Jyri Hietala, Riitta Hänninen, Matleena Kniivilä, Anne Toppinen. (2019). Networks in international opportunity recognition among Finnish wood product industry SMEs. Silva Fennica vol. 53 no. 4 article id 10151. https://doi.org/10.14214/sf.10151

Keywords: wood products; business networks; institutional networks; internationalization; opportunity recognition; social networks

Highlights: In line with earlier literature, we found the networks in our study to positively impact international opportunity recognition; Despite the reliance on various network forms and levels, a strategic stance towards opportunity recognition can be characterized as being more reactive than proactive; Institutional networks represented a more systematic way of recognizing international opportunities among case companies.

Abstract | Full text in HTML | Full text in PDF | Author Info

Bioeconomy development will create new opportunities for firms operating in the international wood products markets, and identifying and exploiting these opportunities is emphasized as a key concept to achieving business success. Our study will attempt to address a gap in the literature on sawmill industry business development from the viewpoint of international opportunity recognition. The aim of our study is to provide a holistic description on how small and medium-sized enterprises (SMEs) in the wood products industry recognize and exploit international business opportunities, and how they utilize network perspectives in this context. The subject was examined through Finnish wood product industry SMEs by interviewing 11 managers and industry representatives. The results suggest that SMEs recognize international opportunities reactively per se. Social networks formed in professional forums were an important information channel for identifying international opportunities. Through vertical business networks, such as sales agents, firms have been able to increase their international market presence and free their own resources for other important activities. Horizontal dyadic business networks were seen to facilitate new international opportunities through cooperation, while excessive reliance on vertical networks raised concerns and seemed not to be effective in international opportunity recognition. Institutional networks formed a systematic way of recognizing international opportunities, but more so at the initial market entry stage.

Hietala, United Bankers, Aleksanterinkatu 21 A, FI-00100 Helsinki, Finland E-mail: jyri.hietala@unitedbankers.fi
Hänninen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: riitta.hanninen@luke.fi
Kniivilä, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: matleena.kniivila@luke.fi
Toppinen, University of Helsinki, Helsinki Institute of Sustainability Science, Latokartanonkaari 7, P.O. 27, FI-00014 University of Helsinki, Finland E-mail: anne.toppinen@helsinki.fi

article id 10068, category Research article

Lari Melander, Risto Ritala, Markus Strandström. (2019). Classifying soil stoniness based on the excavator boom vibration data in mounding operations. Silva Fennica vol. 53 no. 2 article id 10068. https://doi.org/10.14214/sf.10068

Keywords: spot mounding; activity recognition; stoniness classification; supervised machine learning

Highlights: An excavator was equipped with an inertial measurement unit for taking automatic measurements of soil stoniness during mounding work; Supervised machine-learning classifiers were trained utilizing both the automatically measured data and manual stoniness measurements; The class prediction for the soil stoniness achieved an accuracy of 70% when assigned to constant grid cells.

Abstract | Full text in HTML | Full text in PDF | Author Info

The stoniness index of forest soil describes the stone content in the upper soil layer at depths of 20–30 centimeters. This index is not available in any existing map databases, and traditional measurements for the stoniness of the soil have always necessitated laborious soil-penetration methods. Knowledge of the stone content of a forest site could be of use in a variety of forestry operations. This paper presents a novel approach to obtaining automatic measurements of soil stoniness during an excavator-based mounding operation. The excavator was equipped with only a low-cost inertial measurement unit and a satellite navigation receiver. Using the data from these sensors and manually conducted soil stoniness measurements, supervised machine learning methods were utilized to build a model that is capable of predicting the stoniness class of a given mounding location. This study compares different classifiers and feature selection methods to find the most promising solution for this learning problem. The discussion includes a proposition for a meaningful measurement resolution of the soil’s stoniness, and a practical method for evaluating the variability of the stone content of the soil. The results indicate that it is possible to predict the soil stoniness class with 70% accuracy using only the inertial and location measurements.

Melander, Automation Technology and Mechanical Engineering, Tampere University, FI-33014 Tampere University, Finland http://orcid.org/0000-0003-3662-5187 E-mail: lari.melander@tuni.fi
Ritala, Automation Technology and Mechanical Engineering, Tampere University, FI-33014 Tampere University, Finland http://orcid.org/0000-0003-0721-9948 E-mail: risto.ritala@tuni.fi
Strandström, Metsäteho Oy, Vernissakatu 1, FI-01300 Vantaa, Finland E-mail: markus.strandstrom@metsateho.fi

article id 155, category Research article

Minna Räty, Annika Kangas. (2010). Segmentation of model localization sub-areas by Getis statistics. Silva Fennica vol. 44 no. 2 article id 155. https://doi.org/10.14214/sf.155

Keywords: eCognition; form height; Getis statistics; image segmentation; local indicators of spatial association

Abstract | View details | Full text in PDF | Author Info

Models for large areas (global models) are often biased in smaller sub-areas, even when the model is unbiased for the whole area. Localization of the global model removes the local bias, but the problem is to find homogenous sub-areas in which to localize the function. In this study, we used the eCognition Professional 4.0 (later versions called Definies Pro) segmentation process to segment the study area into homogeneous sub-areas with respect to residuals of the global model of the form height and/or local Getis statistics calculated for the residuals, i.e., G_i^*-indices. The segmentation resulted in four different rasters: 1) residuals of the global model, 2) the local G_i^*-index, and 3) residuals and the local G_i^*-index weighted by the inverse of the variance, and 4) without weighting. The global model was then localized (re-fitted) for these sub-areas. The number of resulting sub-areas varied from 4 to 366. On average, the root mean squared errors (RMSEs) were 3.6% lower after localization than the global model RMSEs in sub-areas before localization. However, the localization actually increased the RMSE in some sub-areas, indicating the sub-area were not appropriate for local fitting. For 56% of the sub-areas, coordinates and distance from coastline were not statistically significant variables, in other words these areas were spatially homogenous. To compare the segmentations, we calculated an aggregate standard error of the RMSEs of the single sub-areas in the segmentation. The segmentations in which the local index was present had slightly lower standard errors than segmentations based on residuals.

Räty, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland E-mail: minna.s.raty@helsinki.fi
Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland E-mail: ak@nn.fi

Click this link to register to Silva Fennica.

If you are a registered user, log in to save your selected articles for later access.

Contents alert

Your selected articles

Follow @SilvaFennica