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Articles by Juha Heikkinen

Category : Special section

article id 287, category Special section

Mikko Peltoniemi, Juha Heikkinen, Raisa Mäkipää. (2007). Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils. Silva Fennica vol. 41 no. 3 article id 287. https://doi.org/10.14214/sf.287

Keywords: uncertainty; soil carbon; anticipated variance; forest soil; monitoring; repeated measurement; soil survey; stratified sampling

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Monitoring changes in soil C has recently received interest due to reporting under the Kyoto Protocol. Model-based approaches to estimate changes in soil C stocks exist, but they cannot fully replace repeated measurements. Measuring changes in soil C is laborious due to small expected changes and large spatial variation. Stratification of soil sampling allows the reduction of sample size without reducing precision. If there are no previous measurements, the stratification can be made with model-predictions of target variable. Our aim was to present a simulation-based stratification method, and to estimate how much stratification of inventory plots could improve the efficiency of the sampling. The effect of large uncertainties related to soil C change measurements and simulated predictions was targeted since they may considerably decrease the efficiency of stratification. According to our simulations, stratification can be useful with a feasible soil sample number if other uncertainties (simulated predictions and forecasted forest management) can be controlled. For example, the optimal (Neyman) allocation of plots to 4 strata with 10 soil samples from each plot (unpaired repeated sampling) reduced the standard error (SE) of the stratified mean by 9–34% from that of simple random sampling, depending on the assumptions of uncertainties. When the uncertainties of measurements and simulations were not accounted for in the division to strata, the decreases of SEs were 2–9 units less. Stratified sampling scheme that accounts for the uncertainties in measured material and in the correlates (simulated predictions) is recommended for the sampling design of soil C stock changes.

Peltoniemi, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: mikko.peltoniemi@metla.fi
Heikkinen, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jh@nn.fi
Mäkipää, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: raisa.makipaa@metla.fi

Category : Research article

article id 10662, category Research article

Kari T. Korhonen, Arto Ahola, Juha Heikkinen, Helena M. Henttonen, Juha-Pekka Hotanen, Antti Ihalainen, Markus Melin, Juho Pitkänen, Minna Räty, Maria Sirviö, Mikael Strandström. (2021). Forests of Finland 2014–2018 and their development 1921–2018. Silva Fennica vol. 55 no. 5 article id 10662. https://doi.org/10.14214/sf.10662

Keywords: biodiversity; National Forest Inventory; growing stock; forest resources; forest damage

Highlights: Current volume of growing stock, 2500 M m³, is 1.7 times the volume in the 1920s; Annual volume increment is 107.8 M m³, which is double the increment estimated in the 1930s; Serious damage is observed on 2% of the forests available for wood supply; The amount of dead wood is on average 5.8 m³ per ha on productive forest.

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We describe the methodology applied in the 12th national forest inventory of Finland (NFI12) and describe the state of Finland’s forests as well as the development of some key parameters since 1920s. According to the NFI12, the area of forestry land (consisting of productive and poorly productive forest, unproductive land, and other forestry land) is 26.2 M ha. The area of forestry land has decreased from 1920s to 1960s due to expansion of agriculture and built-up land. 20% of the forestry land is not available for wood supply and 13% is only partly available for wood supply. The area of peatlands is 8.8 M ha, which is one third of the forestry land. 53% of the current area of peatlands is drained. The volume of growing stock, 2500 M m³, is 1.7 times the volume estimated in NFI1 in the 1920s for the current territory of Finland. The estimated annual volume increment is 107.8 M m³. The increment estimate has doubled since the estimate of NFI2 implemented in late 1930s. The annual mortality is estimated to 7 M m³, which is 0.5 M m³ more than according to the previous inventory. Serious or complete damage was observed on 2% of the productive forest available for wood supply. The amount of dead wood is on average 5.8 m³ ha^–1 in productive forests. Since the NFI9 (1996–2003) the amount of dead wood has increased in South Finland and decreased in North Finland both in protected forests and forests available for wood supply (FAWS). The area of natural or almost natural forests on productive forest is 380 000 ha, out of this, 42 000 ha are in FAWS and 340 000 ha in protected forests.

Korhonen, Natural Resources Institute Finland (Luke), P.O. Box 68, FI-80100 Joensuu, Finland E-mail: kari.t.korhonen@luke.fi
Ahola, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: arto.ahola@luke.fi
Heikkinen, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: juha.heikkinen@luke.fi
Henttonen, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: helena.henttonen@luke.fi
Hotanen, Natural Resources Institute Finland (Luke), P.O. Box 68, FI-80100 Joensuu, Finland E-mail: juha-pekka.hotanen@luke.fi
Ihalainen, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: anttivj.ihalainen@elisanet.fi
Melin, Natural Resources Institute Finland (Luke), P.O. Box 68, FI-80100 Joensuu, Finland E-mail: markus.melin@luke.fi
Pitkänen, Natural Resources Institute Finland (Luke), P.O. Box 68, FI-80100 Joensuu, Finland E-mail: juho.pitkanen@luke.fi
Räty, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: minna.raty@luke.fi
Sirviö, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: maria.sirvio@uudenmaanliitto.fi
Strandström, Natural Resources Institute Finland (Luke), P.O. Box 2, FI-00790, Helsinki, Finland E-mail: mikael.strandstrom@luke.fi

article id 10539, category Research article

Jaakko Repola, Juha Heikkinen, Jari Lindblad. (2021). Pulpwood green density prediction models and sampling-based calibration. Silva Fennica vol. 55 no. 4 article id 10539. https://doi.org/10.14214/sf.10539

Keywords: pulpwood; conversion factor; green density

Highlights: The developed models provided a realistic description of the observed seasonal variation in pulpwood green density; The model predictions were more reliable than those obtained with current practices.

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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

article id 10269, category Research article

Annika Kangas, Helena M. Henttonen, Timo P. Pitkänen, Sakari Sarkkola, Juha Heikkinen. (2020). Re-calibrating stem volume models – is there change in the tree trunk form from the 1970s to the 2010s in Finland? Silva Fennica vol. 54 no. 4 article id 10269. https://doi.org/10.14214/sf.10269

Keywords: TLS; OLS; regression; terrestrial laser scanning

Highlights: TLS data showed that trunk form has changed in Finland from the 1970s; Significant differences were observed for all tree species; The trees in TLS data are on average more slender than in the old data.

Abstract | Full text in HTML | Full text in PDF | Author Info

The tree stem volume models of Norway spruce, Scots pine and silver and downy birch currently used in Finland are based on data collected during 1968–1972. These models include four different formulations of a volume model, with three different combinations of independent variables: 1) diameter at height of 1.3 m above ground (dbh), 2) dbh and tree height (h) and 3) dbh, h and upper diameter at height of 6 m (d6). In recent National Forest Inventories of Finland, a difference in the mean volume prediction between the models with and without the upper diameter as predictor has been observed. To analyze the causes of this difference, terrestrial laser scanning (TLS) was used to acquire a large dataset in Finland during 2017–2018. Field-measured predictors and volumes predicted using spline functions fitted to the TLS data were used to re-calibrate the current volume models. The trunk form is different in these two datasets. The form height is larger in the new data for all diameter classes, which indicates that the tree trunks are more slender than they used to be. One probable reason for this change is the increase in stand densities, which is at least partly due to changed forest management. In models with both dbh and h as predictors, the volume is smaller a given h class in the data new data than in the old data, and vice versa for the diameter classes. The differences between the old and new models were largest with pine and smallest with birch.

Kangas, Natural Resources Institute Finland (Luke), Yliopistokatu 6 B, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
Henttonen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: helena.henttonen@luke.fi
Pitkänen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0001-5389-8713 E-mail: timo.p.pitkanen@luke.fi
Sarkkola, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: sakari.sarkkola@luke.fi
Heikkinen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0003-3527-774X E-mail: juha.heikkinen@luke.fi

article id 587, category Research article

Erkki Tomppo, Kari T. Korhonen, Juha Heikkinen, Hannu Yli-Kojola. (2001). Multi-source inventory of the forests of the Hebei Forestry Bureau, Heilongjiang, China. Silva Fennica vol. 35 no. 3 article id 587. https://doi.org/10.14214/sf.587

Keywords: models; forest inventory; satellite images; k-nearest neighbour method; China

Abstract | View details | Full text in PDF | Author Info

A multi-source forest inventory method is applied to the estimation of forest resources in the area of the Hebei Forest Bureau in Heilongjiang province in North-East China. A stratified systematic cluster sampling design was utilised in field measurements. The design was constructed on the basis of information from earlier stand-level inventories, aerial orthophotographs, experiences from other sampling inventories and the available budget. Sample tree volumes were estimated by means of existing models. New models were constructed and their parameters estimated for tallied tree volumes and volume increments. The estimates for the area of the Bureau were computed from field measurements, and for the areas of the forest farms estimated from field measurements and satellite images. A k-nearest neighbour method was utilised. This method employing satellite image data makes it possible to estimate all variables, particularly for smaller areas than that possible using field measurements only. The methods presented, or their modifications, could also be applied to the planning and realisation of forest inventories elsewhere in Temperate or Boreal zones. The inventory in question gave an estimate of 114 m3/ha (the multi-source inventory 119 m3/ha) instead of 72 m3/ha as previously estimated from available information. Totally nineteen tree species, genera of species or tree species groups were identified (Appendix 1). The forests were relatively young, 60% of them younger than 40 years and 85% younger than 60 years.

Tomppo, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: erkki.tomppo@metla.fi
Korhonen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: ktk@nn.fi
Heikkinen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: jh@nn.fi
Yli-Kojola, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: hyk@nn.fi

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