Earth observation (EO) is a prerequisite for sustainable land use management, and the open-data Landsat mission is at the forefront of this development. However, increasing data volumes have led to a "digital-divide", and consequently, it is key to develop methods that account for the most data-intensive processing steps, then used for the generation and provision of analysis-ready, standardized, higher-level (Level 2 and Level 3) baseline products for enhanced uptake in environmental monitoring systems. Accordingly, the overarching research task of this dissertation was to develop such a framework with a special emphasis on the yet under-researched drylands of Southern Africa. A fully automatic and memory-resident radiometric preprocessing streamline (Level 2) was implemented. The method was applied to the complete Angolan, Zambian, Zimbabwean, Botswanan, and Namibian Landsat record, amounting 58,731 images with a total data volume of nearly 15 TB. Cloud/shadow detection capabilities were improved for drylands. An integrated correction of atmospheric, topographic and bidirectional effects was implemented, based on radiative theory with corrections for multiple scatterings, and adjacency effects, as well as including a multilayered toolset for estimating aerosol optical depth over persistent dark targets or by falling back on a spatio-temporal climatology. Topographic and bidirectional effects were reduced with a semi-empirical C-correction and a global set of correction parameters, respectively. Gridding and reprojection were already included to facilitate easy and efficient further processing. The selection of phenologically similar observations is a key monitoring requirement for multi-temporal analyses, and hence, the generation of Level 3 products that realize phenological normalization on the pixel-level was pursued. As a prerequisite, coarse resolution Land Surface Phenology (LSP) was derived in a first step, then spatially refined by fusing it with a small number of Level 2 images. For this purpose, a novel data fusion technique was developed, wherein a focal filter based approach employs multi-scale and source prediction proxies. Phenologically normalized composites (Level 3) were generated by coupling the target day (i.e. the main compositing criterion) to the input LSP. The approach was demonstrated by generating peak, end and minimum of season composites, and by comparing these with static composites (fixed target day). It was shown that the phenological normalization accounts for terrain- and land cover class-induced LSP differences, and the use of Level 2 inputs enables a wide range of monitoring options, among them the detection of within state processes like forest degradation. In summary, the developed preprocessing framework is capable of generating several analysis-ready baseline EO satellite products. These datasets can be used for regional case studies, but may also be directly integrated into more operational monitoring systems " e.g. in support of the Reducing Emissions from Deforestation and Forest Degradation (REDD) incentive. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Trier University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.

Evapotranspiration (ET) is one of the most important variables in hydrological studies. In the ET process, energy exchange and water transfer are involved. ET consists of transpiration and evaporation. The amount of plants transpiration dominates in ET. Especially in the forest regions, the ratio of transpiration to ET is in general 80-90 %. Meteorological variables, vegetation properties, precipitation and soil moisture are critical influence factors for ET generation. The study area is located in the forest area of Nahe catchment (Rhineland-Palatinate, Germany). The Nahe catchment is highly wooded. About 54.6 % of this area is covered by forest, with deciduous forest and coniferous forest are two primary types. A hydrological model, WaSiM-ETH, was employed for a long-term simulation from 1971-2003 in the Nahe catchment. In WaSiM-ETH, the potential evapotranspiration (ETP) was firstly calculated by the Penman-Monteith equation, and subsequently reduced according to the soil water content to obtain the actual evapotranspiration (ETA). The Penman-Monteith equation has been widely used and recommended for ETP estimation. The difficulties in applying this equation are the high demand of ground-measured meteorological data and the determination of surface resistance. A method combined remote sensing images with ground-measured meteorological data was also used to retrieve the ETA. This method is based on the surface properties such as surface albedo, fractional vegetation cover (FVC) and land surface temperature (LST) to obtain the latent heat flux (LE, corresponding to ETA) through the surface energy balance equation. LST is a critical variable for surface energy components estimation. It was retrieved from the TM/ETM+ thermal infrared (TIR) band. Due to the high-quality and cloudy-free requirements for TM/ETM+ data selection as well as the overlapping cycle of TM/ETM+ sensor is 16 days, images on only five dates are available during 1971-2003 (model ran) " May 15, 2000, July 05, 2001, July 19, August 04 and September 21 in 2003. It is found that the climate conditions of 2000, 2001 and 2003 are wet, medium wet and dry, respectively. Therefore, the remote sensing-retrieved observations are noncontinuous in a limited number over time but contain multiple climate conditions. Aerodynamic resistance and surface resistance are two most important parameters in the Penman-Monteith equation. However, for forest area, the aerodynamic resistance is calculated by a function of wind speed in the model. Since transpiration and evaporation are separately calculated by the Penman-Monteith equation in the model, the surface resistance was divided into canopy surface resistance rsc and soil surface resistance rse. rsc is related to the plants transpiration and rse is related to the bare soil evaporation. The interception evaporation was not taken into account due to its negligible contribution to ET rate under a dry-canopy (no rainfall) condition. Based on the remote sensing-retrieved observations, rsc and rse were calibrated in the WaSiM-ETH model for both forest types: for deciduous forest, rsc = 150 sm−1, rse = 250 sm−1; for coniferous forest, rsc = 300 sm−1, rse = 650 sm−1. We also carried out sensitivity analysis on rsc and rse. The appropriate value ranges of rsc and rse were determined as (annual maximum): for deciduous forest, [100,225] sm−1 for rsc and [50,450] sm−1 for rse; for coniferous forest, [225,375] sm−1 for rsc and [350,1200] sm−1 for rse. Due to the features of the observations that are in a limited number but contain multiple climate conditions, the statistical indices for model performance evaluation are required to be sensitive to extreme values. In this study, boxplots were found to well exhibit the model performance at both spatial and temporal scale. Nush-Sutcliffe efficiency (NSE), RMSE-observations standard deviation ratio (RSR), percent bias (PBIAS), mean bias error (MBE), mean variance of error distribution (S2d), index of agreement (d), root mean square error (RMSE) were found as appropriate statistical indices to provide additional evaluation information to the boxplots. The model performance can be judged as satisfactory if NSE > 0.5, RSR ≤ 0.7, PBIAS < -±12, MBE < -±0.45, S2d < 1.11, d > 0.79, RMSE < 0.97. rsc played a more important role than rse in ETP and ETA estimation by the Penman-Monteith equation, which is attributed to the fact that transpiration dominates in ET. The ETP estimation was found the most correlated to the relative humidity (RH), followed by air temperature (T), relative sunshine duration (SSD) and wind speed (WS). Under wet or medium wet climate conditions, ETA estimation was found the most correlated to T, followed by RH, SSD and WS. Under a water-stress condition, there were very small correlations between ETA and each meteorological variable.

Time series archives of remotely sensed data offer many possibilities to observe and analyse dynamic environmental processes at the Earth- surface. Based on these hypertemporal archives, which offer continuous observations of vegetation indices, typically at repetition rates from one to two weeks, sets of phenological parameters or metrics can be derived. Examples of such parameters are the beginning and end of the annual growing period, as well as its length. Even though these parameters do not correspond exactly to conventional observations of phenological events, they nevertheless provide indications of the dynamic processes occurring in the biosphere. The development of robust algorithms for the derivation of phenological metrics can be challenging. Currently, such algorithms are most commonly based on digital filters or the Fourier analysis of time series. Polynomial spline models offer a useful alternative to existing methods. The possibilities of using spline models in the analytical description of time series are numerous, and their specific mathematical properties may help to avoid known problems occurring with the more common methods for deriving phenological metrics. Based on a selection of different polynomial spline models suitable for the analysis of remotely sensed time series of vegetation indices, a method to derive various phenological parameters from such time series was developed and implemented in this work. Using an example data set from an intensively used agricultural area showing highly dynamic variations in vegetation phenology, the newly developed method was verified by a comparison of the results of the spline based approach to the results of two alternative, well established methods.

The main research question of this thesis was to set up a framework to allow for the identification of land use changes in drylands and reveal their underlying drivers. The concept of describing land cover change processes in a framework of global change syndrome was introduced by Schellnhuber et al. (1997). In a first step the syndrome approach was implemented for semi-natural areas of the Iberian Peninsula based on time series analysis of the MEDOKADS archive. In the subsequent study the approach was expanded and adapted to other land cover strata. Furthermore, results of an analysis of the relationship of annual NDVI and rainfall data were incorporated to designate areas that show a significant relationship indicating that at least a part of the variability found in NDVI time series was caused by precipitation. Additionally, a first step was taken towards the integration of socio-economic data into the analysis; population density changes between 1961 and 2008 were utilized to support the identification of processes related to land abandonment accompanied by cessation of agricultural practices on the one hand and urbanization on the other. The main findings of the studies comprise three major land cover change processes caused by human interaction: (i) shrub and woody vegetation encroachment in the wake of land abandonment of marginal areas, (ii) intensification of non-irrigated and irrigated, intensively used fertile regions and (iii) urbanization trends along the coastline caused by migration and the increase of mass tourism. Land abandonment of cultivated fields and the give-up of grazing areas in marginal mountainous areas often lead to the encroachment of shrubs and woody vegetation in the course of succession or reforestation. Whereas this cover change has positive effects concerning soil stabilization and carbon sequestration the increase of biomass involves also negative consequences for ecosystem goods and services; these include decreased water yield as a result of increased evapotranspiration, increasing fire risk, decreasing biodiversity due to landscape homogenization and loss of aesthetic value. Arable land in intensively used fertile zones of Spain was further intensified including the expansion of irrigated arable land. The intensification of agriculture has also generated land abandonment in these areas because less people are needed in the agricultural labour sector due to mechanization. Urbanization effects due to migration and the growth of the tourism sector were mapped along the eastern Mediterranean coast. Urban sprawl was only partly detectable by means of the MEDOKADS archive as the changes of urbanization are often too subtle to be detected by data with a spatial resolution of 1 km-². This is in line with a comparison of a Landsat TM time series and the NOAA AVHRR archive for a study area in the Greece that showed that small scale changes cannot be detected based on this approach, even though they might be of high relevance for local management of resources. This underlines the fact that land degradation processes are multi-scale problems and that data of several spatial and temporal scales are mandatory to build a comprehensive dryland observation system. Further land cover processes related to a decrease of greenness did not play an important role in the observation period. Thus, only few patches were identified, suggesting that no large-scale land degradation processes are taking place in the sense of decline of primary productivity after disturbances. Nevertheless, the land cover processes detected impact ecosystem functioning and using the example of shrub encroachment, bear risks for the provision of goods and services which can be valued as land degradation in the sense of a decline of important ecosystem goods and services. This risk is not only confined to the affected ecosystem itself but can also impact adjacent ecosystems due to inter-linkages. In drylands water availability is of major importance and the management of water resources is an important political issue. In view of climate change this topic will become even more important because aridity in Spain did increase within the last decades and is likely to further do so. In addition, the land cover changes detected by the syndrome approach could even augment water scarcity problems. Whereas the water yield of marginal areas, which often serve as headwaters of rivers, decreases with increasing biomass, water demand of agriculture and tourism is not expected to decline. In this context it will be of major importance to evaluate the trade-offs between different land uses and to take decisions that maintain the future functioning of the ecosystems for human well-being.

Das Ziel dieser Forschungsarbeit liegt in der Entwicklung einer innovativen Klassifikationsstrategie zur satellitengestützten Forstinventur in einem europäischen Mittelgebirgsraum. Über die Ableitung von thematischen Karten der flächenscharfen Verbreitung von fünf Baumartengruppen (Eiche, Buche, Fichte, Douglasie und Kiefer) sowie drei Entwicklungsphasen (Qualifizierung, Dimensionierung und Reife) werden wichtige für eine nachhaltige Bewirtschaftung von Wäldern erforderliche Grundlagendaten bereitgestellt. rnDie nachhaltige Bewirtschaftung der Vielfachfunktionen von Wäldern (Nutz-, Schutz- und Erholungsfunktionen) sowie der steigende Informationsbedarf in Folge nationaler und internationaler Monitoring- und Berichtspflichten (u.a. Montréal Prozess und Kyoto Protokoll) erfordern aktuelle und flächendeckende Informationen über den Zustand der Wälder. In diesem Kontext können fernerkundliche Daten und Methoden zur Unterstützung konventioneller terrestrischer Verfahren zum Einsatz kommen.rnDas Untersuchungsgebiet dieser Studie umfasst den südlichen und östlichen Teil der rheinland-pfälzischen Eifel mit einer Fläche von mehr als 5200 km-², davon rund 2080 km-² bewaldet. Die naturräumliche Heterogenität, die wuchsklimatischen Unterschiede, die Variabilität von Relief und Topographie, die große Zahl vorkommender Baumarten sowie die kulturhistorische Waldentwicklung in der Eifel stellen eine besondere Herausforderung für satellitengestützte Inventurmethoden dar.rnDurch die bevorzugte Verwendung von Referenzdaten aus der unmittelbaren räumlichen Umgebung eines zu klassifizierenden Bereichs wird bei der Parametrisierung des Klassifikationsansatzes die jeweilige naturräumliche und wuchsklimatische Charakteristik berücksichtigt. Der Vergleich dieses räumlich adaptiven Klassifikationsansatzes mit einer konventionellen Maximum-Likelihood Klassifikation zeigt, dass eine Verbesserung der Klassifikationsgenauigkeit um 12 Prozentpunkte erreicht werden konnte. Die Adaptierung der Klassifikationsstrategie an die naturräumlichen und wuchsklimatischen Bedingungen sowie die Anpassung an bestehende Erhebungsmethoden und Datenorganisation bilden die Grundlage für eine erfolgreiche Anwendung des Verfahrens in einem heterogenen Mittelgebirgsraum. Die hohe erreichte Gesamtgenauigkeit des Klassifikationsergebnisses von rund 74% (über 87% für die fünf Hauptbaumarten) erlaubt die Einbindung der Methode in operationelle Erhebungsverfahren zur Unterstützung der terrestrischen Forstinventur.