Articles containing the keyword 'prediction'

The purpose of this study was to compare the Weibull distributions estimated for the entire growing stock of a stand and separately for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H. Karst.) in describing the basal area diameter distributions in mixed stands. The material for this study was obtained by measuring 553 stands located in eastern Finland. The parameters of the Weibull distribution were estimated using the method of maximum likelihood. The models for these parameters were derived using regression analysis. Also, some parameter models from previous studies were compared with the measured distribution. The obtained distributions were compared using the diameter sums of the entire growing stock, diameter sums by tree species and of the sawtimber part of the growing stock. The results showed that far more accurate results were obtained when the distributions were formed using parameter models separately for the different tree species than when using parameter models for the entire growing stock. This was already true when considering the entire growing stock of the stand and especially when the results were examined by tree species. When the models for the entire growing stock were applied by tree species in relation to basal areas, the results obtained were overestimates for Norway spruce and underestimates for Scots pine. The models from earlier studies, where parameter models were estimated separately for tree species from the National Forest Inventory data, showed good fits also in regard to the data of this study.

• Maltamo, E-mail: mm@mm.unknown

A gap-model was used with forest inventory data in taking ground-true site, soil and tree characteristics into account in predicting the effects of climate change on forests. A total of 910 permanent sample plots established in the course of national forest inventory (NFI) in Finland and located on mineral soil sites in southern Finland were selected as the input data. The climatological input used in the simulations consisted of interpolated means of and deviations from long-term local temperature and precipitation records. The policy-oriented climate scenarios of SILMU (Finnish Research Programme on Climate Change) were used to describe the climate change. The temperature changes in the climate scenarios were increases of ca. +1.1 °C (low), +4.4 °C (medium) and +6.6 °C (high) compared to the current climate in 110 years. The simulation period was 110 years covering the time years 1990–2100.

Southern Finland, divided into fifteen forestry board districts, was used as the study region. Regional development of stand volume, cutting yield, and total wood production of forests under different climate scenarios were examined. The annual average growth in simulations under current climate was close to that observed in NFL Forests benefited from a modest temperature increase (Scenario 2), but under Scenario 1 the growing stock remained at a lower level than under the current climate in all parts of the study region. In wood production and cutting yield there were regional differences. In the southern part of the study regional wood production under Scenario 1 was ca. 10% lower than under the current climate, but in the eastern and western parts wood production was 5–15% higher under Scenario 1 than under the current climate. The relative values of total wood production and cutting yield indicated that the response of forests to climate change varied by geographical location and the magnitude of climate change. This may be a consequence of not just varying climatic (e.g. temperature and precipitation) and site conditions, but of varying responses by different kind of forests (e.g. forests differing in tree species composition and age).

• Talkkari, E-mail: at@mm.unknown

The single tree growth models presented in this study were based on about 4,000 trees measured in 50 even-aged Scots pine (Pinus sylvestris L.) sample plots with varying density, spatial pattern of trees and stand age. Predictors that used information about tree locations decreased the relative standard error of estimate by 10 percentage points (15%), if past growth was not used as a predictor, and about 15 percentage points (30%) when past growth was one of the predictors. When ranked according to the degree of determination, the best growth models were obtained for the basal area increment, the next best for relative growth, and the poorest for diameter increment. The past growth decreased the relative standard error of estimate by 15–20 percentage points, but did not make the spatial predictors unnecessary. The degree of determination of the spatial basal area growth model was almost 80% if the past growth was unknown and almost 90% if the past growth was known. Variables that described the amount of removed competition did not improve the growth models.

The PDF includes an abstract in Finnish.

• Pukkala, E-mail: tp@mm.unknown

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

Bilberry (Vaccinium myrtillus L.) and lingonberry (V. vitis-idaea L.) can be a part of healthy diet and are important for many animals. Two approaches are described to assessing their vegetation cover and berry yield via national forest inventory (NFI) observations. The aim was to provide estimates and predictions of the abundance and yield of the species at regional and national levels in Finland and Sweden. In Finland, the model-based predictions are used in evaluating the impacts of cutting intensity on forest berries needed in forest-related decision making. In Sweden, seasonal inventory-based estimates are used to evaluate the annual national and regional berry yields, and in a forecasting system aimed at large public and berry enterprises. Based on the NFI sample plots measured between 2014 and 2018, the total annual yields are estimated to be 208 Mkg of bilberry and 246 Mkg of lingonberry on productive forest land (increment at least 1 m³ ha^–1 year^–1) in Finland, and 336 and 382 Mkg respectively in Sweden (average of NFI inventories in 2015–2019). The predicted development of berry yields is related to the intensity of cuttings in alternative forest management scenarios: lower removals favoured bilberry, and higher removals lingonberry. The model-based method describes the effects of stand development and management on berry yields, whereas the inventory-based method can calibrate seasonal estimates through field observations. In providing spatially and timely more accurate information concerning seasonal berry yields, an assessment of berry yields should involve the elements of both inventory-based and model-based approaches described in this study.

• Miina, Natural Resources Institute Finland (Luke), Yliopistokatu 6 B, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-8639-4383 E-mail: jari.miina@luke.fi
• Bohlin, Swedish University of Agricultural sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, S-901 83 Umeå, Sweden E-mail: inka.bohlin@slu.se
• Lind, Swedish University of Agricultural sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, S-901 83 Umeå, Sweden E-mail: torgny.lind@slu.se
• Dahlgren, Swedish University of Agricultural sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, S-901 83 Umeå, Sweden E-mail: jonas.dahlgren@slu.se
• Härkönen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: kari.harkonen@luke.fi
• Packalen, Natural Resources Institute Finland (Luke), Yliopistokatu 6 B, FI-80100 Joensuu, Finland; Ministry of Agriculture and Forestry of Finland, P.O. Box 30, FI-00023 Government, Finland E-mail: tuula.packalen@mmm.fi
• Tolvanen, Natural Resources Institute Finland (Luke), Paavo Havaksentie 3, FI-90014 University of Oulu, Finland E-mail: anne.tolvanen@luke.fi

The diameter at any point on a stem and tree volume are some of the most important types of information used in forest management planning. One of the methods to predict the diameter at any point on a stem is to develop taper models. Black locust (Robinia pseudoacacia L.) occurs in almost all forests in Poland, with the largest concentration in the western part of the country. Using empirical data obtained from 13 black locust stands (48 felled trees), seven taper models with different numbers of estimated parameters were analysed for section diameters both over and under bark using fixed and mixed-effects modelling approaches. Assuming a lack of additional measurements, the best fitted taper models were used for the prediction of over bark volume using both methods. The predicted volume was compared with the results from different volume equations available for black locust. The variable-form taper model with eight estimated parameters fitted the data the best. The lowest root mean square error for volume prediction was achieved for the elaborated fixed-effects taper model (0.0476), followed by the mixed-effects taper model (0.0489). At the same time, the difference between the volume relative errors achieved based on the taper models does not differ significantly from the results obtained using the volume equations already available for black locust (two of the three analysed).

• Bronisz, Institute of Forest Sciences, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, PL 02-776 Warsaw, Poland E-mail: karol.bronisz@wl.sggw.pl
• Zasada, Institute of Forest Sciences, Warsaw University of Life Sciences-SGGW, Nowoursynowska 159, PL 02-776 Warsaw, Poland https://orcid.org/0000-0002-4881-296X E-mail: Michal.Zasada@wl.sggw.pl

In remote sensing-based forest inventories 3D point cloud data, such as acquired from airborne laser scanning, are well suited for estimating the volume of growing stock and stand height, but tree species recognition often requires additional optical imagery. A combination of 3D data and optical imagery can be acquired based on aerial imaging only, by using stereo photogrammetric 3D canopy modeling. The use of aerial imagery is well suited for large-area forest inventories, due to low costs, good area coverage and temporally rapid cycle of data acquisition. Stereo-photogrammetric canopy modeling can also be applied to previously acquired imagery, such as for aerial ortho-mosaic production, assuming that the imagery has sufficient stereo overlap. In this study we compared two stereo-photogrammetric canopy models combined with contemporary satellite imagery in forest inventory. One canopy model was based on standard archived imagery acquired primarily for ortho-mosaic production, and another was based on aerial imagery whose acquisition parameters were better oriented for stereo-photogrammetric canopy modeling, including higher imaging resolution and greater stereo-coverage. Aerial and satellite data were tested in the estimation of growing stock volume, volumes of main tree species, basal area and diameter and height. Despite the better quality of the latter canopy model, the difference of the accuracy of the forest estimates based on the two different data sets was relatively small for most variables (differences in RMSEs were 0–20%, depending on variable). However, the estimates based on stereo-photogrammetrically oriented aerial data retained better the original variation of the forest variables present in the study area.

• Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: sakari.tuominen@luke.fi
• Balazs, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: andras.balazs@luke.fi
• Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: annika.kangas@luke.fi

Detailed pre-harvest information about the volumes and properties of growing stocks is needed for increased precision in wood procurement planning for just-in-time wood deliveries by cut-to-length (CTL) harvesters. In the study, the non-parametric Most Similar Neighbour (MSN) methodology was evaluated for predicting external quality of Scots pine and Norway spruce, expressed as stem sections fulfilling the saw log dimension and quality requirements of Finnish forest industry, as they affect the recovery of timber assortments and the value of a pre-harvest stand. Effects of external tree quality were evaluated using saw log recovery and saw log reduction caused by stem defects, as well as total timber value (€) and average unit value (€ m^–3) in a stand. Root mean square error (RMSE) of saw log recovery and reduction were 9.12 percentile points (pp) for Scots pine and 6.38 pp for Norway spruce stands. In the unit value considerations, the predictions compared with measurements resulted in the RMSE of 3.50 € m^–3 and the bias of 0.58 € m^–3 in Scots pine stands and 2.60 € m^–3, and 0.35 € m^–3 in Norway spruce stands, respectively. The presented MSN based approach together with the utilization of the external stem quality database included in the ARVO software could provide dimension and external quality predictions usable for pre-harvest assessment of timber stock at a stand level. This prediction methodology is usable especially in analyses where timber assortment recoveries, values and unit prices are compared when different bucking objectives are used.

• Malinen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: jukka.malinen@uef.fi
• Kilpeläinen, Natural Resources Institute Finland (Luke), Production systems, Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: harri.kilpelainen@luke.fi
• Verkasalo, Natural Resources Institute Finland (Luke), Production systems, Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: erkki.verkasalo@luke.fi

The pine weevil Hylobius abietis L. is an economically important pest insect that kills high proportions of conifer seedlings in reforestation areas. It is present in conifer forests all over Europe but weevil abundance and risk for damage varies considerably between areas. This study aimed to obtain a useful model for predicting damage risks by analyzing survey data from 292 regular forest plantations in northern Sweden. A model of pine weevil attack was constructed using various site characteristics, including both climatic factors and factors related to forest management activities. The optimal model was rather imprecise but showed that the risk of pine weevil attack can be predicted approximatively with three principal variables: 1) the proportion of seedlings expected to be planted in mineral soil rather than soil covered with duff and debris, 2) age of clear-cut at the time of planting, and 3) calculated temperature sum at the location. The model was constructed using long-run average temperature sums for epoch 2010, and so effects of climate change can be inferred from the model by adjustment to future epochs. Increased damage risks with a warmer climate are strongly indicated by the model. Effects of a warmer climate on the geographical distribution and abundance of the pine weevil are also discussed. The new tool to better estimate the risk of damage should provide a basis for foresters in their choice of countermeasures against pine weevil damage in northern Europe.

• Nordlander, Swedish University of Agricultural Sciences (SLU), Department of Ecology, P.O. Box 7044, SE-750 07 Uppsala, Sweden E-mail: Goran.Nordlander@slu.se
• Mason, University of Canterbury, School of Forestry, Private Bag 4800, Christchurch 8140, New Zealand; Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, SE-230 53 Alnarp, Sweden http://orcid.org/0000-0001-9024-9106 E-mail: euan.mason@canterbury.ac.nz
• Hjelm, Skogforsk, The Forest Research Institute of Sweden, Ekebo 2250, SE-268 90 Svalöv, Sweden; Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, P.O. Box 49, SE-230 53 Alnarp, Sweden E-mail: karin.hjelm@skogforsk.se
• Nordenhem, Swedish University of Agricultural Sciences (SLU), Department of Ecology, P.O. Box 7044, SE-750 07 Uppsala, Sweden E-mail: h.nordenhem@telia.com
• Hellqvist, Swedish University of Agricultural Sciences (SLU), Department of Ecology, P.O. Box 7044, SE-750 07 Uppsala, Sweden E-mail: Claes.Hellqvist@slu.se

• Ferrarese, College of Forestry and Conservation, The University of Montana, Missoula, MT, USA; (present) Center for the Environmental Management of Military Lands, 1490 Campus Delivery, Colorado State University, Fort Collins, CO 80523, USA E-mail: jena.ferrarese@colostate.edu
• Affleck, College of Forestry and Conservation, University of Montana, Missoula, MT, USA E-mail: david.affleck@cfc.umt.edu
• Seielstad, College of Forestry and Conservation, University of Montana, Missoula, MT, USA E-mail: carl.seielstad@firecenter.umt.edu

• Siipilehto, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi
• Mehtätalo, University of Eastern Finland, School of Computing, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.mehtatalo@uef.fi

• Mäkinen, Simosol Oy, Rautatietori 4, FI-11130 Riihimäki, Finland E-mail: antti.makinen@simosol.fi
• Kangas, University of Helsinki, Department of Forest Sciences, Helsinki, Finland E-mail: ak@nn.fi
• Nurmi, University of Helsinki, Department of Forest Sciences, Helsinki, Finland E-mail: mn@nn.fi

• Siipilehto, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi

• Pietilä, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ip@nn.fi
• Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
• Mäkinen, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: am@nn.fi
• Mehtätalo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lm@nn.fi

• Kangas, Department of Forest Resources Management, P.O.Box 27, FI-00014 University of Helsinki, Finland E-mail: ak@nn.fi
• Mehtätalo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lm@nn.fi
• Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi

• Siipilehto, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi

• Palahí, Centre Tecnológic Forestal de Catalunya. Passeig Lluis Companys, 23, 08010, Barcelona, Spain E-mail: marc.palahi@ctfc.es
• Pukkala, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland E-mail: tp@nn.fi
• Trasobares, Foreco Technologies, Av. Diagonal 416, Estudio 2, Barcelona 08037, Spain E-mail: at@nn.es

• Siipilehto, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi

• Kangas, University of Helsinki, Dept. of Forest Resources Management, P.O. Box 27, 00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
• Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland E-mail: mm@nn.fi

• Nyström, SLU, Department of Forest Resource Management and Geomatics, SE-901 83 Umeå, Sweden E-mail: kenneth.nystrom@resgeom.slu.se
• Ståhl, SLU, Department of Forest Resource Management and Geomatics, SE-901 83 Umeå, Sweden E-mail: gs@nn.se

• Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: annika.kangas@metla.fi
• Maltamo, Finnish Forest Research Institute, Joensuu Research Station, P.O. Box 68, FIN-80101 Joensuu, Finland E-mail: mm@nn.fi

• Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: annika.kangas@metla.fi
• Maltamo, Finnish Forest Research Institute, Joensuu Research Station, P.O. Box 68, FIN-80101 Joensuu, Finland E-mail: mm@nn.fi

• Magnussen, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: smagnussen@pfc.forestry.ca
• Boudewyn, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: pb@nn.ca
• Wulder, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: mw@nn.ca
• Seemann, Canadian Forest Service, 506 West Burnside Road, Victoria B.C., Canada V8Z 1M5 E-mail: ds@nn.ca

• Siipilehto, Finnish Forest Research Institute, Vantaa Research Centre, P.O. Box 18, FIN-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi

• Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: annika.kangas@metla.fi
• Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi

• Siipilehto, Finnish Forest Research Institute, Vantaa Research Centre, P.O. Box 18, FIN-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi