Canopy processes, fluxes and microclimate in a pine forest

Interaction between forests and the atmosphere occurs by radiative and turbulent transport. The fluxes of energy and mass between surface and the atmosphere directly influence the properties of the lower atmosphere and in longer time scales the global climate. Boreal forest ecosystems are central in the global climate system, and its responses to human activities, because they are significant sources and sinks of greenhouse gases and of aerosol particles. The aim of the present work was to improve our understanding on the existing interplay between biologically active canopy, microenvironment and turbulent flow and quantify. In specific, the aim was to quantify the contribution of different canopy layers to whole forest fluxes. For this purpose, long-term micrometeorological and ecological measurements made in a Scots pine (Pinus sylvestris) forest at SMEAR II research station in Southern Finland were used. The properties of turbulent flow are strongly modified by the interaction between the canopy elements: momentum is efficiently absorbed in the upper layers of the canopy, mean wind speed and turbulence intensities decrease rapidly towards the forest floor and power spectra is modulated by spectral short-cut . In the relative open forest, diabatic stability above the canopy explained much of the changes in velocity statistics within the canopy except in strongly stable stratification. Large eddies, ranging from tens to hundred meters in size, were responsible for the major fraction of turbulent transport between a forest and the atmosphere. Because of this, the eddy-covariance (EC) method proved to be successful for measuring energy and mass exchange inside a forest canopy with exception of strongly stable conditions. Vertical variations of within canopy microclimate, light attenuation in particular, affect strongly the assimilation and transpiration rates. According to model simulations, assimilation rate decreases with height more rapidly than stomatal conductance (gs) and transpiration and, consequently, the vertical source-sink distributions for carbon dioxide (CO2) and water vapor (H2O) diverge. Upscaling from a shoot scale to canopy scale was found to be sensitive to chosen stomatal control description. The upscaled canopy level CO2 fluxes can vary as much as 15 % and H2O fluxes 30 % even if the gs models are calibrated against same leaf-level dataset. A pine forest has distinct overstory and understory layers, which both contribute significantly to canopy scale fluxes. The forest floor vegetation and soil accounted between 18 and 25 % of evapotranspiration and between 10 and 20 % of sensible heat exchange. Forest floor was also an important deposition surface for aerosol particles; between 10 and 35 % of dry deposition of particles within size range 10 30 nm occurred there. Because of the northern latitudes, seasonal cycle of climatic factors strongly influence the surface fluxes. Besides the seasonal constraints, partitioning of available energy to sensible and latent heat depends, through stomatal control, on the physiological state of the vegetation. In spring, available energy is consumed mainly as sensible heat and latent heat flux peaked about two months later, in July August. On the other hand, annual evapotranspiration remains rather stable over range of environmental conditions and thus any increase of accumulated radiation affects primarily the sensible heat exchange. Finally, autumn temperature had strong effect on ecosystem respiration but its influence on photosynthetic CO2 uptake was restricted by low radiation levels. Therefore, the projected autumn warming in the coming decades will presumably reduce the positive effects of earlier spring recovery in terms of carbon uptake potential of boreal forests.

Sustainability and Mission Drift in Microfinance

Microfinance institutions (MFIs) are constrained by double bottom-lines: meeting social obligations (the first bottom-line) and obtaining financial self-sufficiency (the second bottom-line). The proponents of the first bottom-line, however, are increasingly concerned that there is a trade-off between these two bottom-lines—i.e., getting hold of financial self-sufficiency may lead MFIs to drift away from their original social mission of serving the very poor, commonly known as mission drift in microfinance which is still a controversial issue. This study aims at addressing the concerns for mission drift in microfinance in a performance analysis framework. Chapter 1 deals with theoretical background, motivation and objectives of the topic. Then the study explores the validity of three major and related present-day concerns. Chapter 2 explores the impact of profitability on outreach-quality in MFIs, commonly known as mission drift, using a unique panel database that contains 4-9 years’ observations from 253 MFIs in 69 countries. Chapter 3 introduces factor analysis, a multivariate tool, in the process of analysing mission drift in microfinance and the exercise in this chapter demonstrates how the statistical tool of factor analysis can be utilised to examine this conjecture. In order to explore why some microfinance institutions (MFIs) perform better than others, Chapter 4 looks at factors which have an impact on several performance indicators of MFIs—profitability or sustainability, repayment status and cost indicators—based on quality-data on 353 institutions in 77 countries. The study also demonstrates whether such mission drift can be avoided while having self-sustainability. In Chapter 5 we examine the impact of capital and financing structure on the performance of microfinance institutions where estimations with instruments have been performed using a panel dataset of 782 MFIs in 92 countries for the period 2000-2007. Finally, Chapter 6 concludes the study by summarising the results from the previous chapters and suggesting some directions for future studies.

The Effect of Obesity on Wages and Employment: The Difference Between Having a High BMI and Being Fat

In this paper, we re-examine the relationship between overweight and labour market success, using indicators of individual body composition along with BMI (Body Mass Index). We use the dataset from Finland in which weight, height, fat mass and waist circumference are not self-reported, but obtained as part of the overall health examination. We find that waist circumference, but not weight or fat mass, has a negative effect on wages for women, whereas all measures of obesity have negative effects on women’s employment probabilities. For men, the only obesity measure that is significant for men’s employment probabilities is fat mass. One interpretation of our findings is that the negative wage effects of overweight on wages run through the discrimination channel, but that the negative effects of overweight on employment have more to do with ill health. All in all, measures of body composition provide a more refined picture about the effects of obesity on wages and employment.

Methods in general model localization

The aim of this study was to evaluate and test methods which could improve local estimates of a general model fitted to a large area. In the first three studies, the intention was to divide the study area into sub-areas that were as homogeneous as possible according to the residuals of the general model, and in the fourth study, the localization was based on the local neighbourhood. According to spatial autocorrelation (SA), points closer together in space are more likely to be similar than those that are farther apart. Local indicators of SA (LISAs) test the similarity of data clusters. A LISA was calculated for every observation in the dataset, and together with the spatial position and residual of the global model, the data were segmented using two different methods: classification and regression trees (CART) and the multiresolution segmentation algorithm (MS) of the eCognition software. The general model was then re-fitted (localized) to the formed sub-areas. In kriging, the SA is modelled with a variogram, and the spatial correlation is a function of the distance (and direction) between the observation and the point of calculation. A general trend is corrected with the residual information of the neighbourhood, whose size is controlled by the number of the nearest neighbours. Nearness is measured as Euclidian distance. With all methods, the root mean square errors (RMSEs) were lower, but with the methods that segmented the study area, the deviance in single localized RMSEs was wide. Therefore, an element capable of controlling the division or localization should be included in the segmentation-localization process. Kriging, on the other hand, provided stable estimates when the number of neighbours was sufficient (over 30), thus offering the best potential for further studies. Even CART could be combined with kriging or non-parametric methods, such as most similar neighbours (MSN).

PERFECT Stroke : PERFormance, Effectiveness, and Costs of treatment episodes in Stroke

Stroke is a major cause of death and disability, incurs significant costs to healthcare systems, and inflicts severe burden to the whole society. Stroke care in Finland has been described in several population-based studies between 1967 and 1998, but not since. In the PERFECT Stroke study presented here, a system for monitoring the Performance, Effectiveness, and Costs of Treatment episodes in Stroke was developed in Finland. Existing nationwide administrative registries were linked at individual patient level with personal identification numbers to depict whole episodes of care, from acute stroke, through rehabilitation, until the patients went home, were admitted to permanent institutional care, or died. For comparisons in time and between providers, patient case-mix was adjusted for. The PERFECT Stroke database includes 104 899 first-ever stroke patients over the years 1999 to 2008, of whom 79% had ischemic stroke (IS), 14% intracerebral hemorrhage (ICH), and 7% subarachnoid hemorrhage (SAH). A 18% decrease in the age and sex adjusted incidence of stroke was observed over the study period, 1.8% improvement annually. All-cause 1-year case-fatality rate improved from 28.6% to 24.6%, or 0.5% annually. The expected median lifetime after stroke increased by 2 years for IS patients, to 7 years and 7 months, and by 1 year for ICH patients, to 4 years 5 months. No change could be seen in median SAH patient survival, >10 years. Stroke prevalence was 82 000, 1.5% of total population of Finland, in 2008. Modern stroke center care was shown to be associated with a decrease in both death and risk of institutional care of stroke patients. Number needed to treat to prevent these poor outcomes at one year from stroke was 32 (95% confidence intervals 26 to 42). Despite improvements over the study period, more than a third of Finnish stroke patients did not have access to stroke center care. The mean first-year healthcare cost of a stroke patient was ~20 000 , and among survivors ~10 000 annually thereafter. Only part of this cost was incurred by stroke, as the same patients cost ~5000 over the year prior to stroke. Total lifetime costs after first-ever stroke were ~85 000 . A total of 1.1 Billion , 7% of all healthcare expenditure, is used in the treatment of stroke patients annually. Despite a rapidly aging population, the number of new stroke patients is decreasing, and the patients are more likely to survive. This is explained in part by stroke center care, which is effective, and should be made available for all stroke patients. It is possible, in a suitable setting with high-quality administrative registries and a common identifier, to avoid the huge workload and associated costs of setting up a conventional stroke registry, and still acquire a fairly comprehensive dataset on stroke care and outcome.

On formation, growth and concentrations of air ions

Aerosol particles have effect on climate, visibility, air quality and human health. However, the strength of which aerosol particles affect our everyday life is not well described or entirely understood. Therefore, investigations of different processes and phenomena including e.g. primary particle sources, initial steps of secondary particle formation and growth, significance of charged particles in particle formation, as well as redistribution mechanisms in the atmosphere are required. In this work sources, sinks and concentrations of air ions (charged molecules, cluster and particles) were investigated directly by measuring air molecule ionising components (i.e. radon activity concentrations and external radiation dose rates) and charged particle size distributions, as well as based on literature review. The obtained results gave comprehensive and valuable picture of the spatial and temporal variation of the air ion sources, sinks and concentrations to use as input parameters in local and global scale climate models. Newly developed air ion spectrometers (Airel Ltd.) offered a possibility to investigate atmospheric (charged) particle formation and growth at sub-3 nm sizes. Therefore, new visual classification schemes for charged particle formation events were developed, and a newly developed particle growth rate method was tested with over one year dataset. These data analysis methods have been widely utilised by other researchers since introducing them. This thesis resulted interesting characteristics of atmospheric particle formation and growth: e.g. particle growth may sometimes be suppressed before detection limit (~ 3 nm) of traditional aerosol instruments, particle formation may take place during daytime as well as in the evening, growth rates of sub-3 nm particles were quite constant throughout the year while growth rates of larger particles (3-20 nm in diameter) were higher during summer compared to winter. These observations were thought to be a consequence of availability of condensing vapours. The observations of this thesis offered new understanding of the particle formation in the atmosphere. However, the role of ions in particle formation, which is not well understood with current knowledge, requires further research in future.

Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables

In recent years, thanks to developments in information technology, large-dimensional datasets have been increasingly available. Researchers now have access to thousands of economic series and the information contained in them can be used to create accurate forecasts and to test economic theories. To exploit this large amount of information, researchers and policymakers need an appropriate econometric model.Usual time series models, vector autoregression for example, cannot incorporate more than a few variables. There are two ways to solve this problem: use variable selection procedures or gather the information contained in the series to create an index model. This thesis focuses on one of the most widespread index model, the dynamic factor model (the theory behind this model, based on previous literature, is the core of the first part of this study), and its use in forecasting Finnish macroeconomic indicators (which is the focus of the second part of the thesis). In particular, I forecast economic activity indicators (e.g. GDP) and price indicators (e.g. consumer price index), from 3 large Finnish datasets. The first dataset contains a large series of aggregated data obtained from the Statistics Finland database. The second dataset is composed by economic indicators from Bank of Finland. The last dataset is formed by disaggregated data from Statistic Finland, which I call micro dataset. The forecasts are computed following a two steps procedure: in the first step I estimate a set of common factors from the original dataset. The second step consists in formulating forecasting equations including the factors extracted previously. The predictions are evaluated using relative mean squared forecast error, where the benchmark model is a univariate autoregressive model. The results are dataset-dependent. The forecasts based on factor models are very accurate for the first dataset (the Statistics Finland one), while they are considerably worse for the Bank of Finland dataset. The forecasts derived from the micro dataset are still good, but less accurate than the ones obtained in the first case. This work leads to multiple research developments. The results here obtained can be replicated for longer datasets. The non-aggregated data can be represented in an even more disaggregated form (firm level). Finally, the use of the micro data, one of the major contributions of this thesis, can be useful in the imputation of missing values and the creation of flash estimates of macroeconomic indicator (nowcasting).

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.

The Radiation, snow characteristics and albedo at summit (RASCALS) expedition report

The RASCALS expedition spent over three weeks at the Summit camp research station near the top of the Greenland Ice Sheet during polar summer 2010. During this time, detailed measurements of the physical and optical properties of Arctic perennial snow were carried out concurrently with snow albedo and reflectance measurements. Favorable weather conditions during the campaign enabled the collection of a large dataset on Arctic snow albedo and associated quantities for use in developing and validating remote sensing algorithms for snow albedo using satellites. This report provides a description of the measurements and conditions during the campaign. RASCALS-retkikunnan tehtävä oli tutkia Grönlannin mannerjäätikön lumen fysikaalisia ja optisia ominaisuuksia sekä Auringon valon vuorovaikutusta lumen kanssa. Retikunta vietti hieman yli kolme viikkoa mannerjäätikön keskellä sijaitsevalla Summit Camp-tutkimusasemalla tehden mittauksia. Sääolot suosivat kampanjaa, jonka seurauksena onnistuttiin keräämään laaja ja monipuolinen tietoaineisto mannerjäätikön lumen pintakerroksesta ja eritoten lumen heijastavuuden (albedon)käyttäytymisestä. Aineisto on hyödyllinen kehitettäessä ja varmennettaessa lumen albedon kaukokartoitusmenetelmiä satelliiteilla.

Taxonomy and phylogeny of the manna lichens and allied species (Megasporaceae)

This dissertation is focused on the taxonomy, phylogeny, and ecology of the vagrant, erratic and allied terricolous and saxicolous species of the genera Aspicilia A. Massal. and Circinaria Link (Megasporaceae), particularly those traditionally referred to as manna lichens . The group has previously been defined on the basis of few morphological characters. The phylogeny of the family Megasporaceae is inferred from the combined dataset of nuLSU and mtSSU sequences. Five genera Aspicilia, Circinaria, Lobothallia, Megaspora, and Sagedia are recognized. Lobothallia is sister of the four other genera, while Aspicilia and Sagedia form the next clade. All these genera have small asci with eight spores. Circinaria is a sister genus of Megaspora, and these two have in common asci with (1 4) 6 8 large spores. Circinaria forms a monophyletic group and sphaerothallioid species form a monophyletic group within Circinaria. The presence of certain morphological characters such as pseudocyphellae, thickness of cortex and medulla layers, as well as ecological differences in sphaerothallioid species distinguish it from some other crustose species, especially those containing aspicilin and characterised by thin cortex and medulla layers, conidium length c. 6 12 µm and absence of pseudocyphellae. If sphaerothallioid species are accepted as a distinct genus, the rest of the Circinaria species would remain as a paraphyletic assemblage. Currently, the genus Circinaria includes all the sphaerothallioid species and its generic position is confirmed and accepted. Thus, it is proposed as a correct generic name also for the manna lichens described originally in other genera. Phylogeny at the species level was studied using nrITS sequence data. Traditionally, morphological characters have been used for the recognition of species. They were re-evaluated in the light of molecular data. Since characters such as vagrant, erratic and crustose growth forms proved to be misleading for the recognition of some species, a combination of several characters (including molecular data) is recommended. Vagrant growth form seems to have evolved several times among the distantly related lineages and even within a single population. The reasons behind the high plasticity in the external morphology of the sphaerothallioid Circinaria remain, however, unknown. Six new species are recognized: Aspicilia tibetica, Circinaria arida, C. digitata nom provis., C. gyrosa nom. provis., C. rogeri nom. provis., and C. rostamii nom. provis. Based on an analysis of nrITS dataset, three new erratic, vagrant and crustose species were also recognized, but these require additional study. The results also reveal that C. elmorei and C. hispida are not monophyletic as currently understood. In addition, 13 new combinations in the genus Circinaria are proposed.

Mapping Road Infrastructure in Developing Countries Applying Remote Sensing and GIS - The Case of the Taita Hills, Kenya

Road transport and infrastructure has a fundamental meaning for the developing world. Poor quality and inadequate coverage of roads, lack of maintenance operations and outdated road maps continue to hinder economic and social development in the developing countries. This thesis focuses on studying the present state of road infrastructure and its mapping in the Taita Hills, south-east Kenya. The study is included as a part of the TAITA-project by the Department of Geography, University of Helsinki. The road infrastructure of the study area is studied by remote sensing and GIS based methodology. As the principal dataset, true colour airborne digital camera data from 2004, was used to generate an aerial image mosaic of the study area. Auxiliary data includes SPOT satellite imagery from 2003, field spectrometry data of road surfaces and relevant literature. Road infrastructure characteristics are interpreted from three test sites using pixel-based supervised classification, object-oriented supervised classifications and visual interpretation. Road infrastructure of the test sites is interpreted visually from a SPOT image. Road centrelines are then extracted from the object-oriented classification results with an automatic vectorisation process. The road infrastructure of the entire image mosaic is mapped by applying the most appropriate assessed data and techniques. The spectral characteristics and reflectance of various road surfaces are considered with the acquired field spectra and relevant literature. The results are compared with the experimented road mapping methods. This study concludes that classification and extraction of roads remains a difficult task, and that the accuracy of the results is inadequate regardless of the high spatial resolution of the image mosaic used in this thesis. Visual interpretation, out of all the experimented methods in this thesis is the most straightforward, accurate and valid technique for road mapping. Certain road surfaces have similar spectral characteristics and reflectance values with other land cover and land use. This has a great influence for digital analysis techniques in particular. Road mapping is made even more complicated by rich vegetation and tree canopy, clouds, shadows, low contrast between roads and surroundings and the width of narrow roads in relation to the spatial resolution of the imagery used. The results of this thesis may be applied to road infrastructure mapping in developing countries on a more general context, although with certain limits. In particular, unclassified rural roads require updated road mapping schemas to intensify road transport possibilities and to assist in the development of the developing world.

Water quality estimation by optical remote sensing in boreal lakes

In lake-rich regions, the gathering of information about water quality is challenging because only a small proportion of the lakes can be assessed each year by conventional methods. One of the techniques for improving the spatial and temporal representativeness of lake monitoring is remote sensing from satellites and aircrafts. The experimental material included detailed optical measurements in 11 lakes, air- and spaceborne remote sensing measurements with concurrent field sampling, automatic raft measurements and a national dataset of routine water quality measurements from over 1100 lakes. The analyses of the spatially high-resolution airborne remote sensing data from eutrophic and mesotrophic lakes showed that one or a few discrete water quality observations using conventional monitoring can yield a clear over- or underestimation of the overall water quality in a lake. The use of TM-type satellite instruments in addition to routine monitoring results substantially increases the number of lakes for which water quality information can be obtained. The preliminary results indicated that coloured dissolved organic matter (CDOM) can be estimated with TM-type satellite instruments, which could possibly be utilised as an aid in estimating the role of lakes in global carbon budgets. Based on the results of reflectance modelling and experimental data, MERIS satellite instrument has optimal or near-optimal channels for the estimation of turbidity, chlorophyll a and CDOM in Finnish lakes. MERIS images with a 300 m spatial resolution can provide water quality information in different parts of large and medium-sized lakes, and in filling in the gaps resulting from conventional monitoring. Algorithms that would not require simultaneous field data for algorithm training would increase the amount of remote sensing-based information available for lake monitoring. The MERIS Boreal Lakes processor, trained with the optical data and concentration ranges provided by this study, enabled turbidity estimations with good accuracy without the need for algorithm correction with field measurements, while chlorophyll a and CDOM estimations require further development of the processor. The accuracy of interpreting chlorophyll a via semi empirical algorithms can be improved by classifying lakes prior to interpretation according to their CDOM level and trophic status. Optical modelling indicated that the spectral diffuse attenuation coefficient can be estimated with reasonable accuracy from the measured water quality concentrations. This provides more detailed information on light attenuation from routine monitoring measurements than is available through the Secchi disk transparency. The results of this study improve the interpretation of lake water quality by remote sensing and encourage the use of remote sensing in lake monitoring.