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.
2005
Abstract
We investigate ecosystem dynamics by analyzing time series of measured variables. The information content and the complexity of these data are quantifed by methods from information theory.When applied to runoff (stream discharge) from catchments, the information/complexity relation reveals a simple non-trivial property for a large ensemble (more than 1800) of time series. This behaviour is so far not understood in hydrology.Using a multi-agent network receiving input resembling rainfall and producing output, we are able to reproduce the observed behaviour for the first time. The reconstruction is based on the identification and subsequent replacement of general patterns in the input. We thus consider runoff dynamics as the expression of an interactive learning problem of agents in an ecosystem.
Abstract
We investigate a data set of 160 river runoff time series at daily resolution from catchments in Southern Germany. Our aim is to seek spatial patterns for best parametrization of extreme value distributions to these data sets on one hand, and to analyze temporal instationarities of parameter estimates and extreme value attributes on the other. Conventional extreme value statistics and the calculation of return periods implicitly assume that the most extreme events are statistically independent. We demonstrate that this assumption is invalid, and that correlations, temporal as well as spatial, of arbitrary extent prevail instead. An important consequence is that the concept of return periods is obsolete. In order to find explanatory variables for the observed patterns, features of the waiting time distribution at a given relative threshold are correlated to catchment properties, such as size, mean runoff volume, elevation, and others. Finally, the effect of varying temporal resolution on the duration periods is exhibited. http://www.cosis.net/abstracts/EGU05/03192/EGU05-J-03192.pdf
Abstract
Instationarities in runoff time series are ubiquitous. However, simple trend analyses are often obscured by the presence of long-term correlations, and some instationarities are not simply changes in the mean or periodicities. Thus, wherever feasible, instationarities should be based on the full frequency distribution, or the cumulative distribution function (cdf), of the series. In this paper, we investigate the time-dependence of the empirical cdfs of 97 runoff datasets from the upper Danube basin applying a new pairwise test statistic, KSSUM, based on integrated differences of the cdfs. This is an improvement to the Kolmogorov-Smirnov (KS) test and was applied on different time scales, i.e. windows of varying size. If desired, the influence of drifts in the mean as well as heteroscedasticity can be excluded via z-transformations. The resulting time series of the KSSUM variable, either within a runoff series for different windows, or across series for the same period, is then subjected to the detection of spatiotemporal patterns with different methods. For most of the time series the underlying distributions move towards higher values in the long run. We also observed a periodic drift in the mean across all analysed gauges. It is furthermore possible to separate exceedingly variable runoff series from those with intermediate or small changes in value distribution on a regional basis, and thus to separate overall trends from local deviations at individual gauges. It is demonstrated that KSSUM is a sensitive method to investigate instationarities in sets of time series based on pairwise comparisons. An extension to a proper multivariate comparison is a possible further development. http://www.cosis.net/abstracts/EGU05/04198/EGU05-J-04198.pdf
Authors
Svein SolbergAbstract
Forest damage will result in two general effects: defoliation and/or discolouration. The two available techniques in remote sensing of forests today, LiDAR and spectroscopy, are promising tools for monitoring these two, respectively. Merging data on foliar mass, estimated by LiDAR, with data on chlorophyll concentrations, estimated by spectroscopy, can provide data on chlorophyll mass pr area unit. Monitoring the temporal changes of this is likely to be a very good measure for variations in forest health.In order to check out the possibilities for this, we are now working on building relationships between foliar mass data and LiDAR data for single spruce trees. In total we have measurements of position and stem diameter on about 2000 trees distributed on 16 plots, where 64 trees are intensively sampled for estimating foliar mass, as well as crown size.We need to parameterize a relationship between the LiDAR data for each of these trees and their foliar mass (or leaf area). If we succeed to build this relationship, we will scale it up to provide foliar mass (or leaf area) estimates for every 10x10 m pixels in two SPOT images of the area.Together with a similar up-scaling of chlorophyll concentrations, based on spectroscopy, we will test the possibility of estimating chlorophyll mass per area from SPOT or other satellites. In addition, we have visually assessed data on crown density for all the trees, being a rough, but valuable data-set for validating the relationship.The work, being in progress now, includes several tasks:a) finding an appropriate canopy surface modelb) segmentation of treesc) estimating crown volume, and evt d) handling of smaller trees standing below (this is a heterogenous canopy layer forest) and e) handling of the relative influence of stem and branches.Additionally, we see some other benefits from using LiDAR together with airborne hyperspectral data and satellite data in general. Firstly, the combination of high resolution LiDAR and hyper-spectral data, is a good basis for separating the signals from ground vegetation and from the tree canopy. Secondly, LiDAR provides both a DTM and a canopy surface model, and they are two alternative surface models for the geo-referencing of other data, and for appropriate handling of effects of shadowing and obstacles from tall trees.
Authors
Päivi Rinne Carl Gunnar Fossdal Sissel Torre Heather Danforth Aksel Granhus Gunnhild Søgaard John Einset Harald Kvaalen Øystein Johnsen Chris Van der SchootAbstract
No abstract has been registered
Abstract
Considerable knowledge exists about the effect of aluminium (Al) on root vitality, but whether elevated levels of Al affect soil microorganisms is largely unknown. We thus compared soils from Al-treated and control plots of a field experiment with respect to microbial and chemical parameters, as well as root growth and vitality. Soil from a field experiment established in a 50 year old Norway spruce (Picea abies L.) stand where low concentrations of aluminum (0.5 mM AlCl3) had been added weekly or bi-weekly during the growth season for seven years was compared to a control treatment with respect to microbial and chemical parameters, as well as root growth and vitality. Analysis of soil solutions collected using zero tension lysimeters and porous suction cups showed that Al treatment lead to increased concentrations of Al, Ca and Mg and lower pH and [Ca+Mg]/[Al] molar ratio. Corresponding soil analyses showed that soil pH remained unaffected (pH 3.8), that Al increased, while extractable Ca and Mg decreased due to the Al treatment. Root ingrowth into cores placed in the upper 20 cm of the soil during 28 months was not affected by Al additions, neither was the mortality of these roots. The biomass of some taxonomical groups of soil microorganisms in the humus layer, analyzed using specific membrane components (phospholipid fatty acids; PLFAs), was clearly affected by the imposed Al treatment, but less so in the underlying mineral soil. Microbial community structure in the humus layer was also clearly modified by the Al treatment, whereas differences in the mineral horizon were less clear. Shifts in PLFA trans/cis ratios indicative of short term physiological stress were not observed. Yet, aluminium stress was indicated both by changes in community structure and in ratios of single PLFAs for treated/untreated plots. Thus, soil microorganisms were more sensitive indicators of subtle chemical changes in soil than chemical composition and vitality of roots.
Authors
Paivi L.H. Rinne Carl Gunnar Fossdal Sissel Torre Heather Danforth Aksel Granhus Gunnhild Søgaard John Einset Harald Kvaalen Øystein Johnsen Christiaan van der SchootAbstract
No abstract has been registered
Authors
Toril Drabløs Eldhuset Holger Lange Nicholas Clarke Per Otto Flæte Erlend Ystrøm Haartveit Gro Wollebæk O. Janne KjønaasAbstract
Root and needle litter are the most important sources of organic carbon in forest soils. Their decomposition is thus important for the long-term storage of C in, and release of CO2 from, the soil. Different components in the organic matter will decompose with different speeds. NIRS (Near InfraRed Spectroscopy) is a relatively simple and promising way of analysing the composition of organic matter, but its use in forest soil and litter studies has been limited up to now. We will present preliminary results from litter decomposition studies in two forest ecosystems: Picea abies stands (30 and 120 years old) from Nordmoen, Norway, and uneven-aged P. abies stands with a mean age of 90 years and under different N treatments at Gårdsjön, Sweden. ags with litter collected from the stands have been buried in the soil for different time periods and have been analysed using a CHN-analyzer and NIRS. Two aspects will be discussed: a) model calibration and validation for C and N concentrations, and b) assessment of decomposability using NIRS.
2004
Authors
Svein Solberg Kjell Andreassen Nicholas Clarke Kjetil Tørseth Ole Einar Tveito Geir-Harald Strand Stein Michael TomterAbstract
No abstract has been registered
Abstract
Extensive monitoring of forest health in Europe has been carried out for two decades, based mainly on defoliation and discolouration. Together these two variables reflect chlorophyll amounts in the tree crown, i.e. as an indicator of foliar mass, and chlorophyll concentration in the foliage, respectively.In a current project we try to apply remote sensing techniques to estimate canopy chlorophyll mass, being a suitable forest health variable. So far, we limit this to Norway spruce only. LIDAR data here play an important role, together with optical and spectral data, either from survey flights or from satellites. We intend to model relationships between foliar mass and LIDAR data for sample trees, and then scale up this to foliar mass estimates for the entire LIDAR area.Similarly, we try to scale up chlorophyll concentrations in sample trees, by modelling a relationship between sample tree chlorophyll and hyper-spectral data. The estimates of foliar mass and chlorophyll concentrations are then aggregated to every 10x10 m pixel of a SPOT satellite scene which is also covered by airborne data, providing an up-scaled ground truth. If we are successful with this, it might be a starting point for developing a new nationwide forest health monitoring system in Norway.