Articles by Jouni Siipilehto

The following treatments were compared in three Scots pine (Pinus sylvestris L.) reforestation areas on a scarified moist mineral-soil site in southern Finland, planted with 1+1 bareroot stock in spring 1987: (a) no weed control treatment; (b) mulching with a fibre slurry produced by mixing wastepaper with water and applied 1 cm deep to an area of 60 cm in diameter around the seedling soon after planting; (c) glyphosate (at 2 kg ha^-1) sprayed on a 1 m² spot around the seedling in early August 1987; (d) terbuthylazine (at 10 kg ha^-1) applied as (c). Monitoring of the trials over a 4-year period between 1987–90 showed that none of the treatments reduced surface vegetation to an extent that would have benefited pine. The percentage cover development of the vegetation, dominated by Agrostis capillaris, Calamagrostis arundinaceae, Deschampsia flexuosa, Festuca ovina, Epilobioum angustifolium and Pteridium aquillinum, followed much the same pattern in all treatments, with (c) slightly favouring forbs. Survival of pine at the end of the study period was about 90%, with non-significant differences between treatments. Mulching and terbuthylazine treatment slightly reduced seedling height growth in the second year. Growth was better in glyphosate treatment than in terbuthylazine treatment in the lowest (<30%) and the highest (>60%) pre-treatment weed cover classes, and in the latter also better than in untreated control. Mulching gave variable results; at its best it provided also good control of weeds for several years, without, however, improving the initial development of pine in these trials.

• Siipilehto, E-mail: js@mm.unknown
• Lyly, E-mail: ol@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

Ageing and competition reduce trees’ ability to capture resources, which predisposes them to death. In this study, the effect of senescence on the survival probability of Norway spruce (Picea abies (L.) Karst.) was analysed by fitting alternative survival probability models. Different model formulations were compared in the dataset, which comprised managed and unmanaged plots in long-term forest experiments in Finland and Norway, as well as old-growth stands in Finland. Stand total age ranged from 19 to 290 years. Two models were formulated without an age variable, such that the negative coefficient for the squared stem diameter described a decreasing survival probability for the largest trees. One of the models included stand age as a separate independent variable, and three models included an interaction term between stem diameter and stand age. According to the model including stand age and its interaction with stem diameter, the survival probability curves could intersect each other in stands with a similar structure but a different mean age. Models that did not include stand age underestimated the survival rate of the largest trees in the managed stands and overestimated their survival rate in the old-growth stands. Models that included stand age produced more plausible predictions, especially for the largest trees. The results supported the hypothesis that the stand age and senescence of trees decreases the survival probability of trees, and that the ageing effect improves survival probability models for Norway spruce.

• Siipilehto, Natural Resources Institute Finland (Luke), Natural resources, Latokartanonkaari 9, P.O. Box 2, FI-00790 Helsinki, Finland E-mail: jouni.siipilehto@luke.fi
• Mäkinen, Natural Resources Institute Finland (Luke), Production systems, Latokartanonkaari 9, P.O. Box 2, FI-00790 Helsinki, Finland https://orcid.org/0000-0002-1820-6264 E-mail: harri.makinen@luke.fi
• Andreassen, Norwegian Institute of Bioeconomy Research (NIBIO), NO-1431 Ås, Norway E-mail: kjellandreassen@gmail.com
• Peltoniemi, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, P.O. Box 2, FI-00790 Helsinki, Finland https://orcid.org/0000-0003-2028-6969 E-mail: mikko.peltoniemi@luke.fi

New mortality models were developed for the purpose of improving long-term growth and yield simulations in Finland, Norway, and Sweden and were based on permanent national forest inventory plots from Sweden and Norway. Mortality was modelled in two steps. The first model predicts the probability of survival, while the second model predicts the proportion of basal area in surviving trees for plots where mortality has occurred. In both models, the logistic function was used. The models incorporate the variation in prediction period length and in plot size. Validation of both models indicated unbiased mortality rates with respect to various stand characteristics such as stand density, average tree diameter, stand age, and the proportion of different tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), and broadleaves. When testing against an independent dataset of unmanaged spruce-dominated stands in Finland, the models provided unbiased prediction with respect to stand age.

• Siipilehto, Natural Resources Institute Finland (Luke), Natural resources, P.O. Box 2, FI-00790 Helsinki, Finland E-mail: jouni.siipilehto@luke.fi
• Allen, Norwegian Institute of Bioeconomy Research (NIBIO), Division of Forest and Forest Products, NO-1431 Ås, Norway; Larson and McGowin Inc., Mobile, AL 36607, USA https://orcid.org/0000-0002-7824-2849 E-mail: micky.allen@nibio.no
• Nilsson, Swedish University of Agricultural Sciences (SLU), Southern Swedish Forest Research Centre, SE-23053 Alnarp, Sweden https://orcid.org/0000-0002-7624-4031 E-mail: urban.nilsson@slu.se
• Brunner, Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management, NO-1432 Ås, Norway https://orcid.org/0000-0003-1668-9714 E-mail: andreas.brunner@nmbu.no
• Huuskonen, Natural Resources Institute Finland (Luke), Natural resources, P.O. Box 2, FI-00790 Helsinki, Finland https://orcid.org/0000-0001-8630-3982 E-mail: saija.huuskonen@luke.fi
• Haikarainen, Natural Resources Institute Finland (Luke), Natural resources, P.O. Box 2, FI-00790 Helsinki, Finland https://orcid.org/0000-0001-8703-3689 E-mail: soili.haikarainen@luke.fi
• Subramanian, Swedish University of Agricultural Sciences (SLU), Southern Swedish Forest Research Centre, SE-23053 Alnarp, Sweden https://orcid.org/0000-0003-2777-3241 E-mail: narayanan.subramanian@slu.se
• Antón-Fernández, Norwegian Institute of Bioeconomy Research (NIBIO), Division of Forest and Forest Products, NO-1431 Ås, Norway https://orcid.org/0000-0001-5545-3320 E-mail: clara.anton.fernandez@nibio.no
• Holmström, Swedish University of Agricultural Sciences (SLU), Southern Swedish Forest Research Centre, SE-23053 Alnarp, Sweden https://orcid.org/0000-0003-2025-1942 E-mail: emma.holmstrom@slu.se
• Andreassen, Norwegian Institute of Bioeconomy Research (NIBIO), Division of Forest and Forest Products, NO-1431 Ås, Norway https://orcid.org/0000-0003-4272-3744 E-mail: kjellandreassen@gmail.com
• Hynynen, Natural Resources Institute Finland (Luke), Natural resources, P.O. Box 2, FI-00790 Helsinki, Finland E-mail: jari.hynynen@luke.fi

This study examined a theoretical model for stand structures from the volumes of pulpwood and saw logs of clear-cut stands. The average stem size was used to estimate the number of cut trees. The distribution was solved using nonlinear derivative-free optimization. The truncated 2-parameter Weibull distribution was used to describe the stand structure of the commercial stems. This method was first tested with harvester data collected from seven clear-cut stands in southern Finland. Validation included reliability in the stand characteristics and goodness-of-fit of the species-specific distributions. The distributions provided unbiased estimates for the saw log volume, while the bias in the estimated pulpwood volume was 2%. The standard stand characteristics from the Weibull distributions corresponded notably well with the harvester data. A Kolmogorov-Smirnov (KS) test rejected two distributions out of 21 cases, when the accurate input variables were available for the theoretical model. The results of the study suggest that the presented method is a relevant option for predicting the stand structure. In practice, the reliability of the presented method was dependent on the quality of the information available from the stand prior to cutting. With a timber trade data set, the solution for the distribution for a clear-cut section was found. The goodness-of-fit was dependent on the accuracy of the visually assessed timber trade variables. Especially the average stem size proved difficult to assess due to high number of understorey pulpwood stems. Due to overestimated average stem sizes, the solved number of harvested trees was underestimated. Less than 50% of the distributions predicted for clear-cut sections passed the KS test.

• Siipilehto, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, P.O. Box 2, FI-00790 Helsinki, Finland E-mail: jouni.siipilehto@luke.fi
• Rajala, Metsä Group, Revontulenpuisto 2, P.O. Box 10, 02020 METSÄ, FI-02100 Espoo, Finland E-mail: miika.rajala@metsagroup.com

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

• 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

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

• Siipilehto, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@metla.fi
• Sarkkola, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: ss@nn.fi
• Mehtätalo, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland E-mail: lm@nn.fi

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

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

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

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

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

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