Zusammenfassung Kenntnisse über den Zustand und die Entwicklung von Böden sind entscheidend für die Charakterisierung von Ökosystemen und deren Veränderungen. Die weltweite Verbreitung von Eisenoxiden und ihre von der Bodenentwicklung abhängige Konzentration und mineralogische Zusammensetzung machen sie zu geeigneten Indikatoren. Methoden der optische Fernerkundung wurden angewandt, um am Beispiel des Cabo de Gata- Níjar Naturparks, einem semi-ariden Ökosystem in Südostspanien, die Konzentrationen von Eisenoxiden im Boden zu bestimmen und zu kartieren. In der zuerst durchgeführten laborspektroskopischen Studie wurde eine Methode entwickelt, welche den Eisenoxidgehalt (Fed, Citrat-Dithionit extrahierbares Eisenoxid) mit den Eisenabsorptionsbanden verknüpft. Korngrößenabhängige Fed Vorhersagemodelle wurden sowohl für sand- als auch ton-schluff-haltige Proben erstellt. Beide liefern hochgenaue Schätzungen mit weniger als 15% Vorhersagefehler. Ähnliche Werte wurden für korngrößenunabhängige Modelle erreicht. Korngrößenunabhängige Modelle wurden zur Analyse der HyMap-Bilddaten verwendet, da eine pixelbezogene Bestimmung der vorherrschenden Bodentextur nicht möglich war. Die räumliche Verteilung der Fed Konzentration im Untersuchungsgebiet wurde mit einer den Laborergebnissen vergleichbaren Genauigkeit bestimmt. Laboruntersuchungen zum Vegetationseinfluss in Bezug auf Vitalität und Bedeckungsgrad auf die Bodenreflektionsspektren und die Fed Vorhersagegenauigkeit zeigten, dass zuverlässige Abschätzungen bis zu einer Vegetationsbedeckung von ca. 20 % möglich sind. Dementsprechend wurden drei Vorhersagegenauigkeitsklassen definiert, basierend auf der gemeinsamen Detektierbarkeit von Vegetation und Eisenabsorptionsbanden im Bildpixel. Die abgeleitete Fed Verteilungskarte dient der Einschätzung des vorliegenden Bodenzustands und dem Ausweisen von erodierten Oberflächen. Die entwickelte Methode hat aufgrund ihrer Einfachheit ein großes Potential für ein globales Monitoring von sensitiven Gebieten unter der Verwendung von gegenwärtig verfügbaren als auch zukünftigen satellitengestützten Sensoren. / Abstract The knowledge of the soil condition and development is decisive when characterizing and monitoring the change of ecosystems. The global presence of iron oxides and their highly variable concentration and mineralogy reflecting different soil conditions make them a suitable indicator. Optical remote sensing methods are employed to determine and map the soil iron oxide concentrations on the example of the Cabo de Gata-Níjar Natural Park, a semi-arid ecosystem in SE Spain. In an initial laboratory spectroscopy study, a methodology is developed that links iron oxide content (Fed, citrate-dithionite extractable iron oxides) with iron spectral absorption bands. Texture-dependent Fed prediction models are developed for sand- and clay-silt-dominated samples. They yield highly accurate estimations with less than 15 % prediction error. Similar accuracies are achieved from texture-independent models. Texture-independent models are applied to the HyMap image data because a pixel-wise determination of the predominating soil texture is not possible. However, the spatial distribution of Fed concentration in the study area is determined with comparable accuracy as in the laboratory. Laboratory analysis of vegetation vitality and density impact on the soil reflectance spectra and Fed prediction accuracy has shown that reliable estimations are possible until about 20 % leaf cover. Accordingly, three Fed prediction accuracy levels are defined based on the joint detectability of vegetation and iron absorption features. The final Fed prediction map is used to evaluate the current soil conditions and identify potentially eroded soils surfaces. The present method has due to low complexity a high potential for the global monitoring of such sensitive areas from current and future spaceborne sensors.

As well as the many benefits associated with the evolution of multispectral sensors into hyperspectral sensors there is also a considerable increase in storage space and the computational load to process the data. Consequently the remote sensing ommunity is investigating and developing statistical methods to alleviate these problems. / The research presented here investigates several approaches to reducing the dimensionality of hyperspectral remotely sensed data while maintaining the levels of accuracy achieved using the full dimensionality of the data. It was conducted with an emphasis on applications in maximum likelihood classification (MLC) of hyperspectral image data. An inherent characteristic of hyperspectral data is that adjacent bands are typically highly correlated and this results in a high level of redundancy in the data. The high correlations between adjacent bands can be exploited to realise significant reductions in the dimensionality of the data, for a negligible reduction in classification accuracy. / The high correlations between neighbouring bands is related to their response functions overlapping with each other by a large amount. The spectral band filter functions were modelled for the HyMap instrument that acquires hyperspectral data used in this study. The results were compared with measured filter function data from a similar, more recent HyMap instrument. The results indicated that on average HyMap spectral band filter functions exhibit overlaps with their neighbouring bands of approximately 60%. This is considerable and partly accounts for the high correlation between neighbouring spectral bands on hyperspectral instruments. / A hyperspectral HyMap image acquired over an agricultural region in the south west of Western Australia has been used for this research. The image is composed of 512 × 512 pixels, with each pixel having a spatial resolution of 3.5 m. The data was initially reduced from 128 spectral bands to 82 spectral bands by removing the highly overlapping spectral bands, those which exhibit high levels of noise and those bands located at strong atmospheric absorption wavelengths. The image was examined and found to contain 15 distinct spectral classes. Training data was selected for each of these classes and class spectral mean and covariance matrices were generated. / The discriminant function for MLC makes use of not only the measured pixel spectra but also the sample class covariance matrices. This thesis first examines reducing the parameterization of these covariance matrices for use by the MLC algorithm. The full dimensional spectra are still used for the classification but the number of parameters needed to describe the covariance information is significantly reduced. When a threshold of 0.04 was used in conjunction with the partial correlation matrices to identify low values in the inverse covariance matrices, the resulting classification accuracy was 96.42%. This was achieved using only 68% of the elements in the original covariance matrices. / Both wavelet techniques and cubic splines were investigated as a means of representing the measured pixel spectra with considerably fewer bands. Of the different mother wavelets used, it was found that the Daubechies-4 wavelet performed slightly better than the Haar and Daubechies-6 wavelets at generating accurate spectra with the least number of parameters. The wavelet techniques investigated produced more accurately modelled spectra compared with cubic splines with various knot selection approaches. A backward stepwise knot selection technique was identified to be more effective at approximating the spectra than using regularly spaced knots. A forward stepwise selection technique was investigated but was determined to be unsuited to this process. / All approaches were adapted to process an entire hyperspectral image and the subsequent images were classified using MLC. Wavelet approximation coefficients gave slightly better classification results than wavelet detail coefficients and the Haar wavelet proved to be a more superior wavelet for classification purposes. With 6 approximation coefficients, the Haar wavelet could be used to classify the data with an accuracy of 95.6%. For 11 approximation coefficients this figure increased to 96.1%. / First and second derivative spectra were also used in the classification of the image. The first and second derivatives were determined for each of the class spectral means and for each band the standard deviations were calculated of both the first and second derivatives. Bands were then ranked in order of decreasing standard deviation. Bands showing the highest standard deviations were identified and the derivatives were generated for the entire image at these wavelengths. The resulting first and second derivative images were then classified using MLC. Using 25 spectral bands classification accuracies of approximately 96% and 95% were achieved using the first and second derivative images respectively. These results are comparable with those from using wavelets although wavelets produced higher classification accuracies when fewer coefficients were used.

Durch den Prozess der Urbanisierung verändert die Menschheit die Erdoberfläche in großem Ausmaß und auf unwiederbringliche Weise. Die optische Fernerkundung ist eine Art der Erdbeobachtung, die das Verständnis dieses dynamischen Prozesses und seiner Auswirkungen erweitern kann. Die vorliegende Arbeit untersucht, inwiefern hyperspektrale Daten Informationen über Versiegelung liefern können, die der integrierten Analyse urbaner Mensch-Umwelt-Beziehungen dienen. Hierzu wird die Verarbeitungskette von Vorverarbeitung der Rohdaten bis zur Erstellung referenzierter Karten zu Landbedeckung und Versiegelung am Beispiel von Hyperspectral Mapper Daten von Berlin ganzheitlich untersucht. Die traditionelle Verarbeitungskette wird mehrmals erweitert bzw. abgewandelt. So wird die radiometrische Vorverarbeitung um die Normalisierung von Helligkeitsgradienten erweitert, welche durch die direktionellen Reflexionseigenschaften urbaner Oberflächen entstehen. Die Klassifikation in fünf spektral komplexe Landnutzungsklassen wird mit Support Vector Maschinen ohne zusätzliche Merkmalsextraktion oder Differenzierung von Subklassen durchgeführt. Eine detaillierte Ergebnisvalidierung erfolgt mittels vielfältiger Referenzdaten. Es wird gezeigt, dass die Kartengenauigkeit von allen Verarbeitungsschritten abhängt: Support Vector Maschinen klassifizieren Hyperspektraldaten akkurat aber die Kartengenauigkeit wird durch die Georeferenzierung deutlich gemindert; die Versiegelungskartierung stellt die Situation am Boden gut dar, aber die Überdeckung versiegelter Flächen durch Bäume bedingt systematische Fehlschätzungen; eine Bildsegmentierung führt zu keiner Verbesserung der Klassifikationsergebnisse, bietet jedoch eine sinnvolle Möglichkeit zur effektiveren Prozessierung durch Datenkomprimierung. Auf diesem Weg ermöglicht die vorliegende Arbeit Rückschlüsse zur Verlässlichkeit von Datenprodukten, die eine Ausweitung fernerkundlicher Analysen in weniger gut dokumentierte urbane Räume voranbringt. / Urbanization is one of the most powerful and irreversible processes by which humans modify the Earth''s surface. Optical remote sensing is a main source of Earth observation products which help to better understand this dynamic process and its consequences. This work investigates the potential of airborne hyperspectral data to provide information on urban imperviousness that is needed for an integrated analysis of the coupled natural and human systems therein. For this purpose the complete processing workflow from preprocessing of the raw image to the generation of geocoded maps on land cover and impervious surface coverage is performed using Hyperspectral Mapper data acquired over Berlin, Germany. The traditional workflow for hyperspectral data is extended or modified at several points: a normalization of brightness gradients that are caused by directional reflectance properties of urban surfaces is included into radiometric preprocessing; support vector machines are used to classify five spectrally complex land cover classes without previous feature extraction or the definition of sub-classes. A detailed assessment of such maps is performed based on various reference products. Results show that the accuracy of derived maps depends on several steps within the processing workflow. For example, the support vector machine classification of hyperspectral data itself is accurate but geocoding without detailed terrain information introduces critical errors; impervious surface estimates correlate well with ground data but trees covering impervious surface below generally causes offsets; image segmentation does not enhance spectral classification accuracy of the spatially heterogeneous area but offers an interesting way of data compression and more time effective processing. Findings from this work help judging the reliability of data products and in doing so advance a possible extension of urban remote sensing approaches to areas where only little additional data exists.

This thesis describes the research undertaken for the degree of Doctor of Philosophy, testing the hypothesis that spectrometer data can be used to establish usable relationships for prediction of pasture quality attributes. The research data consisted of reflectance measurements of various temperate pasture types recorded at four different times (years 2000 to 2002), recorded by three hyperspectral sensors, the in situ ASD, the airborne HyMap and the satellite-borne Hyperion. Corresponding ground-based pasture samples were analysed for content of chlorophyll, water, crude protein, digestibility, lignin and cellulose at three study sites in rural Victoria, Australia. This context was used to evaluate effects of sensor differences, data processing and enhancement, analytical methods and sample variability on the predictive capacity of derived prediction models. Although hyperspectral data analysis is being applied in many areas very few studies on temperate pastures have been conducted and hardly any encompass the variability and heterogeneity of these southern Australian examples. The research into the relationship between the spectrometer data and pasture quality attribute assays was designed using knowledge gained from assessment of other hyperspectral remote sensing and near-infrared spectroscopy research, including bio-chemical and physical properties of pastures, as well as practical issues of the grazing industries and carbon cycling/modelling. Processing and enhancement of the spectral data followed methods used by other hyperspectral researchers with modifications deemed essential to produce better relationships with pasture assay data. As many different methods are in use for the analysis of hyperspectral data several alternative approaches were investigated and evaluated to determine reliability, robustness and suitability for retrieval of temperate pasture quality attributes. The analyses employed included stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR). The research showed that the spectral research data had a higher potential to be used for prediction of crude protein and digestibility than for the plant fibres lignin and cellulose. Spectral transformation such as continuum removal and derivatives enhanced the results. By using a modified approach based on sample subsets identified by a matrix of subjective bio-physical and ancillary data parameters, the performance of the models were enhanced. Prediction models from PLSR developed on ASD in situ spectral data, HyMap airborne imagery and Hyperion and corresponding pasture assays showed potential for predicting the two important pasture quality attributes crude protein and digestibility in hyperspectral imagery at a few quantised levels corresponding to levels currently used in commercial feed testing. It was concluded that imaging spectrometry has potential to offer synoptic, simultaneous and spatially continuous information valuable to feed based enterprises in temperate Victoria. The thesis provide a significant contribution to the field of hyperspectral remote sensing and good guidance for future hyperspectral researchers embarking on similar tasks. As the research is based on temperate pastures in Victoria, Australia, which are dominated by northern hemisphere species, the findings should be applicable to analysis of temperate pastures elsewhere, for example in Western Australia, New Zealand, South Africa, North America, Europe and northern Asia (China).

Urbanisierungsprozesse beeinflussen die Funktionen natürlicher Ökosysteme signifikant. Bereitgestellte ökosystemare Leistungen unterliegen Veränderungen, die direkt die Lebensbedingungen des Menschen beeinflussen. Funktionale Freiflächen mit Baum- und Gehölzbeständen sind wichtige Bestandteile urbaner Ökosysteme und lindern lokal Einflüsse der Urbanisierung. Die Umweltbedingungen in der Stadt weichen von den natürlichen ab und mindern die Funktionsfähigkeit der Bäume. Gegenüber etablierten, terrestrischen Untersuchungen des Baumzustands ermöglicht die Fernerkundung räumlich und zeitlich flexible Vitalitätscharakterisierungen. In dieser Dissertation wird ein fernerkundlicher Ansatz zur Baumzustandserhebung erarbeitet. Bei Nutzung hyperspektraler Bilddaten werden biophysikalische und strukturelle Baumparameter mit dem quantitativen Ansatz der Strahlungstransfermodellierung (RTM) abgeleitet. Schwerpunkt dieser Arbeit war die Adaption der RTM auf einzelne Bäume. Hierzu wurden drei Anpassungen vorgenommen: die Korrektur höhenbedingter Bildpunktversätze, die Selektion von Bildelementen und die Nutzung eines geometrisch-optischen Modells, um Einflüsse der Kronentopographie zu korrigieren. Eine Validierung der Modelle und abgeleiteter Parameter basierte auf umfangreichen Geländeerhebungen. Beispielhaft wurde die weißblühende Rosskastanie, eine Hauptbaumart Berlins, untersucht. Ein Modelltest zeigte die Eignung der modifizierten RTM, die optischen Eigenschaften von Kastanien zu modellieren. Mit einer Sensitivitätsanalyse wurde der Einfluss spezifischer Baumeigenschaften auf die Modellergebnisse quantifiziert, was die Optimierung des Verfahrens ermöglichte. Durch Invertierung der Modelle konnten die vitalitätsrelevanten Parameter Chlorophyll- und Wassergehalt realistisch abgeleitet und in ein Baumkataster eingebunden werden. Damit leistet diese Dissertation einen Beitrag, das thematische Spektrum verfügbarer Biogeodaten zur Untersuchung stadtökologische Prozesse zu erweitern. / Urbanisation affects natural ecosystems functions significantly. This leads to changes in ecosystem services which are directly linked to human living conditions. Functional open spaces, especially when covered with wood and trees, are a major component of urban ecosystems and lessen the impact of urbanisation. Environmental conditions of urban areas are fare away from non urban, natural ones. The functionality of trees decreases as a consequence of these unfavourable conditions. In comparison to terrestrial surveys, remote sensing enables spatially and temporally flexible descriptions of tree vitality. In the present work a remote sensing based approach to investigate conditions of urban trees was developed. Radiative transfer models (RTM) were used to derive biophysical and structural parameters from hyperspectral data. In order to adapt the modelling approach to single trees three modifications were made: a) the correction of pixel shifts due to the height of trees and non-nadir view angles of the sensor; b) the selection of spectrally pure pixels; c) the correction of albedo effects induced by crown geometry. A detailed validation of the models and the estimated parameters is performed based on data of an extensive ground survey. Being one of Berlin’s main tree species, the horse chestnut (lat. Aesculus hippocastanum) was exemplary investigated. An Evaluation of the modified RTM showed that they are able to model the optical properties of chestnut canopies. A sensitivity analysis was performed to quantify the impact of tree specific effects on the results of the modelling approach. The findings allowed an optimization of the method. Vitality parameters such as chlorophyll and water content could be realistically estimated by model inversion. Results were finally linked to an existing cadastral system. Results from this work help widen the thematic spectrum of environmental biogeodata applications and investigating multiple processes of urban ecology.

Les roches mafiques à ultramafiques permettent de tracer les processus de formation et d'évolution des surfaces planétaires. Pour caractériser ces surfaces, la spectroscopie de réflectance visible-proche infrarouge est une technique adaptée de part sa sensibilité aux absorptions du fer présent dans les minéraux. L'objectif de cette thèse est de déterminer la composition modale des roches, ainsi que la composition chimique des minéraux constitutifs.<br />Nous avons développé une procédure basée sur la mise en oeuvre du Modèle Gaussien Modifié (MGM) qui permet de modéliser les spectres pour des assemblages minéralogiques complexes (olivine - orthopyroxène - clinopyroxène). Après validation de cette approche sur des données simples (poudres), la méthode a été appliquée à des roches naturelles complexes (météorites martiennes et roches d'Oman). Fort de l'expertise acquise dans cette étape intermédiaire, des cartographies minéralogiques ont alors pu être réalisées à partir de données spatiales et aéroportées, respectivement pour le volcan Syrtis Major sur Mars et le massif ophiolitique de Sumail (Oman). Nous avons ainsi montré que les laves de l'édifice volcanique présentent un enrichissement en olivine (Fo50-80) et que les pyroxènes, suivant les conditions de mise en place, peuvent avoir des compositions allant des augites aux enstatites. Pour l'ophiolite d'Oman, nous avons mis en évidence et cartographié pour la première fois des variations spatiales organisées de composition modale au sein même de l'unité harzburgitique. Nous avons également caractérisé des variations de composition dans les clinopyroxènes de la zone crustale ayant des implications pétrogénétiques.<br />Ces apports sont essentiels dans la caractérisation et la compréhension des processus pétrologiques inhérents à la formation des surfaces planétaires et devraient stimuler l'utilisation de l'imagerie spectroscopique à des fins géologiques.