Inventory of sparse forest populations using adaptive cluster sampling

Abstract

Comparison of randomized branch sampling with and without replacement at the first stage

Abstract

Sampling in chemical analysis

The uncertainty of any analytical determination depends on analysis and sampling. Uncertainty arising from sampling is usually not controlled and methods for its evaluation are still little known. Pierre Gy’s sampling theory is currently the most complete theory about samplingwhich also takes the design of the sampling equipment into account. Guides dealing with the practical issues of sampling also exist, published by international organizations such as EURACHEM, IUPAC (International Union of Pure and Applied Chemistry) and ISO (International Organization for Standardization). In this work Gy’s sampling theory was applied to several cases, including the analysis of chromite concentration estimated on SEM (Scanning Electron Microscope) images and estimation of the total uncertainty of a drug dissolution procedure. The results clearly show that Gy’s sampling theory can be utilized in both of the above-mentioned cases and that the uncertainties achieved are reliable. Variographic experiments introduced in Gy’s sampling theory are beneficially applied in analyzing the uncertainty of auto-correlated data sets such as industrial process data and environmental discharges. The periodic behaviour of these kinds of processes can be observed by variographic analysis as well as with fast Fourier transformation and auto-correlation functions. With variographic analysis, the uncertainties are estimated as a function of the sampling interval. This is advantageous when environmental data or process data are analyzed as it can be easily estimated how the sampling interval is affecting the overall uncertainty. If the sampling frequency is too high, unnecessary resources will be used. On the other hand, if a frequency is too low, the uncertainty of the determination may be unacceptably high. Variographic methods can also be utilized to estimate the uncertainty of spectral data produced by modern instruments. Since spectral data are multivariate, methods such as Principal Component Analysis (PCA) are needed when the data are analyzed. Optimization of a sampling plan increases the reliability of the analytical process which might at the end have beneficial effects on the economics of chemical analysis,

Stratification of regional sampling by model-predicted changes of carbon stocks in Forested mineral soils

Selective papers of the workshop on "Development of models and forest soil surveys for monitoring of soil carbon", Koli, Finland, April 5-9 2006.

Elemental analysis of wood materials by external millibeam thick target PIXE

PIXE (Particle Induce X-ray Emission spectrometry) was used for analysing stem bark and stem wood of Scots pine, Norway spruce and Silver birch. Thick samples were irradiated, in laboratory atmosphere, with 3 MeV protons and the beam current was measured indirectly using a photo multiplicator (PM) tube. Both point scans and bulk analyses were performed with the 1 mm diameter proton beam. In bulk analyses, whole bark and sectors of discs of the stem wood were dry ashed at 550 ˚C. The ashes were homogenised by shaking and prepared to target pellets for PIXE analyses. This procedure generated representative samples to be analysed, but the enrichment also enabled quantification of some additional trace elements. The ash contents obtained as a product of the sample preparation procedure also showed to be of great importance in the evaluation of results in environmental studies. Spot scans from the pith of pine wood outwards, showed clearly highest concentrations of manganese, calcium and zinc in the first spot irradiated, or 2-3 times higher than in the surrounding wood. For stem wood from the crown part of a pine this higher concentration level was found in the first four spots/mms, including the pith and the two following growth rings. Zinc showed increasing concentrations outwards in sapwood of the pine stem, with the over-all lowest concentrations in the inner half of the sapwood. This could indicate emigration of this element from sapwood being under transformation to heartwood. Point scans across sapwood of pine and spruce showed more distinct variations in concentrations relative to hearth wood. Higher concentrations of e.g. zinc, calcium and manganese were found in earlywood than in denser latewood. Very high concentrations of iron and copper were also seen for some earlywood increments. The ash content of stem bark is up to and order higher than for the stem wood. However, when the elemental concentration in ashes of bark and wood of the same disc were compared, these are very similar – this when trees are growing at spots with no anthropogenic contamination from the atmosphere. The largest difference was obtained for calcium which appeared at two times high concentrations in ashes of bark than in ashes of the wood (ratio of 2). Pine bark is often used in monitoring of atmospheric pollution, where concentrations in bark samples are compared. Here an alternative approach is suggested: Bark and the underlying stem wood of a pine trees are dry ashed and analysed. The elemental concentration in the bark ash is then compared to the concentration of the same element in the wood ash. Comparing bark to wood includes a normalisation for the varying availability of an element from the soil at different sites. When this comparison is done for the ashes of the materials, a normalisation is also obtained for the general and locally different enrichment of inorganic elements from wood to bark. Already a ratio >2 between the concentration in the bark ash and the concentration in the wood ash could indicate atmospheric pollution. For monitoring where bark is used, this way of “inwards” comparison is suggested - instead of comparing to results from analyses of bark from other trees (read reference areas), growing at sites with different soil and, locally, different climate conditions. This approach also enables evaluation of atmospheric pollution from sampling of only relative few individual trees –preferable during forest felling.

Improving quality at the preanalytical phase of blood sampling : literature review

Julkaisumaa: 158 TW TWN Taiwan

Variational Ensemble Kalman Filtering in Hydrology

The current thesis manuscript studies the suitability of a recent data assimilation method, the Variational Ensemble Kalman Filter (VEnKF), to real-life fluid dynamic problems in hydrology. VEnKF combines a variational formulation of the data assimilation problem based on minimizing an energy functional with an Ensemble Kalman filter approximation to the Hessian matrix that also serves as an approximation to the inverse of the error covariance matrix. One of the significant features of VEnKF is the very frequent re-sampling of the ensemble: resampling is done at every observation step. This unusual feature is further exacerbated by observation interpolation that is seen beneficial for numerical stability. In this case the ensemble is resampled every time step of the numerical model. VEnKF is implemented in several configurations to data from a real laboratory-scale dam break problem modelled with the shallow water equations. It is also tried in a two-layer Quasi- Geostrophic atmospheric flow problem. In both cases VEnKF proves to be an efficient and accurate data assimilation method that renders the analysis more realistic than the numerical model alone. It also proves to be robust against filter instability by its adaptive nature.