finland E-mail: hannu.hokka@metla.fi
• Repola, Finnish Forest Research Institute, Rovaniemi Research Unit, FI-96301 Rovaniemi, Finland E-mail: jr@nn.fi
• Moilanen, Finnish Forest Research Institute, Muhos Research Unit, Muhos, Finland E-mail: mm@nn.fi
• Saarinen, Finnish Forest Research Institute, Parkano Research Unit, Parkano, Finland E-mail: ms@nn.fi

• Repola, Finnish Forest Research Institute, Rovaniemi Research Unit, P.O. Box 16, FI-96301 Rovaniemi, Finland E-mail: jaakko.repola@metla.fi

• Repola, Finnish Forest Research Institute, Rovaniemi Research Unit, P.O. Box 16, FI-96301 Rovaniemi, Finland E-mail: jaakko.repola@metla.fi

• Repola, Finnish Forest Research Institute, Rovaniemi Research Unit, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: jaakko.repola@metla.fi

• Repola, Finnish Forest Research Institute, Rovaniemi Research Unit, P.O. Box 16, FI-96301 Rovaniemi, Finland E-mail: jaakko.repola@metla.fi
• Hökkä, Finnish Forest Research Institute, Rovaniemi Research Unit, P.O. Box 16, FI-96301 Rovaniemi, Finland E-mail: hh@nn.fi
• Penttilä, Finnish Forest Research Institute, Rovaniemi Research Unit, P.O. Box 16, FI-96301 Rovaniemi, Finland E-mail: tp@nn.fi