Biomass is an important ecological variable for understanding the responses of vegetation to the currently observed global change. The impact of changes in vegetation biomass on the global ecosystem is also of high relevance. The vegetation in the arid and semi-arid environments of Kazakhstan is expected to be affected particularly strongly by future climate change. Therefore, it is of great interest to observe large-scale vegetation dynamics and biomass distribution in Kazakhstan. At the beginning of this dissertation, previous research activities and remote-sensing-based methods for biomass estimation in semi-arid regions have been comprehensively reviewed for the first time. The review revealed that the biggest challenge is the transferability of methods in time and space. Empirical approaches, which are predominantly applied, proved to be hardly transferable. Remote-sensing-based Net Primary Productivity (NPP) models, on the other hand, allow for regional to continental modelling of NPP time-series and are potentially transferable to new regions. This thesis thus deals with modelling and analysis of NPP time-series for Kazakhstan and presents a methodological concept for derivation of above-ground biomass estimates based on NPP data. For validation of the results, biomass field data were collected in three study areas in Kazakhstan. For the selection of an appropriate model, two remote-sensing-based NPP models were applied to a study area in Central Kazakhstan. The first is the Regional Biomass Model (RBM). The second is the Biosphere Energy Transfer Hydrology Model (BETHY/DLR). Both models were applied to Kazakhstan for the first time in this dissertation. Differences in the modelling approaches, intermediate products, and calculated NPP, as well as their temporal characteristics were analysed and discussed. The model BETHY/DLR was then used to calculate NPP for Kazakhstan for 2003–2011. The results were analysed regarding spatial, intra-annual, and inter-annual variations. In addition, the correlation between NPP and meteorological parameters was analysed. In the last part of this dissertation, a methodological concept for derivation of above-ground biomass estimates of natural vegetation from NPP time-series has been developed. The concept is based on the NPP time-series, information about fractional cover of herbaceous and woody vegetation, and plants’ relative growth rates (RGRs). It has been the first time that these parameters are combined for biomass estimation in semi-arid regions. The developed approach was finally applied to estimate biomass for the three study areas in Kazakhstan and validated with field data. The results of this dissertation provide information about the vegetation dynamics in Kazakhstan for 2003–2011. This is valuable information for a sustainable land management and the identification of regions that are potentially affected by a changing climate. Furthermore, a methodological concept for the estimation of biomass based on NPP time-series is presented. The developed method is potentially transferable. Providing that the required information regarding vegetation distribution and fractional cover is available, the method will allow for repeated and large-area biomass estimation for natural vegetation in Kazakhstan and other semi-arid environments.

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The localization of autonomous ground vehicles in dense urban environments poses a challenge. Applications in classical outdoor robotics rely on the availability of GPS systems in order to estimate the position. However, the presence of complex building structures in dense urban environments hampers a reliable localization based on GPS. Alternative approaches have to be applied In order to tackle this problem. This thesis proposes an approach which combines observations of a single perspective camera and odometry in a probabilistic framework. In particular, the localization in the space of appearance is addressed. First, a topological map of reference places in the environment is built. Each reference place is associated with a set of visual features. A feature selection is carried out in order to obtain distinctive reference places. The topological map is extended to a hybrid representation by the use of metric information from Geographic Information Systems (GIS) and satellite images. The localization is solved in terms of the recognition of reference places. A particle lter implementation incorporating this and the vehicle's odometry is presented. The proposed system is evaluated based on multiple experiments in exemplary urban environments characterized by high building structures and a multitude of dynamic objects.

The localization of autonomous ground vehicles in dense urban environments poses a challenge. Applications in classical outdoor robotics rely on the availability of GPS systems in order to estimate the position. However, the presence of complex building structures in dense urban environments hampers a reliable localization based on GPS. Alternative approaches have to be applied In order to tackle this problem. This thesis proposes an approach which combines observations of a single perspective camera and odometry in a probabilistic framework. In particular, the localization in the space of appearance is addressed. First, a topological map of reference places in the environment is built. Each reference place is associated with a set of visual features. A feature selection is carried out in order to obtain distinctive reference places. The topological map is extended to a hybrid representation by the use of metric information from Geographic Information Systems (GIS) and satellite images. The localization is solved in terms of the recognition of reference places. A particle lter implementation incorporating this and the vehicle's odometry is presented. The proposed system is evaluated based on multiple experiments in exemplary urban environments characterized by high building structures and a multitude of dynamic objects.

The study was conducted at the vicinity of the rural town of Doka in an area of about 55 x 40 km2. The aim of the study was to map and model the influences of the introduction of mechanized rain-fed agriculture and its rapid expansion on the natural vegetation in the southern Gadarif Region. To achieve these objectives the study utilized a series of techniques. Beside the intensive use of remote sensing imagery, interviews with key informants and farmers as well as detailed field surveys were carried out. Multi-temporal analyses of remote sensing imagery showed that during the seventies the average natural vegetation clearing rate increased most rapidly and then began to slow down. Farmers are aware that land degradation, in various forms, is taking place on their cultivated agricultural land. This is based on their perception and the interpretation of indicators such as weed infestation, reduced soil fertility and soil compaction. Continuous cropping, mono-cropping, rainfall shortage and the use of inferior seeds were the main reasons of land degradation indicated by the farmers. Abandonment of agricultural land to restore soil fertility is a common practice among farmers in the Gadarif Region. The study proved that the subsequent natural regeneration of plant species and the vegetation development on abandoned agricultural land are subject to the previous cultivation period and the duration of the fallow. The current regeneration capacity of the abandoned land may not be sufficient to reach full restoration of the previous vegetation climax except for some pockets which received more regenerative resources. Field surveys in conjunction with remotely sensed and topographic data have the potential to explain the restoration and rehabilitation patterns of degraded/abandoned agricultural land to a good extent. The findings of the study seem to be representative not only for the whole Gadarif Region or other areas in Sudan, but also for other regions in the Sahel Zone with similar problems and environmental and social conditions. One of the most practical conservation approaches is to let farmers play an active role in managing their abandoned land. Such management aims to allow for a certain level of use and benefits while maintaining the natural vegetation development on theses area in order to achieve maximal restoration. Although the study investigated the vegetation development in abandoned mechanized rainfed agricultural land, a full understanding of the path-way needs surveys that include more types of abandoned land and investigation of the effects of other local environmental factors (e.g. fire, grazing, distance from forests etc.) for more than one season.

Inwieweit verbinden uns Materialien, die uns umgeben mit den Orten von denen sie stammen? Wie verändern sich Landschaften und soziotechnische Infrastrukturen wenn Gebrauchsmaterialien in alternativen industriellen Symbiosen produziert werden? Und welche Rolle spielt die (karto)-grafische Dokumentation in ergebnisoffenen und transdisziplinären Forschungs- und Gestaltungsprozessen?

Angesichts der Herausforderungen von Klimawandel und Biodiversitätskrise werden städtische Grünräume immer wichtiger. Insbesondere den Stadtbäumen kommt eine Schlüsselrolle zu. Sie erbringen essenzielle Ökosystemleistungen und haben einen wesentlichen Einfluss auf das menschliche Wohlbefinden (Davies et. al. 2017; Blaser et. al. 2016). Zudem sind sie von zentraler Bedeutung für die städtische Biodiversität (Gloor & Göldi 2018). Während ihr Stellenwert von wissenschaftlicher Seite immer differenzierter erkannt wird, haben sich diese Erkenntnisse in den Kartengrundlagen zur Planung und Kommunikation noch nicht etabliert: Wesentliche Eigenschaften von Stadtbäumen, darunter insbesondere die ökologischen Leistungen und die artspezifischen Anforderungen, werden in den Kartengrundlagen kaum repräsentiert. Eine Unter- bzw. Nichtrepräsentation in Karten kann jedoch negative Folgen haben, denn sowohl Karteninhalt als auch -design haben Einfluss darauf, wie Informationen und Botschaften verstanden werden. Sie tragen somit massgeblich dazu bei, welche Entscheidungen getroffen werden.

Kartotrak software allows optimal waste classification and avoids unnecessary remediation. It has been designed for those - site owners, safety authorities or contractors, involved in environmental site characterization projects - who need to locate and estimate contaminated soil volumes confidently.

Die Kartierung und Lokalisierung eines mobilen Roboters in seiner Umgebung ist eine wichtige Voraussetzung für dessen Autonomie. In dieser Arbeit wird der Einsatz eines 3D-Laserscanners zur Erfüllung dieser Aufgaben untersucht. Durch die optimierte Anordnung eines rotierenden 2D-Laserscanners werden hochauflösende Bereiche vorgegeben. Zudem wird mit Hilfe von ICP die Kartierung und Lokalisierung im Stillstand durchgeführt. Bei der Betrachtung zur Verbesserung der Bewegungsschätzung wird auch eine Möglichkeit zur Lokalisierung während der Bewegung mit 3D-Scans vorgestellt. Die vorgestellten Algorithmen werden durch Experimente mit realer Hardware evaluiert.