Οι δασικές πυρκαγιές αποτελούν αναπόσπαστο κομμάτι των Μεσογειακών οικοσυστημάτων επηρεάζοντας το φυσικό κύκλο διαδοχής της βλάστησης, αλλά και τη δομή και λειτουργία τους. Τα τελευταία χρόνια παρατηρείται αύξηση των δασικών πυρκαγιών αυξάνοντας ιδιαίτερα το επιστημονικό ενδιαφέρον. Η χρησιμοποίηση της δορυφορικής τηλεπισκόπησης στη χαρτογράφηση των καμένων εκτάσεων έχει τριάντα χρόνια ιστορία ως εργαλείο χαρτογράφησης αλλά και παρακολούθησης της εξέλιξης των καμένων εκτάσεων. Η χαρτογράφηση των δασικών πυρκαγιών με τη χρήση δορυφορικών δεδομένων είναι και σήμερα ένα εν ενεργεία αντικείμενο έρευνας της τηλεπισκόπησης. Πολλά χαρακτηριστικά παραδείγματα υπάρχουν στη διεθνή βιβλιογραφία με ερευνητικό αντικείμενο τη χαρτογράφηση των καμένων εκτάσεων με τη χρήση πολλαπλών τύπων δορυφορικών δεδομένων, όμως ο αριθμός αυτών που διαπραγματεύονται για την ίδια πυρκαγιά πολλούς τύπους δεδομένων είναι περιορισμένος. Στην παρούσα διδακτορική διατριβή επιχειρείται για πρώτη φορά η χαρτογράφηση των καμένων εκτάσεων με εκτεταμένη χρήση διαφόρων τύπων δορυφορικών εικόνων πολλαπλής φασματικής και χωρικής διακριτικής ικανότητας που έχουν αποκτηθεί για την ίδια πυρκαγιά (Πάρνηθα, 2007). Πιο συγκεκριμένα, αντικείμενο έρευνας αποτέλεσε η χαρτογράφηση των άκαυτων νησίδων εσωτερικά της περιμέτρου της πυρκαγιάς, καθώς και η διερεύνηση των παραγόντων που διαμορφώνουν την ακρίβεια της χαρτογράφησης, η διερεύνηση της ευαισθησίας των τιμών ανάκλασης σε διαφορετικές αναλογίες καμένου/βλάστησης, καθώς και η εφαρμογή και αξιολόγηση διαφόρων δεικτών βλάστησης. Τα δορυφορικά δεδομένα που αξιολογήθηκαν προέρχονται από τους δορυφορικούς ανιχνευτές IKONOS, LANDSAT, ASTER και MODIS. Παράλληλα με τα αρχικά δεδομένα δημιουργήθηκε ένα σύνολο εικόνων πολλαπλής φασματικής και χωρικής κλίμακας. Αρχικά, εφαρμόστηκαν κλασικοί αλγόριθμοι επεξεργασίας εικόνας για τη γεωμετρική, ραδιομετρική και ατμοσφαιρική διόρθωση των δορυφορικών εικόνων. Στη συνεχεία, επεξεργάστηκε η υψηλής ανάλυσης εικόνα IKONOS, η οποία αποτέλεσε τη βάση για τον υπολογισμό του ποσοστού κάλυψης των καμένων εκτάσεων, της βλάστησης και του γυμνού εδάφους σε επίπεδο εικονοστοιχείου. Λαμβάνοντας υπόψη διαφορετικούς συνδυασμούς φασματικών και χωρικών αναλύσεων πραγματοποιήθηκαν συνολικά 420 ταξινομήσεις. Επιπλέον, οι φασματικοί δίαυλοι καθώς και 57 δείκτες βλάστησης που υπολογίστηκαν, συσχετίστηκαν με περιοχές διαφορετικών αναλογιών καμένης και άκαυτης βλάστησης, με σκοπό τη διερεύνηση της ευαισθησίας τους στην εκτίμηση του ποσοστού των καμένων και μη καμένων περιοχών. Συμπερασματικά, η χωρική διακριτική ικανότητα αποδεικνύεται ως ο σημαντικότερος παράγοντας για την αποτύπωση των άκαυτων νησίδων εσωτερικά της περιμέτρου της πυρκαγιάς, ενώ διαπιστώθηκε ότι συσχετίζεται άμεσα με τον αριθμό των χαρτογραφημένων νησίδων. Επιπλέον, το κοντινό και μέσο υπέρυθρο τμήμα του φάσματος αποδείχτηκαν σημαντικά για την εκτίμηση του ποσοστού του καμένου, ενώ το κόκκινο και κοντινό υπέρυθρο για την εκτίμηση του ποσοστού της βλάστησης. Το τελευταίο φαίνεται ότι διαδραματίζει σημαντικό ρόλο στον υπολογισμό του ποσοστού των καμένων εκτάσεων, ενώ το μέσο υπέρυθρο στον υπολογισμό του ποσοστού της βλάστησης. Οι δείκτες βλάστησης ελαχιστοποιούν τις επιδράσεις εξωτερικών παραγόντων, όπως είναι η επίδραση του εδάφους. Έτσι, οι ενδιάμεσες κατηγορίες κρίθηκαν πιο σύμφωνες φασματικά με τις διαφορετικές αναλογίες καμένου/βλάστησης, σε σχέση με τους αρχικούς φασματικούς δίαυλους, βάσει των οποίων υπολογίζονται οι δείκτες. Οι κλασικοί δείκτες, οι οποίοι ενσωματώνουν το κόκκινο και κοντινό υπέρυθρο μήκος κύματος έδειξαν καλύτερη προσαρμογή στην εκτίμηση του ποσοστού της βλάστησης. Αντίθετα, η τροποποιημένη εκδοχή τους, αντικαθιστώντας το κόκκινο με το μέσο υπέρυθρο τμήμα του φάσματος έδειξαν καλύτερη προσαρμογή στην εκτίμηση του ποσοστού των καμένων περιοχών, ταυτόχρονα με την υψηλή προσαρμογή για την εκτίμηση της βλάστησης. Τέλος, πραγματοποιήθηκε η ανασύσταση της πρόσφατης ιστορίας των πυρκαγιών (1984-2011) για την Αττική, εφαρμόζοντας πρόσφατα ανεπτυγμένες (ημι)αυτόματες τεχνικές χαρτογράφησης σε διαχρονικά LANDSAT δορυφορικά δεδομένα μεσαίας χωρικής διακριτικής ικανότητας. Τα αποτελέσματα αυτής της διαδικασίας οδήγησαν στη χαρτογράφηση των περιμέτρων των πυρκαγιών με σχετικά μεγάλη ακρίβεια, ενώ από τα μοντέλα παλινδρόμησης διαπιστώθηκε ότι οι διαφορές μεταξύ της καμένης έκτασης που υπολογίζεται από τα δορυφορικά δεδομένα και αυτά τα οποία καταγράφονται από τη Δασική Υπηρεσία αποδίδονται στον αριθμό των δορυφορικών εικόνων που χρησιμοποιούνται καθώς και στην ημερομηνία απόκτησης της πρώτης δορυφορικής εικόνας. / Forest fires, an integral part of Mediterranean ecosystems, affect the natural cycle of vegetation succession and the ecosystem’s structure and function. Recently, the increment in frequency of fires has increased the concern of the scientific community. The use of remote sensing in burned land mapping has a 30 year long history as tool in mapping and monitoring of forest fire. Despite this long period, burned land mapping using satellite data is still an active research topic in satellite remote sensing. Many characteristic examples of satellite remote sensing studies of burned land mapping and monitoring can be found in the literature, however studies dealing with a multisource data set for the same fire event are limited. The present thesis attempted to map burned surfaces using a multisource satellite data set of multiple spectral and spatial resolution acquired for the same fire event (Parnitha, 2007). In particular, the aims of the thesis were to delineate the unburned patches within fire scar perimeter and explore the factors influence the classification accuracy, to explore the sensitivity of spectral reflectance values to different burn and vegetation ratios, as well as to examine and evaluate some vegetation indices. The satellite data used were acquired from IKONOS, LANDSAT, ASTER and MODIS. Along with the basic data set, a spatially degraded satellite data over a range of coarser resolutions were created. Firstly, classical image processing algorithms were applied to correct geometrically, radiometrically and atmospherically the satellite images used. The pan-sharpened IKONOS served as the basis to estimate the percent of cover of burned areas, vegetation and bare land, at pixel level. Totally 420 classifications have been implemented considering different combinations of spectral and spatial resolutions. Additionally, the spectral bands and 57 versions of some classical vegetation indices were correlated with different burned and vegetation ratios in order to explore their sensitivity. Conclusively, spatial resolution is the most important factor for the delineation of the unburned patches within the fire scar perimeter, while proved to be strongly correlated with the number of the mapped islands. Moreover, the near and middle infrared channels were the most important ones to estimate the percentage of burned area, while the red and near infrared were the most important channels to estimate the percentage of vegetation. The latter, seemed to play a more significant role in estimating the percent of burned area while the middle infrared seemed to play a more significant role in estimating the percent of vegetation. Vegetation indices are less sensitive to external parameters of the vegetation by minimizing external effects, such as soil impact. Thus, the semi-burned classes were spectrally more consistent to their different fractions of scorched and non-scorched vegetation, than the original spectral channels based on which these indices are estimated. The classical indices, which incorporate the red-near infrared space showed better performance to estimate the percent of the vegetation. In contrast, the modified version of the classical indices, by replacing the red with the middle infrared channel showed the highest performance to estimate the percent of burned areas, apart from the high performance in the estimation of the vegetation. Finally, in the present thesis maps with the reconstruction of the recent fire history of Attica region were created, in a spatially explicit mode using (semi)automated image processing techniques in a series of multi-temporal medium-resolution LANDSAT images. The results showed that the fire-scar perimeters were captured with considerably high accuracy, while regression modeling showed that the differences between the area burned estimated from satellite data and that recorded by the forest service can be explained by the number of satellite images used followed by the acquisition date of the first image.

Soil landscape characterization into landform elements for precision agriculture has become an important issue. As soil properties and crop yields change over the landscape, delineating landform elements as a basis for site-specific application of crop inputs has become a reality. Two different methods of delineating landform elements from agricultural fields were tested and compared. The first method delineated landform elements from digital elevation maps with the use of the LandMapR(tm) software, the second method delineated classes from IKONOS high resolution panchromatic images using an unsupervised classification algorithm. The LandMapR(tm) model delineated landform elements from true elevation data collected in the field and was considered the reference dataset to which the image classification maps were compared to. The IKONOS imagery was processed using a combination of one filtering algorithm and one unsupervised classification method prior to being compared to the classified DEM. A total of 20 filtering algorithms and two unsupervised methods were used for each of the five study sites. The study sites consisted of four agricultural fields covered with crop stubble and one field in summer fallow. Image classification accuracy assessment was reported as overall, producer’s and user’s accuracy as well as Kappa statistic. Results showed that filtering algorithms and classification methods had no effects on image classification accuracies. Highest classification accuracy of image map to landform element map comparison achieved for all study sites was 17.9 %. Classification accuracy was affected by the heterogeneity of the ground surface cover found in each field. However, the classification accuracy of the fallow field was not superior to the stubble fields.

Soil landscape characterization into landform elements for precision agriculture has become an important issue. As soil properties and crop yields change over the landscape, delineating landform elements as a basis for site-specific application of crop inputs has become a reality. Two different methods of delineating landform elements from agricultural fields were tested and compared. The first method delineated landform elements from digital elevation maps with the use of the LandMapR(tm) software, the second method delineated classes from IKONOS high resolution panchromatic images using an unsupervised classification algorithm. The LandMapR(tm) model delineated landform elements from true elevation data collected in the field and was considered the reference dataset to which the image classification maps were compared to. The IKONOS imagery was processed using a combination of one filtering algorithm and one unsupervised classification method prior to being compared to the classified DEM. A total of 20 filtering algorithms and two unsupervised methods were used for each of the five study sites. The study sites consisted of four agricultural fields covered with crop stubble and one field in summer fallow. Image classification accuracy assessment was reported as overall, producer’s and user’s accuracy as well as Kappa statistic. Results showed that filtering algorithms and classification methods had no effects on image classification accuracies. Highest classification accuracy of image map to landform element map comparison achieved for all study sites was 17.9 %. Classification accuracy was affected by the heterogeneity of the ground surface cover found in each field. However, the classification accuracy of the fallow field was not superior to the stubble fields.

Coral reefs are one of the most productive, diverse and complex ecosystems on Earth. They are very important ecologically, economically and socially, but are subject to increasing deleterious disturbances. To protect coral reefs and manage the sustainable use of their resources it is necessary to understand how coral communities respond to disturbances and to use this understanding to project the likely ecological trajectories of disturbed coral reefs in spatial and temporal contexts. Three powerful tools exist to address this issue: (1) in situ monitoring that describes ecological transitions of coral communities at very fine spatial scale; (2) time-series of maps derived from high spatial resolution remote sensing images that provide multi-temporal synoptic views of the reefs; and (3) spatially- and temporally-explicit models that are able to handle ecosystems complexity and represent their spatial dynamics. The combination of these three tools to map and monitor coral communities remained to be addressed. This dissertation developed an integrative approach to characterise, map and model coral communities’ responses to disturbances. This approach provides a basis for monitoring coral reefs at temporal and spatial scales matched to disturbance impacts and coral reefs patchiness. This was achieved by investigating the dynamics of three different Indo-Pacific reefs and by following four steps: - Developing and applying a method to characterise how detailed coral communities can be mapped before and after a major cyclone event from a short time-series of high spatial resolution images (IKONOS, Quickbird) on Aboré Reef (New-Caledonia); - Using the methods developed in the first step to assess whether decadal-scale coral dynamics can be retraced and monitored from time-series of aerial photographs and satellite images spanning at least 30 years on Saint-Leu (Réunion Island) and Heron (Australia) Reefs; - Developing a spatially- and temporally-explicit model of coral communities’ dynamics with cellular agent-based formalism on the western section of Heron reef flat; and - Assessing the relevance of the mapping, monitoring and modelling tools developed in this work, into an integrated approach for coral reef monitoring. For the first step, accurate monitoring requires that descriptions of the reef features are coherent with the local scale of disturbance impacts in space and time. While such a monitoring paradigm is applied in terrestrial environments, it is not the case for coral reefs. A before-after cyclone time-series of satellite images from Aboré Reef was used to test this paradigm on coral reefs. In situ data provided a new three-level hierarchical coral community typology (45 classes at the finest level). Photo-interpretation and hierarchical mapping methods were applied to an IKONOS image and a Quickbird image taken before and after cyclone Erica respectively. Application of this paradigm yielded a highly detailed multi-temporal maps of pre- and post-cyclone coral communities and recommendations to design reef-scale monitoring protocols. For the second step, the temporal scale of monitoring projects needs also to match the inherent reef dynamics. To assess the applicability of this temporal component of the paradigm at a decadal scale, the hierarchical mapping approaches developed for Aboré Reef were applied to a 33-year time-series of satellite images (two Quickbird images) and airborne photographs (five scanned images) of Saint-Leu Reef. The mapping approach overcame challenges due to different images qualities and to the lack of in situ observations in time and space before cyclone Firinga in 1989. This demonstrated the potential for further applications of the approach in reef monitoring protocols based on complementary in situ and remote sensing data to help understand the dynamics of reef-top coral reef communities and geomorphology over years to decades. In the next step, the modelling component of this work focused on a proof-of-concept for spatially-explicit modelling of coral growth by simulating maps of reef flat colonisation on a 16 686 m2 section of Heron Reef. To do this a 35-year time-series of two satellite Quickbird pan-sharpened images and five aerial photographs of Heron Reef was first used to hierarchically map and quantify the areal expansion of coral on the reef flat. The coral growth was driven by several artificially induced local sea-level rises associated with engineering works on the reef flat. Vertical and horizontal growth rates were quantified in terms of percentage of the total area colonised each year by corals. Coral community maps and coral growth rates estimated from the image time-series were used to constrain an accretive cellular growth model. Although only preliminary the model produced coral growth likelihood maps corresponding to observed fine-scale coral growth patterns. This suggested the tool had promise for further applications in reef management. This dissertation developed an integrative approach to characterise, map and model coral communities’ responses to disturbances, providing a basis for monitoring coral reefs at ecological, temporal, and spatial scales matching the patchiness of the communities’ distribution and disturbance impacts. The contributions of the work to the applied fields of coral reef mapping, modelling and monitoring were demonstrated through the results achieved and the development of protocols that do not require specialized image processing algorithms and methods. This opens perspectives for further development of the approach on other coral reefs around the world.

Population estimation in inter-censual years has many important applications. In this research, high-resolution pan-sharpened IKONOS image, LiDAR data, and parcel data are used to estimate small-area population in the eastern part of the city of Denton, Texas. Residential buildings are extracted through object-based classification techniques supported by shape indices and spectral signatures. Three population indicators -building count, building volume and building area at block level are derived using spatial joining and zonal statistics in GIS. Linear regression and geographically weighted regression (GWR) models generated using the three variables and the census data are used to estimate population at the census block level. The maximum total estimation accuracy that can be attained by the models is 94.21%. Accuracy assessments suggest that the GWR models outperformed linear regression models due to their better handling of spatial heterogeneity. Models generated from building volume and area gave better results. The models have lower accuracy in both densely populated census blocks and sparsely populated census blocks, which could be partly attributed to the lower accuracy of the LiDAR data used.

The political history of U.S. commercial remote sensing began in 1984 when the U.S. government first attempted to commercialize its civil earth observation satellite system " Landsat. Since then, the high technology of earth imaging satellite systems has generated intense debates and policy conflicts, primarily centered on U.S. government concerns over the national security and foreign policy implications of high-resolution commercial satellite systems. Conversely, proponents of commercial observation satellites have urged U.S. policymakers to recognize the scientific and socio-economic utility of commercial remote sensing and thus craft and implement regulatory regimes that allow for a greater degree of information openness and transparency in using earth observation satellite imagery. This dissertation traces and analyzes that tumultuous political history and examines the policy issues and social construction of commercial remote sensing to determine the role of knowledge in the effective crafting and execution of commercial remote sensing laws and policies. Although individual and organizational perspectives, interests, missions, and cultures play a significant role in the social construction of commercial observation satellite systems and programs, the problem of insufficient knowledge of the myriad dimensions and complex nature of commercial remote sensing is a little studied but important component of this social construction process. Knowledge gaps concerning commercial remote sensing extend to various dimensions of the subject matter, such as the global, economic, technical, and legal/policy aspects. Numerous examples of knowledge voids are examined to suggest a connection between deficient knowledge and divergent policy perceptions as they relate to commercial remote sensing. Relevant knowledge voids are then structurally categorized to demonstrate the vastness and complexity of commercial remote sensing policy issues and to offer recommendations on how to fill such knowledge gaps to effect increased collaboration between the US government and the U.S. commercial remote sensing industry. Finally, the dissertation offers suggestions for future STS studies on policy issues, particularly those that focus on the global dimensions of commercial remote sensing or on applying the knowledge gap concept advanced by this dissertation to other areas of science and technology policymaking. / Ph. D.

Die Fernerkundung war in den vergangenen Jahren von einschneidenden Umbrüchen gekennzeichnet, die sich besonders in der stark gestiegenen geometrischen Bodenauflösung der Sensoren und den damit einhergehenden Veränderungen der Verarbeitungs- und Auswertungsverfahren widerspiegeln. Sehr hoch auflösende Satellitenbilddaten - definiert durch eine Auflösung zwischen einem halben und einem Meter - existieren seit dem Start von IKONOS Ende 1999. Etwa im selben Zeitraum wurden extrem hoch auflösende digitale Flugzeugkameras (0,1 bis 0,5 m) entwickelt. Dieser Arbeit liegen IKONOS-Daten mit einer Auflösung von einem (panchromatischer Kanal) bzw. vier Metern (Multispektraldaten) zugrunde. Bedingt durch die Eigenschaften sehr hoch aufgelöster Bilddaten (z. B. Detailgehalt, starke spektrale Variabilität, Datenmenge) lassen sich bisher verfügbare Standardverfahren der Bildverarbeitung nur eingeschränkt anwenden. Die Ergebnisse der in dieser Arbeit getesteten Verfahren verdeutlichen, dass die Methoden- bzw. Softwareentwicklung mit den technischen Neuerungen nicht Schritt halten konnte. Einige Verfahren werden erst allmählich für sehr hoch auflösende Daten nutzbar (z. B. atmosphärisch-topographische Korrektur). Die vorliegende Arbeit zeigt, dass Daten dieses Auflösungsbereiches mit bisher verwendeten pixelbasierten, statistischen Klassifikationsverfahren nur unzulänglich ausgewertet werden können. Die hier untersuchte Anwendung von Bildsegmentierungsmethoden hilft, die Nachteile pixelbasierter Verfahren zu überwinden. Dies wurde durch einen Vergleich pixel- und segmentbasierter Klassifikationsverfahren belegt. Im Rahmen einer Segmentierung werden homogene Bildbereiche zu Regionen verschmolzen, welche die Grundlage für die anschließende Klassifikation bilden. Hierzu stehen über die spektralen Eigenschaften hinaus Form-, Textur- und Kontextmerkmale zur Verfügung. In der verwendeten Software eCognition lassen sich diese Klassifikationsmerkmale zudem auf Grundlage des fuzzy-logic-Konzeptes in einer Wissensbasis (Entscheidungsbaum) umsetzen. Ein Vergleich verschiedener, derzeit verfügbarer Segmentierungsverfahren zeigt darüber hinaus, dass sich mit der genutzten Software eine hohe Segmentierungsqualität erzielen lässt. Der wachsende Bedarf an aktuellen Geobasisdaten stellt für sehr hoch auflösende Fernerkundungsdaten eine wichtige Einsatzmöglichkeit dar. Durch eine gezielte Klassifikation der Bilddaten lassen sich Arbeitsgrundlagen für die hier betrachteten Anwendungsfelder Landschaftsplanung und Landschaftsökologie schaffen. Die dargestellten Beispiele von Landschaftsanalysen durch die segmentbasierte Auswertung von IKONOS-Daten zeigen, dass sich eine Klassifikationsgüte von 90 % und höher erreichen lässt. Zudem können die infolge der Segmentierung abgegrenzten Landschaftseinheiten eine Grundlage für die Berechnung von Landschaftsstrukturmaßen bilden. Nationale Naturschutzziele sowie internationale Vereinbarungen zwingen darüber hinaus zur kontinuierlichen Erfassung des Landschaftsinventars und dessen Veränderungen. Fernerkundungsdaten können in diesem Bereich zur Etablierung automatisierter und operationell einsatzfähiger Verfahren beitragen. Das Beispiel Biotop- und Landnutzungskartierung zeigt, dass eine Erfassung von Landnutzungseinheiten mit hoher Qualität möglich ist. Bedingt durch das Auswertungsverfahren sowie die Dateneigenschaften entspricht die Güte der Ergebnisse noch nicht vollständig den Ansprüchen der Anwender, insbesondere hinsichtlich der erreichbaren Klassifikationstiefe. Die Qualität der Ergebnisse lässt sich durch die Nutzung von Zusatzdaten (z. B. GIS-Daten, Objekthöhenmodelle) künftig weiter steigern. Insgesamt verdeutlicht die Arbeit den Trend zur sehr hoch auflösenden digitalen Erderkundung. Für eine breite Nutzung dieser Datenquellen ist die weitere Entwicklung automatisierter und operationell anwendbarer Verarbeitungs- und Analysemethoden unerlässlich. / In recent years remote sensing has been characterised by dramatic changes. This is reflected especially by the highly increased geometrical resolution of imaging sensors and as a consequence thereof by the developments in processing and analysis methods. Very high resolution satellite imagery (VHR) - defined by a resolution between 0.5 and 1 m - exists since the start of IKONOS at the end of 1999. At about the same time extreme high resolution digital airborne sensors (0.1 till 0.5 m) have been developed. The basis of investigation for this dissertation is IKONOS imagery with a resolution of one meter (panchromatic) respectively four meters (multispectral). Due to the characteristics of such high resolution data (e.g. level of detail, high spectral variability, amount of data) the use of previously available standard methods of image processing is limited. The results of the procedures tested within this work demonstrate that the development of methods and software was not able to keep up with the technical innovations. Some procedures are only gradually becoming suitable for VHR data (e.g. atmospheric-topographic correction). Additionally, this work shows that VHR imagery can be analysed only inadequately using traditional pixel-based statistical classifiers. The herein researched application of image segmentation methods helps to overcome drawbacks of pixel-wise procedures. This is demonstrated by a comparison of pixel and segment-based classification. Within a segmentaion, homogeneous image areas are merged into regions which are the basis for the subsequent classification. For this purpose, in addition to spectral features also formal, textural and contextual properties are available. Furthermore, the applied software eCognition allows the definition of the features for classification based on fuzzy logic in a knowledge base (decision tree). An evaluation of different, currently available segmentation approaches illustrates that a high segmentation quality is achievable with the used software. The increasing demand for geospatial base data offers an important field of application for VHR remote sensing data. With a targeted classification of the imagery the creation of working bases for the herein considered usage for landscape planning and landscape ecology is possible. The given examples of landscape analyses using a segment-based processsing of IKONOS data show an achievable classification accuracy of 90 % and more. The landscape units delineated by image segmentation could be used for the calculation of landscape metrics. National aims of nature conservation as well as international agreements constrain a continuous survey of the landscape inventory and the monitoring of its changes. Remote sensing imagery can support the establishment of automated and operational methods in this field. The example of biotope and land use type mapping illustrates the possibility to detect land use units with a high precision. Depending on the analysis method and the data characteristics the quality of the results is not fully equivalent to the user?s demands at the moment, especially concerning the achievable depth of classification. The quality of the results can be enhanced by using additional thematic data (e.g. GIS data, object elevation models). To summarize this dissertation underlines the trend towards very high resolution digital earth observation. Thus, for a wide use of this kind of data it is essentially to further develop automated and operationally useable processing and analysis methods.

Die Fernerkundung war in den vergangenen Jahren von einschneidenden Umbrüchen gekennzeichnet, die sich besonders in der stark gestiegenen geometrischen Bodenauflösung der Sensoren und den damit einhergehenden Veränderungen der Verarbeitungs- und Auswertungsverfahren widerspiegeln. Sehr hoch auflösende Satellitenbilddaten - definiert durch eine Auflösung zwischen einem halben und einem Meter - existieren seit dem Start von IKONOS Ende 1999. Etwa im selben Zeitraum wurden extrem hoch auflösende digitale Flugzeugkameras (0,1 bis 0,5 m) entwickelt. Dieser Arbeit liegen IKONOS-Daten mit einer Auflösung von einem (panchromatischer Kanal) bzw. vier Metern (Multispektraldaten) zugrunde. Bedingt durch die Eigenschaften sehr hoch aufgelöster Bilddaten (z. B. Detailgehalt, starke spektrale Variabilität, Datenmenge) lassen sich bisher verfügbare Standardverfahren der Bildverarbeitung nur eingeschränkt anwenden. Die Ergebnisse der in dieser Arbeit getesteten Verfahren verdeutlichen, dass die Methoden- bzw. Softwareentwicklung mit den technischen Neuerungen nicht Schritt halten konnte. Einige Verfahren werden erst allmählich für sehr hoch auflösende Daten nutzbar (z. B. atmosphärisch-topographische Korrektur). Die vorliegende Arbeit zeigt, dass Daten dieses Auflösungsbereiches mit bisher verwendeten pixelbasierten, statistischen Klassifikationsverfahren nur unzulänglich ausgewertet werden können. Die hier untersuchte Anwendung von Bildsegmentierungsmethoden hilft, die Nachteile pixelbasierter Verfahren zu überwinden. Dies wurde durch einen Vergleich pixel- und segmentbasierter Klassifikationsverfahren belegt. Im Rahmen einer Segmentierung werden homogene Bildbereiche zu Regionen verschmolzen, welche die Grundlage für die anschließende Klassifikation bilden. Hierzu stehen über die spektralen Eigenschaften hinaus Form-, Textur- und Kontextmerkmale zur Verfügung. In der verwendeten Software eCognition lassen sich diese Klassifikationsmerkmale zudem auf Grundlage des fuzzy-logic-Konzeptes in einer Wissensbasis (Entscheidungsbaum) umsetzen. Ein Vergleich verschiedener, derzeit verfügbarer Segmentierungsverfahren zeigt darüber hinaus, dass sich mit der genutzten Software eine hohe Segmentierungsqualität erzielen lässt. Der wachsende Bedarf an aktuellen Geobasisdaten stellt für sehr hoch auflösende Fernerkundungsdaten eine wichtige Einsatzmöglichkeit dar. Durch eine gezielte Klassifikation der Bilddaten lassen sich Arbeitsgrundlagen für die hier betrachteten Anwendungsfelder Landschaftsplanung und Landschaftsökologie schaffen. Die dargestellten Beispiele von Landschaftsanalysen durch die segmentbasierte Auswertung von IKONOS-Daten zeigen, dass sich eine Klassifikationsgüte von 90 % und höher erreichen lässt. Zudem können die infolge der Segmentierung abgegrenzten Landschaftseinheiten eine Grundlage für die Berechnung von Landschaftsstrukturmaßen bilden. Nationale Naturschutzziele sowie internationale Vereinbarungen zwingen darüber hinaus zur kontinuierlichen Erfassung des Landschaftsinventars und dessen Veränderungen. Fernerkundungsdaten können in diesem Bereich zur Etablierung automatisierter und operationell einsatzfähiger Verfahren beitragen. Das Beispiel Biotop- und Landnutzungskartierung zeigt, dass eine Erfassung von Landnutzungseinheiten mit hoher Qualität möglich ist. Bedingt durch das Auswertungsverfahren sowie die Dateneigenschaften entspricht die Güte der Ergebnisse noch nicht vollständig den Ansprüchen der Anwender, insbesondere hinsichtlich der erreichbaren Klassifikationstiefe. Die Qualität der Ergebnisse lässt sich durch die Nutzung von Zusatzdaten (z. B. GIS-Daten, Objekthöhenmodelle) künftig weiter steigern. Insgesamt verdeutlicht die Arbeit den Trend zur sehr hoch auflösenden digitalen Erderkundung. Für eine breite Nutzung dieser Datenquellen ist die weitere Entwicklung automatisierter und operationell anwendbarer Verarbeitungs- und Analysemethoden unerlässlich. / In recent years remote sensing has been characterised by dramatic changes. This is reflected especially by the highly increased geometrical resolution of imaging sensors and as a consequence thereof by the developments in processing and analysis methods. Very high resolution satellite imagery (VHR) - defined by a resolution between 0.5 and 1 m - exists since the start of IKONOS at the end of 1999. At about the same time extreme high resolution digital airborne sensors (0.1 till 0.5 m) have been developed. The basis of investigation for this dissertation is IKONOS imagery with a resolution of one meter (panchromatic) respectively four meters (multispectral). Due to the characteristics of such high resolution data (e.g. level of detail, high spectral variability, amount of data) the use of previously available standard methods of image processing is limited. The results of the procedures tested within this work demonstrate that the development of methods and software was not able to keep up with the technical innovations. Some procedures are only gradually becoming suitable for VHR data (e.g. atmospheric-topographic correction). Additionally, this work shows that VHR imagery can be analysed only inadequately using traditional pixel-based statistical classifiers. The herein researched application of image segmentation methods helps to overcome drawbacks of pixel-wise procedures. This is demonstrated by a comparison of pixel and segment-based classification. Within a segmentaion, homogeneous image areas are merged into regions which are the basis for the subsequent classification. For this purpose, in addition to spectral features also formal, textural and contextual properties are available. Furthermore, the applied software eCognition allows the definition of the features for classification based on fuzzy logic in a knowledge base (decision tree). An evaluation of different, currently available segmentation approaches illustrates that a high segmentation quality is achievable with the used software. The increasing demand for geospatial base data offers an important field of application for VHR remote sensing data. With a targeted classification of the imagery the creation of working bases for the herein considered usage for landscape planning and landscape ecology is possible. The given examples of landscape analyses using a segment-based processsing of IKONOS data show an achievable classification accuracy of 90 % and more. The landscape units delineated by image segmentation could be used for the calculation of landscape metrics. National aims of nature conservation as well as international agreements constrain a continuous survey of the landscape inventory and the monitoring of its changes. Remote sensing imagery can support the establishment of automated and operational methods in this field. The example of biotope and land use type mapping illustrates the possibility to detect land use units with a high precision. Depending on the analysis method and the data characteristics the quality of the results is not fully equivalent to the user?s demands at the moment, especially concerning the achievable depth of classification. The quality of the results can be enhanced by using additional thematic data (e.g. GIS data, object elevation models). To summarize this dissertation underlines the trend towards very high resolution digital earth observation. Thus, for a wide use of this kind of data it is essentially to further develop automated and operationally useable processing and analysis methods.

A growing number of scientists are investigating applications of landscape ecology principles to marine studies, yet few coral reef scientists have examined spatial patterns across entire reefscapes with a holistic ecosystem-based view. This study was an effort to better understand reefscape ecology by quantitatively assessing spatial structures and habitat arrangements using remote sensing and geographic information systems (GIS). Quantifying recurring patterns in reef systems has implications for improving the efficiency of mapping efforts and lowering costs associated with collecting field data and acquiring satellite imagery. If a representative example of a reef is mapped with high accuracy, the data derived from habitat configurations could be extrapolated over a larger region to aid management decisions and focus conservation efforts. The aim of this project was to measure repeating spatial patterns at multiple scales (10s m2 to 10s km2) and to explain the environmental mechanisms which have formed the observed patterns. Because power laws have been recognized in size-frequency distributions of reef habitat patches, this study further investigated whether the property exists for expansive reefs with diverse geologic histories. Intra- and inter-reef patch relationships were studied at three sites: Andavadoaka (Madagascar), Vieques (Puerto Rico), and Saipan (Commonwealth of the Northern Mariana Islands). In situ ecological information, including benthic species composition and abundance, as well as substrate type, was collected with georeferenced video transects. LiDAR (Light Detection and Ranging) surveys were assembled into digital elevation models (DEMs), while vessel-based acoustic surveys were utilized to empirically tune bathymetry models where LiDAR data were unavailable. A GIS for each site was compiled by overlying groundtruth data, classifications, DEMs, and satellite images. Benthic cover classes were then digitized and analyzed based on a suite of metrics (e.g. patch complexity, principle axes ratio, and neighborhood transitions). Results from metric analyses were extremely comparable between sites suggesting that spatial prediction of habitat arrangements is very plausible. Further implications discussed include developing an automated habitat mapping technique and improving conservation planning and delimitation of marine protected areas.

Cette thèse détaille la conception, le développement et l'analyse d'un nouvel outil de caractérisation des textures exploitant les modèles de prédiction linéaire complexe sur les espaces couleur perceptuels séparant l'intensité lumineuse de la partie chromatique. Des modèles multicanaux 2-d causaux et non-causaux ont été utilisés pour l'estimation simultanée des densités spectrales de puissance d'une image " bi-canal ", le premier contenant les valeurs réelles de l'intensité et le deuxième les valeurs complexes de la partie chromatique. Les bonnes performances en terme de biais et de variance de ces estimations ainsi que l'usage d'une distance appropriée entre deux spectres assurent la robustesse et la pertinence de l'approche pour la classification de textures. Une mesure de l'interférence existante entre l'intensité et la partie chromatique à partir de l'analyse spectrale est introduite afin de comparer les transformations associées aux espaces couleur. Des résultats expérimentaux en classification de textures sur différents ensembles de tests, dans différents espaces couleur (RGB, IHLS et L*a*b*) sont présentés et discutés. Ces résultats montrent que la structure spatiale associée à la partie chromatique d'une texture couleur est mieux caractérisée à l'aide de l'espace L*a*b* et de ce fait, cet espace permet d'obtenir les meilleurs résultats pour classifier les textures à l'aide de leur structure spatiale et des modèles de prédiction linéaire. Une méthode bayésienne de segmentation d'images texturées couleur a aussi été développée à partir de l'erreur de prédiction linéaire multicanale. La contribution principale de la méthode réside dans la proposition d'approximations paramétriques robustes pour la distribution de l'erreur de prédiction linéaire multicanale : la distribution de Wishart et une approximation multimodale exploitant les lois de mélanges gaussiennes multivariées. Un autre aspect original de l'approche consiste en la fusion d'un terme d'énergie sur la taille des régions avec l'énergie du modèle de Potts afin de modéliser le champ des labels de classe à l'aide d'un modèle de champ aléatoire possédant une distribution de Gibbs. Ce modèle de champ aléatoire est ainsi utilisé pour régulariser spatialement un champ de labels initial obtenu à partir des différentes approximations de la distribution de l'erreur de prédiction. Des résultats expérimentaux en segmentation d'images texturées couleur synthétiques et d'images satellites hautes résolutions QuickBird et IKONOS ont permis de valider l'application de la méthode aux images fortement texturées. De plus les résultats montrent l'intérêt d'utiliser les approximations de la distribution de l'erreur de prédiction proposées ainsi que le modèle de champ de labels amélioré par le terme d'énergie qui pénalise les petites régions. Les segmentations réalisées dans l'espace L*a*b* sont meilleures que celles obtenues dans les autres espaces couleur (RGB et IHLS) montrant à nouveau la pertinence de caractériser les textures couleur par la prédiction linéaire multicanale complexe à l'aide de cet espace couleur.