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.
2014
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No abstract has been registered
2013
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No abstract has been registered
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There is a need for monitoring methods for forest volume, biomass and carbon based on satellite remote sensing. In the present study we tested interferometric X-band SAR (InSAR) from the Tandem-X mission. The aim of the study was to describe how accurate volume and biomass could be estimated from InSAR height and test whether the relationships were curvilinear or not. The study area was a spruce dominated forest in southeast Norway. We selected 28 stands in which we established 192 circular sample plots of 250 m2, accurately positioned by a Differential Global Positioning System (dGPS). Plot level data on stem volume and aboveground biomass were derived from field inventory. Stem volume ranged fromzero to 596 m3/ha, and aboveground biomass up to 338 t/ha.We generated 2 Digital Surface Models (DSMs) fromInSAR processing of two co-registered, HH-polarized TanDEM-X image pairs – one ascending and one descending pair.We used a Digital TerrainModel (DTM) from airborne laser scanning (ALS) as a reference and derived a 10 m × 10 m Canopy Height Model (CHM), or InSAR height model. We assigned each plot to the nearest 10 m × 10 m InSAR height pixel. We applied a nonlinear, mixed model for the volume and biomass modeling, and from a full model we removed effects with a backward stepwise approach. InSAR heightwas proportional to volume and aboveground biomass, where a 1 m increase in InSAR height corresponded to a volume increase of 23 m3/ha and a biomass increase of 14 t/ha. Root Mean Square Error (RMSE) values were 43–44% at the plot level and 19–20% at the stand level.
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No abstract has been registered
2012
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Et overordnet samfunnsmål er å sikre en bærekraftig bruk og forvaltning av Norges arealressurser. Det krever en kontinuerlig leveranse av pålitelig og oppdatert informasjon til beslutningstakere. For å være i stand til å levere denne informasjonen, produserer Norsk institutt for skog og landskap blant annet arealressursstatistikk for alle kommuner i Norge. Statistikken produseres også på fylkesnivå og for hele landet. Arealtallene hentes ut fra en kombinasjon av ulike nasjonale datasett i ulike målestokker sammen med tolkning av satellittbilder. Gjennom en omklassifisering beregnes statistikk for visse landressursklasser som dyrka jord, beite, skog basert på produktivitet, ferskvann, snø og isbre, snaumark og bebygd område. Skog og landskap har de siste par årene brukt åpen kildekode. Hele produksjonslinje utføres ved hjelp av slik programvare. Resultatene lagres i XML-filer som legges ut på internett. Produksjonen krever behandling av flere databaser med nasjonal dekning og må håndtere geometriske operasjoner effektivt og uten feil. Den åpne kildekodeløsningen er pålitelig, stabil og rask.
Abstract
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.
2011
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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.
2010
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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 CLC2000 was completed by the Norwegian Forest and Landscape Institute (Skog og landskap) in 2008 and was produced from existing national land cover datasets wherever available. CLC has a standardized nomenclature with 44 classes. 31 classes are represented in the Norwegian dataset. CLC is a small scale map showing built up areas, agriculture, forest and semi-natural areas, wetlands and water bodies. CLC has a minimum mapping unit of 25 ha. CLC2000 can be used for visualization of the general land cover patterns in Norway at a scale 1:250 000 or smaller. CLC2000 is representing the land cover situation close to year 20001. This report presents the Norwegian CLC2000 project and the methods and automatic generalization processes that were used in the project. CORINE Land Cover is one of four land cover maps (AR5, AR50, AR250 and CLC) published by Skog og landskap. CLC2000 was produced with support from the European Environmental Agency (EEA) who has joint ownership to the product....
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....