Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2012
Abstract
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Abstract
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Abstract
K-nearest neighbor (kNN) approaches are popular statistical methods for predicting forest attributes in airborne laser scanning (ALS) based inventories. Their main upsides are the simplicity to predict multivariate response variables and their freeness of distributional assumptions on the conditional response.One of their largest draw-backs is that predictions outside the range of the reference data inherently result in an under- or overestimation. This property of kNN approaches is known as extrapolation bias and aggravates with an increasing number of neighbors (k) used for the prediction.This study presents one possibility to reduce extrapolation biases of predictions based on the area-based approach (ABA) by using individual tree crown (ITC) approaches within those specific areas of a low density ALS acquisition where the point density might be sufficiently high for using ITC methods.In the proposed strategy, additional (or artificial) reference plots augmented field measured plots. Artificial plots were created by applying ITC segmentation to a canopy height model derived from high density ALS data. The response variable biomass per hectare was predicted for every segment following a semi-ITC approach.The segment predictions were aggregated on the artificial plot level. The artificial plots were then treated in the same way as the original reference data to make predictions in areas with low density ALS data based on the ABA. It was hereby assumed that the predicted plot level response on the artificial plots is equivalent with the observed plot level response on the original reference data.The data consisted of 110 reference plots with a smaller data range than the 201 independent validation plots. Considerable extrapolation bias was visible if only the reference plots were used for the prediction. Almost no extrapolation bias was found if the prediction was based on reference plots augmented by artificial plots. The root mean squared error (RMSE) of the biomass predictions based on the reference plots was 39.1%. The RMSE reduced to 29.8% if the reference plots were augmented by artificial plots.
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.
Authors
Franҫois Lefèvre Jarkko Koskela Jason Hubert Hojka Kraigher Roman Longauer Ditte C. Olrik Silvio Schüler Michele Bozzano Paraskevi Alizoti Remigijus Bakys Cathleen Baldwin Dalibor Ballian Sanna Black-Samuelsson Dagmar Bednarova Sándor Bordács Eric Collin Bart De Cuyper Sven M.G. De Vries Thröstur Eysteinsson Josef Frýdl Michaela Haverkamp Mladen Ivankovic Heino Konrad Czeslaw Koziol Tiit Maaten Eduardo Notivol Paino Hikmet Öztürk Ivanova Denitsa Pandeva Gheorghe Parnuta Andrej Pilipovic Dragos Postolache Cathal Ryan Arne Steffenrem Maria Carolina Varela Federico Vessella Roman T. Volosyanchuk Marjana Westergren Frank Wolter Leena Yrjänä Inga ZarinaAbstract
Dynamic conservation of forest genetic resources (FGR) means maintaining the genetic diversity of trees within an evolutionary process and allowing generation turnover in the forest. We assessed the network of forests areas managed for the dynamic conservation of FGR (conservation units) across Europe (33 countries). On the basis of information available in the European Information System on FGR (EUFGIS Portal), species distribution maps, and environmental stratification of the continent, we developed ecogeographic indicators, a marginality index, and demographic indicators to assess and monitor forest conservation efforts. The pan-European network has 1967 conservation units, 2737 populations of target trees, and 86 species of target trees. We detected a poor coincidence between FGR conservation and other biodiversity conservation objectives within this network. We identified 2 complementary strategies: a species-oriented strategy in which national conservation networks are specifically designed for key target species and a site-oriented strategy in which multiple-target units include so-called secondary species conserved within a few sites. The network is highly unbalanced in terms of species representation, and 7 key target species are conserved in 60% of the conservation units. We performed specific gap analyses for 11 tree species, including assessment of ecogeographic, demographic, and genetic criteria. For each species, we identified gaps, particularly in the marginal parts of their distribution range, and found multiple redundant conservation units in other areas. The Mediterranean forests and to a lesser extent the boreal forests are underrepresented. Monitoring the conservation efficiency of each unit remains challenging; however, <2% of the conserved populations seem to be at risk of extinction. On the basis of our results, we recommend combining species-oriented and site-oriented strategies.
Abstract
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Abstract
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Popular scientific article – Skogsfugl og forvaltning - jaktas kjøreregler
Per Wegge, Jørund Rolstad
Abstract
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Abstract
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Abstract
Storfuglen (tiur og røy) er ofte blitt betegnet som en `gammelskogart`. Tre årtier med skogsfuglforskning på Varaldskogen har imidlertid vist at den har klart overgangen til yngre kulturskog bedre enn ventet. Det betyr likevel ikke at alt er `såre vel`.