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  • 1.
    Heder Brandt, Petter
    et al.
    University West, School of Business, Economics and IT.
    Olsson, Anders
    School of Business, Economics and Law, University of Gothenburg, Gothenburg (SWE).
    Dahlquist, Karl
    University West, School of Business, Economics and IT, Division of Urban Planing and Development.
    Inal, Tuba
    University West, School of Business, Economics and IT, Division of Urban Planing and Development.
    “Profitability is sustainability”: framing of forest management practices by the Swedish forest industry2023In: Scandinavian Journal of Forest Research, ISSN 0282-7581, E-ISSN 1651-1891, Vol. 38, no 7-8, p. 429-441Article in journal (Refereed)
    Abstract [en]

    This article investigates how the Swedish forest industry, as represented by the three largest Swedish private forest companies (Svenska Cellulosa AB, Stora Enso, and Holmen), through their main public relations (PR) channels frame the current dominant Swedish forestry model and alternative models that are promoted by the European Union (EU). The content analysis of the three companies’ trade magazines published between 2019 and 2022 explores the patterns in the PR framing of the forest management models with respect to economic, environmental, and social aspects. The time interval is centered by the July 2021 announcement of the EU’s new Forest Strategy for 2030. The magazines’ target audience is private forest owners, from whom Svenska Cellulosa AB, Stora Enso, and Holmen buy 40–50% of the timber used in production. The main finding of the study is that these corporations did not present alternative methods as viable options to replace the Swedish forestry model. The magazines, with some individual variations, respond to the alternative methods promoted by the EU and environmental associations by an increased emphasis on the benefits, mainly environmental, of the Swedish forestry model–framing the model as not only the most profitable but also the most ecologically sustainable. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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  • 2.
    Steen, Josefin
    et al.
    University West, Department of Engineering Science.
    Svensson, Tobias
    University West, Department of Engineering Science.
    Kartläggning av invasiv trädart med satellitbaserad fjärranalys: Klassificering och identifiering av Pinus Contorta2023Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Invasive plants are an increasing problem in society and pose a major threat to our ecosystem, infrastructure, health and biodiversity. The high cost of controlling the spread of invasive plants is a fact and modern satellites can provide the data needed to map the presence of these invasive plant species. Previous studies for mapping invasive plants have used classification methods such as Support Vector Machine (SVM), Random Forest (RF) and texture analysis algorithms. Information on whether more simple classification methods can be used to perform a similar classification of vegetation is limited. Likewise, studies for mapping only the invasive tree species Pinus contorta. This study relies on the use of both pixel-based and object-based classifications to identify and classify the contorta pine. Which form of classification method is appropriate varies as all plants have different spatial and spectral characteristics. A method that works for classifying one species does not necessarily mean that it will be applicable to another species. The data used in the study are satellite images from 2018 collected by Sentinel-2 with a resolution of 10 x 10 meters. The data was then processed in ArcMap 10.8 software during all stages of the analysis. Four different forms of classification were performed to find the most appropriate method for classifying the contorta pine. The classification results were validated against the Swedish University of Agricultural Sciences (SLU) raster map of contorta stands in the selected study area located north of Härnösand and showed inconclusive results. Each classification was compared to SLU's mapping and with the help of a critical success index (CSI) that shows the performance of the classification model successful hits (A), overestimated (B) and miss (C), the results could be more easily analyzed and interpreted. The methods used in the classification model of Pinus contorta showed 0.5-0.9% correct classification. 

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