@phdthesis{Wohlfart2018,
  author    = {Wohlfart, Christian},
  title     = {The Yellow River Basin in Transition - Multi-faceted Land Cover Change Analysis in the Yellow River Basin in the Context of Global Change Using Multi-sensor Remote Sensing Imagery},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-163724},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {As a cradle of ancient Chinese civilization, the Yellow River Basin has a very long human-environment interrelationship, where early anthropogenic activities re- sulted in large scale landscape modifications. Today, the impact of this relationship has intensified further as the basin plays a vital role for China's continued economic development. It is one of the most densely-populated, fastest growing, and most dynamic regions of China with abundant natural and environmental resources providing a livelihood for almost 190 million people. Triggered by fundamental economic reforms, the basin has witnessed a spectacular economic boom during the last decades and can be considered as an exemplary blueprint region for contemporary dynamic Global Change processes occurring throughout the country, which is currently transitioning from an agrarian-dominated economy into a modern urbanized society. However, this resourcesdemanding growth has led to profound land use changes with adverse effects on the Yellow River social-ecological systems, where complex challenges arise threatening a long-term sustainable development. Consistent and continuous remote sensing-based monitoring of recent and past land cover and land use change is a fundamental requirement to mitigate the adverse impacts of Global Change processes. Nowadays, technical advancement and the multitude of available satellite sensors, in combination with the opening of data archives, allow the creation of new research perspectives in regional land cover applications over heterogeneous landscapes at large spatial scales. Despite the urgent need to better understand the prevailing dynamics and underlying factors influencing the current processes, detailed regional specific land cover data and change information are surprisingly absent for this region. In view of the noted research gaps and contemporary developments, three major objectives are defined in this thesis. First (i), the current and most pressing social-ecological challenges are elaborated and policy and management instruments towards more sustainability are discussed. Second (ii), this thesis provides new and improved insights on the current land cover state and dynamics of the entire Yellow River Basin. Finally (iii), the most dominant processes related to mining, agriculture, forest, and urban dynamics are determined on finer spatial and temporal scales. The complex and manifold problems and challenges that result from long-term abuse of the water and land resources in the basin have been underpinned by policy choices, cultural attitude, and institutions that have evolved over centuries in China. The tremendous economic growth that has been mainly achieved by extracting water and exploiting land resources in a rigorous, but unsustainable manner, might not only offset the economic benefits, but could also foster social unrest. Since the early emergence of the first Chinese dynasties, flooding was considered historically as a primary issue in river management and major achievements have been made to tame the wild nature of the Yellow River. Whereas flooding is therefore largely now under control, new environmental and social problems have evolved, including soil and water pollution, ecological degradation, biodiversity decline, and food security, all being further aggravated by anthropogenic climate change. To resolve the contemporary and complex challenges, many individual environmental laws and regulations have been enacted by various Chinese ministries. However, these policies often pursue different, often contradictory goals, are too general to tackle specific problems and are usually implemented by a strong top-down approach. Recently, more flexible economic and market-based incentives (pricing, tradable permits, investments) have been successfully adopted, which are specifically tailored to the respective needs, shifting now away from the pure command and regulating instruments. One way towards a more holistic and integrated river basin management could be the establishment of a common platform (e.g. a Geographical Information System) for data handling and sharing, possibly operated by the Yellow River Basin Conservancy Commission (YRCC), where available spatial data, statistical information and in-situ measures are coalesced, on which sustainable decision-making could be based. So far, the collected data is hardly accessible, fragmented, inconsistent, or outdated. The first step to address the absence and lack of consistent and spatially up-to-date information for the entire basin capturing the heterogeneous landscape conditions was taken up in this thesis. Land cover characteristics and dynamics were derived from the last decade for the years 2003 and 2013, based on optical medium-resolution hightemporal MODIS Normalized Differenced Vegetation Index (NDVI) time series at 250 m. To minimize the inherent influence of atmospheric and geometric interferences found in raw high temporal data, the applied adaptive Savitzky-Golay filter successfully smoothed the time series and substantially reduced noise. Based on the smoothed time series data, a large variety of intra-annual phenology metrics as well as spectral and multispectral annual statistics were derived, which served as input variables for random forest (RF) classifiers. High quality reference data sets were derived from very high resolution imagery for each year independently of which 70 \% trained the RF models. The accuracy assessments for all regionally specific defined thematic classes were based on the remaining 30 \% reference data split and yielded overall accuracies of 87 \% and 84 \% for 2003 and 2013, respectively. The first regional adapted Yellow River Land Cover Products (YRB LC) depict the detail spatial extent and distribution of the current land cover status and dynamics. The novel products overall differentiate overall 18 land cover and use classes, including classes of natural vegetation (terrestrial and aquatic), cultivated classes, mosaic classes, non-vegetated, and artificial classes, which are not presented in previous land cover studies so far. Building on this, an extended multi-faceted land cover analysis on the most prominent land cover change types at finer spatial and temporal scales provides a better and more detailed picture of the Yellow River Basin dynamics. Precise spatio-temporal products about mining, agriculture, forest, and urban areas were examined from long-trem Landsat satellite time series monitored at annual scales to capture the rapid rate of change in four selected focus regions. All archived Landsat images between 2000 and 2015 were used to derive spatially continuous spectral-temporal, multi-spectral, and textural metrics. For each thematic region and year RF models were built, trained and tested based on a stablepixels reference data set. The automated adaptive signature (AASG) algorithm identifies those pixels that did not change between the investigated time periods to generate a mono-temporal reference stable-pixels data set to keep manual sampling requirements to a minimum level. Derived results gained high accuracies ranging from 88 \% to 98 \%. Throughout the basin, afforestation on the Central Loess Plateau and urban sprawl are identified as most prominent drivers of land cover change, whereas agricultural land remained stable, only showing local small-scale dynamics. Mining operations started in 2004 on the Qinghai-Tibet Plateau, which resulted in a substantial loss of pristine alpine meadows and wetlands. In this thesis, a novel and unique regional specific view of current and past land cover characteristics in a complex and heterogeneous landscape was presented by using a multi-source remote sensing approach. The delineated products hold great potential for various model and management applications. They could serve as valuable components for effective and sustainable land and water management to adapt and mitigate the predicted consequences of Global Change processes.},
  subject      = {Fernerkundung},
  language  = {en}
}
@phdthesis{Knauer2018,
  author    = {Knauer, Kim},
  title     = {Vegetation Dynamics in West Africa - Spatio-temporal Data Fusion for the Monitoring of Agricultural Expansion},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-164776},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {West Africa is one of the fastest growing regions in the world with annual population growth rates of more than three percent for several countries. Since the 1950s, West Africa experienced a fivefold increase of inhabitants, from 71 to 353 million people in 2015 and it is expected that the region's population will continue to grow to almost 800 million people by the year 2050. This strong trend has and will have serious consequences for food security since agricultural productivity is still on a comparatively low level in most countries of West Africa. In order to compensate for this low productivity, an expansion of agricultural areas is rapidly progressing. The mapping and monitoring of agricultural areas in West Africa is a difficult task even on the basis of remote sensing. The small scale extensive farming practices with a low level of agricultural inputs and mechanization make the delineation of cultivated land from other land cover and land use (LULC) types highly challenging. In addition, the frequent cloud coverage in the region considerably decreases the availability of earth observation datasets. For the accurate mapping of agricultural area in West Africa, high temporal as well as spatial resolution is necessary to delineate the small-sized fields and to obtain data from periods where different LULC types are distinguishable. However, such consistent time series are currently not available for West Africa. Thus, a spatio-temporal data fusion framework was developed in this thesis for the generation of high spatial and temporal resolution time series. Data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) enjoyed increasing popularity during recent years but they have hardly been used for the application on larger scales. In order to make it applicable for this purpose and to increase the input data availability, especially in cloud-prone areas such as West Africa, the ESTARFM framework was developed in this thesis introducing several enhancements. An automatic filling of cloud gaps was included in the framework in order to use even partly cloud-covered Landsat images for the fusion without producing gaps on the output images. In addition, the ESTARFM algorithm was improved to automatically account for regional differences in the heterogeneity of the study region. Further improvements comprise the automation of the time series generation as well as the significant acceleration of the processing speed through parallelization. The performance of the developed ESTARFM framework was tested by fusing an 8-day NDVI time series from Landsat and MODIS data for a focus area of 98,000 km² in the border region between Burkina Faso and Ghana. The results of this test show the capability of the ESTARFM framework to accurately produce high temporal resolution time series while maintaining the spatial detail, even in such a heterogeneous and cloud-prone region. The successfully tested framework was subsequently applied to generate consistent time series as the basis for the mapping of agricultural area in Burkina Faso for the years 2001, 2007, and 2014. In a first step, high temporal (8-day) and high spatial (30 m) resolution NDVI time series for the entire country and the three years were derived with the ESTARFM framework. More than 500 Landsat scenes and 3000 MODIS scenes were automatically processed for this purpose. From the fused ESTARFM NDVI time series, phenological metrics were extracted and together with the single time steps of NDVI served as input for the delineation of rainfed agricultural areas, irrigated agricultural areas and plantations. The classification was conducted with the random forest algorithm at a 30 m spatial resolution for entire Burkina Faso and the three years 2001, 2007, and 2014. For the training and validation of the classifier, a randomly sampled reference dataset was generated from Google Earth images based on expert knowledge of the region. The overall classification accuracies of 92\% (2001), 91\% (2007), and 91\% (2014) indicate the well-functioning of the developed methodology. The resulting maps show an expansion of agricultural area of 91\% from about 61,000 km² in 2001 to 116,900 km² in 2014. While rainfed agricultural areas account for the major part of this increase, irrigated areas and plantations also spread considerably. Especially the expansion of irrigation systems and plantation area can be explained by the promotion through various national and international development projects. The increase of agricultural areas goes in line with the rapid population growth in most of Burkina Faso's provinces which still had available land resources for an expansion of agricultural area. An analysis of the development of agricultural areas in the vicinity of protected areas highlighted the increased human pressure on these reserves. The protection of the remnant habitats for flora and fauna while at the same time improving food security for a rapidly growing population, are the major challenges for the region in the future. The developed ESTARFM framework showed great potential beyond its utilization for the mapping of agricultural area. Other large-scale research that requires a sufficiently high temporal and spatial resolution such as the monitoring of land degradation or the investigation of land surface phenology could greatly benefit from the application of this framework.},
  subject      = {Fernerkundung},
  language  = {en}
}
@phdthesis{Cord2012,
  author    = {Cord, Anna},
  title     = {Potential of multi-temporal remote sensing data for modeling tree species distributions and species richness in Mexico},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-71021},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2012},
  abstract  = {Current changes of biodiversity result almost exclusively from human activities. This anthropogenic conversion of natural ecosystems during the last decades has led to the so-called 'biodiversity crisis', which comprises the loss of species as well as changes in the global distribution patterns of organisms. Species richness is unevenly distributed worldwide. Altogether, 17 so-called 'megadiverse' nations cover less than 10\% of the earth's land surface but support nearly 70\% of global species richness. Mexico, the study area of this thesis, is one of those countries. However, due to Mexico's large extent and geographical complexity, it is impossible to conduct reliable and spatially explicit assessments of species distribution ranges based on these collection data and field work alone. In the last two decades, Species distribution models (SDMs) have been established as important tools for extrapolating such in situ observations. SDMs analyze empirical correlations between geo-referenced species occurrence data and environmental variables to obtain spatially explicit surfaces indicating the probability of species occurrence. Remote sensing can provide such variables which describe biophysical land surface characteristics with high effective spatial resolutions. Especially during the last three to five years, the number of studies making use of remote sensing data for modeling species distributions has therefore multiplied. Due to the novelty of this field of research, the published literature consists mostly of selective case studies. A systematic framework for modeling species distributions by means of remote sensing is still missing. This research gap was taken up by this thesis and specific studies were designed which addressed the combination of climate and remote sensing data in SDMs, the suitability of continuous remote sensing variables in comparison with categorical land cover classification data, the criteria for selecting appropriate remote sensing data depending on species characteristics, and the effects of inter-annual variability in remotely sensed time series on the performance of species distribution models. The corresponding novel analyses were conducted with the Maximum Entropy algorithm developed by Phillips et al. (2004). In this thesis, a more comprehensive set of remote sensing predictors than in the existing literature was utilized for species distribution modeling. The products were selected based on their ecological relevance for characterizing species distributions. Two 1 km Terra-MODIS Land 16-day composite standard products including the Enhanced Vegetation Index (EVI), Reflectance Data, and Land Surface Temperature (LST) were assembled into enhanced time series for the time period of 2001 to 2009. These high-dimensional time series data were then transformed into 18 phenological and 35 statistical metrics that were selected based on an extensive literature review. Spatial distributions of twelve tree species were modeled in a hierarchical framework which integrated climate (WorldClim) and MODIS remote sensing data. The species are representative of the major Mexican forest types and cover a variety of ecological traits, such as range size and biotope specificity. Trees were selected because they have a high probability of detection in the field and since mapping vegetation has a long tradition in remote sensing. The result of this thesis showed that the integration of remote sensing data into species distribution models has a significant potential for improving and both spatial detail and accuracy of the model predictions.},
  subject      = {Fernerkundung},
  language  = {en}
}
@phdthesis{Machwitz2010,
  author    = {Machwitz, Miriam},
  title     = {Eine raum-zeitliche Modellierung der Kohlenstoffbilanz mit Fernerkundungsdaten auf regionaler Ebene in Westafrika},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-55136},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2010},
  abstract  = {Der Klimawandel und insbesondere die globale Erw{\"a}rmung geh{\"o}ren aktuell zu den gr{\"o}ßten Herausforderungen an Politik und Wissenschaft. Steigende CO2-Emissionen sind hierbei maßgeblich f{\"u}r die Klimaerw{\"a}rmung verantwortlich. Ein regulierender Faktor beim CO2-Austausch mit der Atmosph{\"a}re ist die Vegetation, welche als CO2-Senke aber auch als CO2-Quelle fungieren kann. Diese Funktionen k{\"o}nnen durch Analysen der Landbedeckungs{\"a}nderung in Kombination mit Modellierungen der Kohlenstoffbilanz quantifiziert werden, was insbesondere von aktuellen und zuk{\"u}nftigen politischen Instrumenten wie CDM (Clean Development Mechanism) oder REDD (Reducing Emissions from Deforestation and Degradation) gefordert wird. Vor allem in Regionen mit starker Landbedeckungs{\"a}nderung und hoher Bev{\"o}lkerungsdichte sowie bei geringem Wissen {\"u}ber die Produktivit{\"a}t und CO2-Speicherpotentiale der Vegetation, bedarf es einer Erforschung und Quantifizierung der terrestrischen Kohlenstoffspeicher. Eine Region, f{\"u}r die dies in besonderem Maße zutrifft, ist Westafrika. J{\"u}ngste Studien haben gezeigt, dass sich einerseits die Folgen des Klimawandels und Umweltver{\"a}nderungen sehr stark in Westafrika auswirken werden und andererseits Bev{\"o}lkerungswachstum eine starke {\"A}nderung der Landbedeckung f{\"u}r die Nutzung als agrarische Fl{\"a}che bewirkt hat. Folglich sind in dieser Region die terrestrischen Kohlenstoffspeicher durch Ausdehnung der Landwirtschaft und Waldrodung besonders gef{\"a}hrdet. Große Fl{\"a}chen agieren anstelle ihrer urspr{\"u}nglichen Funktion als CO2-Senke bereits als CO2-Quelle. [...]},
  subject      = {Carbon dioxide capture and storage},
  language  = {de}
}
@phdthesis{Gessner2010,
  author    = {Geßner, Ursula},
  title     = {R{\"a}umliche und zeitliche Muster der Vegetationsstruktur in Savannen des s{\"u}dlichen Afrika},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-55128},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2010},
  abstract  = {Die Ver{\"a}nderung der terrestrischen {\"O}kosysteme, ist ein grundlegendes Element des Globalen Wandels. In diesem Kontext unterliegt auch eines der gr{\"o}ßten Biome der Erde, die tropische und subtropische Savanne, immer st{\"a}rkeren Ver{\"a}nderungen. Dieses Biom in sozio{\"o}konomischer und {\"o}kologischer Hinsicht von besonderer Bedeutung. F{\"u}r einen rasch wachsenden Teil der Weltbev{\"o}lkerung bildet es die Grundlage f{\"u}r das Betreiben von Weidewirtschaft, Ackerbau und Tourismus. In nationalen und internationalen Forschungsprogrammen zum Globalen Wandel hat die Analyse von Landnutzungs- und Landbedeckungs{\"a}nderungen in den vergangenen Jahrzehnten zunehmend an Bedeutung gewonnen. Die Landbedeckungsdynamik von Savannen{\"o}kosystemen ist jedoch noch nicht hinreichend verstanden, so dass diese {\"O}kosysteme in globalen Studien nur ansatzweise ber{\"u}cksichtigt werden k{\"o}nnen. Besondere Herausforderungen bei der Erfassung der Landbedeckung und ihrer Dynamik liegen im Falle der Savannen in der heterogenen r{\"a}umlichen Verteilung der Wuchsformen, in den graduellen {\"U}berg{\"a}ngen zwischen Landbedeckungsklassen und in der hohen inner- und interannuellen Variabilit{\"a}t der Vegetationsdecke. Vor diesem Hintergrund besch{\"a}ftigt sich diese Dissertation mit der fernerkundungsbasierten Erfassung und Interpretation der Vegetationsstruktur und der Vegetationsdynamik von Savannen am Beispiel ausgew{\"a}hlter afrikanischer Untersuchungsregionen. Die Vegetationsstruktur wird in dieser Dissertation in Form von Bedeckungsgraden holziger Vegetation, krautiger Vegetation und vegetationsloser Fl{\"a}che erfasst. Es kommt ein mehrskaliges Verfahren zum Einsatz, in dem h{\"o}chstaufgel{\"o}ste IKONOS- und QuickBird-Daten, Landsat-Daten und annuelle MODIS-Zeitreihen ausgewertet werden. Der Ansatz basiert auf der Methodik der Ensemble-Regeressionb{\"a}ume und stellt eine Erweiterung und Optimierung der Herangehensweise des MODIS-Standardproduktes Vegetation Continuous Fields (VCF) nach Hansen et al. (2002) dar. Beim Vergleich mit unabh{\"a}ngigen Validierungsdaten der n{\"a}chst h{\"o}heren Aufl{\"o}sungsebene zeigt sich das Potenzial der vorgestellten Methodik. Die r{\"a}umliche {\"U}bertragbarkeit der Regressionsb{\"a}ume wird am Beispiel von zwei Vegetationstypen innerhalb der Zentralnamibischen Savanne dargestellt. In diesem Zusammenhang zeigt sich der hohe Stellenwert einer optimalen Auswahl an Trainingsdaten mit einer repr{\"a}sentativen Abdeckung der Wertespanne aller existierenden Bedeckungsgrade. Die erarbeiteten Resultate unterstreichen, die optimale Eignung der Subpixel-Bedeckungsgrade, gerade zur Beschreibung von Savannenlandschaften. In der Kombination von herk{\"o}mmlichen, diskreten Landbedeckungs- oder Vegetationskarten mit Informationen zu Bedeckungsgraden wird ein besonderer Mehrwert f{\"u}r weiterf{\"u}hrende Analysen gesehen. Die Dynamik der Savannenvegetation wird in dieser Arbeit sowohl auf biannueller als auch auf mehrj{\"a}hriger Skala charakterisiert. Bei der biannuellen Analyse werden die Ver{\"a}nderungen der holzigen Vegetationsbedeckung zwischen den Jahren 2003/04 und 2006/07 erfasst. Hierf{\"u}r findet eine zeitliche {\"U}bertragung des zuvor vorgestellten Verfahrens zur Ableitung von Bedeckungsanteilen statt. Im Rahmen der biannuellen Untersuchungen k{\"o}nnen Ver{\"a}nderungsfl{\"a}chen identifiziert werden, ohne Einschr{\"a}nkung auf {\"U}berg{\"a}nge zwischen fest definierten Klassengrenzen. In Erg{\"a}nzung der biannuellen Analysen werden aus MODIS-EVI- und Niederschlagszeitreihen Maßzahlen abgeleitet, die den Zusammenhang zwischen Niederschlag und Vegetationsentwicklung, die Variabilit{\"a}t und die Trends der Vegetation {\"u}ber einen Zeitraum von acht Jahren beschreiben. Hierbei kommen beispielsweise Korrelationsanalysen zwischen Vegetationsindex- und Niederschlagszeitreihen zum Einsatz. Zudem werden Trendanalysen der Vegetationsindex-Zeitreihen durchgef{\"u}hrt. Die Trends werden einerseits allein aus den Zeitreihen der Vegetationsindizes ermittelt, andererseits wird bei der Berechnung von Restrends (Residual Trends) der Einfluss des Niederschlags ber{\"u}cksichtigt. Neben den Korrelations- und Trendanalysen werden unterschiedliche Variabilit{\"a}tsmaße der Vegetationsindex-Zeitreihen genutzt, um die mehrj{\"a}hrige Vegetationsdynamik zu beschreiben. Durch die Kombination von Fernerkundungsdaten unterschiedlicher r{\"a}umlicher und zeitlicher Aufl{\"o}sungen wird in dieser Dissertation die heterogene Vegetationsstruktur und die komplexe Vegetationsdynamik ausgew{\"a}hlter afrikanischer Savannen{\"o}kosysteme beschreiben.},
  subject      = {Savanne},
  language  = {de}
}
@phdthesis{NoellieAhouRUETH2010,
  author    = {Noellie Ahou RUETH, geb. YAO},
  title     = {Mapping Bushfire Distribution and Burn Severity in West Africa Using Remote Sensing Observations},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-54244},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2010},
  abstract  = {Fire has long been considered to be the main ecological factor explaining the origin and maintenance of West African savannas. It has a very high occurrence in these savannas due to high human pressure caused by strong demographic growth and, concomitantly, is used to transform natural savannas into farmland and is also used as a provider of energy. This study was carried out with the support of the BIOTA project funded by the German ministry for Research and Education. The objective of this study is to establish the spatial and temporal distribution of bushfires during a long observation period from 2000 to 2009 as well as to assess fire impact on vegetation through mapping of the burn severity; based on remote sensing and field data collections. Remote sensing was used for this study because of the advantages that it offers in collecting data for long time periods and on different scales. In this case, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument at 1km resolution is used to assess active fires, and understand the seasonality of fire, its occurrence and its frequency within the vegetation types on a regional scale. Landsat ETM+ imagery at 30 m and field data collections were used to define the characteristics of burn severity related to the biomass loss on a local scale. At a regional scale, the occurrence of fires and rainfall per month correlated very well (R2 = 0.951, r = -0.878, P < 0.01), which shows that the lower the amount of rainfall, the higher the fire occurrence and vice versa. In the dry season, four fire seasons were determined on a regional scale, namely very early fires, which announce the beginning of the fires, early and late fires making up the peak of fire in December/January and very late fires showing the end of the fire season and the beginning of the rainy season. Considerable fire activity was shown to take place in the vegetation zones between the Forest and the Sahel areas. Within these zones, parts of the Sudano-Guinean and the Guinean zones showed a high pixel frequency, i.e. fires occurred in the same place in many years. This high pixel frequency was also found in most protected areas in these zones. As to the kinds of land cover affected by fire, the highest fire occurrence is observed within the Deciduous woodlands and Deciduous shrublands. Concerning the burn severity, which was observed at a local scale, field data correlated closely with the ΔNBR derived from Landsat scenes of Pendjari National Park (R2 = 0.76). The correlation coefficient according to Pearson is r = 0.84 and according to Spearman-Rho, the correlation coefficient is r = 0.86. Very low and low burn severity (with ΔNBR value from 0 to 0.40) affected the vegetation weakly (0-35 percent of biomass loss) whereas moderate and high burn severity greatly affected the vegetation, leading to up to 100 percent of biomass loss, with the ΔNBR value ranging from 0.41 to 0.99. It can be seen from these results that remotely sensed images offer a tool to determine the fire distribution over large regions in savannas and that the Normalised Burn Ratio index can be applied to West Africa savannas. The outcomes of this thesis will hopefully contribute to understanding and, eventually, improving fire regimes in West Africa and their response to climate change and changes in vegetation diversity.},
  subject      = {Westafrika},
  language  = {en}
}
@phdthesis{Heldens2010,
  author    = {Heldens, Wieke},
  title     = {Use of airborne hyperspectral data and height information to support urban micro climate characterisation},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-48935},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2010},
  abstract  = {The urban micro climate has been increasingly recognised as an important aspect for urban planning. Therefore, urban planners need reliable information on the micro climatic characteristics of the urban environment. A suitable spatial scale and large spatial coverage are important requirements for such information. This thesis presents a conceptual framework for the use of airborne hyperspectral data to support urban micro climate characterisation, taking into account the information needs of urban planning. The potential of hyperspectral remote sensing in characterising the micro climate is demonstrated and evaluated by applying HyMap airborne hyperspectral and height data to a case study of the German city of Munich. The developed conceptual framework consists of three parts. The first is concerned with the capabilities of airborne hyperspectral remote sensing to map physical urban characteristics. The high spatial resolution of the sensor allows to separate the relatively small urban objects. The high spectral resolution enables the identification of the large range of surface materials that are used in an urban area at up to sub-pixel level. The surface materials are representative for the urban objects of which the urban landscape is composed. These spatial urban characteristics strongly influence the urban micro climate. The second part of the conceptual framework provides an approach to use the hyperspectral surface information for the characterisation of the urban micro climate. This can be achieved by integrating the remote sensing material map into a micro climate model. Also spatial indicators were found to provide useful information on the micro climate for urban planners. They are commonly used in urban planning to describe building blocks and are related to several micro climatic parameters such as temperature and humidity. The third part of the conceptual framework addresses the combination and presentation of the derived indicators and simulation results under consideration of the planning requirements. Building blocks and urban structural types were found to be an adequate means to group and present the derived information for micro climate related questions to urban planners. The conceptual framework was successfully applied to a case study in Munich. Airborne hyperspectral HyMap data has been used to derive a material map at sub-pixel level by multiple endmember linear spectral unmixing. This technique was developed by the German Research Centre for Geosciences (GFZ) for applications in Dresden and Potsdam. A priori information on building locations was used to support the separation between spectrally similar materials used both on building roofs and non-built surfaces. In addition, surface albedo and leaf area index are derived from the HyMap data. The sub-pixel material map supported by object height data is then used to derive spatial indicators, such as imperviousness or building density. To provide a more detailed micro climate characterisation at building block level, the surface materials, albedo, leaf area index (LAI) and object height are used as input for simulations with the micro climate model ENVI-met. Concluding, this thesis demonstrated the potential of hyperspectral remote sensing to support urban micro climate characterisation. A detailed mapping of surface materials at sub-pixel level could be performed. This provides valuable, detailed information on a large range of spatial characteristics relevant to the assessment of the urban micro climate. The developed conceptual framework has been proven to be applicable to the case study, providing a means to characterise the urban micro climate. The remote sensing products and subsequent micro climatic information are presented at a suitable spatial scale and in understandable maps and graphics. The use of well-known spatial indicators and the framework of urban structural types can simplify the communication with urban planners on the findings on the micro climate. Further research is needed primarily on the sensitivity of the micro climate model towards the remote sensing based input parameters and on the general relation between climate parameters and spatial indicators by comparison with other cities.},
  subject      = {Mikroklima},
  language  = {en}
}
@phdthesis{Colditz2007,
  author    = {Colditz, Rene Roland},
  title     = {Time Series Generation and Classification of MODIS Data for Land Cover Mapping},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-25908},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2007},
  abstract  = {Processes of the Earth's surface occur at different scales of time and intensity. Climate in particular determines the activity and seasonal development of vegetation. These dynamics are predominantly driven by temperature in the humid mid-latitudes and by the availability of water in semi-arid regions. Human activities are a modifying parameter for many ecosystems and can become the prime force in well-developed regions with an intensively managed environment. Accounting for these dynamics, i.e. seasonal dynamics of ecosystems and short- to long-term changes in land-cover composition, requires multiple measurements in time. With respect to the characterization of the Earth surface and its transformation due to global warming and human-induced global change, there is a need for appropriate data and methods to determine the activity of vegetation and the change of land cover. Space-borne remote sensing is capable of monitoring the activity and development of vegetation as well as changes of the land surface. In many instances, satellite images are the only means to comprehensively assess the surface characteristics of large areas. A high temporal frequency of image acquisition, forming a time series of satellite data, can be employed for mapping the development of vegetation in space and time. Time series allow for detecting and assessing changes and multi-year transformation processes of high and low intensity, or even abrupt events such as fire and flooding. The operational processing of satellite data and automated information-extraction techniques are the basis for consistent and continuous long-term product generation. This provides the potential for directly using remote-sensing data and products for analyzing the land surface in relation to global warming and global change, including deforestation and land transformation. This study aims at the development of an advanced approach to time-series generation using data-quality indicators. A second goal focuses on the application of time series for automated land-cover classification and update, using fractional cover estimates to accommodate for the comparatively coarse spatial resolution. Requirements of this study are the robustness and high accuracy of the approaches as well as the full transferability to other regions and datasets. In this respect, the developments of this study form a methodological framework, which can be filled with appropriate modules for a specific sensor and application. In order to attain the first goal, time-series compilation, a stand-alone software application called TiSeG (Time Series Generator) has been developed. TiSeG evaluates the pixel-level quality indicators provided with each MODIS land product. It computes two important data-availability indicators, the number of invalid pixels and the maximum gap length. Both indices are visualized in time and space, indicating the feasibility of temporal interpolation. The level of desired data quality can be modified spatially and temporally to account for distinct environments in a larger study area and for seasonal differences. Pixels regarded as invalid are either masked or interpolated with spatial or temporal techniques.},
  subject      = {Zeitreihe},
  language  = {en}
}
@phdthesis{Esch2006,
  author    = {Esch, Thomas},
  title     = {Automatisierte Analyse von Siedlungsfl{\"a}chen auf der Basis h{\"o}chstaufl{\"o}sender Radardaten},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-18863},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2006},
  abstract  = {St{\"a}dtische Agglomerationen zeichnen sich durch eine zunehmende Dynamik {\"o}kologischer, {\"o}konomischer und sozialer Ver{\"a}nderungen aus. Um eine nachhaltige Entwicklung urbaner R{\"a}ume zu gew{\"a}hrleisten, bedarf es verst{\"a}rkt innovativer Methoden zur Erfassung der raumwirksamen Ver{\"a}nderungen. Diesbez{\"u}glich hat sich die satellitengest{\"u}tzte Erdbeobachtung als kosteng{\"u}nstiges Instrumentarium zur Erhebung planungsrelevanter Informationen erwiesen. Dabei wird in naher Zukunft eine neue Generation von Radarsatelliten zur Verf{\"u}gung stehen, deren Leistungsverm{\"o}gen erstmals die operationelle Analyse von Siedlungsfl{\"a}chen auf Grundlage von Radardaten erm{\"o}glicht. Vor diesem Hintergrund ist es das Ziel der Dissertation, auf der Basis einer nutzerorientierten Methodik das Potential hochaufl{\"o}sender SAR-Daten zur automatisierten Erfassung und Analyse von Siedlungsfl{\"a}chen zu untersuchen. Die Methodik setzt auf dem objektorientierten Bildanalysekonzept der Software eCognition auf. Dabei haben sich der SAR-Speckle sowie Schw{\"a}chen hinsichtlich der G{\"u}te der Bildsegmentierung bzw. der Bestimmung geeigneter Segmentierungseinstellungen als Limitierungen erwiesen. Folglich liegt ein erster Schwerpunkt auf der Optimierung und Stabilisierung einer segmentbasierten Auswertung von Radardaten. Hier hat sich gezeigt, dass mit Blick auf Siedlungsareale weiterhin Optimierungsbedarf hinsichtlich einer strukturerhaltenden Bildgl{\"a}ttung besteht. Daher wird zun{\"a}chst ein neuer Filteransatz entwickelt, der gegen{\"u}ber den etablierten Techniken eine konsequentere Reduzierung des Speckle in homogenen Bildarealen gew{\"a}hrleistet und dabei gleichsam die hochfrequente Information in stark strukturierten Aufnahmebereichen bewahrt. Die Schwierigkeiten im Zusammenhang mit der G{\"u}te und {\"U}bertragbarkeit der Bildsegmentierung werden ebenso wie die Schw{\"a}chen im Hinblick auf die zielgerichtete Definition der optimalen Segmentierungsparameter durch die Entwicklung eines klassenbasierten Ansatzes zur Segmentoptimierung in der Software-Umgebung von eCognition reduziert. Der zweite Schwerpunkt dieser Dissertation widmet sich der Entwicklung von Konzepten zur automatisierten Analyse der regionalen und lokalen Siedlungsstruktur. Im regionalen Kontext liegen die Identifizierung von Siedlungsfl{\"a}chen und die Erfassung einfacher Landnutzungsklassen im Fokus der Arbeiten. Dazu wird ein Regelwerk zur Auswertung einfach-polarisierter SAR-Aufnahmen erstellt, das sich maßgeblich auf r{\"a}umlich und zeitlich robuste textur-, kontext- und hierarchiebezogene Merkmale st{\"u}tzt. Diese Wissensbasis wird anschließend so erweitert, dass sie die Analyse dual-polarisierter, bifrequenter oder kombinierter optischer und SAR-basierter Bilddaten erm{\"o}glicht. Wie die Ergebnisse zeigen, k{\"o}nnen Siedlungsfl{\"a}chen und Landnutzungsklassen bereits {\"u}ber einfach-polarisierte SAR-Aufnahmen mit Genauigkeiten von rund 90 Prozent erfasst werden. Durch die Einbindung einer weiteren Polarisation, Frequenz oder optischer Daten l{\"a}sst sich diese G{\"u}te auf Werte von bis zu 95 Prozent steigern. Die lokalen Analysen zielen auf die thematisch und r{\"a}umlich differenzierte Erfassung der Landnutzung innerhalb bebauter Areale ab. Die Untersuchung basiert auf der synergetischen Auswertung einer hochaufl{\"o}senden Radaraufnahme und eines bedeutend geringer aufgel{\"o}sten optischen Datensatzes. Die isolierte Analyse von SAR-Aufnahmen reichte hingegen selbst bei der Kombination verschiedener Frequenzen oder Polarisationen nicht zur Charakterisierung der kleinteiligen, heterogenen Stadtlandschaft aus. Im Kontext der synergetischen Auswertung dient die SAR-Aufnahme vornehmlich zur Extraktion der urbanen Topografie, w{\"a}hrend der optische Datensatz wichtige Merkmale zur Differenzierung der erfassten Struktureinheiten in die Kategorien Geb{\"a}ude, versiegelte Freifl{\"a}che, unversiegelte Freifl{\"a}che und Baumbestand beisteuert. Das Resultat zeigt, dass sich trotz des synergetischen Ansatzes lediglich eine Genauigkeit von 65 Prozent erzielen l{\"a}sst. Dennoch k{\"o}nnen Geb{\"a}ude dabei mit einer G{\"u}te von 72 Prozent vergleichsweise akkurat erfasst werden. Im Hinblick auf die Demonstration des siedlungsbezogenen Anwendungspotentials h{\"o}chstaufl{\"o}sender SAR-Daten l{\"a}sst sich res{\"u}mieren, dass eine automatische Ableitung siedlungsstruktureller Merkmale im komplexen st{\"a}dtischen Umfeld aufgrund der eingeschr{\"a}nkten spektralen Aussagekraft und der starken Geometrieabh{\"a}ngigkeit des Signals mit signifikanten Schwierigkeiten verbunden ist. Dennoch hat sich gezeigt, dass diese Limitierungen in gewissem Umfang {\"u}ber den Ansatz der multiskaligen, objektorientierten Klassifizierung kompensiert werden k{\"o}nnen. Dabei lassen sich die regionalen Siedlungs- und Landnutzungsmuster mit {\"u}berzeugenden Genauigkeiten erfassen, w{\"a}hrend die Betrachtung der lokalen Siedlungsstruktur eindeutig die Grenzen der Radartechnik im Hinblick auf die Analyse komplex strukturierter Stadtlandschaften aufzeigt.},
  subject      = {Fernerkundung},
  language  = {de}
}
@misc{Walz2004,
  type      = {Master Thesis},
  author    = {Walz, Yvonne},
  title     = {Measuring burn severity in forests of South-West Western Australia using MODIS},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-14745},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2004},
  abstract  = {Burn severity was measured within the Mediterranean sclerophyll forests of south-west Western Australia (WA) using remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The region of south-west WA is considered as a high fire prone landscape and is managed by the state government's Department of Conservation and Land Management (CALM). Prescribed fuel reduction burning is used as a management tool in this region. The measurement of burn severity with remote sensing data focused on monitoring the success and impact of prescribed burning and wildfire in this environment. The high temporal resolution of MODIS with twice daily overpasses in this area was considered highly favourable, as opportunities for prescribed burning are temporally limited by climatic conditions. The Normalised Burn Ratio (NBR) was investigated to measure burn severity in the forested area of south-west WA. This index has its heritage based on data from the Landsat TM/ETM+ sensors (Key and Benson, 1999 [1],[2]) and was transferred from Landsat to MODIS data. The measurement principally addresses the biomass consumption due to fire, whereas the change detected between the pre-fire image and the post-fire image is quantified by the {\"A}NBR. The NBR and the Normalised Difference Vegetation Index (NDVI) have been applied to MODIS and Landsat TM/ETM+ data. The spectral properties and the index values of the remote sensing data have been analysed within different burnt areas. The influence of atmospheric and BRDF effects on MODIS data has been investigated by comparing uncorrected top of atmosphere reflectance and atmospheric and BRDF corrected reflectance. The definition of burn severity classes has been established in a field trip to the study area. However, heterogeneous fire behaviour and patchy distribution of different vegetation structure made field classification difficult. Ground truth data has been collected in two different types of vegetation structure present in the burnt area. The burn severity measurement of high resolution Landsat data was assessed based on ground truth data. However, field data was not sufficient for rigorous validation of remote sensing data. The NBR index images of both sensors have been calibrated based on training areas in the high resolution Landsat image. The burn severity classifications of both sensors are comparable, which demonstrates the feasibility of a burn severity measurement using moderate spatial resolution 250m MODIS data. The normalisation through index calculation reduced atmospheric and BRDF effects, and thus MODIS top of at-mosphere data has been considered suitable for the burn severity measurement. The NBR could not be uniformly applied, as different structures of vegetation influenced the range of index values. Furthermore, the index was sensitive to variability in moisture content. However, the study concluded that the NBR on MODIS data is a useful measure of burn severity in the forested area of south-west WA.},
  subject      = {Westaustralien},
  language  = {en}
}