Articles containing the keyword 'ramet'

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

The use of random parameter models in forestry has been proposed as one method of incorporating different levels of information into prediction equations. By explicitly considering the variance-covariance structure of observations and considering some model parameters as random rather than fixed, one can incorporate more complex error structures in analysing data.

Competition indices and variance component techniques were applied to 92 Scots pine (Pinus sylvestris L.) -dominated permanent sample plots on drained peatlands in Northern Finland. By quantifying stand, plot, and tree level variation, it was possible to identify the level (stand, plot or tree) at which the explanatory variables contributed to the model. The replication of plots within stands revealed little variation among plots within a single stand but significant variation occurred at stand and tree levels. Positive and negative effects of inter-tree competition are identified by examining simple correlation statistics and the random parameter model.

• Penner, E-mail: mp@mm.unknown
• Hökkä, E-mail: hh@mm.unknown
• Penttilä, E-mail: tp@mm.unknown

Semiparametric models, ordinary regression models and mixed models were compared for modelling stem volume in National Forest Inventory data. MSE was lowest for the mixed model. Examination of spatial distribution of residuals showed that spatial correlation of residuals is lower for semiparametric and mixed models than for parametric models with fixed regressors. Mixed models and semiparametric models can both be used for describing the effect of geographic location on stem form.

• Kangas, E-mail: ak@mm.unknown
• Korhonen, E-mail: kk@mm.unknown

The paper demonstrates the possibility of using data from small relascope sample plots in the derivation of the regression models which predict the Weibull function parameters for the dbh-distribution. The Weibull parameters describing the basal area dbh-distribution were estimated for relascope sample plots from the Finnish National Forest Inventory. In the first stage of the estimation nonlinear regression analysis was employed to derive initial parameter estimates for the second stage, in which the maximum likelihood method was used. The parameter estimates were employed as dependent variables for the derivation of the regression models; the independent variables comprised of the compartment-wise stand variables generally estimated in ocular inventories.

The PDF includes an abstract in Finnish.

• Kilkki, E-mail: pk@mm.unknown
• Päivinen, E-mail: rp@mm.unknown

The mean has a great importance in statistics in general and also in forest statistical calculations. The meaning of the average tree and its characteristics is important also for the practical forest mensuration work. However, the question is how are the statistical numbers of a mean tree related to the statistical numbers of the stand.   

Study is based on the strip-wise survey of forests in southern Finland. From that information the 30 sample plots were chosen, 10 of each of most typical forest site types, MT, VT and CT. The stands are of different ages and development classes, varying from 14 to 159 years.

For the determination of the average tree are the statistical numbers of five characteristics needed: volume, basal area, diameter, height and form factor. The stereometric mean tree of the stand can be calculated with only one statistical method and that solution is absolute.

Theoretically and statistically absolute solution for the problem is the continuous solution by the mean that is weighted with the number of stems. This solution however is not very useful in practical sense.

A simple, practical and adequately exact solution for determining the average tree by approximation procedure of a certain arithmetic mean. 

• Lönnroth, E-mail: el@mm.unknown

A method was developed for relative radiometric calibration of single multitemporal Landsat TM image, several multitemporal images covering each other, and several multitemporal images covering different geographical locations. The radiometrically calibrated different images were used for detecting rapid changes on forest stands. The nonparametric Kernel method was applied for change detection. The accuracy of the change detection was estimated by inspecting the image analysis results in field.

The change classification was applied for controlling the quality of the continuously updated forest stand information. The aim was to ensure that all the manmade changes and any forest damages were correctly updated including the attribute and stand delineation information. The image analysis results were compared with the registered treatments and the stand information base. The stands with discrepancies between these two information sources were recommended to be field inspected.

• Varjo, E-mail: jv@mm.unknown

The applicability of five mathematical programming methods, namely standard linear programming, parametric programming, goal programming, mixed integer programming and integer programming is discussed as a planning tool for the choice of wood procurement method.

Theoretically, the goal programming approach seems to be the best routine for mathematical handling of problems related to wood procurement. The parametric approach must include enough large post-optimality analysis routine. If the effect of the variables expressed with different measures is to be studied, interpretation of the economic information given by the approach becomes a problem. The other drawback is that the approach does not allow determination of the hierarchy of the goals objectively as they depend on the subjective preferences of the decision maker.

From the practical point of view, standard linear programming is the best method if the objective function can be formulated in economic terms, for instance. If there are several goals to be attained or satisfied the best method is goal programming.

According to the sub-studies, every method under consideration can be used as a solution routine for the minimization of wood procurement costs. In cost minimization the best methods are goal programming and standard linear programming. The best method for harvesting system evaluation purposes is parametric because it allows varied cost calculations within a certain cost range. The best method for harvesting equipment investment planning is mixed integer programming with binary decision variables.

The more complicated and restricted the problem environment is, the better the mathematical programming approach will be, also in harvesting related problems.

The PDF includes a summary in English.

• Mikkonen, E-mail: em@mm.unknown

Hybrid aspen (Populus tremula L. × P. tremuloides Michx.) is known with outstanding growth rate and some favourable wood characteristics, but models for stand management have not yet been prepared in northern Europe. This study introduces methods and models to predict tree dimensions, diameter at breast height (dbh) and tree height for a hybrid aspen plantation using data from repeatedly measured permanent sample plots established in clonal plantations in southern Finland. Dbh distributions using parameter recovery method for the Weibull function was used with Näslund’s height curve to model tree heights. According to the goodness-of-fit statistics of Kolmogorov-Smirnov and the Error Index, the arithmetic mean diameter (D) and basal area-weighted mean diameter (DG) provided more stable parameter recovery for the Weibull distribution than the median diameter (DM) and basal area-weighted median diameter (DGM), while DG showed the best overall fit. Thus, Näslund’s height curve was modelled using DG with Lorey’s height (HG), age, basal area (BA), and tree dbh (Model 1). Also, Model 2 was tested using all predictors of Model 1 with the number of trees per ha (TPH). All predictors were shown to be significant in both Models, showing slightly different behaviour. Model 1 was sensitive to the mean characteristics, DG and HG, while Model 2 was sensitive to stand density, including both BA and TPH as predictors. Model 1 was considered more reasonable to apply based on our results. Consequently, the parameter recovery method using DG and Näslund’s models were applicable for predicting tree diameter and height.

• Lee, Natural Resources Institute Finland (Luke), Natural resources, Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0003-1586-9385 E-mail: daesung.lee@luke.fi
• Siipilehto, Natural Resources Institute Finland (Luke), Natural resources, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: jouni.siipilehto@luke.fi
• Hynynen, Natural Resources Institute Finland (Luke), Natural resources, Vipusenkuja 5, FI-57200 Savonlinna, Finland https://orcid.org/0000-0002-9132-8612 E-mail: jari.hynynen@luke.fi

Genetic parameters of growth and stem quality traits were estimated for open-pollinated silver birch Betula pendula Roth progenies in Latvia at the age of 10 and 14 years. Tree height and stem volume were found to be under strong genetic control at both inventories (narrow-sense heritabilities varied from 0.41 to 0.66). Mainly low heritabilities were found for stem defects, yet genetic control of branch diameter, stem straightness and overall stem quality varied from low to high depending on study site. High additive genetic coefficient of variation was found for stem volume (25.3–32.5%). Genetic correlations among growth traits were strong and positive (0.90–0.99). Mainly weak genetic correlations between growth and quality traits implied simultaneous improvement. Still, strong negative correlations between branch angle and stem straightness might result in enlarged knot size for straighter logs. The genetic age-age correlations were strong. Weak genotype by environment interaction and stability of best genotypes over different sites was indicated by strong genetic correlations between trials. Each growth or quality trait alone showed substantial improvement in terms of estimated genetic gain (up to 62% over trial mean for stem volume). Therefore, selection index combining both growth and stem quality may be developed.

• Gailis, Latvian State Forest Research Institute Silava, 111 Rigas street, Salaspils LV-2169, Latvia E-mail: arnis.gailis@silava.lv
• Zeltiņš, Latvian State Forest Research Institute Silava, 111 Rigas street, Salaspils LV-2169, Latvia https://orcid.org/0000-0002-6286-5814 E-mail: pauls.zeltins@silava.lv
• Purviņš, Latvian State Forest Research Institute Silava, 111 Rigas street, Salaspils LV-2169, Latvia E-mail: andis.purvins@silava.lv
• Augustovs, Latvian State Forest Research Institute Silava, 111 Rigas street, Salaspils LV-2169, Latvia E-mail: juris.augustovs@silava.lv
• Vīndedzis, Latvian State Forest Research Institute Silava, 111 Rigas street, Salaspils LV-2169, Latvia E-mail: valts.vindedzis@silava.lv
• Zariņa, Latvian State Forest Research Institute Silava, 111 Rigas street, Salaspils LV-2169, Latvia E-mail: inga.zarina@silava.lv
• Jansons, Latvian State Forest Research Institute Silava, 111 Rigas street, Salaspils LV-2169, Latvia E-mail: aris.jansons@silava.lv

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

Accurate timber assortment information is required before cuttings to optimize wood allocation and logging activities. Timber assortments can be derived from diameter-height distribution that is most often predicted from the stand characteristics provided by forest inventory. The aim of this study was to assess and compare the accuracy of three different pre-harvest inventory methods in predicting the structure of mainly Scots pine-dominated, clear-cut stands. The investigated methods were an area-based approach (ABA) based on airborne laser scanning data, the smartphone-based forest inventory Trestima app and the more conventional pre-harvest inventory method called EMO. The estimates of diameter-height distributions based on each method were compared to accurate tree taper data measured and registered by the harvester’s measurement systems during the final cut. According to our results, grid-level ABA and Trestima were generally the most accurate methods for predicting diameter-height distribution. ABA provides predictions for systematic 16 m × 16 m grids from which stand-wise characteristics are aggregated. In order to enable multimodal stand-wise distributions, distributions must be predicted for each grid cell and then aggregated for the stand level, instead of predicting a distribution from the aggregated stand-level characteristics. Trestima required a sufficient sample for reliable results. EMO provided accurate results for the dominating Scots pine but, it could not capture minor admixtures. ABA seemed rather trustworthy in predicting stand characteristics and diameter distribution of standing trees prior to harvesting. Therefore, if up-to-date ABA information is available, only limited benefits can be obtained from stand-specific inventory using Trestima or EMO in mature pine or spruce-dominated forests.

• Siipilehto, Natural Research Institute Finland (Luke), Management and Production of Renewable Resources, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@luke.fi
• Lindeman,  Natural Research Institute Finland, Green Technology, Kaironiementie 15, 39700 Parkano E-mail: harri.lindeman@luke.fi
• Vastaranta, University of Helsinki, Department of Forest Sciences, P.O. Box 62 (Viikinkaari 11), FI-00014 University of Helsinki E-mail: mikko.vastaranta@helsinki.fi
• Yu, Finnish Geospatial Research Institute (FGI), Department of Remote Sensing and Photogrammetry, National Land Survey of Finland, P.O. Box 15 (Geodeetinrinne 2), FI-02431, Masala, Finland E-mail: xiaowei.yu@maanmittauslaitos.fi
• Uusitalo,  Natural Research Institute Finland, Green Technology, Kaironiementie 15, 39700 Parkano E-mail: jori.uusitalo@luke.fi

• 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

• Haara, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: arto.haara@joensuu.fi
• Leskinen, Finnish Environment Institute, Research Programme for Production and Consumption, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: pl@nn.fi

• Peuhkurinen, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: jp@nn.fi
• Maltamo, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi
• Malinen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: jm@nn.fi

• Insinna, Office for Environmental Protection Liechtenstein, Climate Change Division, P.O. Box 684, FL-9490 Vaduz, Liechtenstein E-mail: patrick.insinna@aus.llv.li
• Jalkanen, Rovaniemi Research Unit, Finnish Forest Research Institute, P.O. Box 16, FI-96301 Rovaniemi, Finland E-mail: rj@nn.fi
• Götz, Eberswalde University of Applied Sciences, Department of Forestry, Forest-Botanical Gardens, D-16225 Eberswalde, Germany E-mail: bg@nn.de

• 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

• Muinonen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: eero.muinonen@metla.fi

• Mehtätalo, Finnish Forest Research Institute, Joensuu Research Centre, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: lauri.mehtatalo@metla.fi

• Haara, Finnish Forest Research Institute, Joensuu Research Centre, P.O.Box 68, FIN-80101 Joensuu, Finland E-mail: arto.haara@metla.fi

• Malinen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: jukka.malinen@joensuu.fi

• Sironen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: susanna.sironen@forest.joensuu.fi
• Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: ak@nn.fi
• Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, 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: jk@nn.fi

• Maltamo, Faculty of Forestry, University of Joensuu, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: matti.maltamo@forest.joensuu.fi
• Eerikäinen, Faculty of Forestry, University of Joensuu, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: ke@nn.fi

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

• Nikkanen, Finnish Forest Research Institute, Punkaharju Research Station, FIN-58450 Punkaharju, Finland E-mail: teijo.nikkanen@metla.fi
• Ruotsalainen, Finnish Forest Research Institute, Punkaharju Research Station, FIN-58450 Punkaharju, Finland E-mail: sr@nn.fi

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

Populations of tree species with a wide geographic range, such as silver birch (Betula pendula Roth), show genetic specialization to native environments, while maintaining high phenotypical plasticity. Accordingly, assessment of local specialization is essential for adaptive management. The aim of the study was to detect geographic patterns of local adaptation of growth and stem quality based on two open-pollinated progeny trials in Latvia testing local material. Two provenance regions differing by continentality were distinguished, which also differed in genetic control of growth traits, likely originating from the post-glacial recolonization of vegetation and subsequent natural adaptation. Heritability of the traits was estimated for each of the distinguished regions, indicating differing patterns of genetic adaptation and potential for future selection. Trees from the more continental inland showed superior growth and possessed higher heritability. The coastal provenance region showed slower growth and intermediate heritability of the respective traits. Moderate to high heritability for stem quality traits was estimated irrespectively of region. Overall, better growth and higher heritability suggests that anthropogenic selection within the best inland provenances may constitute better performing and adaptable breeding population compared to the coastal one. Still, overlapping phenotypical variation and heritability of quality traits implies improved stemwood quality for plywood regardless of the provenance region. High adaptive capacity of silver birch genotypes suggests ability to cope with climatic changes, highlighting its potential for climate-smart forestry.

• Gailis, Latvian State Forest Research Institute Silava, 111 Rigas street, LV-2169, Salaspils, Latvia E-mail: arnis.gailis@silava.lv
• Zeltiņš, Latvian State Forest Research Institute Silava, 111 Rigas street, LV-2169, Salaspils, Latvia E-mail: pauls.zeltins@silava.lv
• Matisons, Latvian State Forest Research Institute Silava, 111 Rigas street, LV-2169, Salaspils, Latvia https://orcid.org/0000-0002-4042-0689 E-mail: roberts.matisons@silava.lv
• Purviņš, Latvian State Forest Research Institute Silava, 111 Rigas street, LV-2169, Salaspils, Latvia E-mail: andis.purvins@silava.lv
• Augustovs, Latvian State Forest Research Institute Silava, 111 Rigas street, LV-2169, Salaspils, Latvia E-mail: juris.augustovs@silava.lv
• Vīndedzis, Latvian State Forest Research Institute Silava, 111 Rigas street, LV-2169, Salaspils, Latvia E-mail: valts.vindedzis@silava.lv
• Jansons, Latvian State Forest Research Institute Silava, 111 Rigas street, LV-2169, Salaspils, Latvia https://orcid.org/0000-0001-7981-4346 E-mail: aris.jansons@silava.lv

• Fekedulegn, Department of Statistics, West Virginia University, Eberly College of Arts and Sciences, P.O. Box 6330, Morgantown, WV26506, USA E-mail: fdesta@stat.wvu.edu
• Mac Siurtain, University College Dublin, Ireland E-mail: mpms@nn.ie
• Colbert, USDA Forest Service, Northeastern Research Station, Morgantown, West Virginia E-mail: jjc@nn.us