Hyperspectral data for classification of vegetation of alpine treeless in the Krkonoše Mts. ABSTRACT The Master Thesis is a part of the HyMountEcos project, which deals with a complex evaluation of mountain's ecosystems in the Giant Mountains National Park using the hyperspectral data. The area of interest is alpine treeless in the Giant Mountains National Park. The main goal of this thesis was to create detailed methodology for classification of vegetation cover using hyperspectral data from AISA DUAL and APEX sensors, to find a classification method, which would improve the accuracy of the results compared to those found in the literature, and to compare the accuracy reached with these two types of the data. Many different classification algorithms (Spectral Angle Mapper, Linear Spectral Unmixing, Support Vector Machine, MESMA a Neural Net) were applied and the classification results were statistically evaluated and compared in the next part of the work. The classification method Neural Net was found as the most accurate one, as it gives the most accurate results for APEX data (the overall accuracy 96 %, Kappa coefficient 0,95) as well as for AISA DUAL data (the overall accuracy 90 %, Kappa coefficient 0,88). The resulting accuracy of the classification (the overall one and also for some classes) reached...

In the last decades the population living in cities has substantially increased. According to the United Nations, by 2050 two thirds of the world population will be living in urban areas. Demographic pressure, through influx of residents or internal growth results in expansion and densification of urban areas and goes hand in hand with increased imperviousness, putting pressure on the provision of urban green. Urban green offers a range of direct and indirect benefits to the urban ecosystem. Green in the city reduces rainwater runoff and flooding risk while improving water quality; it improves air quality, provides natural cooling and contributes to reducing the urban heat island effect. Being the main source of contact with nature, urban green has also been shown to contribute to the physical and psychological wellbeing of urban citizens. The environmental concern for urban nature and re-naturing of cities are thus at the heart of developing more «ecological approaches to sustainable urban design and planning». In the framework of this research, it implies: understanding the (spatial) distribution of green space in relation to the built-up area of the city at different scale levels – the benefits they provide, their quality and proximity for urban residents – and; how to develop diagnostic, analytical and projective capabilities aimed at improving their (urban green) provision to address a host of sustainability challenges related to climate change, demographic growth and densification of the urban area. The research focuses on the development of evidence-based frameworks for planning that incorporate citizens’ needs and that are built on an interdisciplinary foundation. With this scope and focus, this study contributes to the development of a more ecological framework for sustainable urban design and planning aimed at integrating nature in the city more effectively and in an evidence-based way. The first part of the research focuses on the development of a spatially explicit tool for green space quality and proximity assessment reflecting user’s perception. Application of the model in the Brussels context reveals that user’s perception of qualities of urban green spaces such as naturalness and spaciousness can be linked to green space characteristics as described by available GIS-based data. As such GIS-based modelling allows for an extrapolation of questionnaire-based quality assessments for a selection of parks to other public green spaces. Analysis of the proximity of urban green spaces based on user’s perception shows spatial inequalities in green space provision, with less than 50% of Brussels’ citizens having good access to small (residential and play green) and to large green spaces (city and metropolitan green). By coupling multi-scale proximity assessment with quality assessment of green spaces, it is demonstrated that nearly two third of the Brussels population has no access to high quality public green spaces. Through collaborative research by design workshops involving different stakeholders, indicators produced by the quality-proximity model are used to indicate and tackle problem areas. Three alternative scenarios for public green space development are defined. The scenario analysis demonstrates that actions to provide low-income neighborhoods with a good accessibility to public green spaces will require creative solutions, dealing with complex property and management issues, and levels of investment that go well beyond the cost of regular green space development. The second part of the study presents a GIS- and design-based approach to assess potential land cover change for the Brussels-Capital Region anticipating expected population growth. The methodology proposed can be used to assess the impact of spatial policies and the implementation of building codes on future urban land cover. By studying the everyday processes for parcel infill and densification, and by defining a densification process based on the principles of sustainable urban design (e.g. walkable and high-density urban areas near mobility hubs, compact building typologies, preserving valuable natural areas, creative approaches to increasing the provision of urban green (green roofs, bioswales, etc.) space for water and floodscapes, etc.), two land use evolution scenarios are formulated; a business-as-usual and a sustainable scenario. One of the main conclusions of the case study on the Brussels-Capital Region is that densification can be deployed as a vehicle for positive land cover change and greening of the city. / Doctorat en Art de bâtir et urbanisme (Architecture) / info:eu-repo/semantics/nonPublished

Following the hierarchical nature of forest resource management, the present work focuses on the natural forest cover at various abstraction levels of details, i.e. categorical land use/land cover (LU/LC) level and a continuous empirical estimation of local operational level. As no single sensor presently covers absolutely all the requirements of the entire levels of forest resource assessment, multisource imagery (i.e. RapidEye, TERRA ASTER and LANDSAT TM), in addition to other data and knowledge have been examined. To deal with this structure, an object-based image analysis (OBIA) approach has been assessed in the destabilized Blue Nile region of Sudan as a potential solution to gather the required information for future forest planning and decision making. Moreover, the spatial heterogeneity as well as the rapid changes observed in the region motivates the inspection for more efficient, flexible and accurate methods to update the desired information. An OBIA approach has been proposed as an alternative analysis framework that can mitigate the deficiency associated with the pixel-based approach. In this sense, the study examines the most popular pixel-based maximum likelihood classifier, as an example of the behavior of spectral classifier toward respective data and regional specifics. In contrast, the OBIA approach analyzes remotely sensed data by incorporating expert analyst knowledge and complimentary ancillary data in a way that somehow simulates human intelligence for image interpretation based on the real-world representation of the features. As the segment is the basic processing unit, various combinations of segmentation criteria were tested to separate similar spectral values into groups of relatively homogeneous pixels. At the categorical subtraction level, rules were developed and optimum features were extracted for each particular class. Two methods were allocated (i.e. Rule Based (RB) and Nearest Neighbour (NN) Classifier) to assign segmented objects to their corresponding classes. Moreover, the study attempts to answer the questions whether OBIA is inherently more precise at fine spatial resolution than at coarser resolution, and how both pixel-based and OBIA approaches can be compared regarding relative accuracy in function of spatial resolution. As anticipated, this work emphasizes that the OBIA approach is can be proposed as an advanced solution particulary for high resolution imagery, since the accuracies were improved at the different scales applied compare with those of pixel-based approach. Meanwhile, the results achieved by the two approaches are consistently high at a finer RapidEye spatial resolution, and much significantly enhanced with OBIA. Since the change in LU/LC is rapid and the region is heterogeneous as well as the data vary regarding the date of acquisition and data source, this motivated the implementation of post-classification change detection rather than radiometric transformation methods. Based on thematic LU/LC maps, series of optimized algorithms have been developed to depict the dynamics in LU/LC entities. Therefore, detailed change “from-to” information classes as well as changes statistics were produced. Furthermore, the produced change maps were assessed, which reveals that the accuracy of the change maps is consistently high. Aggregated to the community-level, social survey of household data provides a comprehensive perspective additionally to EO data. The predetermined hot spots of degraded and successfully recovered areas were investigated. Thus, the study utilized a well-designed questionnaire to address the factors affecting land-cover dynamics and the possible solutions based on local community's perception. At the operational structural forest stand level, the rationale for incorporating these analyses are to offer a semi-automatic OBIA metrics estimates from which forest attribute is acquired through automated segmentation algorithms at the level of delineated tree crowns or clusters of crowns. Correlation and regression analyses were applied to identify the relations between a wide range of spectral and textural metrics and the field derived forest attributes. The acquired results from the OBIA framework reveal strong relationships and precise estimates. Furthermore, the best fitted models were cross-validated with an independent set of field samples, which revealed a high degree of precision. An important question is how the spatial resolution and spectral range used affect the quality of the developed model this was also discussed based on the different sensors examined. To conclude, the study reveals that the OBIA has proven capability as an efficient and accurate approach for gaining knowledge about the land features, whether at the operational forest structural attributes or categorical LU/LC level. Moreover, the methodological framework exhibits a potential solution to attain precise facts and figures about the change dynamics and its driving forces. / Da das Waldressourcenmanagement hierarchisch strukturiert ist, beschäftigt sich die vorliegende Arbeit mit der natürlichen Waldbedeckung auf verschiedenen Abstraktionsebenen, das heißt insbesondere mit der Ebene der kategorischen Landnutzung / Landbedeckung (LU/LC) sowie mit der kontinuierlichen empirischen Abschätzung auf lokaler operativer Ebene. Da zurzeit kein Sensor die Anforderungen aller Ebenen der Bewertung von Waldressourcen und von Multisource-Bildmaterialien (d.h. RapidEye, TERRA ASTER und LANDSAT TM) erfüllen kann, wurden zusätzlich andere Formen von Daten und Wissen untersucht und in die Arbeit mit eingebracht. Es wurde eine objekt-basierte Bildanalyse (OBIA) in einer destabilisierten Region des Blauen Nils im Sudan eingesetzt, um nach möglichen Lösungen zu suchen, erforderliche Informationen für die zukünftigen Waldplanung und die Entscheidungsfindung zu sammeln. Außerdem wurden die räumliche Heterogenität, sowie die sehr schnellen Änderungen in der Region untersucht. Dies motiviert nach effizienteren, flexibleren und genaueren Methoden zu suchen, um die gewünschten aktuellen Informationen zu erhalten. Das Konzept von OBIA wurde als Substitution-Analyse-Rahmen vorgeschlagen, um die Mängel vom früheren pixel-basierten Konzept abzumildern. In diesem Sinne untersucht die Studie die beliebtesten Maximum-Likelihood-Klassifikatoren des pixel-basierten Konzeptes als Beispiel für das Verhalten der spektralen Klassifikatoren in dem jeweiligen Datenbereich und der Region. Im Gegensatz dazu analysiert OBIA Fernerkundungsdaten durch den Einbau von Wissen des Analytikers sowie kostenlose Zusatzdaten in einer Art und Weise, die menschliche Intelligenz für die Bildinterpretation als eine reale Darstellung der Funktion simuliert. Als ein Segment einer Basisverarbeitungseinheit wurden verschiedene Kombinationen von Segmentierungskriterien getestet um ähnliche spektrale Werte in Gruppen von relativ homogenen Pixeln zu trennen. An der kategorische Subtraktionsebene wurden Regeln entwickelt und optimale Eigenschaften für jede besondere Klasse extrahiert. Zwei Verfahren (Rule Based (RB) und Nearest Neighbour (NN) Classifier) wurden zugeteilt um die segmentierten Objekte der entsprechenden Klasse zuzuweisen. Außerdem versucht die Studie die Fragen zu beantworten, ob OBIA in feiner räumlicher Auflösung grundsätzlich genauer ist als eine gröbere Auflösung, und wie beide, das pixel-basierte und das OBIA Konzept sich in einer relativen Genauigkeit als eine Funktion der räumlichen Auflösung vergleichen lassen. Diese Arbeit zeigt insbesondere, dass das OBIA Konzept eine fortschrittliche Lösung für die Bildanalyse ist, da die Genauigkeiten - an den verschiedenen Skalen angewandt - im Vergleich mit denen der Pixel-basierten Konzept verbessert wurden. Unterdessen waren die berichteten Ergebnisse der feineren räumlichen Auflösung nicht nur für die beiden Ansätze konsequent hoch, sondern durch das OBIA Konzept deutlich verbessert. Die schnellen Veränderungen und die Heterogenität der Region sowie die unterschiedliche Datenherkunft haben dazu geführt, dass die Umsetzung von Post-Klassifizierungs- Änderungserkennung besser geeignet ist als radiometrische Transformationsmethoden. Basierend auf thematische LU/LC Karten wurden Serien von optimierten Algorithmen entwickelt, um die Dynamik in LU/LC Einheiten darzustellen. Deshalb wurden für Detailänderung "von-bis"-Informationsklassen sowie Veränderungsstatistiken erstellt. Ferner wurden die erzeugten Änderungskarten bewertet, was zeigte, dass die Genauigkeit der Änderungskarten konstant hoch ist. Aggregiert auf die Gemeinde-Ebene bieten Sozialerhebungen der Haushaltsdaten eine umfassende zusätzliche Sichtweise auf die Fernerkundungsdaten. Die vorher festgelegten degradierten und erfolgreich wiederhergestellten Hot Spots wurden untersucht. Die Studie verwendet einen gut gestalteten Fragebogen um Faktoren die die Dynamik der Änderung der Landbedeckung und mögliche Lösungen, die auf der Wahrnehmung der Gemeinden basieren, anzusprechen. Auf der Ebene des operativen strukturellen Waldbestandes wird die Begründung für die Einbeziehung dieser Analysen angegeben um semi-automatische OBIA Metriken zu schätzen, die aus dem Wald-Attribut durch automatisierte Segmentierungsalgorithmen in den Baumkronen abgegrenzt oder Cluster von Kronen Ebenen erworben wird. Korrelations- und Regressionsanalysen wurden angewandt, um die Beziehungen zwischen einer Vielzahl von spektralen und strukturellen Metriken und den aus den Untersuchungsgebieten abgeleiteten Waldattributen zu identifizieren. Die Ergebnisse des OBIA Rahmens zeigen starke Beziehungen und präzise Schätzungen. Die besten Modelle waren mit einem unabhängigen Satz von kreuz-validierten Feldproben ausgestattet, welche hohe Genauigkeiten ergaben. Eine wichtige Frage ist, wie die räumliche Auflösung und die verwendete Bandbreite die Qualität der entwickelten Modelle auch auf der Grundlage der verschiedenen untersuchten Sensoren beeinflussen. Schließlich zeigt die Studie, dass OBIA in der Lage ist, als ein effizienter und genauer Ansatz Kenntnisse über die Landfunktionen zu erlangen, sei es bei operativen Attributen der Waldstruktur oder auch auf der kategorischen LU/LC Ebene. Außerdem zeigt der methodischen Rahmen eine mögliche Lösung um präzise Fakten und Zahlen über die Veränderungsdynamik und ihre Antriebskräfte zu ermitteln.

Risks and uncertainties are unavoidable in agriculture in Sudan, due to its dependence on climatic factors and to the imperfect nature of the agricultural decisions and policies attributed to land cover and land use changes that occur. The current study was conducted in the Gash Agricultural Scheme (GAS) - Kassala State, as a semi-arid land in eastern Sudan. The scheme has been established to contribute to the rural development, to help stability of the nomadic population in eastern Sudan, particularly the local population around the Gash river areas, and to facilitate utilizing the river flood in growing cotton and other cash crops. In the last decade, the scheme production has declined, because of drought periods, which hit the region, sand invasion and the spread of invasive mesquite trees, in addition to administrative negligence. These have resulted also in poor agricultural productivity and the displacement of farmers away from the scheme area. Recently, the scheme is heavily disturbed by human intervention in many aspects. Consequently, resources of cultivated land have shrunk and declined during the period of the study, which in turn have led to dissatisfaction and increasing failure of satisfying increasing farmer’s income and demand for local consumption. Remote sensing applications and geospatial techniques have played a key role in studying different types of hazards whether they are natural or manmade. Multi-temporal satellite data combined with ancillary data were used to monitor, analyze and to assess land use and land cover (LULC) changes and the impact of land degradation on the scheme production, which provides the managers and decision makers with current and improved data for the purposes of proper administration of natural resources in the GAS. Information about patterns of LULC changes through time in the GAS is not only important for the management and planning, but also for a better understanding of human dimensions of environmental changes at regional scale. This study attempts to map and assess the impacts of LULC change and land degradation in GAS during a period of 38 years from 1972-2010. Dry season multi-temporal satellite imagery collected by different sensor systems was selected such as three cloud-free Landsat (MSS 1972, TM 1987 and ETM+ 1999) and ASTER (2010) satellite imagery. This imagery was geo-referenced and radiometrically and atmospherically calibrated using dark object subtraction (DOS). Two approaches of classification (object-oriented and pixel-based) were applied for classification and comparison of LULC. In addition, the study compares between the two approaches to determine which one is more compatible for classification of LULC of the GAS. The pixel-based approach performed slightly better than the object-oriented approach in the classification of LULC in the study area. Application of multi-temporal remote sensing data proved to be successful for the identification and mapping of LULC into five main classes as follows: woodland dominated by dense mesquite trees, grass and shrubs dominated by less dense mesquite trees, bare and cultivated land, stabilized fine sand and mobile sand. After image enhancement successful classification of imagery was achieved using pixel and object based approaches as well as subsequent change detection (image differencing and change matrix), supported by classification accuracy assessments and post-classification. Comparison of LULC changes shows that the land cover of GAS has changed dramatically during the investigated period. It has been discovered that more significant of LULC change processes occurred during the second studied period (1987 to 1999) than during the first period (1972-1987). In the second period nearly half of bare and cultivated lands was changed from 41372.74 ha (20.22 %) in 1987 to 28020.80 ha (13.60 %) in 1999, which was mainly due to the drought that hit the region during the mentioned period. However, the results revealed a drastic loss of bare and cultivated land, equivalent to more than 40% during the entire period (1972-2010). Throughout the whole period of study, drought and invasion of both mesquite trees and sand were responsible for the loss of more than 40% of the total productive lands. Change vector analysis (CVA) as a useful approach was applied for estimating change detection in both magnitude and direction of change. The promising approach of multivariate alteration detection (MAD) and subsequent maximum autocorrelation factor (MAD/MAF) transformation was used to support change detection via assessment of maximum correlation between the transformed variates and the specific original image bands related to specific land cover classes. However, both CVA and MAD/MAD strongly prove the fact that bare and cultivated land have dramatically changed and decreased continuously during the studied period. Both CVA and MAD/MAD demonstrate adequate potentials for monitoring, detecting, identifying and mapping the changes. Moreover, this research demonstrated that CVA and MAD/MAF are superior in providing qualitative details about the nature of all kinds of change. Vegetation indices (VI) such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified adjusted vegetation index (MSAVI) and grain soil index (GSI) were applied to measure the quantitative characterization of temporal and spatial vegetation cover patterns and change. All indices remain very sensitive to structure variation of LULC. The results reveal that the NDVI is more effective for detecting the amount and status of the vegetation cover in the study area than SAVI, MSAVI and GSI. Therefore, it can be stated that NDVI can be used as a response variable to identify drought disturbance and land degradation in semi-arid land such as the GAS area. Results of detecting vegetation cover observed by using SAVI were found to be more reasonable than using MSAVI, although MSAVI reduces the background of bare soil better than SAVI. GSI proves high efficiency in determining the different types of surface soils, and producing a change map of top soil grain size, which is useful in assessment of land degradation in the study area. The linkage between socio-economic data and remotely sensed data was applied to determine the relationships between the different factors derived and to analyze the reasons for change in LULC and land degradation and its effects in the study area. The results indicate a strong relationship between LULC derived from remotely sensed data and the influencing socioeconomic variables. The results obtained from analyzing socioeconomic data confirm the findings of remote sensing data analysis, which assure that the decline and degradation of agricultural land is a result of further spread of mesquite trees and of increased invasion of sand during the study period. High livestock density and overgrazing, drought, invasion of sand, spread of invasive mesquite trees, overexploitation of land, improper management, and population growth were considered as the main direct factors responsible for degradation in the study area.

The work presented in this thesis mainly focuses on two subjects related to the application of remote sensing data: (1) for land cover classification combining optical sensor, texture features generated from spectral information and synthetic aperture radar (SAR) features, and (2) to develop a non-destructive approach for above ground biomass (AGB) and forest attributes estimation employing multi-source remote sensing data (i.e. optical data, SAR backscatter) combined with in-situ data. Information provided by reliable land cover map is useful for management of forest resources to support sustainable forest management, whereas the generation of the non-destructive approach to model forest biophysical properties (e.g. AGB and stem volume) is required to assess the forest resources more efficiently and cost-effective, and coupled with remote sensing data the model can be applied over large forest areas. This work considers study sites over tropical rain forest landscape in Indonesia characterized by different successional stages and complex vegetation structure including tropical peatland forests. The thesis begins with a brief introduction and the state of the art explaining recent trends on monitoring and modeling of forest resources using remote sensing data and approach. The research works on the integration of spectral information and texture features for forest cover mapping is presented subsequently, followed by development of a non-destructive approach for AGB and forest parameters predictions and modeling. Ultimately, this work evaluates the potential of mosaic SAR data for AGB modeling and the fusion of optical and SAR data for peatlands discrimination. The results show that the inclusion of geostatistics texture features improved the classification accuracy of optical Landsat ETM data. Moreover, the fusion of SAR and optical data enhanced the peatlands discrimination over tropical peat swamp forest. For forest stand parameters modeling, neural networks method resulted in lower error estimate than standard multi-linear regression technique, and the combination of non-destructive measurement (i.e. stem number) and remote sensing data improved the model accuracy. The up scaling of stem volume and biomass estimates using Kriging method and bi-temporal ETM image also provide favorable estimate results upon comparison with the land cover map. / Die in dieser Dissertation präsentierten Ergebnisse konzentrieren sich hauptsächlich auf zwei Themen mit Bezug zur angewandten Fernerkundung: 1) Der Klassifizierung von Oberflächenbedeckung basierend auf der Verknüpfung von optischen Sensoren, Textureigenschaften erzeugt durch Spektraldaten und Synthetic-Aperture-Radar (SAR) features und 2) die Entwicklung eines nichtdestruktiven Verfahrens zur Bestimmung oberirdischer Biomasse (AGB) und weiterer Waldeigenschaften mittels multi-source Fernerkundungsdaten (optische Daten, SAR Rückstreuung) sowie in-situ Daten. Eine zuverlässige Karte der Landbedeckung dient der Unterstützung von nachhaltigem Waldmanagement, während eine nichtdestruktive Herangehensweise zur Modellierung von biophysikalischen Waldeigenschaften (z.B. AGB und Stammvolumen) für eine effiziente und kostengünstige Beurteilung der Waldressourcen notwendig ist. Durch die Kopplung mit Fernerkundungsdaten kann das Modell auf große Waldflächen übertragen werden. Die vorliegende Arbeit berücksichtigt Untersuchungsgebiete im tropischen Regenwald Indonesiens, welche durch verschiedene Regenerations- und Sukzessionsstadien sowie komplexe Vegetationsstrukturen, inklusive tropischer Torfwälder, gekennzeichnet sind. Am Anfang der Arbeit werden in einer kurzen Einleitung der Stand der Forschung und die neuesten Forschungstrends in der Überwachung und Modellierung von Waldressourcen mithilfe von Fernerkundungsdaten dargestellt. Anschließend werden die Forschungsergebnisse der Kombination von Spektraleigenschaften und Textureigenschaften zur Waldbedeckungskartierung erläutert. Desweiteren folgen Ergebnisse zur Entwicklung eines nichtdestruktiven Ansatzes zur Vorhersage und Modellierung von AGB und Waldeigenschaften, zur Auswertung von Mosaik- SAR Daten für die Modellierung von AGB, sowie zur Fusion optischer mit SAR Daten für die Identifizierung von Torfwäldern. Die Ergebnisse zeigen, dass die Einbeziehung von geostatistischen Textureigenschaften die Genauigkeit der Klassifikation von optischen Landsat ETM Daten gesteigert hat. Desweiteren führte die Fusion von SAR und optischen Daten zu einer Verbesserung der Unterscheidung zwischen Torfwäldern und tropischen Sumpfwäldern. Bei der Modellierung der Waldparameter führte die Neural-Network-Methode zu niedrigeren Fehlerschätzungen als die multiple Regressions. Die Kombination von nichtdestruktiven Messungen (z.B. Stammzahl) und Fernerkundungsdaten führte zu einer Steigerung der Modellgenauigkeit. Die Hochskalierung des Stammvolumens und Schätzungen der Biomasse mithilfe von Kriging und bi-temporalen ETM Daten lieferten positive Schätzergebnisse im Vergleich zur Landbedeckungskarte.

Agricultural production is becoming increasingly important as the world demand increases. On the other hand, there are several factors threatening that production such as the climate change. Therefore, monitoring and management of different parameters affecting the production are important. The current study is dedicated to two key parameters, namely agricultural land cover and soil-moisture mapping using X- and L-Band Synthetic Aperture Radar (SAR) data. Land-cover mapping plays an essential role in various applications like irrigation management, yield estimation and subsidy control. A model of multi-direction/multi-distance texture analysis on SAR data and its use for agricultural land cover classification was developed. The model is built and implemented in ESRI ArcGIS software and integrated with “R Environment”. Sets of texture measures can be calculated on a plot basis and stored in an attribute table for further classification. The classification module provides various classification approaches such as support vector machine and artificial neural network, in addition to different feature-selection methods. The model has been tested for a typical Mid-European agricultural and horticultural land use pattern south to the town of Pirna (Saxony/Germany), where the high-resolution SAR data, TerraSAR-X and ALOS/PALSAR (HH/HV) imagery, were used for land-cover mapping. The results indicate that an integrated classification using textural information of SAR data has a high potential for land-cover mapping. Moreover, the multi-dimensional SAR data approach improved the overall accuracy. Soil moisture (SM) is important for various applications such as crop-water management and hydrological modelling. The above-mentioned TerraSAR-X data were utilised for soil-moisture mapping verified by synchronous field measurements. Different speckle-reduction techniques were applied and the most representative filtered image was determined. Then the soil moisture was calculated for the mapped area using the obtained linear regression equations for each corresponding land-cover type. The results proved the efficiency of SAR data in soil-moisture mapping for bare soils and at the early growing stage of fieldcrops. / Landwirtschaftliche Produktion erlangt mit weltweit steigender Nahrungsmittelnachfrage zunehmende Bedeutung. Zahlreiche Faktoren bedrohen die landwirtschaftliche Produktion wie beispielsweise die globale Klimaveränderung einschließlich ihrer indirekten Nebenwirkungen. Somit ist das Monitoring der Produktion selbst und der wesentlichen Produktionsparameter eine zweifelsfrei wichtige Aufgabe. Die vorliegende Studie widmet sich in diesem Kontext zwei Schlüsselinformationen, der Aufnahme landwirtschaftlicher Kulturen und den Bodenfeuchteverhältnissen, jeweils unter Nutzung von Satellitenbilddaten von Radarsensoren mit Synthetischer Apertur, die im X- und L-Band operieren. Landnutzungskartierung spielt eine essentielle Rolle für zahlreiche agrarische Anwendungen; genannt seien hier nur Bewässerungsmaßnahmen, Ernteschätzung und Fördermittelkontrolle. In der vorliegenden Arbeit wurde ein Modell entwickelt, welches auf Grundlage einer Texturanalyse der genannten SAR-Daten für variable Richtungen und Distanzen eine Klassifikation landwirtschaftlicher Nutzungsformen ermöglicht. Das Modell wurde als zusätzliche Funktionalität für die ArcGIS-Software implementiert. Es bindet dabei Klassifikationsverfahren ein, die aus dem Funktionsschatz der Sprache „R“ entnommen sind. Zum Konzept: Ein Bündel von Texturparametern wird durch das vorliegende Programm auf Schlagbasis berechnet und in einer Polygonattributtabelle der landwirtschaftlichen Schläge abgelegt. Auf diese Attributtabelle greift das nachfolgend einzusetzende Klassifikationsmodul zu. Die Software erlaubt nun die Suche nach „aussagekräftigen“ Teilmengen innerhalb des umfangreichen Texturmerkmalsraumes. Im Klassifikationsprozess kann aus verschiedenen Ansätzen gewählt werden. Genannt seien „Support Vector Machine“ und künstliche neuronale Netze. Das Modell wurde für einen typischen mitteleuropäischen Untersuchungsraum mit landwirtschaftlicher und gartenbaulicher Nutzung getestet. Er liegt südlich von Pirna im Freistaat Sachsen. Zum Test lagen für den Untersuchungsraum Daten von TerraSAR-X und ALOS/PALSAR (HH/HV) aus identischen Aufnahmetagen vor. Die Untersuchungen beweisen ein hohes Potenzial der Texturinformation aus hoch aufgelösten SAR-Daten für die landwirtschaftliche Nutzungserkennung. Auch die erhöhte Dimensionalität durch die Kombination von zwei Sensoren erbrachte eine Verbesserung der Klassifikationsgüte. Kenntnisse der Bodenfeuchteverteilung sind u.a. bedeutsam für Bewässerungsanwendungen und hydrologische Modellierung. Die oben genannten SAR-Datensätze wurden auch zur Bodenfeuchteermittlung genutzt. Eine Verifikation wurde durch synchrone Feldmessungen ermöglicht. Initial musste der Radar-typische „Speckle“ in den Bildern durch Filterung verringert werden. Verschiedene Filtertechniken wurden getestet und das beste Resultat genutzt. Die Bodenfeuchtebestimmung erfolgte in Abhängigkeit vom Nutzungstyp über Regressionsanalyse. Auch die Resultate für die Bodenfeuchtebestimmung bewiesen das Nutzpotenzial der genutzten SAR-Daten für offene Ackerböden und Stadien, in denen die Kulturpflanzen noch einen geringen Bedeckungsgrad aufweisen.

Beaver Creek is an impaired streams that is not supporting its designated use for recreation due to Escherichia coli (E.coli), and sediment. To address this problem, this thesis was divided into two studies. The first study explored changes in Land Use Land Cover (LULC), and its impact on surface water quality. Changes in E.coli load between 1997-2001 and 2014-2018 were analyzed. Also, Landsat data of 2001, and 2018 were examined in Terrset 18.31. Mann-Whitney test only showed a significant reduction in E.coli for one site. Negative correlation was established between E.coli load, and Developed LULC, Forest LULC, and Cultivated LULC. The second study modelled discharge for Beaver Creek watershed using HEC-HMS. This study simulated discharge in an upstream sub-watershed of Beaver Creek, and the full Beaver Creek with a Nash-Sutcliffe of 0.007, and R2 0.20. Sub-basins with high discharge were identified for further examination for possible high sediment load.

The aim of the project is to perform object based change detection of land cover in specific areas of Czech republic. Landsat 2000 and Spot 2006 satellite images are used as input data. The method used for evaluation of changes is Multivariate Alteration Detection unsupervised method which is based on statistical procedures and is available from e-Cognition software. The results of detection are compared with Corine Land Cover changes database to evaluate degree of parity on detected areas. Different mapping unit is used to be able to detect smaller changes than Corine database. First part of the work is review of literature sources aimed on processing of satellite images, description of the spectral behavior of landscape objects, origins of Corine Land Cover database and principle of change detection using MAD. Second part deals with data adjustment, change detection process and comparison of reached results with Corine. Keywords: object based change detection, satellite images, Corine Land Cover, mapping unit of changes, Multivariate Alteration Detection, e-Cognition

Urban areas around the world are changing constantly and therefore it is necessary to update urban land cover maps regularly. Remote sensing techniques have been used to monitor changes and update land-use/land-cover information in urban areas for decades. Optical imaging systems have received most of the attention in urban studies. The development of SAR applications in urban monitoring has been accelerated with more and more advanced SAR systems operating in space.   This research investigated object-based and rule-based classification methodologies for extracting urban land-cover information from high resolution SAR data. The study area is located in the north and northwest part of the Greater Toronto Area (GTA), Ontario, Canada, which has been undergoing rapid urban growth during the past decades. Five-date RADARSAT-1 fine-beam C-HH SAR images with a spatial resolution of 10 meters were acquired during May to August in 2002. Three-date RADARSAT-2 ultra-fine-beam C-HH SAR images with a spatial resolution of 3 meters were acquired during June to September in 2008.   SAR images were pre-processed and then segmented using multi-resolution segmentation algorithm. Specific features such as geometric and texture features were selected and calculated for image objects derived from the segmentation of SAR images. Both neural network (NN) and support vector machines (SVM) were investigated for the supervised classification of image objects of RADARSAT-1 SAR images, while SVM was employed to classify image objects of RADARSAT-2 SAR images. Knowledge-based rules were developed and applied to resolve the confusion among some classes in the object-based classification results.   The classification of both RADARSAT-1 and RADARSAT-2 SAR images yielded relatively high accuracies (over 80%). SVM classifier generated better result than NN classifier for the object-based supervised classification of RADARSAT-1 SAR images. Well-designed knowledge-based rules could increase the accuracies of some classes after the object-based supervised classification. The comparison of the classification results of RADARSAT-1 and RADARSAT-2 SAR images showed that SAR images with higher resolution could reveal more details, but might produce lower classification accuracies for certain land cover classes due to the increasing complexity of the images. Overall, the classification results indicate that the proposed object-based and rule-based approaches have potential for operational urban land cover mapping from high-resolution space borne SAR images. / QC 20101209

Vegetation forms the basis for the existence of animal and human. Due to changes in climate and human perturbation, most of the natural vegetation of the world has undergone some form of transformation both in composition and structure. Increased anthropogenic activities over the last decades had pose serious threat on the natural vegetation in Nigeria, many vegetated areas are either transformed to other land use such as deforestation for agricultural purpose or completely lost due to indiscriminate removal of trees for charcoal, fuelwood and timber production. This study therefore aims at examining the rate of change in vegetation cover, the degree of change and the application of Principal Component Analysis (PCA) in the dry sub-humid region of Nigeria using Normalized Difference Vegetation Index (NDVI) data spanning from 1983-2011. The method used for the analysis is the T-mode orientation approach also known as standardized PCA, while trends are examined using ordinary least square, median trend (Theil-Sen) and monotonic trend. The result of the trend analysis shows both positive and negative trend in vegetation change dynamics over the 29 years period examined. Five components were used for the Principal Component Analysis. The results of the first component explains about 98 % of the total variance of the vegetation (NDVI) while components 2-5 have lower variance percentage (< 1%). Two ancillary land use land cover data of 2000 and 2009 from European Space Agency (ESA) were used to further explain changes observed in the Normalized Difference Vegetation Index. The result of the land use data shows changes in land use pattern which can be attributed to anthropogenic activities such as cutting of trees for charcoal production, fuelwood and agricultural practices. The result of this study shows the ability of remote sensing data for monitoring vegetation change in the dry-sub humid region of Nigeria.