Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniversitet, 2004. / Härtill 5 uppsatser.

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.

Tree species identification is an important element in many forest resources applications such as wildlife habitat management, inventory, and forest damage assessment. Field data collection for large or mountainous areas is often cost prohibitive, and good estimates of the number and spatial arrangement of species or species groups cannot be obtained. Knowledge-based and neural network species classification models were constructed for remotely sensed data of conifer stands located in the lower mountain regions near McCall, Idaho, and compared to field data. Analyses for each modeling system were made based on multi-spectral sensor (MSS) data alone and MSS plus LiDAR (light detection and ranging) data. The neural network system produced models identifying five of six species with 41% to 88% producer accuracies and greater overall accuracies than the knowledge-based system. The neural network analysis that included a LiDAR derived elevation variable plus multi-spectral variables gave the best overall accuracy at 63%.

Avec l'expansion des zones urbaines, la pollution de l'air et l'effet d'îlot de chaleur augmentent, entraînant des problèmes de santé pour les habitants et des changements climatiques mondiaux. Dans ce contexte, les arbres urbains sont une ressource précieuse pour améliorer la qualité de l'air et promouvoir les îlot de fraîcheur. D'autre part, les canopées sont soumises à des conditions spécifiques dans l'environnement urbain, causant la propagation de maladies et la diminution de l'espérance de vie parmi les arbres. Cette thèse explore le potentiel de la télédétection pour la cartographie automatique des arbres urbains, de la détection des couronnes d'arbres à l'estimation des espèces, une tâche préliminaire essentielle pour la conception des futures villes vertes, et pour une surveillance efficace de la végétation. Fondé sur des données hyperspectrales aéroportées, panchromatiques et un modèle numérique de surface, le premier objectif de cette thèse consiste à tirer parti de plusieurs sources de données pour améliorer les cartes d'arbres urbains existants, en testant différentes stratégies de fusion (fusion de caractéristiques et fusion de décision). La nature des résultats nous a conduit à optimiser la complémentarité des sources. En particulier, le deuxième objectif est d'étudier en profondeur la richesse des données hyperspectrales, en développant une approche d'ensemble classifier fondée sur des indices de végétation, où les "classifier" sont spécifiques aux espèces. Enfin, la première partie a mis en évidence l'intérêt de distinguer les arbres de rue des autres structures d'arbres urbains. Dans un cadre de Marked Point Process, le troisième objectif est de détecter les arbres en alignement urbain. Par le premier objectif, cette thèse démontre que les données hyperspectrales sont le principal moteur de la précision de la prédiction des espèces. La stratégie de fusion au niveau de décision est la plus appropriée pour améliorer la performance en comparaison des données hyperspectrales seules, mais de légères améliorations sont obtenues (quelques %) en raison de la faible complémentarité des caractéristiques texturales et structurelles en plus des caractéristiques spectrales. L'approche d'ensemble classifier développée dans la deuxième partie permet de classer les espèces d'arbres à partir de références au sol, avec des améliorations significatives par rapport à une approche standard de classification au niveau des caractéristiques. Chaque classifieur d'espèces extrait reflète les attributs spectraux discriminants de l'espèce et peut être relié à l'expertise des botanistes. Enfin, les arbres de rue peuvent être cartographiés grâce au terme d'interaction des MPP proposé qui modélise leurs caractéristiques contextuelles (alignement et hauteurs similaires). De nombreuses améliorations doivent être explorées comme la délimitation plus précise de la couronne de l'arbre, et plusieurs perspectives sont envisageables après cette thèse, parmi lesquelles le suivi de l'état de santé des arbres urbains. / With the expansion of urban areas, air pollution and heat island effect are increasing, leading to state of health issues for the inhabitants and global climate changes. In this context, urban trees are a valuable resource for both improving air quality and promoting freshness islands. On the other hand, canopies are subject to specific conditions in the urban environment, causing the spread of diseases and life expectancy decreases among the trees. This thesis explores the potential of remote sensing for the automatic urban tree mapping, from the detection of the individual tree crowns to their species estimation, an essential preliminary task for designing the future green cities, and for an effective vegetation monitoring. Based on airborne hyperspectral, panchromatic and Digital Surface Model data, the first objective of this thesis consists in taking advantage of several data sources for improving the existing urban tree maps, by testing different fusion strategies (feature and decision level fusion). The nature of the results led us to optimize the complementarity of the sources. In particular, the second objective is to investigate deeply the richness of the hyperspectral data, by developing an ensemble classifiers approach based on vegetation indices, where the classifiers are species specific. Finally, the first part highlighted to interest of discriminating the street trees from the other structures of urban trees. In a Marked Point Process framework, the third objective is to detect trees in urban alignment. Through the first objective, this thesis demonstrates that the hyperspectral data are the main driver of the species prediction accuracy. The decision level fusion strategy is the most appropriate one for improving the performance in comparison the hyperspectral data alone, but slight improvements are obtained (a few percent) due to the low complementarity of textural and structural features in addition to the spectral ones. The ensemble classifiers approach developed in the second part allows the tree species to be classified from ground-based references, with significant improvements in comparison to a standard feature level classification approach. Each extracted species classifier reflects the discriminative spectral attributes of the species and can be related to the expertise of botanists. Finally, the street trees can be mapped thanks to the proposed MPP interaction term which models their contextual features (alignment and similar heights). Many improvements have to be explored such as the more accurate tree crown delineation, and several perspectives are conceivable after this thesis, among which the state of health monitoring of the urban trees.

In forest management, knowledge about a forest's distribution of tree species is key. Being able to automate tree species classification for large forest areas is of great interest, since it is tedious and costly labour doing it manually. In this project, the aim was to investigate the efficiency of classifying individual tree species (pine, spruce and deciduous forest) from 3D point clouds acquired by airborne laser scanning (ALS), using convolutional neural networks. Raw data consisted of 3D point clouds and photographic images of forests in northern Sweden, collected from a helicopter flying at low altitudes. The point cloud of each individual tree was connected to its representation in the photos, which allowed for manual labeling of training data to be used for training of convolutional neural networks. The training data consisted of labels and 2D projections created from the point clouds, represented as images. Two different convolutional neural networks were trained and tested; an adaptation of the LeNet architecture and the ResNet architecture. Both networks reached an accuracy close to 98 %, the LeNet adaptation having a slightly lower loss score for both validation and test data compared to that of ResNet. Confusion matrices for both networks showed similar F1 scores for all tree species, between 97 % and 98 %. The accuracies computed for both networks were found higher than those achieved in similar studies using ALS data to classify individual tree species. However, the results in this project were never tested against a true population sample to confirm the accuracy. To conclude, the use of convolutional neural networks is indeed an efficient method for classification of tree species, but further studies on unbiased data is needed to validate these results.

Master of Science / Department of Statistics / Leigh Murray / Using remotely sensed reflectance data is an appealing tool for controlling invasive species of grasses by rangeland managers. Recent developments in functional data analysis include the functional boxplot (FBP) which is shown here to be a useful tool in the visualization of reflectance data. Functional boxplots are a novel method of visually inspecting functional data and determining the presence of outliers in the data. Implementation and interpretation of FBPs are both straightforward and intuitive. The goal of this study is to examine the use of FBPs for visualizing reflectance data, and to determine the efficacy of using the FBP to distinguish between native tall grasses and invasive Old World Bluestem (OWB, Bothriochloa spp.) monocultures in a Kansas prairie. Validation trials were conducted in order to determine the stability of the FBP when used to analyze spectral data. FBPs were shown to be highly stable for use with both native and OWB grasses at all times and subsets of wavelengths tested. Identification trials were conducted by introducing a single OWB observation to a test set of native tall grass observations and constructing a FBP. Results indicate that using observations recorded early in the growing season, the functional boxplot is able to successfully identify the OWB observation as an outlier in a test set of native tall grass observations with an estimated probability 100% and 95.45% when considering the visible and cellular spectrums, respectively. A 95% lower bound for the probability of successfully identifying the OWB observation using the cellular spectrum in May is found to be 89.67%.

Scrapter Lepeletier de Saint-Fargeau & Audinet-Serville, 1828 (Hymenoptera: Aculeatea: Anthophila: Colletidae) is a genus of solitary bees largely endemic to southern Africa. This dissertation investigated the phylogenetic systematics of the genus. Eleven new species of Scrapter are described, principally from the Succulent Karoo biome of South Africa, bringing the total number of species in the genus to 42. An updated dichotomous key to facilitate identification is provided. The previously unknown females of S. albifumus Eardley and S. amplispinatus Eardley are also described. The genus is recorded from outside southern Africa for the first time with the collection of S. nitidus (Friese) in Kenya. This constitutes a significant range extension of the genus. The taxonomic status of five species described by Cockerell in 1944, and subsequently overlooked, is addressed. They are all found to be synonyms of other Scrapter species, except one, which is found to be a Ctenoplectrina species (Apidae: Apinae: Ctenoplectrini). The new synonymies are: S. subincertus Cockerell = S. niger Lepeletier de Saint-Fargeau & Audinet-Serville; S. brunneipennis Cockerell = S. niger Lepeletier de Saint-Fargeau & Audinet-Serville; S. merescens Cockerell = S. leonis Cockerell; S. sinophilus Cockerell = S. algoensis (Friese). Scrapter ugandica Cockerell becomes Ctenoplectrina ugandica (Cockerell) as a new combination. Investigation of selected morphological features (e.g. postmentum, facial fovea, galea) revealed much diversity in Scrapter. The monophyly of Scrapter is not supported by unambiguous apomorphies, but is defensible by the congruence of various qualitative characters (e.g. premental fovea, T2 fovea, hindleg and sternal scopa in [females], two submarginal cells). A cladistic analysis using 25 morphological characters recovered numerous most parsimonious trees under both equal- and successive-weighting. To aid in resolution, several taxa known from only one sex or from very limited material, and with many unknown states, were deleted from the matrix. Analysis using this reduced matrix under equal- and successive-weighting resulted in better resolution, although with low consistency index values. Several subclades were common to both cladograms, and likely represent monophyla. The low consistency indices and general lack of unique synapomorphies upholding these subclades, however, dictated against making any classificatory re-arrangements. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2006.

In this paper the brief overview of methods available for individual tree stems quality evaluation and tree species classification has been performed. The use of Near Infrared photometry based on conifer’s canopy reflectance measurement in near infrared range of spectrum has been evaluated for the use in autonomous forest harvesting. Photometric method based on the image processing of the bark pattern has been proposed to perform classification between main construction timber tree species in Scandinavia: Norway spruce (Picea abies) and Scots Pine (Pinus sylvestris). Several feature extraction algorithms have been evaluated, resulting two methods selected: Statistical Analysis using gray level co-occurrence matrix and maximally stable extremal regions feature detector. Feedforward Neural Network with Backpropagation training algorithm and Support Vector Machine classifiers have been implemented and compared. The verification of the proposed algorithm has been performed by real-time testing.

Bonatea Willd. (Orchidaceae: Habernariinae) is a small genus confined to the African continent and Arabia. Phylogenetic and morphometric analyses were undertaken in order to evaluate phylogenetic relationships and species delimitations within Bonatea. In the phylogenetic analyses, little congruence was found between ITS and matK molecular data, while morphological results were largely congruent with those of the ITS region. There is little sequence variation within and between Bonatea species, which could indicate a recent and rapid radiation. The generic characters for Bonatea were reevaluated. Bonatea is closely related to Habenaria but differs in having a galeate middle rostellum lobe that is clearly separated from the vertical anther thecae. By contrast, species of Habenaria have short anthers that are slightly arcuate and flank the rostellum. Morphometric analyses were used to determine taxon boundaries within the Bonatea speciosa and Bonatea cassidea complexes, respectively. Principle component and cluster analyses of morphological variation support the recognition of Bonatea antennifera Rolfe, Bonatea boltonii (Harv.) Bolus and Bonatea speciosa (L.f.) Willd. as distinct species. Morphological evidence supports the inclusion of Bonatea porrecta (Bolus) Summerh. and Bonatea volkensiana (Kraenzl.) Rolfe in the B. speciosa c1ade and this is corroborated by molecular data for the former. Clinal variation in petal lobe dimensions and colour across the distribution range of Bonatea cassidea Sond. encompasses the taxon Bonatea saundersiae (Harv.) T.Durand & Schinz, which is reduced to synonymy. Bonatea saundersioides (Kraenzl. & Schltr.) Cortesi, the sister species to B. cassidea, also exhibits colour variation in its petals. A revision of Bonatea is presented recognizing 14 species. Bonatea eminii (Kraenzl.) Rolfe was excluded due to insufficient information. Full descriptions are provided with diagnostic characters and distributional maps. Bonatea bracteata G.McDonald & McMurtry and Bonatea tentaculifera Summerh. are removed from Bonatea based on their rostellum structure which is inconsistent with the revised generic concept. Bonatea bracteata was transferred as Habenaria transvaalensis Schltr. and B. tentaculifera was renamed Habenaria bonateoides M.Ponsie, as the specific epithet is currently occupied within Habenaria. / Thesis (M.Sc.)-University of KwaZulu-Natal, 2006.

Vorliegende Arbeit zeigt die jüngsten Möglichkeiten hochauflösender Fernerkundung am Beispiel von Stadtbäumen in Berlin, Deutschland. Es wurden neuste methodische Ansätze eingesetzt, wie beispielsweise maschinelles Lernens und individuelle Baumdetektion. Sie erwiesen sich von großem Vorteil für die detaillierte Analyse urbaner Ökosystemdienstleistungen in einer heterogenen Umwelt. Neueste Fernerkundung von hoher zeitlicher Auflösung hat Möglichkeiten gezeigt, Veränderungen des Stadtwaldes präziser zu untersuchen. Diesbezüglich konnten Baumspezies klassifiziert werden auf Grundlage saisonaler Veränderungen, die mittels Fernerkundungsdaten aufgenommen wurden. Dies ist für den urbanen Bereich einmalig und über große Flächen noch nicht durchgeführt worden. Darüber hinaus haben diese Baumarten einzelnen Bäumen zugeordnet werden können, deren Abmessung fernerkundlich erfasst worden ist. Diese neu erzeugten Umweltinformationen einzelner Bäume können damit verbundene urbane Ökosystemdienstleistungen präzise aktualisieren. Zum Beispiel haben so Unsicherheiten in der Schätzung zur Kohlenstoffspeicherung städtischer Wälder reduziert werden können. Es ist zudem von Vorteil gewesen, den gegenwärtigen Mangel an räumlich expliziten dreidimensionalen Informationen über Stadtwälder anzusprechen. Allerdings ist die Rolle städtischen Wälder, das Treibhausgas CO2 langfristig auszugleichen, immer noch wenig untersucht. Gerade der Mangel an präzisen, konsistenten und aktuellen Details führt zu großen Unsicherheiten im Rahmen von Lebenszyklus-Analysen. Auf Grund des aktuellen Fortschritts in hochauflösender Fernerkundung könnten diese Unsicherheiten reduziert werden. Dazu werden Möglichkeiten ausgiebig kritisch bewertet und anhand einer Lebenszyklus-Analyse am Beispiel Berlin andiskutiert, inwieweit sie präzisere langfristige Prognosen zum Stadtwald als Kohlenstoffspeicher liefern. / This work shows recent options for implementing high resolution remote sensing in assessing urban trees in Berlin, Germany. State-of-the-art methodological approaches like machine learning and individual tree detection proved to be highly advantageous for analyzing details of urban ecosystem services within a heterogeneous urban environment. Recent remote sensing of high temporal resolution offers new options for more precisely addressing urban forest dynamics. This successfully shows that tree species could be identified from seasonal changes of remotely sensed imagery, though this has not yet been applied across cities. Furthermore, these tree species results could be combined with remotely sensed individual tree dimensions. This newly generated data can be suggested to update spatially explicit information on related urban ecosystem services. For example, this could reduce the uncertainties of such estimates as urban forest carbon storage, and also address the present lack of spatially explicit three-dimensional information on urban forests. However, few studies have considered the local scale of urban forests to effectively evaluate their potential long-term carbon offset. The lack of precise, consistent and up-to-date forest details is challenging within the scope of life cycle assessments. This can cause high uncertainties in urban forest carbon offset. Although, recent progress in high resolution remote sensing is promising to reduce these uncertainties. For this purpose, remote sensing options are extensively reviewed and briefly discussed using an example of life cycle assessment for Berlin, which allow more precise long-term prognoses of urban forest carbon offset.