Articles containing the keyword 'deciduous trees'

Moose (Alces alces L.) browsing was studied in young Scots pine (Pinus sylvestris L.) stands mixed with deciduous trees in high-density winter ranges. The proportional use of twig biomass decreased as the availability increased. The total as well as proportional biomass consumption were higher on the moist than on the dry type of forest. The per tree consumption of pine was higher on the moist type, where the availability of pine was lower. Deciduous trees were more consumed on the moist type, where their availability was relatively high. The consumption of pine saplings increased as the availability of birch increased. Pine stem breakages were most numerous when birch occurred as overgrowth above pine and at high birch densities. The availability of other deciduous tree species did not correlate with browsing intensity of Scots pine. Moose browsing had seriously inhibited the development of Scots pines in 6% of the stands, over 60% of available biomass having been removed. Rowan and aspen were commonly over-browsed and their height growth was inhibited, which occurred rarely by birch. There was no difference in the proportion of young stands in forest areas with high and low moose density. A high proportion of peatland forests was found to indicate relatively good feeding habitats in the high-density areas.

The PDF includes an abstract in Finnish.

• Heikkilä, E-mail: rh@mm.unknown
• Härkönen, E-mail: sh@mm.unknown

The aim of the study was to update knowledge of natural range of English oak (Quercus robur L.), European ash (Fraxinus exelsior L.), Norway maple (Acer platanoides L.), small-leaved lime (Tilia cordata Miller), wych elm (Ulmus glabra Mill.) and European white elm (Ulmus laevis Pall.) in Finland, and estimate how far north they could be grown as forest trees or as park trees. The study is based on literature and questionnaires sent to cities and towns, District Forestry Boards, districts of Forest Service, Forestry Management Associations and railway stations.

The northern borders in the natural range of the species succeed one another from south to north as follows: English oak, European ash, Norway maple, wych elm, and small-leaved lime. Occurrence of European white elm is sporadic. The English oak forms forests in the southernmost Finland, while the other species grow only as small stands, groups or solitary trees. According to experiences of planted stands or trees, the northern limits of the species succeed one another from south to north as follows: European ash, English oak, Norway maple, European white elm, wych elm and small-leaved lime. All the species are grown in parks fairly generally up to the district of Kuopio-Vaasa (63 °). The northern limits where the species can be grown as park trees reach considerably further north in the western part of the country than in the east.

The article includes a summary in English.

• Ollinmaa, E-mail: po@mm.unknown

Snow cover and ground frost was studied in 29 forest stands in Southern and Central Finland in 1957–1959. The tree species influenced greatly accumulation of snow on the forest floor. Norway spruce (Picea abies (L.) Karst.) retains snow in its crown. In addition, snow and water falling from the branches compress the snow cover under the trees, and the ground freezes deeper because of the shallow snow cover. In the spring, the dense crown prevents rain and radiation reaching the ground, which remains cold longer. However, ground frost may protect spruce, which has a weak root system, from wind damages.

Scots pine (Pinus sylvestris L.) has similar, but milder, effects on snow cover within the forest. The crowns of pine seedlings and young trees pass snow easily, but later the crowns intercept it considerably. The lower branches are, however, high up and the snow is evenly spread on the ground. The deciduous trees intercept little snow and in the spring the snow smelts and the frozen soil thaws early. The snow conditions of deciduous forests are, however, changed by a spruce undergrowth.

It can be assumed that the unfavourable conditions in spruce forests can be alleviated by thinning. Also, mixture of pine and deciduous trees can transform the conditions more favourable in the spruce stands.

The PDF includes a summary in English.

• Yli-Vakkuri, E-mail: py@mm.unknown

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

• Nirhamo, School of Forest Sciences, University of Eastern Finland, Joensuu, Finland https://orcid.org/0000-0002-1487-533X E-mail: aleksi.nirhamo@uef.fi
• Pykälä, Finnish Environment Institute, Helsinki, Finland https://orcid.org/0000-0002-7566-9310 E-mail: juha.pykala@syke.fi
• Jääskeläinen, Kuopio Museum of Natural History, Kuopio, Finland E-mail: jaaskimmo@gmail.com
• Kouki, School of Forest Sciences, University of Eastern Finland, Joensuu, Finland https://orcid.org/0000-0003-2624-8592 E-mail: jari.kouki@uef.fi

Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen (Populus tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests (Pinus sylvestris L., Picea abies [L.] Karst., Betula spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably.

• Hardenbol, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-0615-505X E-mail: alwin.hardenbol@uef.fi
• Kuzmin, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland; University of Eastern Finland, Department of Geographical and Historical Studies, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: anton.kuzmin@uef.fi
• Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
• Korpelainen, University of Eastern Finland, Department of Geographical and Historical Studies, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: pasi.korpelainen@uef.fi
• Kumpula, University of Eastern Finland, Department of Geographical and Historical Studies, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: timo.kumpula@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
• Kouki, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: jari.kouki@uef.fi

• Li, Key Laboratory for Silviculture and Conservation, Ministry of Education, Beijing 100083, China E-mail: gl@nn.cn
• Liu, Key Laboratory for Silviculture and Conservation, Ministry of Education, Beijing 100083, China E-mail: lyong@bjfu.edu.cn
• Zhu, Key Laboratory for Silviculture and Conservation, Ministry of Education, Beijing 100083, China E-mail: yz@nn.cn
• Li, Research Institute of Forestry, Chinese Academy of Forestry; Key Laboratory of Forest Silviculture of State Forestry Administration, Beijing 100091, China E-mail: qml@nn.cn
• Dumroese, US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Moscow, ID, USA E-mail: rkd@nn.us

• Givnish, Dept of Botany, University of Wisconsin, Madison, WI 53706, USA E-mail: givnish@facstaff.wisc.edu