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
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.
Abstract
Bark beetles cause widespread damages in the coniferous-dominated forests of central Europe and North America. In the future, areas affected by bark beetles may further increase due to climate change. However, the early detection of the bark beetle green attack can guide management decisions to prevent larger damages. For this reason, a field-based bark beetle monitoring program is currently implemented in Germany. The combination of remote sensing and field data may help minimizing the reaction time and reducing costs of monitoring programs covering large forested areas. In this case study, RapidEye and TerraSAR-X data were analyzed separately and in combination to detect bark beetle green attack. The remote sensing data were acquired in May 2009 for a study site in south-west Germany. In order to distinguish healthy areas and areas affected by bark beetle green attack, three statistical approaches were compared: generalized linear models (GLM), maximum entropy (ME) and random forest (RF). The spatial scale (minimum mapping unit) was 78.5 m2. TerraSAR-X data resulted in fair classification accuracy with a cross-validated Cohen’s Kappa Coefficient (kappa) of 0.23. RapidEye data resulted in moderate classification accuracy with a kappa of 0.51. The highest classification accuracy was obtained by combining the TerraSAR-X and RapidEye data, resulting in a kappa of 0.74. The accuracy of ME models was considerably higher than the accuracy of GLM and RF models.
2012
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.
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
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
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
No abstract has been registered
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
Abstract
This study is a part of a larger project designed to find out the causes of top dieback symptoms in Norway spruce in SE Norway. Because sapwood tracheids constitute a water transport system while parenchyma serves as a reserve tissue (Sellin, 1991), the separation and quantification of the sapwood and heartwood may contribute to understanding of the healthy tree functioning. As the extent of sapwood is related to tree vitality, it reflects the tree growth, health and effect of environmental factors (Sandberg & Sterley, 2009). Therefore, the sapwood cross-sectional area is widely used as a biometric parameter indicating the tree vitality, although its estimation and evaluation is prone to scaling errors....
Authors
Johannes BreidenbachAbstract
The aim in the analysis of sample surveys is frequently to derive estimates of sub-population characteristics. This task is denoted small area estimation (SAE). Often, the sample available for the sub-population is, however, too small to allow a reliable estimate. Frequently, auxiliary variables exist that are correlated with the variable of interest. Several estimators can make use of auxiliary information which may reduce the variance of the estimate.