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
Abstract
No abstract has been registered
Abstract
National Forest Inventories (NFIs) provide estimates of forest parameters for national and regional scales. Many key variables of interest, such as biomass and timber volume, cannot be measured directly in the field. Instead, models are used to predict those variables from measurements of other field variables. Therefore, the uncertainty or variability of NFI estimates results not only from selecting a sample of the population but also from uncertainties in the models used to predict the variables of interest. The aim of this study was to quantify the model-related variability of Norway spruce (Picea abies [L.] Karst) biomass stock and change estimates for the Norwegian NFI. The model-related variability of the estimates stems from uncertainty in parameter estimates of biomass models as well as residual variability and was quantified using a Monte Carlo simulation technique. Uncertainties in model parameter estimates, which are often not available for published biomass models, had considerable influence on the model-related variability of biomass stock and change estimates. The assumption that the residual variability is larger than documented for the models and the correlation of within-plot model residuals influenced the model-related variability of biomass stock change estimates much more than estimates of the biomass stock. The larger influence on the stock change resulted from the large influence of harvests on the stock change, although harvests were observed rarely on the NFI sample plots in the 5-year period that was considered. In addition, the temporal correlation between model residuals due to changes in the allometry had considerable influence on the model-related variability of the biomass stock change estimate. The allometry may, however, be assumed to be rather stable over a 5-year period. Because the effects of model-related variability of the biomass stock and change estimates were much smaller than those of the sampling-related variability, efforts to increase the precision of estimates should focus on reducing the sampling variability. If the model-related variability is to be decreased, the focus should be on the tree fractions of living branches as well as stump and roots.
2013
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.
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.
Abstract
The use of forest biomass for bioenergy purposes, directly or through refinement processes, has increased in the last decade. One example of such use is the utilization of logging residues. Branch biomass constitutes typically a considerable part of the logging residues, and should be quantified and included in future forest inventories. Airborne laser scanning (ALS) is widely used when collecting data for forest inventories, and even methods to derive information at the single-tree level has been described. Procedures for estimation of single-tree branch biomass of Norway spruce using features derived from ALS data are proposed in the present study. As field reference data the dry weight branch biomass of 50 trees were obtained through destructive sampling. Variables were further derived from the ALS echoes from each tree, including crown volume calculated from an interpolated crown surface constructed with a radial basis function. Spatial information derived from the pulse vectors were also incorporated when calculating the crown volume. Regression models with branch biomass as response variable were fit to the data, and the prediction accuracy assessed through a cross-validation procedure. Random forest regression models were compared to stepwise and simple linear least squares models. In the present study branch biomass was estimated with a higher accuracy by the best ALS-based models than by existing allometric biomass equations based on field measurements. An improved prediction accuracy was observed when incorporating information from the laser pulse vectors into the calculation of the crown volume variable, and a linear model with the crown volume as a single predictor gave the best overall results with a root mean square error of 35% in the validation.
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
No abstract has been registered
Abstract
Modelling stem taper and volume is crucial in many forest management and planning systems. Taper models are used for diameter prediction at any location along the stem of a sample tree. Furthermore, taper models are flexible means to provide information on the stem volume and assortment structure of a forest stand or other management units. Usually, taper functions are mean functions of multiple linear or nonlinear regression models with diameter at breast height and tree height as predictor variables. In large-scale inventories, an upper diameter is often considered as an additional predictor variable to improve the reliability of taper and volume predictions. Most studies on stem taper focus on accurately modelling the mean function; the error structure of the regression model is neglected or treated as secondary. We present a semi-parametric linear mixed model where the population mean diameter at an arbitrary stem location is a smooth function of relative height. Observed tree-individual diameter deviations from the population mean are assumed to be realizations of a smooth Gaussian process with the covariance depending on the sampled diameter locations. In addition to the smooth random deviation from the population average, we consider independent zero mean residual errors in order to describe the deviations of the observed diameter measurements from the tree-individual smooth stem taper. The smooth model components are approximated by cubic spline functions with a B-spline basis and a small number of knots. The B-spline coefficients of the population mean function are treated as fixed effects, whereas coefficients of the smooth tree-individual deviation are modelled as random effects with zero mean and a symmetric positive definite covariance matrix. The taper of a tree is predicted using an arbitrary number of diameter and corresponding height measurements at arbitrary positions along the stem to calibrate the tree-individual random deviation from the population mean estimated by the fixed effects. This allows a flexible application of the method in practice. Volume predictions are calculated as the integral over cross-sectional areas estimated from the calibrated taper curve. Approximate estimators for the mean squared errors of volume estimates are provided. If the tree height is estimated or measured with error, we use the “law of total expectation and variance” to derive approximate diameter and volume predictions with associated confidence and prediction intervals. All methods presented in this study are implemented in the R-package TapeR.