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.
2012
Abstract
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Authors
I.N Bolotov Yu. V. Bespalaya A.A Makhrov Paul Eric Aspholm A.S Aksenov M. Yu Gofarov G.A. Dvoryankin O.V. Usacheva I.V. Vikhrev S.E. Sokolova A.A. Pashinin A. DavydovAbstract
No abstract has been registered
Authors
Arne Oddvar Skjelvåg Arnold Arnoldussen Ove Mindor Klakegg Ole Einar TveitoAbstract
No abstract has been registered
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Authors
Helge BerglannAbstract
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Authors
L. Dupont T Decaens Emmanuel Lapied V. Chassany R. Marichal F. Dubs M. Maillot V. RoyAbstract
No abstract has been registered
Abstract
The purpose of the study was to explore and compare three different methods for modelling potential natural vegetation (PNV), a hypothetic natural state of vegetation that shows nature's biotic potential in the absence of human influence and disturbance. The vegetation was mapped in a south-central Norwegian mountain region, in a 34.2 km2 area around the village of Beitostølen, in 2009. The actual vegetation map (AVM) formed the basis for the development of PNV using three different modelling methods: (1) an expert-based manual modelling (EMM), (2) rule-based envelope GIS-modelling (RBM), and (3) a statistical predictive GIS-modelling method (Maxent). The article shows that the three modelling methods have different advantages, challenges and preconditions. The findings indicate that: (1) the EMM method should preferably be used only as a supplementary method in highly disturbed areas, (2) both the RBM and the Maxent methods perform well, (3) RBM performs especially well, but also Maxent are more objective methods than EMM and they are much easier to develop and re-run after model validation, (4) Maxent probably underestimates the potential distribution of some vegetation types, whereas RBM overestimates, (5) the Maxent output is relative probabilities of distribution, giving higher model variation than RBM.
Abstract
The paper focuses on the use of thermogravimetric analysis (TGA) as a fast method for estimating the change of lignocellulosic materials during fungal degradation in laboratory trials. Traditionally, evaluations of durability tests are based on mass loss. However, to gain more knowledge of the reasons for differences in durability and strength between wooden materials, information on the chemical changes is needed. Pinus sylvestris sapwood was incubated with the brown rot fungus Gloeophyllum trabeum and the white rot fungus Trametes versicolor. The TGA approach used was found to be reproducible between laboratories. The TGA method did not prove useful for wood deteriorated by white rot, but the TGA showed to be a convenient tool for fast estimation of lignocellulosic components both in sound wood and wood decayed by brown rot.
Abstract
Climate change is a factor that largely contributes to the increase of forest areas affected by natural damages. Therefore, the development of methodologies for forest monitoring and rapid assessment of affected areas is required. Space-borne synthetic aperture radar (SAR) imagery with high resolution is now available for large-scale forest mapping and forest monitoring applications. However, a correct interpretation of SAR images requires an adequate preprocessing of the data consisting of orthorectification and radiometric calibration. The resolution and quality of the digital elevation model (DEM) used as reference is crucial for this purpose. Therefore, the primary aim of this study was to analyze the influence of the DEM quality used in the preprocessing of the SAR data on the mapping accuracy of forest types. In order to examine TerraSAR-X images to map forest dominated by deciduous and coniferous trees, High Resolution SpotLight images were acquired for two study sites in southern Germany. The SAR images were preprocessed with a Shuttle Radar Topography Mission (SRTM) DEM (resolution approximately 90 m), an airborne laser scanning (ALS) digital terrain model (DTM) (5 m resolution), and an ALS digital surface model (DSM) (5 m resolution). The orthorectification of the SAR images using high resolution ALS DEMs was found to be important for the reduction of errors in pixel location and to increase the classification accuracy of forest types. SAR images preprocessed with ALS DTMs resulted in the highest classification accuracies, with kappa coefficients of 0.49 and 0.41, respectively. SAR images preprocessed with ALS DTMs resulted in greater accuracy than those preprocessed with ALS DSMs in most cases. The classification accuracy of forest types using SAR images preprocessed with the SRTM DEM was fair, with kappa coefficients of 0.23 and 0.32, respectively.Analysis of the radar backscatter indicated that sample plots dominated by coniferous trees tended to have lower scattering coefficients than plots dominated by deciduous trees. Leaf-off images were only slightly better suited for the classification than leaf-on images. The combination of leaf-off and leaf-on improved the classification accuracy considerably since the backscatter changed between seasons, especially in deciduous-dominated forest.