Linda Aune-Lundberg
Research Scientist
(+47) 995 78 533
linda.aune-lundberg@nibio.no
Place
Tromsø
Visiting address
Holtvegen 66, 9016 Tromsø
Abstract
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Abstract
The aim of the article is to assess whether agricultural landscapes play a role in the perception of Norway held by tourists and residents. An additional aim is to analyse whether information accompanying images on social media indicate that the photographers have acknowledged the agricultural landscape. The authors used geotagged images uploaded to the image-sharing platform Flickr in their analyses. They selected photos from within the agricultural landscapes, inspected them, and categorized them according to extent and content. Additionally, they analysed the accompanying hashtags. The findings revealed that a large proportion of the photos contained agricultural landscapes, and thus confirmed the importance of the agricultural landscape for visual perception of and access to Norwegian landscapes. In addition, the lack of agricultural-related hashtags strengthened the authors’ suspicions that this might not have been widely recognized by the photographers. Thus, while agricultural landscapes commonly are considered primarily as landscapes of food production, the authors conclude that these landscapes also fulfil other functions and that their contribution to the perception of Norway is important. Additionally, many of the landscape elements seen and analysed in the sample of photos are elements that play a role in providing cultural ecosystem services.
Abstract
Up-to-date and reliable information on land cover and land use status is important in many aspects of human activities. Knowledge about the reference dataset, its coverage, nomenclature, thematic and geometric accuracy, spatial resolution is crucial for appropriate selection of reference samples used in the classification process. In this study, we examined the impact of the selection and pre-processing of reference samples for the classification accuracy. The classification based on Random Forest algorithm was performed using firstly the automatically selected reference samples derived directly from the national databases, and secondly using the pre-processed and verified reference samples. The verification procedures involved the iterative analysis of histogram of spectral features derived from the Sentinel-2 data for individual land cover classes. The verification of the reference samples improved the accuracy of delineation of all land cover classes. The highest improvement was achieved for the woodland broadleaved and non- and sparce vegetation classes, with the overall accuracy increasing from 51% to 73%, and from 33% to 74%, respectively. The second objective of this study was to derive the best possible land cover classification over the mountain area in Norway, therefore we examined whether the use of the Digital Elevation Model (DEM) can improve the classification results. Classifications were carried out based on Sentinel-2 data and a combination of Sentinel-2 and DEM. Using the DEM the accuracy for nine out of ten land cover classes was improved. The highest improvement was achieved for classes located at higher altitudes: low vegetation and non- and sparse vegetation.
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Authors
Svein Olav Krøgli Wendy Fjellstad Linda Aune-Lundberg Milena Chmielewska Agata Hościło Aneta LewandowskaAbstract
No abstract has been registered
Authors
Wendy Fjellstad Svein Olav Krøgli Linda Aune-Lundberg Milena Chmielewska Agata Hościło Kamila BelkaAbstract
No abstract has been registered
Authors
Wendy Fjellstad Svein Olav Krøgli Linda Aune-Lundberg Aneta Lewandowska Agata Hościło Milena ChmielewskaAbstract
No abstract has been registered
Authors
Wendy Fjellstad Svein Olav Krøgli Linda Aune-Lundberg Aneta Lewandowska Agata Hościło Milena ChmielewskaAbstract
No abstract has been registered
Authors
Svein Olav Krøgli Wendy Fjellstad Linda Aune-Lundberg Agata Hościło Aneta Lewandowska Milena ChmielewskaAbstract
No abstract has been registered
Abstract
The CORINE Land Cover dataset for Norway for the reference year 2018 (CLC2018) was compared to detailed national land cover and land use data. This allowed us to describe the thematic composition of the CLC-polygons and aggregate the information into statistical profiles for each CLC-class. We compared the results to the class definitions found in the CLC mapping instructions, while considering the generalization and minimal mapping units required for CLC. The study showed that CLC2018 in general complied with the definitions. Non-conformities were mainly found for detailed and (in a Norwegian context) marginal classes. The classification can still be improved by complementing visual interpretation with classification based on the statistical profile of each polygon when detailed land use and land cover information is available. The use of auxiliary information at the polygon level can thus provide a better, thematically more accurate CLC dataset for use in European land monitoring.
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Detailed descriptions of individual vegetation types shown on vegetation maps can improve the ways in which the composition and spatial structure within the types are understood. The authors therefore examined dwarf shrub heath, a vegetation type covering large areas and found in many parts of the Norwegian mountains. They used data from point samples obtained in a wall-to-wall area frame survey. The point sampling method provided data that gave a good understanding of the composition and structure of the vegetation type, but also revealed a difference between variation within the vegetation type itself (intra-class variation) and variation resulting from the inclusion of other types of vegetation inside the map polygons (landscape variation). Intra-class variation reflected differences in the botanical composition of the vegetation type itself, whereas landscape variation represented differences in the land-cover composition of the broader landscape in which the vegetation type was found. Both types of variation were related to environmental gradients. The authors conclude that integrated point sampling method is an efficient way to achieve increased understanding of the content of a vegetation map and can be implemented as a supporting activity during a survey.
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Authors
Anders Bryn Hans P. Kristoffersen Michael Angeloff Ingvild Nystuen Linda Aune-Lundberg Dag Terje Filip Endresen Christian Svindseth Yngve RekdalAbstract
No abstract has been registered
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The objective of this paper is to examine a method for estimation of land cover statistics for local environments from available area frame surveys of larger, surrounding areas. The method is a simple version of the small-area estimation methodology. The starting point is a national area frame survey of land cover. This survey is post-stratified using a coarse land cover map based on topographic maps and segmentation of satellite images. The approach is to describe the land cover composition of each stratum and subsequently use the results to calculate land cover statistics for a smaller area where the relative distribution of the strata is known. The method was applied to a mountain environment in Gausdal in Eastern Norway and the result was compared to reference data from a complete in situ land cover map of the study area. The overall correlation (Pearson’s rho) between the observed and the estimated land cover figures was r = 0.95. The method does not produce a map of the target area and the estimation error was large for a few of the land cover classes. The overall conclusion is, however, that the method is applicable when the objective is to produce land cover statistics and the interest is the general composition of land cover classes – not the precise estimate of each class. The method will be applied in outfield pasture management in Norway, where it offers a cost-efficient way to screen the management units and identify local areas with a land cover composition suitable for grazing. The limited resources available for in situ land cover mapping can then be allocated efficiently to in-depth studies of the areas with the highest grazing potential. It is also expected that the method can be used to compile land cover statistics for other purposes as well, provided that the motivation is to describe the overall land cover composition and not to provide exact estimates for the individual land cover classes.
Abstract
AR-FJELL is the Norwegian land resource database for the mountain areas. AR-FJELL is not distributed as a separate product from Skog og landskap, but does – together with topographic data (series N50) from the Norwegian Mapping Authority (Statens kartverk) form the basis for the classification of mountain areas in the national land resource maps AR50 and AR250. The five Norwegian AR-FJELL classes are documented through descriptive statistical “profiles” of the actual content of each class. Profiles of the AR-FJELL classes were obtained through a GIS overlay operation between AR-FJELL and the available AR18X18 (Land resource accounting for the Norwegian outfields) survey plots. The distribution of vegetation classes for each AR-FJELL class was compiled from this overlay. The report also consider the distribution of the AR-FJELL classes by elevation asl and the distribution of the vegetation types in the AR18X18 sample. AR18X18 is (2011) only available for parts of Norway. The study should be repeated when a full national coverage is available. This is expected in 2015. The study was carried out with funding from the Norwegian Space Centre.
Abstract
The Norwegian CORINE land cover (CLC2000) was completed autumn 2008. The CLC map was generated automatically from a number of dataset using GIS-techniques for map generalisation. The CLC map has a coarse resolution and it is also using a classification system developed in an environment very different from the Nordic. It is therefore interesting to evaluate both content and correctness of CLC. This study shows that there is a good resemblance between the CLC classes and detailed, large scale maps. The diversity in classes on the other hand, is lost due to the CLC classification system.
Abstract
CORINE Land Cover (CLC) is a seamless European land cover vector database. The Norwegian CLC for the reference year 2006 (CLC2006) was completed by the Norwegian Forest and Landscape Institute (Skog og landskap) in 2009 and was produced according to CLC2006 technical guidelines (EEA 2007). CLC has a common nomenclature with 44 classes that is used throughout Europe. 31 of these classes are found in the Norwegian dataset. A coordinating Technical Team from the European Topic Centre on Land Use and Spatial Information (ETC-LUSI) is coordinating the mapping efforts ensuring that the classification is applied in a similar fashion in each country....
Abstract
All the Norwegian CLC2006 classes are documented through descriptive statistical “profiles” of the actual contents in each class. The CLC2006 profiles are worked through based on an overlay operation between CLC2006 and AR5 (under the timberline) and AR50 (above the timberline). Based on this dataset statistics are generated, that shows the percent distribution of AR5 and AR50 classes in each CLC2006 class. The study was carried out with funding from the Norwegian Space Centre.