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.
2011
Abstract
No abstract has been registered
Abstract
A rapid increase in the frequency of Dutch elm disease (DED), a wilting disease of elm trees caused by bark-beetle vectored fungi, was observed in the early 1990s on several wych elm stands around Oslofjord, southern Norway. To examine the current status of the disease and its impacts on elm population, disease frequency and size distribution of elms were recorded at four locations. Northern parts of Lier, a municipality most affected by DED in Norway 15 years ago, showed in the survey season 4% disease frequency, whereas 13.8% of trees were dead, the dead trees having accumulated over several years in the unmanaged stands. In southern parts of the municipality the mean disease and death percentages were 1.9 and 2.4%. Compatible with their low disease incidence in early 1990s, the other two areas now examined, municipality of Larvik and district of Grenland, showed comparably low frequency of DED. Northern part of Lier showed significantly higher overall density of elm trees per hectare than the other examined areas, and also the small elms below 5 cm in d.b.h. were most frequent in this region. In contrast, the density of large trees was lower in northern Lier than in the other examined areas. These data suggest that regeneration of the tree is not prohibited owing to the disease but that the large trees have been locally reduced in frequency as a result of DED. The superior general density of elm trees in northern Lier, owing to the exceptionally rich soil in the warm southern slopes of the region,> may have contributed to the rapid increase of DED in the area 15 years ago and to the subsequent settlement of the disease outbreak as a chronic stage.
2010
Authors
Jari Vauhkonen Liviu Ene Svein Solberg Marius Hauglin Vegard Lien Petteri Packalén Terje Gobakken Erik Næsset Timo Tokola Matti MaltamoAbstract
The aim of this study was to validate and compare single-tree detection algorithms under different forest conditions. Field data and corresponding airborne laser scanning (ALS) data were acquired from boreal forests in Norway and Sweden, coniferous and broadleaved forests in Germany, and pulpwood plantations in Brazil. The data represented a variety of forest types from pure Eucalyptus stands with known ages and planting densities to conifer-dominated Scandinavian forests and more complex deciduous canopies in Central Europe. ALS data were acquired using different sensors with pulse densities varying between the data sets. Field data in varying extent were associated with each ALS data set for training purposes. Treetop positions were extracted using altogether 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 forest conditions were analyzed. The results showed that forest structure and density strongly affected the performance of all algorithms. 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 conditions and may be of great value for future refinement of the single-tree detection algorithms.
Abstract
While forest inventories based on airborne laser scanning data (ALS) using the area based approach (ABA) have reached operational status, methods using the individual tree crown approach (ITC) have basically remained a research issue. One of the main obstacles for operational applications of ITC is biased results often experienced due to segmentation errors. In this article, we propose a new method, called "semi-ITC" that overcomes the main problems related to ITC by imputing ground truth data within crown segments from the nearest neighboring segment. This may be none, one, or several trees. The distances between segments were derived based on a set of explanatory variables using two nonparametric methods, i.e., most similar neighbor inference (MSN) and random forest (RF). RF favored the imputation of common observations in the data set which resulted in significant biases. Main conclusions are therefore based on MSN. The explanatory variables were calculated by means of small footprint ALS and multispectral data. When testing with empirical data the new method compared favorably to the well-known ABA. Another advantage of the new method over the ABA is that it allowed for the modeling of rare tree species. The results of predicting timber volume with the semi-ITC method were unbiased and the root mean squared error (RMSE) on plot level was smaller than the standard deviation of the observed response variables. The relative RMSEs after cross validation using semi-ITC for total volume and volume of the individual species pine, spruce, birch, and aspen on plot level were 17, 38, 40, 101, and 222%, respectively. Due to the unbiasedness of the estimation, this study is a showcase for how to use crown segments resulting from ITC algorithms in a forest inventory context. (C) 2009 Elsevier Inc. All rights reserved.
Abstract
The semi-individual tree crown approach (semi-ITC) was used to predict crown base heights (CBH) on the level of single crown segments based on airborne laser scanning (ALS) derived metrics. The root-mean-squared-differences (RMSD) on the segment level were smallest for spruce. However, they were larger than the standard deviation of the measured CBH for pine and birch. The RMSD values were also larger compared to other studies. This can in part be explained by the fact that the semi-ITC approach incorporates errors of the segmentation algorithm. As a consequence, all instead of only correctly identified trees were considered in modeling which results in more realistic RMSD values. After aggregating the individual segment predictions to the plot level, the RMSD values were smaller than the standard deviations of the field measurements and comparable to other studies. The relative RMSD values for birch, spruce, pine and all species were 51.61, 35.22, 49.28, and 13.89%, respectively.
Abstract
The airborne laser scanning (ALS) penetration rate, i.e. the ratio of ground echoes to total echoes, is a proxy for gap fraction. Hence, ALS has a potential for monitoring forest properties that are related to gap fraction, such as leaf area index, canopy cover and disturbance. Furthermore, two gap types may be distinguished: While a pulse that only produces a ground echo most likely hit a large, between-tree gap, a pulse that produces a ground echo as the last of several returns most likely hit smaller, within-canopy gaps. This may be utilized to distinguish between disturbance types such as defoliation and tree removal. However, the ALS penetration rate needs to be calibrated with gap fraction measurements in the field, because it is influenced by technical properties of the acquisition. The aim of this study was to quantify the magnitude of this influence, by comparing repeated acquisitions with different technical specifications. We had at hand 12 ALS acquisitions which could be combined into six pairs, from four spruce and pine dominated forests in Norway. We established 20x20 m grids, and for each grid cell we extracted three penetration variables: first echo penetration, last-of-many echo penetration, and total (i.e., first and last echo). We log-transformed the penetration variables (P1 and P2) from two laser acquisitions, and fitted the no-intercept, linear model log(P1) = log(P2), applying total least squares regression analysis. In a majority of the cases, the penetration variables were very similar, i.e. they deviated by <10%. For the first echo penetration the slopes varied from 0.87 to 1.07 and the R2 values ranged between 0.91 and 0.99. For the last-of-many echo penetration, there was generally weaker correspondence with slopes varying from 0.78 to 1.02, and R2 values ranging from 0.60 to 0.94. Finally, for the total penetration there was again stronger agreement with slopes in the range 0.83-1.03 and R2 values from 0.88 to 0.99. In conclusion, it seems that the penetration ability of different ALS scans in many cases are very similar, and further research may reveal ranges of standardized settings for which field inventory can be redundant.
Authors
Stein Tomter Gro Hylen Jan-Erik Ørnelund NilsenAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Svein Solberg Rasmus Astrup Dan Johan Weydahl Erik Næsset Terje Gobakken Ole Martin BollandsåsAbstract
There is an increasing need for forest resource monitoring methods, as more attention is paid to deforestation, bio-energy and forests as habitats. Most national forest inventories are based on networks of field inventory plots, sometimes together with satellite data, and airborne laser scanning (ALS) is increasingly used for local forest mapping. These methods are expensive to establish or carry out, and many countries, including some severely affected by deforestation, do not apply such methods.Satellite based remote sensing methods in use today are hampered by problems caused by clouds and saturation at moderate biomass levels. Satellite SAR is not hampered by cloud problems, and monitoring of canopy surface elevation, which is correlated to key forest resource variables, might be a future method in forest monitoring.We here present the main findings of three studies (Solberg et al. 2010, a, b, c) investigating the potential of interferometric SAR (InSAR) for forest monitoring, by describing the relationship between InSAR height above ground and key forest variables. We based this study on InSAR data from the Shuttle Radar Topographic Mission (SRTM) with its acquisition in February 2000. We obtained SRTM InSAR DEM data from DLR for two forest areas in Norway, and built a ground-truth from the combination of field inventory and ALS.The forest areas were dominated by Norway spruce and Scots pine. In each forest area we laid out a number of field inventory plots, where we recorded standard forest variables such as Dbh and tree height, and from this derived plot aggregated variables of top height, mean height, stand density (mean tree height divided by the mean tree spacing), volume and biomass. We used this to calibrate and validate ALS based models, from which we derived estimates of the same variables for each SRTM pixel. This served as reference data for the SRTM data.From the X-band SRTM digital surface model (DSM) image we subtracted a high quality digital terrain model (DTM) derived from the ALS data. This was based on an extraction of ground echoes from the data provider, and the elevations of these echoes were interpolated into a grid fitting the SRTM grid.This produced data on the RADAR echo height above ground (InSAR height), which we related to the forest variables. With digital stand maps we aggregated the variables to the stand level. The X-band microwaves penetrate a little into the canopy, and the InSAR height was on average about 1.2 m below the mean tree height. InSAR height was strongly related to all forest variables, most strongly to top height.Particularly valuable was that stem volume and biomass, ranging up to 400 m3/ha and 200 t/ha, respectively, were linearly related to InSAR height with an accuracy, RMSE, of 19% at the stand level. However, these relationships had an intercept, which represents the microwave penetration into the vegetation, and due to this the relationships were non-linear for forest stands having heights and biomass values close to zero.With a lower quality DTM derived from topographic maps, the relationships were weaker. However, as long as a forest variable is within the ranges of the linear relationship, any change in InSAR elevation would be proportional to a change in forest height, volume or biomass. And, any logging should be detectable as a sudden decrease in InSAR elevation.Hence, a forest monitoring based on X-band InSAR might be suitable even without a DTM. An application of space borne InSAR for forest monitoring would be feasible for large areas at low cost, whereas an ALS acquisition for a part of the area would serve as reference data for calibration.
Authors
Tuula Kantola Mikko Vastaranta Xiaowei Yu Paivi Lyytikainen-Saarenmaa Markus Holopainen Mervi Talvitie Sanna Kaasalainen Svein Solberg Juha HyyppäAbstract
Climate change has been observed to be related to the increase of forest insect damages in the boreal zone. The prediction of the changes in the distribution of insect-caused forest damages has become a topical issue in the field of forest research. The common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini caused severe growth losses and tree mortality of Scots pine (Pinus sylvestris L.) (Pinaceae). Logistic regression is commonly used in modelling the probability of occurrence of an event. In this study the logistic regression was investigated for predicting the needle loss of individual Scots pines (pine) using the features derived from airborne laser scanning (ALS) data. The defoliation level of 164 trees was determined subjectively in the field. Statistical ALS features were extracted for single trees and used as independent variables in logistic regression models. Classification accuracy of defoliation was 87.8% as respective kappa-value was 0.82. For comparison, only penetration features were selected and classification accuracy of 78.0% was achieved (kappa=0.56). Based on the results, it is concluded that ALS based prediction of needle losses is capable to provide accurate estimates for individual trees.