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.
2013
Abstract
No abstract has been registered
Authors
Tor Myking Erling Johan Solberg Gunnar Austrheim James David Mervyn Speed Fredrik Bøhler Rasmus Astrup Rune EriksenAbstract
Sallow (Salix caprea L.) and rowan (Sorbus aucuparia L.) constitute small proportions of the deciduous tree volume in Scandinavia, but are highly preferred winter forage for moose and red deer, which occur at historically high densities. Thus, a possible decline of these tree species has been indicated. Against this background, we have reviewed the life histories of relevance for browsing, as well as the basic biology and genetics of sallow and rowan. The species show similarities with respect to short lifespan, small size and sympodial growth pattern, which are risk factors in a browsing context. They also have high juvenile growth rate, important for growing quickly out of reach of browsers. Sallow depends strongly on disturbance for establishment and is more demanding with respect to soil and light conditions than rowan, possibly important for the substantially lower abundance of sallow on the Norwegian Forest Inventory plots. Similarly, the relative recruitment of small size classes of sallow is less than for rowan. Although recruitment is reported to be hampered in wintering areas with high moose or red deer densities, the inventory data, however, dating only back to 1994, do not suggest a general decrease in any of the species. Sallow and rowan saplings show low mortality in moose and deer dominated areas and the species can be characterised as rather resilient to browsing. Of more concern is that browsing can constrain the development of mature rowan and sallow trees locally, with possible consequences for associated epiphytic biodiversity.
Abstract
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.
2012
Abstract
No abstract has been registered
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.
Abstract
The Norwegian National Forest Inventory (NNFI) provides estimates of forest parameters on national and regional scales by means of a systematic network of permanent sample plots. One of the biggest challenges for the NNFI is the interest in forest attribute information for small sub-populations such as municipalities or protected areas. Frequently, too few sampled observations are available for such small areas to allow estimates with acceptable precision. However, if an auxiliary variable exists that is correlated with the variable of interest, small area estimation (SAE) techniques may provide means to improve the precision of estimates. The study aimed at estimating the mean above-ground forest biomass for small areas with high precision and accuracy, using SAE techniques. For this purpose, the simple random sampling (SRS) estimator, the generalized regression (GREG) estimator, and the unit-level empirical best linear unbiased prediction (EBLUP) estimator were compared. Mean canopy height obtained from a photogrammetric canopy height model (CHM) was the auxiliary variable available for every population element. The small areas were 14 municipalities within a 2,184 km2 study area for which an estimate of the mean forest biomass was sought. The municipalities were between 31 and 527 km2 and contained 1–35 NNFI sample plots located within forest. The mean canopy height obtained from the CHM was found to have a strong linear correlation with forest biomass. Both the SRS estimator and the GREG estimator result in unstable estimates if they are based on too few observations. Although this is not the case for the EBLUP estimator, the estimators were only compared for municipalities with more than five sample plots. The SRS resulted in the highest standard errors in all municipalities. Whereas the GREG and EBLUP standard errors were similar for small areas with many sample plots, the EBLUP standard error was usually smaller than the GREG standard error. The difference between the EBLUP and GREG standard error increased with a decreasing number of sample plots within the small area. The EBLUP estimates of mean forest biomass within the municipalities ranged between 95.01 and 153.76 Mg ha−1, with standard errors between 8.20 and 12.84 Mg ha−1.
Abstract
In South-east Norway, several scattered observations of reduced growth and dieback symptoms were observed over the last 20 years in 40-60 years old Norway spruce (Picea abies) trees. Typical symptoms start with yellowing in the top and subsequent dieback downwards from the top. These symptoms are often combined with bark beetle (Ips typographus), honey fungus (Armillaria spp.) infections, and a sudden decrease in diameter and height growth. After about 1-5 years, most of the symptomatic trees are dead.We selected 11 representative stands in six counties. In each stand all trees in ten 250 m2 plots were evaluated, in total about 4000 trees. In each of these 110 plots, one symptomatic and one non-symptomatic tree were investigated in more detail. We measured tree diameter, height, took increment cores and assessed crown condition, wounds, resin flow, stem cracks, bark beetle infection and Armillaria presence. In addition, internode lengths of the last 20 years were measured in two of the stands.Preliminary results of internode lengths and increment cores showed a sudden decrease of height and diameter growth in the symptomatic trees. Many of these trees had a secondary infection of bark beetles and Armillaria. Some years appear to be typical problem years for many of the trees. These years also correspond with summer drought, i.e. negative Palmer drought severity indexes which were estimated for each stand. In comparison, the non-symptomatic trees, growing close to the symptomatic ones, showed none or minor growth reductions and discolouration.Climate change and increased summer drought may worsen spruce dieback problems. Management adaptions are uncertain. We conclude that Norway spruce is sensitive to drought, which reduce the growth and weaken the health, and probably reduce the defence against secondary infections.
Authors
Jari Vauhkonen Liviu Theodor Ene Sandeep Gupta Johannes Heinzel Johan Holmgren Juho Pitkänen Svein Solberg Yunsheng Wang Holger Weinacker Marius Hauglin Vegard Lien Petteri Packalén Terje Gobakken Barbara Koch Erik Næsset Timo Tokola Matti MaltamoAbstract
Airborne laser scanning data and corresponding field data were acquired from boreal forests in Norway and Sweden, coniferous and broadleaved forests in Germany and tropical pulpwood plantations in Brazil. Treetop positions were extracted using six different algorithms developed in Finland, Germany, Norway and Sweden, and the accuracy of tree detection and height estimation was assessed. Furthermore, the weaknesses and strengths of the methods under different types of forest were analyzed. The results showed that forest structure strongly affected the performance of all algorithms. Particularly, the success of tree detection was found to be dependent on tree density and clustering. The differences in performance between methods were more pronounced for tree detection than for height estimation. The algorithms showed a slightly better performance in the conditions for which they were developed, while some could be adapted by different parameterization according to training with local data. The results of this study may help guiding the choice of method under different forest types and may be of great value for future refinement of the single-tree detection algorithms.
2011
Authors
Johannes BreidenbachAbstract
Digital aerial images over Vestfold county were acquired by TerraTec in summer 2007 with a Vexcel UltraCamX sensor. The flying height above-ground was approximately 2800-3000 m which resulted in images of approximately 1880x2880 m size. The images were acquired in north-south oriented flight strips with a 20% side and 60% within-strip overlap. Panchromatic image data were acquired in 20 cm ground sampling distance (GSD). Near infrared, red, green and blue image bands were acquired in 60 cm GSD but were pansharpened to a 20 cm pixel size by the data vendor. The original radiometric resolution of the images (12 bit) was resampled to 8 bit for archival storage. The plane location and orientation during image acquisition were logged using a GPS and an inertial navigation system (INS). To increase the accuracy of the external orientation, an aerial triangulation was performed based on 34 ground control points using the software Match-AT.