The Unable Individual : The Actantial Analyses of Three Chinese Films and Discussion on Their Representations of the Individual’s Position in Contemporary Chinese Society

The thesis studies three contemporary Chinese films, Incense (Xianghuo 2003), Beijing Bicycle (Shiqi sui de danche, 2001), and South of the Clouds (Yun de nanfang, 2004). The aim of the thesis is to find out how these films represent the individual, his relationships with others, and his possibilities in society. The films all portray a male individual setting out to pursue a goal, facing one obstacle after the other in the process, and in the end failing to achieve his goal. The other characters in the films are also primarily either unable or unwilling to help the lead character. In the thesis these features of the films are analysed by applying A.J. Greimas’s structuralist semiotic theory about the actantial structure of discourses. The questions about the individual’s position are answered by defining which instances in the films actually represent the actantial positions of the sender, receiver, subject, object, helper and opponent. The results of the actantial analyses of the three films are further discussed in the light of theories regarding individualization. The focus is on the theories of Ulrich Beck, Elisabeth Beck-Gernsheim and Zygmunt Bauman, who see a development towards individualization in societies following the disintegration of traditional social structures. For the most part, these theories do seem to be applicable to the films’ representations of the individual’s position, especially regarding the increased responsibility of the individual. For example, the position of the family is represented as either insignificant or negative in all of the films, which fits with the description of the individualized society, contradicting the idea of traditional Chinese society. On the other hand, the identities and goals of the characters in the films are not represented as fragmented in the way the theories would suggest.

From a tree to a stand in Finnish boreal forests: biomass estimation and comparison of methods

There is an increasing need to compare the results obtained with different methods of estimation of tree biomass in order to reduce the uncertainty in the assessment of forest biomass carbon. In this study, tree biomass was investigated in a 30-year-old Scots pine (Pinus sylvestris) (Young-Stand) and a 130-year-old mixed Norway spruce (Picea abies)-Scots pine stand (Mature-Stand) located in southern Finland (61º50' N, 24º22' E). In particular, a comparison of the results of different estimation methods was conducted to assess the reliability and suitability of their applications. For the trees in Mature-Stand, annual stem biomass increment fluctuated following a sigmoid equation, and the fitting curves reached a maximum level (from about 1 kg/yr for understorey spruce to 7 kg/yr for dominant pine) when the trees were 100 years old. Tree biomass was estimated to be about 70 Mg/ha in Young-Stand and about 220 Mg/ha in Mature-Stand. In the region (58.00-62.13 ºN, 14-34 ºE, ≤ 300 m a.s.l.) surrounding the study stands, the tree biomass accumulation in Norway spruce and Scots pine stands followed a sigmoid equation with stand age, with a maximum of 230 Mg/ha at the age of 140 years. In Mature-Stand, lichen biomass on the trees was 1.63 Mg/ha with more than half of the biomass occurring on dead branches, and the standing crop of litter lichen on the ground was about 0.09 Mg/ha. There were substantial differences among the results estimated by different methods in the stands. These results imply that a possible estimation error should be taken into account when calculating tree biomass in a stand with an indirect approach.

Estimation of local forest attributes, utilizing two-phase sampling and auxiliary data

This thesis examines the feasibility of a forest inventory method based on two-phase sampling in estimating forest attributes at the stand or substand levels for forest management purposes. The method is based on multi-source forest inventory combining auxiliary data consisting of remote sensing imagery or other geographic information and field measurements. Auxiliary data are utilized as first-phase data for covering all inventory units. Various methods were examined for improving the accuracy of the forest estimates. Pre-processing of auxiliary data in the form of correcting the spectral properties of aerial imagery was examined (I), as was the selection of aerial image features for estimating forest attributes (II). Various spatial units were compared for extracting image features in a remote sensing aided forest inventory utilizing very high resolution imagery (III). A number of data sources were combined and different weighting procedures were tested in estimating forest attributes (IV, V). Correction of the spectral properties of aerial images proved to be a straightforward and advantageous method for improving the correlation between the image features and the measured forest attributes. Testing different image features that can be extracted from aerial photographs (and other very high resolution images) showed that the images contain a wealth of relevant information that can be extracted only by utilizing the spatial organization of the image pixel values. Furthermore, careful selection of image features for the inventory task generally gives better results than inputting all extractable features to the estimation procedure. When the spatial units for extracting very high resolution image features were examined, an approach based on image segmentation generally showed advantages compared with a traditional sample plot-based approach. Combining several data sources resulted in more accurate estimates than any of the individual data sources alone. The best combined estimate can be derived by weighting the estimates produced by the individual data sources by the inverse values of their mean square errors. Despite the fact that the plot-level estimation accuracy in two-phase sampling inventory can be improved in many ways, the accuracy of forest estimates based mainly on single-view satellite and aerial imagery is a relatively poor basis for making stand-level management decisions.

Remote sensing of boreal land cover: estimation of forest attributes and extent

Remote sensing provides methods to infer land cover information over large geographical areas at a variety of spatial and temporal resolutions. Land cover is input data for a range of environmental models and information on land cover dynamics is required for monitoring the implications of global change. Such data are also essential in support of environmental management and policymaking. Boreal forests are a key component of the global climate and a major sink of carbon. The northern latitudes are expected to experience a disproportionate and rapid warming, which can have a major impact on vegetation at forest limits. This thesis examines the use of optical remote sensing for estimating aboveground biomass, leaf area index (LAI), tree cover and tree height in the boreal forests and tundra taiga transition zone in Finland. The continuous fields of forest attributes are required, for example, to improve the mapping of forest extent. The thesis focus on studying the feasibility of satellite data at multiple spatial resolutions, assessing the potential of multispectral, -angular and -temporal information, and provides regional evaluation for global land cover data. Preprocessed ASTER, MISR and MODIS products are the principal satellite data. The reference data consist of field measurements, forest inventory data and fine resolution land cover maps. Fine resolution studies demonstrate how statistical relationships between biomass and satellite data are relatively strong in single species and low biomass mountain birch forests in comparison to higher biomass coniferous stands. The combination of forest stand data and fine resolution ASTER images provides a method for biomass estimation using medium resolution MODIS data. The multiangular data improve the accuracy of land cover mapping in the sparsely forested tundra taiga transition zone, particularly in mires. Similarly, multitemporal data improve the accuracy of coarse resolution tree cover estimates in comparison to single date data. Furthermore, the peak of the growing season is not necessarily the optimal time for land cover mapping in the northern boreal regions. The evaluated coarse resolution land cover data sets have considerable shortcomings in northernmost Finland and should be used with caution in similar regions. The quantitative reference data and upscaling methods for integrating multiresolution data are required for calibration of statistical models and evaluation of land cover data sets. The preprocessed image products have potential for wider use as they can considerably reduce the time and effort used for data processing.

Computationally Efficient Methods for MDL-Optimal Density Estimation and Data Clustering

The Minimum Description Length (MDL) principle is a general, well-founded theoretical formalization of statistical modeling. The most important notion of MDL is the stochastic complexity, which can be interpreted as the shortest description length of a given sample of data relative to a model class. The exact definition of the stochastic complexity has gone through several evolutionary steps. The latest instantation is based on the so-called Normalized Maximum Likelihood (NML) distribution which has been shown to possess several important theoretical properties. However, the applications of this modern version of the MDL have been quite rare because of computational complexity problems, i.e., for discrete data, the definition of NML involves an exponential sum, and in the case of continuous data, a multi-dimensional integral usually infeasible to evaluate or even approximate accurately. In this doctoral dissertation, we present mathematical techniques for computing NML efficiently for some model families involving discrete data. We also show how these techniques can be used to apply MDL in two practical applications: histogram density estimation and clustering of multi-dimensional data.

The family Herbertaceae and its novel systematic position within liverworts

Taxonomic relationships of the liverwort genus Herbertus in Asia were examined. In addition, the phylogeny of the family Herbertaceae and its close relatives was investigated and analyses conducted of higher level relationships within the entire liverwort phylum. Species of Herbertus show great plasticity in various morphological characters, resulted in a large number of described species. This study was the first comprehensive revision of Asian Herbertus, with 12 species recognized for the continent. Eleven names were reduced to synonymy under earlier described species, and one species was excluded from the genus. Herbertus buchii Juslén was described as a new species. Phylogenetic analyses based on both molecular and morphological characters resolved the families Vetaformaceae, Lepicoleaceae, and Herbertaceae (including Mastigophoraceae) as a monophyletic entity. This clade is among the most derived groups within the leafy liverworts and comprises mostly isophyllous plants, all of which have bracteolar antheridia. The relationships of Mastigophoraceae have formerly been controversial. My results confirm the view that this family is closely related to Herbertaceae, Lepicoleaceae, and Vetaformaceae. In the proposed new classification Mastigophoraceae is included in Herbertaceae. Phylogenetic relationships within the liverworts were reconstructed using both chloroplast and nuclear sequences as well as morphological characters. These analyses were the most comprehensive to date at the time of publication. Previously it was believed that liverworts had a common ancestor with an erect, radial gametophyte and a tetrahedral apical cell. The leafy liverworts were arranged based on the assumption that similar structures had repeatedly developed in many different suborders, with evolution proceeding from erect and isophyllous to creeping and anisophyllous plants. The complex thalloid liverworts were assumed to be the most derived group. By contrast, our studies resolved a clade comprising Treubia and Haplomitrium as the earliest extant liverwort lineage. According to our results the complex thalloids are also an early diverging lineage, and the simple thalloids, traditionally classified together, are a paraphyletic group. Within leafy liverworts, the hypothesis of repeated evolution from isophyllous to anisophyllous plants based on the assumption of a basal unresolved polytomy was rejected. Fundamentally, the leafy liverworts can be divided into three groups. In conflict with the earlier hypotheses, the isophyllous liverworts, including Herbertaceae, were resolved as derived lineages within the liverworts.

Generalised regression estimation for domain class frequencies

This study examines the properties of Generalised Regression (GREG) estimators for domain class frequencies and proportions. The family of GREG estimators forms the class of design-based model-assisted estimators. All GREG estimators utilise auxiliary information via modelling. The classic GREG estimator with a linear fixed effects assisting model (GREG-lin) is one example. But when estimating class frequencies, the study variable is binary or polytomous. Therefore logistic-type assisting models (e.g. logistic or probit model) should be preferred over the linear one. However, other GREG estimators than GREG-lin are rarely used, and knowledge about their properties is limited. This study examines the properties of L-GREG estimators, which are GREG estimators with fixed-effects logistic-type models. Three research questions are addressed. First, I study whether and when L-GREG estimators are more accurate than GREG-lin. Theoretical results and Monte Carlo experiments which cover both equal and unequal probability sampling designs and a wide variety of model formulations show that in standard situations, the difference between L-GREG and GREG-lin is small. But in the case of a strong assisting model, two interesting situations arise: if the domain sample size is reasonably large, L-GREG is more accurate than GREG-lin, and if the domain sample size is very small, estimation of assisting model parameters may be inaccurate, resulting in bias for L-GREG. Second, I study variance estimation for the L-GREG estimators. The standard variance estimator (S) for all GREG estimators resembles the Sen-Yates-Grundy variance estimator, but it is a double sum of prediction errors, not of the observed values of the study variable. Monte Carlo experiments show that S underestimates the variance of L-GREG especially if the domain sample size is minor, or if the assisting model is strong. Third, since the standard variance estimator S often fails for the L-GREG estimators, I propose a new augmented variance estimator (A). The difference between S and the new estimator A is that the latter takes into account the difference between the sample fit model and the census fit model. In Monte Carlo experiments, the new estimator A outperformed the standard estimator S in terms of bias, root mean square error and coverage rate. Thus the new estimator provides a good alternative to the standard estimator.

An Expenditure-Based Estimation of Self-Employment Income Underreporting in Finland

This paper estimates the extent of income underreporting by the self-employed in Finland using the expenditure based approach developed by Pissarides & Weber (1989). Household spending data are for the years 1994 to 1996. The results suggest that self-employment income in Finland is underreported by some 27% on average. Since income for the self-employed is about 8 % of all incomes in Finland, the size of this part of the black economy in Finland is estimated to be about 2,3% of GDP.