Carbon dioxide, methane and nitrous oxide fluxes from a fire chronosequence in subarctic boreal forests of Canada

Forest fires are one of the most important natural disturbances in boreal forests, and their occurrence and severity are expected to increase as a result of climate warming. A combination of factors induced by fire leads to a thawing of the near-surface permafrost layer in subarctic boreal forest. Earlier studies reported that an increase in the active layer thickness results in higher carbon dioxide (CO2) and methane (CH4) emissions. We studied changes in CO2, CH4 and nitrous oxide (N2O) fluxes in this study, and the significance of several environmental factors that influence the greenhouse gas (GHG) fluxes at three forest sites that last had fires in 2012, 1990 and 1969, and we compared these to a control area that had no fire for at least 100 years. The soils in our study acted as sources of CO2 and N2O and sinks for CH4. The elapsed time since the last forest fire was the only factor that significantly influenced all studied GHG fluxes. Soil temperature affected the uptake of CH4, and the N2O fluxes were significantly influenced by nitrogen and carbon content of the soil, and by the active layer depth. Results of our study confirm that the impacts of a forest fire on GHGs last for a rather long period of time in boreal forests, and are influenced by the fire induced changes in the ecosystem. (C) 2017 Elsevier B.V. All rights reserved.Peer reviewe

Pre-processing for Triangulation of Probabilistic Networks

The currently most efficient algorithm for inference with a probabilistic network builds upon a triangulation of a network's graph. In this paper, we show that pre-processing can help in finding good triangulations forprobabilistic networks, that is, triangulations with a minimal maximum clique size. We provide a set of rules for stepwise reducing a graph, without losing optimality. This reduction allows us to solve the triangulation problem on a smaller graph. From the smaller graph's triangulation, a triangulation of the original graph is obtained by reversing the reduction steps. Our experimental results show that the graphs of some well-known real-life probabilistic networks can be triangulated optimally just by preprocessing; for other networks, huge reductions in their graph's size are obtained.Comment: Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001

Multiskalen-basierte Finite-Differenzen-Verfahren auf adaptiven dünnen Gittern

In der Arbeit werden Lösungsverfahren für partielle Differential- gleichungen vorgestellt, die auf Multiskalen-Ansatzfunktionen (Wavelets) basieren. Zur adaptiven Approximation der numerischen Lösung werden anisotrope Tensorprodukte von Verallgemeinerungen der Hierarchischen Basis (Interpolets) benutzt. Diese erlauben eine sehr effiziente Approximation von Funktionen, z.B. Funktionen mit beschränkter gemischter Ableitung. Weiterhin ist eine einfache Transformation zwischen Knotenwerten bzgl. eines adaptiven Gitters und der Multiskalendarstellung möglich. Für die Diskretisierung von Differentialoperatoren werden ein spezielles biorthogonales Petrov-Galerkin--Verfahren und Finite Differenzen-Verfahren betrachtet. Erstmalig wird für diese Diskretisierungen eine allgemeine Konvergenztheorie angegeben, die auch den adaptiven Fall abdeckt. Dabei wird der Konvergenzfehler über einen Approximationsfehler und einen Konsistenzfehler abgeschätzt. Für den Fall spezieller an die Lösung angepasster adaptiver Basen werden für den Konsistenzfehler a priori Schranken angegeben. Ein weiterer Schwerpunkt ist das schnelle Lösen der bei obiger Diskretisierung entstehenden linearen Gleichungssysteme. Es werden zwei sehr effiziente Vorkonditionier vorgestellt und analysiert, wobei einer auf dem Lifting-Schema basiert. Mit diesem erhält man Konditionszahlen, die unabhängig von der feinsten Maschenweite beschränkt sind. Das Lösungsverfahren wird auf eine Reihe von Testproblemen angewandt, z.B. die adaptive Simulation von zwei- bzw. drei-dimensionalen turbulenten Scherschichten

Incorporating self-reported health measures in risk equalization through constrained regression

Most health insurance markets with premium-rate restrictions include a risk equalization system to compensate insurers for predictable variation in spending. Recent research has shown, however, that even the most sophisticated risk equalization systems tend to undercompensate (overcompensate) groups of people with poor (good) self-reported health, confronting insurers with incentives for risk selection. Self-reported health measures are generally considered infeasible for use as an explicit ‘risk adjuster’ in risk equalization models. This study examines an alternative way to exploit this information, namely through ‘constrained regression’ (CR). To do so, we use administrative data (N = 17 m) and health survey information (N = 380 k) from the Netherlands. We estimate five CR models and compare these models with the actual Dutch risk equalization model of 2016 which was estimated by ordinary least squares (OLS). In the CR models, the estimated coefficients are restricted, such that t

Artifact Reduction Based on Sinogram Interpolation for the 3D Reconstruction of Nanoparticles Using Electron Tomography

Electron tomography is a well-known technique providing a 3D characterization of the morphology and chemical composition of nanoparticles. However, several reasons hamper the acquisition of tilt series with a large number of projection images, which deteriorate the quality of the 3D reconstruction. Here, an inpainting method that is based on sinogram interpolation is proposed, which enables one to reduce artifacts in the reconstruction related to a limited tilt series of projection images. The advantages of the approach will be demonstrated for the 3D characterization of nanoparticles using phantoms and several case studies

Nitrogen balance along a northern boreal forest fire chronosequence

Fire is a major natural disturbance factor in boreal forests, and the frequency of forest fires is predicted to increase due to climate change. Nitrogen (N) is a key determinant of carbon sequestration in boreal forests because the shortage of N limits tree growth. We studied changes in N pools and fluxes, and the overall N balance across a 155-year non stand replacing fire chronosequence in sub-arctic Pinus sylvestris forests in Finland. Two years after the fire, total ecosystem N pool was 622 kg ha(-1) of which 16% was in the vegetation, 8% in the dead biomass and 76% in the soil. 155 years after the fire, total N pool was 960 kg ha(-1), with 27% in the vegetation, 3% in the dead biomass and 69% in the soil. This implies an annual accumulation rate of 2.28 kg ha(-1) which was distributed equally between soil and biomass. The observed changes in N pools were consistent with the computed N balance +2.11 kg ha(-1) yr(-1) over the 155-year post-fire period. Nitrogen deposition was an important component of the N balance. The biological N fixation increased with succession and constituted 9% of the total N input during the 155 post-fire years. N2O fluxes were negligible (<0.01 kg ha(-1) yr(-1)) and did not differ among post-fire age classes. The number and intensity of microbial genes involved in N cycling were lower at the site 60 years after fire compared to the youngest and the oldest sites indicating potential differences in soil N cycling processes. The results suggest that in sub-arctic pine forests, the non-stand-replacing, intermediate severity fires decrease considerably N pools in biomass but changes in soil and total ecosystem N pools are slight. Current fire-return interval does not seem to pose a great threat to ecosystem productivity and N status in these sub-arctic forests.Peer reviewe

The InflateSAR Campaign: Testing SAR Vessel Detection Systems for Refugee Rubber Inflatables

Countless numbers of people lost their lives at Europe’s southern borders in recent years in the attempt to cross to Europe in small rubber inflatables. This work examines satellite-based approaches to build up future systems that can automatically detect those boats. We compare the performance of several automatic vessel detectors using real synthetic aperture radar (SAR) data from X-band and C-band sensors on TerraSAR-X and Sentinel-1. The data was collected in an experimental campaign where an empty boat lies on a lake’s surface to analyse the influence of main sensor parameters (incidence angle, polarization mode, spatial resolution) on the detectability of our inflatable. All detectors are implemented with a moving window and use local clutter statistics from the adjacent water surface. Among tested detectors are well-known intensity-based (CA-CFAR), sublook-based (sublook correlation) and polarimetric-based (PWF, PMF, PNF, entropy, symmetry and iDPolRAD) approaches. Additionally, we introduced a new version of the volume detecting iDPolRAD aimed at detecting surface anomalies and compare two approaches to combine the volume and the surface in one algorithm, producing two new highly performing detectors. The results are compared with receiver operating characteristic (ROC) curves, enabling us to compare detectors independently of threshold selection

Reducing avoidable visual impairment in elderly home healthcare patients by basic ophthalmologic screening

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