Articles containing the keyword 'terrain'

As a subproblem in the joint Nordic Terrain-Machine Project the requirements set by forest road construction on the terrain classifications were studied during the summer 1973 in ten operations, in which either a bulldozer or an excavator method was used.

The most important terrain factors of the ground factors affecting the construction time of the road base were the so-called depth index and the moisture content of the soil, and in addition to these the amount of stumps as a ground roughness factor. These variables explained, however, only a rather minor part of the wide variation in the construction output of the practical operations.

The PDF includes a summary in English.

• Haarlaa, E-mail: rh@mm.unknown

The mobility of logging tractors was tested in the winter 1969 on difficult snow conditions to gather information for planning of logging operations and for logging machinery design. The tractors tested were Clark Ranger 666, Timberjack C, Valmet Terra, Ford Brunett 5000, Fiskars 510, BM-Volvo SM 660, BM Volvo SM 661, Ford Country 6, MF-Robur I and BM-Boxer T-350.

According to the results, there is a preference of tracked vehicles in difficult snow conditions compared to wheeled tractors. Ford Country with long and bearing full-tracks proved to have the best mobility. On downhill grades it was found significant differences between three-quarter-track-tractors and skidders, although the performance on level ground and uphill grades was relatively similar. The tracked vehicles can easier move on the packed snow layer and reach a higher speed.

The driving speed does not increase significantly until the density of snow has entirely changed through getting wet. Wet top layer of snow affects positively on driving, because it increases packing of the snow. Increasing density of the snow improves especially the mobility of broad-tired wheeled tractors. To be able to predict the driving speed of a tractor in winter working conditions one must know the depth of the snow layer and the density of the snow and the grade of the slope. In addition, the passages on the same route and the packing of the snow must be regarded.

The PDF includes a summary in English.

• Silvennoinen, E-mail: us@mm.unknown
• Haarlaa, E-mail: rh@mm.unknown

The goal of this study was to develop a mathematical model for determination of the optimal winching distance in different conditions as based on harvesting costs. In the thinned forest the strip roads are parallel and the winching routes perpendicularly to them. A directed felling of trees is used so that it is easy to make loads to be winched. The stems can also be prepared to timber assortments on the stump area and gathered to loads for skidding alongside the winching routes.

After winching the timber is transported using a forwarder mowing on the strip roads. If the stems have not been bucked in the forests, they are to be prepared to timber assortments before the following transportation, because the problem of turning whole stems in a thinned forest has not yet been solved.

In the mathematical model the formation of the costs was described using 18 variables of which 15 had an effect on the optimum winching distance. Some empirical values were estimated concerning these variables, and the corresponding optimum winching distance were computed. The optimum was mainly determined by the quantity of timber harvested per unit area, the size of the winching load, the regression coefficient of the times which were depended on the winching distance.

According to the model, the deviation from optimum winching distance does not cause a very great change in the analysed total costs. When the winching distance is longer, the increase of the costs is smaller than if it is shorter than optimum. In general, the increase of the costs was so small that in practice one obviously can be satisfied with rather approximate methods in determining the suitable winching distance.

The PDF includes a summary in English.

• Kärkkäinen, E-mail: mk@mm.unknown

The purpose of this study was to explain whether it is possible to affect, in practical working site conditions, by means of logging waste on the strip road, the depth of the track which is formed in terrain transportation and the injuries of the growing stand. Five 20 m long investigation areas with logging waste and five similar areas without logging waste were arranged on one strip road at Teisko logging site in Southern Finland. The logging waste layer was mainly Norway spruce and 10–15 cm thick. A KL–836 B forwarder was used. The type of soil was loam.

The logging waste affected the depth of the track only by decreasing the wear of humus layer. Even decreasing effect of logging waste on the injuries in the growing stand was minor. At Kitee working site in Eastern Finland strip roads were studied. The type of soil was thick, rather mouldered peat. The thickness of logging waste was 3–4 times greater than in Teisko, mainly spruce. A Volvo Nalle SM 460 forwarder was used. The effect of the logging waste on the depth of the tracks was clearly to be noticed. On basis of the appearance of the tracks one could assume that the difference was due to different wear of the humus, and not so much due to the quantity of logging waste that improves the carrying capacity of terrain.

In some extent logging waste was also found to affect the amount and quality of tree injuries. In practical working conditions, the importance might be small, since in the experiments an unrealistically great amount of logging waste was used.

The PDF includes a summary in English.

• Kärkkäinen, E-mail: mk@mm.unknown

The article is a report from the meeting of the Section 32 of the IUFRO in Canada on September 15.–25. 1964, which three Finnish forestry experts participated. The theme of the meeting was classification of forest terrain.

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• Makkonen, E-mail: om@mm.unknown

The possibilities of using microwave techniques in detecting the trafficability of peatlands is discussed. Three microwave methods were tested. 1) A FM-CW radar using 1.0–1.8 GHz frequency was used to measure the frost thickness of peat layer. It was possible to follow the variations in snow and frost layer thickness. Total reflected power might indicate the wetness class of the peatland, which is also a trafficability factor. 2) A short-pulse radar with 100 MHz frequency was tested in summer condition. Good profiling of peat layers was obtained, giving basic information for trafficability analysis. 3) A mapping of brightness of temperature of peatlands in summer conditions using a 790 MHz radiometer. The measured brightness temperature was correlated with vegetation cover, thus giving some information of trafficability.

The PDF includes a summary in English.

• Saarilahti, E-mail: ms@mm.unknown

The paper is the final report of the Inter-Nordic Terrain-machine Project (1972–1975). It deals with the requirements for a terrain classification for forestry, its factors and classes, and presents a terrain classification.

The mechanization of hauling, which took place in the field of forestry in the 1950's, added to the need for a terrain classification. Different terrain classifications based on different terrain factors have been developed in many countries. In the meeting of IUFRO Section 32 held in Montreal in 1964, it was found that a general system was needed for measuring and describing those terrain conditions having a significant influence on forest operations. The requirements for such a classification system are given in the paper. Because some of the requirements are contradictory, the classification must be a compromise. The most important factors from the forestry point of view are presented in the article.

The terrain classification presented in this report consists of two stages. The first stage is a primary terrain classification, in which terrain factors are measured or described objectively. The second is a secondary descriptive classification. Only factors essential to the activity in question are taken into account. After this, in a secondary functional stage, the requirements of the employer of the system, e.g., working method, machines etc., are also taken into account.

The PDF includes a summary in Finnish.

• Eriksson, E-mail: te@mm.unknown
• Nilsson, E-mail: gn@mm.unknown
• Skråmo, E-mail: gs@mm.unknown

The first part of the study includes a summary of conclusions from five earlier reports dealing with terrain transportation costs, effect of terrain, load, snow and additional machine components on the mobility of forest tractors, and the distance a vehicle has to move in forest transportation of timber. In the second part of the study the effect of these separate factors on the transport output have been analysed on the basis of simulating a transportation of 3,000 loads by computer.

It was concluded that no specific terrain factor had a dominating effect on any component of the forwarding output. However, many terrain-factor combinations with a nonsignificant effect on the mobility and also on the forwarding output were identified. The most significant factors affecting the output were the size of load and the distance driven during the cycle.

The PDF includes a summary in Finnish.

• Haarlaa, E-mail: rh@mm.unknown

The purpose of this study was to build a mathematic predictive model for the formation of injuries and to try its usability in predicting. The empirical data consisted of 24 thinning sites, which were situated mainly in forests owned by forestry companies. The timber was made by strip roads. The terrain transportation of timber was carried out by forwarders or by agricultural tractor-trailer combinations.

The model has been compiled by multiple regression analysis. The predictive model has been interpreted so that the formation of injuries is, for one part, dependent of the number of trees exposed to damages, and, on the other hand, on the factors influencing the probability of injuries, the possibility for this existing. The number of trees exposed to injuries is dependent on the density of strip roads and on the amount of standing timber left after logging. The probability of injuries is influenced by the quality of the standing timber, the type of soil and the intensity of the timber harvesting process.

The predictive model has been interpreted in similar way as the number of injured trees. The possibility of formation of injuries is mainly affected by the length of strip roads and the amount of the standing timber. The probability of injuries is influenced, for the part of the number of root injuries by the strength of the soil type, and probably also by transportation equipment. For the amount of stem injuries no valid predictive equation was found in this study.

• Kärkkäinen, E-mail: mk@mm.unknown

The biomass of a secondary evergreen and deciduous broad-leaved mixed forest was comprehensively inventoried in a permanent 2 ha plot in southwestern China. Biomass models, sub-sampling, soil pit method, and published data were utilized to determine the biomass of all components. Results showed that the total biomass of the forest was 158.1 Mg ha⁻¹; the total biomass included the major aboveground (137.7 Mg ha⁻¹) and belowground (20.3 Mg ha⁻¹) biomass components of vascular plants as well as the minor biomass components of bryophytes (0.078 Mg ha⁻¹) and lichens (0.043 Mg ha⁻¹). The necromass was 17.6 Mg ha⁻¹ and included woody debris (9.0 Mg ha⁻¹) and litter (8.6 Mg ha⁻¹). The spatial pattern of the aboveground biomass was determined by the spatial distribution of dominant trees with large diameter, tall height, and dense wood. The belowground biomass differed in terms of root diameter and decreased with increasing soil depth. The belowground biomass in each soil pit in local habitats was not related to the spatial distribution of woody plants and soil pit depth. The karst forest presented lower biomass compared than the nonkarst forests in the subtropical zone. Biomass carbon in the karst terrains would increase substantially if degraded karst vegetation could be successfully restored to the forest. Comprehensive site-based biomass inventory of karst vegetation will contribute not only to provide data for benchmarking global and regional vegetation and carbon models but also for regional carbon inventory and vegetation restoration.

• Liu, State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Lincheng West Road 99, 550081 Guiyang, China; Puding Karst Ecosystem Research Station, Chinese Academy of Sciences, 562100 Puding, China; University of Chinese Academy of Sciences, 100049 Beijing, China E-mail: liulibin@mail.gyig.ac.cn
• Wu, State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Lincheng West Road 99, 550081 Guiyang, China; Puding Karst Ecosystem Research Station, Chinese Academy of Sciences, 562100 Puding, China; University of Chinese Academy of Sciences, 100049 Beijing, China E-mail: wuyang2468@hotmail.com
• Hu, School of Chemistry and Life Science, Guangxi Teachers Education University, 530001 Nanning, China; Key Laboratory of Beibu Gulf Environment Change and Resources Utilization of Ministry of Education, Guangxi Teachers Education University, 530001 Nanning, China E-mail: ahhugang@gmail.com
• Zhang, School of Chemistry and Life Science, Guangxi Teachers Education University, 530001 Nanning, China; Key Laboratory of Beibu Gulf Environment Change and Resources Utilization of Ministry of Education, Guangxi Teachers Education University, 530001 Nanning, China E-mail: gxtczzh@gmail.com
• Cheng, State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Lincheng West Road 99, 550081 Guiyang, China; Puding Karst Ecosystem Research Station, Chinese Academy of Sciences, 562100 Puding, China E-mail: chenganyun@vip.skleg.cn
• Wang, State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Lincheng West Road 99, 550081 Guiyang, China; Puding Karst Ecosystem Research Station, Chinese Academy of Sciences, 562100 Puding, China E-mail: wangshijie@vip.skleg.cn
• Ni, State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Lincheng West Road 99, 550081 Guiyang, China; Puding Karst Ecosystem Research Station, Chinese Academy of Sciences, 562100 Puding, China E-mail: nijian@vip.skleg.cn

• Manner, Swedish University of Agricultural Sciences, Department of Forest Biomaterial and Technology, SE-901 83 Umeå, Sweden E-mail: jussi.manner@slu.se
• Nordfjell, Swedish University of Agricultural Sciences, Department of Forest Biomaterial and Technology, SE-901 83 Umeå, Sweden E-mail: tomas.nordfjell@slu.se
• Lindroos, Swedish University of Agricultural Sciences, Department of Forest Biomaterial and Technology, SE-901 83 Umeå, Sweden E-mail: ola.lindroos@slu.se

• Ersson,  Swedish University of Agricultural Sciences, Department of Forest Biomaterials and Technology, SE-901 83 Umeå, Sweden E-mail: back.tomas.ersson@slu.se
• Jundén,  UMIT Research Lab, Umeå University, SE-901 87 Umeå, Sweden E-mail: linus.junden@gmail.com
• Bergsten,  Swedish University of Agricultural Sciences, Department of Forest Biomaterials and Technology, SE-901 83 Umeå, Sweden E-mail: urban.bergsten@slu.se
• Servin,  UMIT Research Lab, Umeå University, SE-901 87 Umeå, Sweden E-mail: martin.servin@physics.umu.se

• Lideskog, Division of Product and Production Development, Luleå University of Technology, SE-97187 Luleå, Sweden E-mail: hakan.lideskog@ltu.se
• Ersson, Department of Forest Biomaterials and Technology, SLU, SE-90183 Umeå, Sweden E-mail: back.tomas.ersson@slu.se
• Bergsten, Department of Forest Biomaterials and Technology, SLU, SE-90183 Umeå, Sweden E-mail: urban.bergsten@slu.se
• Karlberg, Division of Product and Production Development, Luleå University of Technology, SE-97187 Luleå, Sweden E-mail: magnus.karlberg@ltu.se