Determining species of individual trees is important for forest management. In this thesis we investigate if it is possible to discriminate between Norway spruce, Scots pine and deciduous trees from airborne laser scanning data by using unique probability density functions estimated for each specie. We estimate the probability density functions in three different ways: by fitting a beta distribution, histogram density estimation and kernel density estimation. All these methods classifies single laser returns (and not segments of laser returns). The resulting classification is compared with a reference method based on features extracted from airborne laser scanning data.We measure how well a method performs by using the overall accuracy, that is the proportion of correctly predicted trees. The highest overall accuracy obtained by the methods we developed in this thesis is obtained by using histogram-density estimation where an overall accuracy of 83.4 percent is achieved. This result can be compared with the best result from the reference method that produced an overall accuracy of 84.1 percent. The fact that we achieve a high level of correctly classified trees indicates that it is possible to use these types of methods for identification of tree species. / Att kunna artbestämma enskilda träd är viktigt inom skogsbruket. I denna uppsats undersöker vi om det är möjligt att skilja mellan gran, tall och lövträd med data från en flygburen laserskanner genom att skatta en unik täthetsfunktion för varje trädslag. Täthetsfunktionerna skattas på tre olika sätt: genom att anpassa en beta-fördelning, skatta täthetsfunktionen med histogram samt skatta täthetsfunktionen med en kernel täthetsskattning. Alla dessa metoder klassificerar varje enskild laserretur (och inte segment av laserreturer). Resultaten från vår klassificering jämförs sedan med en referensmetod som bygger på särdrag från laserskanner data. Vi mäter hur väl metoderna presterar genom att jämföra den totala precisionen, vilket är andelen korrektklassificerade träd. Den högsta totala precisionen för de framtagna metoderna i denna uppsats erhölls med metoden som bygger på täthetsskattning med histogram. Precisionen för denna metod var 83,4 procent rättklassicerade träd. Detta kan jämföras med en rättklassificering på 84,1 procent vilket är det bästa resultatet för referensmetoderna. Att vi erhåller en så pass hög grad av rättklassificerade träd tyder på att de metoder som vi använder oss av är användbara för trädslagsklassificering.

No description available.

The purpose of this thesis was to provide a revision and characterization of the species of nongeniculate coralline algae previously ascribed to the now defunct genus Porolithon / to provide a modern account of selected taxa from the genus Hydrolithon and descriptions of taxa found to conform the generic delimitation of Hydrolithon and to use a phenetic cluster analysis to determine the taxonomic relationships between the various taxa ascribed to the genera Polorithon and Hydrolithon.

[Truncated abstract] The southern-temperate spider family Micropholcommatidae is a poorly-studied taxon of uncertain limits and uncertain affinities. Since the first Australian species were described in the early twentieth century, and the family was erected in 1944, the taxonomic status and phylogenetic placement of the Micropholcommatidae have been the subject of ongoing debate. Various phylogenetic hypotheses have been proposed for the family, but these hypotheses have never been tested with a robust phylogenetic analysis – a problem compounded by the inadequate state and confusing history of micropholcommatid taxonomy. To address the many gaps in our understanding of micropholcommatid interrelationships, this thesis will present a comprehensive systematic treatment of the family. Using a combination of molecular phylogenetic, morphological cladistic and taxonomic methods, micropholcommatid diversity is documented and tested at multiple systematic levels, with an alpha-taxonomic and biogeographic focus on the diverse Australian fauna. The taxonomic contribution is substantial throughout, with one new family, two new subfamilies, one new tribe, 14 new genera and 37 new species described. A combined molecular phylogenetic study is presented in Chapter 2, as a 'first pass' exploration of the monophyly, limits and phylogenetic position of the family Micropholcommatidae. The analyses incorporated 50 ingroup spider species, including 23 micropholcommatid taxa, with nucleotide sequences obtained from two nuclear ribosomal RNA genes (18S and 28S). ...The new subfamily Gigiellinae is also described for two enigmatic species in the newly-described genus Gigiella, known only from the temperate Nothofagus rainforests of south-eastern Australia and southern Chile. As a final contribution to micropholcommatid taxonomy, and as an extension to the cladistic analyses presented in Chapter 5, the new spider family Teutoniellidae is proposed in Chapter 6 for three genera from South America, South Africa and Australia. Teutoniellid monophyly is evidenced by at least two unambiguous synapomorphies, and the morphology of the family is described in relation to other symphytognathidan and EbCY spider taxa. The nominate genus Teutoniella is redescribed to include three species from South America, along with an additional new species from Tasmania. Two new teutoniellid genera are also described, each for a single new species from South Africa: Inflaticrus ansieae is described from the Langeberg Range, east of Cape Town; and Woldius hennigi is described from near Pietermaritzburg, north-west of Durban. In summary, this thesis provides a taxonomic and phylogenetic framework for all future research on micropholcommatid spiders. It presents new data on the phylogeny, phylogenetic position, composition, biogeography, molecular evolution and natural history of a previously poorly-known group of spiders, and highlights a number of remaining gaps in our knowledge of micropholcommatid and araneoid systematics. As a novel contribution to scholarship, this thesis synthesises taxonomic and phylogenetic hypotheses at multiple systematic levels, and tests those hypotheses with original, combined datasets.

Thesis--University of Pittsburgh. / Bibliography: p. 247-251.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (p. [248]-277).

Thesis--University of Pittsburgh. / Bibliography: p. 247-251.

Thesis--University of Michigan. / Includes bibliographical references (leaves 451-452).

The main claim of this thesis is that value-judgments should play a profound role in the construction and evaluation of species classifications. The arguments for this claim will be presented over the course of five chapters. These are divided into two main parts; part one, which consists of the two first chapters, presents an argument for a radical form of species pluralism; part two, which comprises the remaining chapters, discusses the implications of radical species pluralism for the role of values in species classification. The content of the five chapters is as follows. Chapter 1 starts with a discussion of the theoretical assumptions concerning species and natural kinds that form the broad framework within which the arguments of the thesis are placed. The aim of this chapter is to introduce a set of relatively uncontroversial assumptions that frame the rest of the thesis. On the basis of these assumptions, chapter 2 presents an argument for radical species pluralism. The chapter substantiates this argument with a broad range of examples, and compares this position to other forms of species pluralism. Chapter 3 returns to the main interest of the thesis, namely, the role of values in species classification. It introduces the notion of values and presents an argument for the value-ladenness of taxonomy on the basis of the considerations in the first two chapters. It then sketches three important views on values in science in the literature. Chapter 4 argues that the case presented in chapter 3 provides strong support for one of these views, called the ‘Aims View’, and against two other prominent views, called the ‘Epistemic Priority View’ and the ‘Value-Free Ideal’. The resulting view, in line with the Aims View, is that value-judgments should play a particularly substantial role in species classification. Chapter 5 then considers the popular assumption that these value-judgments in taxonomy commonly take the shape of generally accepted classificatory norms, and argues that this assumption is not tenable. Finally, a brief concluding chapter points at some implications of the claims and arguments in this thesis.

Ce mémoire de thèse porte sur l’élaboration de systèmes de reconnaissance d’image qui sont robustes à la variabilité géométrique. La compréhension d’une image est un problème difficile, de par le fait qu’elles sont des projections en deux dimensions d’objets 3D. Par ailleurs, des représentations qui doivent appartenir à la même catégorie, par exemple des objets de la même classe en classification, peuvent être visuellement très différentes. Notre but est de rendre ces systèmes robustes à la juste quantité de déformations, celle-ci étant automatiquement déterminée à partir des données. Nos deux contributions sont les suivantes. Nous montrons tout d’abord comment utiliser des exemples virtuels pour rendre les systèmes de classification d’images robustes et nous proposons ensuite une méthodologie pour apprendre des descripteurs de bas niveau robustes, pour la recherche d’image.Nous étudions tout d’abord les exemples virtuels, en tant que transformations de vrais exemples. En représentant une image en tant que sac de descripteurs transformés, nous montrons que l’augmentation de données, c’est-à-dire le fait de les considérer comme de nouveaux exemples iid, est la meilleure manière de les utiliser, pourvu qu’une étape de vote avec les descripteurs transformés soit opérée lors du test. Du fait que les transformations apportent différents niveaux d’information, peuvent être redondants, voire nuire à la performance, nous pro-posons un nouvel algorithme capable de sélectionner un petit nombre d’entre elles,en maximisant la justesse de classification. Nous montrons par ailleurs comment remplacer de vrais exemples par des virtuels, pour alléger les couts d’annotation.Nous rapportons de bons résultats sur des bancs d’essai de classification.Notre seconde contribution vise à améliorer les descripteurs de régions locales utilisés en recherche d’image, et en particulier nous proposons une alternative au populaire descripteur SIFT. Nous proposons un nouveau descripteur, appelé patch-CKN, appris sans supervision. Nous introduisons un nouvel ensemble de données liant les images et les imagettes, construit à partir de reconstruction3D automatique d’images récupérées sur Internet. Nous définissons une méthode pour tester précisément la performance des descripteurs locaux au niveau de l’imagette et de l’image. Notre approche dépasse SIFT et les autres approches à base d’architectures convolutionnelles sur notre banc d’essai, et d’autres couramment utilisés dans la littérature. / This dissertation focuses on designing image recognition systems which are robust to geometric variability. Image understanding is a difficult problem, as images are two-dimensional projections of 3D objects, and representations that must fall into the same category, for instance objects of the same class in classification can display significant differences. Our goal is to make systems robust to the right amount of deformations, this amount being automatically determined from data. Our contributions are twofolds. We show how to use virtual examples to enforce robustness in image classification systems and we propose a framework to learn robust low-level descriptors for image retrieval. We first focus on virtual examples, as transformation of real ones. One image generates a set of descriptors –one for each transformation– and we show that data augmentation, ie considering them all as iid samples, is the best performing method to use them, provided a voting stage with the transformed descriptors is conducted at test time. Because transformations have various levels of information, can be redundant, and can even be harmful to performance, we propose a new algorithm able to select a set of transformations, while maximizing classification accuracy. We show that a small amount of transformations is enough to considerably improve performance for this task. We also show how virtual examples can replace real ones for a reduced annotation cost. We report good performance on standard fine-grained classification datasets. In a second part, we aim at improving the local region descriptors used in image retrieval and in particular to propose an alternative to the popular SIFT descriptor. We propose new convolutional descriptors, called patch-CKN, which are learned without supervision. We introduce a linked patch- and image-retrieval dataset based on structure from motion of web-crawled images, and design a method to accurately test the performance of local descriptors at patch and image levels. Our approach outperforms both SIFT and all tested approaches with convolutional architectures on our patch and image benchmarks, as well as several styate-of-theart datasets.