Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2018
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Eva Lieungh Eriksen Heidrun Asgeirsdatter Ullerud Rune Halvorsen Sigrun Aune Harald Bratli Peter Horvath Inger Kristine Volden Anders Kvalvåg Wollan Anders BrynAbstract
Questions: Substantial variation between observers has been found when comparing parallel land-cover maps, but how can we know which map is better? What magnitude of error and inter-observer variation is expected when assigning land-cover types and is this affected by the hierarchical level of the type system, observer characteristics, and ecosystem properties? Study area: Hvaler, south-east Norway. Methods: Eleven observers assigned mapping units to 120 stratified random points. At each observation point, the observers first assigned a mapping unit to the point independently. The group then decided on a ‘true’ reference mapping unit for that point. The reference was used to estimate total error. ‘Ecological distance’ to the reference was calculated to grade the errors. Results: Individual observers frequently assigned different mapping units to the same point. Deviating assignments were often ecologically close to the reference. Total error, as percentage of assignments that deviated from the reference, was 35.0% and 16.4% for low and high hierarchical levels of the land-covertype system, respectively. The corresponding figures for inter-observer variation were 42.8% and 19.4%, respectively. Observer bias was found. Particularly high error rates were found for land-cover types characterised by human disturbance. Conclusions: Access to a ‘true’ mapping unit for each observation point enabled estimation of error in addition to the inter-observer variation typically estimated by the standard pairwise comparisons method for maps and observers. Three major sources of error in the assignment of land-cover types were observed: dependence on system complexity represented by the hierarchical level of the land-cover-type system, dependence on the experience and personal characteristics of the observers, and dependence on properties of the mapped ecosystem. The results support the necessity of focusing on quality in land-cover mapping, among commissioners, practitioners and other end users.
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2017
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Ragnhild Aabøe Inglingstad Taye Mestawet Tove Gulbrandsen Devold Ellen Kathrine Ulleberg Gerd Elisabeth Vegarud Peer Berg Anna Caroline Holene Nina SvartedalAbstract
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2016
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The report is based on information Norway provided in an electronic questionnaire that was prepared by FAO to collect national data as a contribution to The State of the World’s Biodiversity for Food and Agriculture. The report presents information on the status and trends of biodiversity for food and agriculture, including animals, plants and micro-organisms with a direct or indirect role in agriculture, forestry and/or fisheries. A lot of data on these issues is available in Norway; however it is mostly spread across different monitoring systems and fragmented. The report draws attention to the use and conservation of biodiversity for food and agriculture and to the function(s) of and interactions between its components in production systems. The report focuses more on associated biodiversity, ecosystem services and wild foods than on plant, animal and forest genetic resources as these are presented in other reports. Even if the awareness on the importance of associated biodiversity to food production and food
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Abstract Questions Vegetation mapping based on field surveys is time-consuming and expensive. Distribution modelling might be used to overcome these challenges. What is the performance of distribution modelling of vegetation compared to traditional vegetation mapping when projected locally? Does the modelling performance vary among ecosystems? Does vegetation type distribution and abundance influence the modelling performance? Location Gravfjellet, Øystre Slidre commune, southern Norway. Methods Two comparable neighbouring areas, each of 4 km2, were mapped for species-defined vegetation types. One area was used for model training, the other for model projection. Maximum entropy models were run for six vegetation types, two from each of the ecosystems present in the area: forest, wetland and mountain heath- and shrublands. For each ecosystem, one locally abundant and one locally rare vegetation type were tested. AUC, the area under the receiver operating curve, was used as the model selection criterion. Environmental variables (n = 9) were selected through a backwards selection scheme, and model complexity was kept low. The models were evaluated using independent data. Results Distribution modelling of vegetation types by local projection gave high AUC values, and the results were supported by the evaluation using independent data. The modelling ability was not affected by ecosystem differences. A negative relationship between the number of points used to train the models and the AUC value before evaluation suggests that models for locally rare vegetation types had better predictive performance than the models for abundant types. This result was not significant after evaluation. Conclusion Provided that relevant explanatory variables are available at an appropriate scale, and that field-validated training points are available, distribution modelling can be used for local projection of the six tested vegetation types from the boreal–alpine ecotone.