Assessment of Paleo-Landscape Features using Advanced Remote Sensing Techniques, Modelling and GIS Methods in the Lake Manyara Basin, Northern Tanzania

In researching the evolution of hominids, the East African Rift System acts as a vital region. The rift valleys enabled some of the most sensational hominid findings to date. Various hypotheses have been developed in the last decades, which try to explain the influence of changes in paleo-climate, paleo-landscape and paleo-environment on hominin evolution in the Quaternary. Additionally, the sediments and the morphology of the East African Rift System provide excellent terrestrial archives for paleo-environmental reconstruction. Lake Manyara is located in an endorheic basin in the eastern arm of the East African Rift System in northern Tanzania. The surroundings of the Lake Manyara are in the focus of paleontological and archaeological investigations. For instance, two hominin bearing sites were found within the catchment of the Makuyuni River, as well as artefacts and fossils are periodically uncovered. The study area, which is located east of the present-day lake, provides an insight into relevant geological and geomorphological drivers of paleo-landscape evolution of the whole region. This thesis aims at contributing to the understanding of landscape evolution in the Lake Manyara region. Compared to other regions in the East African rift system, few landscape evolution studies took place for the Lake Manyara basin. As such, an integrative scientific investigation of the spatial situation of paleo-landscape features and of paleo-lake level fluctuations is missing. The proposed study utilizes state-of-the-art remote sensing based research methods in evaluating the landscape, and in concluding from present-day landforms and processes, how the landscape developed during the Pleistocene and Holocene. In striving to accomplish this goal, this cumulative dissertation comprises eight central research questions, which are introduced in a conceptual framework. The research questions have been considered in seven scientific publications, which describe the applied methodologies and results in detail. The framework of the thesis provides a coherent and detailed interpretation and discussion of the scientific findings. The research questions and outcomes of the analyses are listed below. Key drivers of landscape development in the East African Rift System are tectonic and tectonically induced processes. Drainage network, stream longitudinal profiles and basin analysis based on topographic analyses, as well as lineaments extracted from remote sensing images, were successfully used as methods in identifying tectonic activity and related features in rift areas. The application of a gully erosion model suggests that the gully channel systems in the study area are relatively stable and that they had developed prior to the last significant lake regression. The paleo-landscape and the paleo-environment are closely connected to lake level changes of the paleo-Lake Manyara. Hence, a key question concerns the extent of the Manyara Beds, which are lacustrine deposits that indicate the maximum extent of the paleo-Lake Manyara. A combined analysis, utilizing ASTER multispectral indices and topographic parameters from a digital elevation model, led to the spatial delineation of lacustrine sediments. Their extent indicates a relation to lacustrine sediments in the southern part of the basin, and reveals lacustrine / palustrine deposits further east. A methodological comparison of Support Vector Machines and Boosted Regression Trees, which served as classification methods to identify the lacustrine sediments, exhibited high accuracies for both approaches, with minor advantages for Support Vector Machines. Closely related to the previous research question is the question on the spatial distribution of surface substrates. By incorporating a WorldView-2 scene and Synthetic Aperture Radar data to the previously mentioned datasets, it was possible to distinguish between nine topsoil and lithological target classes in the study area. The surface substrates indicate the underlying lithologies, sediments and soils, as well as soil formation processes. Between the village of Makuyuni and the present-day Lake Manyara, paleo-shorelines and terraces were formed by various paleo-lake levels. Questions arise, at which elevation these features occur and what is the maximum elevation, which was reached. ALOS PALSAR and TerraSAR-X backscatter intensity information provided the possibility of an area-wide mapping of those morphological features. Some radiometric dates exist for stromatolites from a distinct paleo-shoreline level, which support the interpretation of the lake fluctuations. The paleo-shoreline, which was identified with the highest elevation, coincides with the elevation of the lowest possible outlet of the closed Manyara basin. It can be assumed that the paleo-Lake Manyara over-spilled into the neighboring Engaruka and Natron-Magadi basins. The question of the location of sites with a high probability of artefact and/or fossil presence is important for future archaeological and paleontological research. ASTER remote sensing data and topographic indices contributed likewise to the predictive modelling of probabilities of archaeological and paleontological sites in the study area. Generally, paleontological sites are found on a higher elevation, compared to Stone Age sites. In addition, fossil sites seem to be related to stable paleo-landscape features according to this study’s findings. The results of this dissertation provide new insights in the landscape development of the Lake Manyara basin. The scientific findings contribute to the understanding of the landscape evolution for the study area, as well as for the neighboring basins in the East African Rift System. The applied geospatial methodologies can be transferred to other study areas with similar research needs

Earth observation for exposome mapping of Germany: analyzing environmental factors relevant to non-communicable diseases

Non-communicable diseases - NCDs - (e.g., asthma, cancer, or diabetes) are a major concern for society and medicine. According to the World Health Organization, NCDs are responsible for > 70 % of global premature deaths. Apart from increasing mortality, these diseases strain one’s immune system which leads to higher susceptibility to transmittable diseases. NCD-susceptibility depends on the genome (genetic predisposition), behavior (lifestyle), and exposome of a person. The exposome is a composition of environmental parameters such as exposure to air pollution, noise, extreme temperatures, or surrounding greenness. Using Earth Observation data, the majority of factors making up the exposome can be monitored over long periods of time at high resolution and with nearly global coverage. Still, exposome maps and products communicating NCD risk are not widely available. In this study, we utilize eight land surface datasets (distance to green spaces, distance to blue spaces, temperature, noise from industry, as well as road, rail, and air traffic, and light pollution) as well as two air pollution datasets (PM2.5 and NO2) to map health-relevant environmental exposure. We use an established cumulative approach and incorporate exposure-response relationships from scientific literature to map environments that impact public health for the complete area of Germany. We present results communicating exposure relevant to myocardial infarction risk. The methodology is transferable to other NCDs and other areas of interest. In the context of the global health burden from NCDs and ongoing global change, this approach supplies findings for communicating health-relevant exposure

Global Offshore Wind Energy Infrastrucutre Dynamics Derived from Sentinel-1 Imagery with CNNs based on Synthetic Training Data

Over the last years, deep learning has become an important component in the Earth observation toolset. Especially the convolutional neural network is the most widely used deep learning model in Earth observation. The supervised optimisation of neural networks relies on large datasets, which are necessary to predict on complex data and train models to be transferable in time and space. In contrast to the efficient processing of large data archives by trained neural networks is their need for large training datasets, which are labour-intensive to create. Another drawback is that only those research questions can be investigated where enough data are available to build datasets large enough to train a deep learning model. In order to solve the problem of labour-intensive data annotation and a potential lack of raw data, we have developed SyntEO, an approach to synthetically generate Earth observation data and corresponding labels simultaneously. This approach specifically addresses the needs of Earth observation data and composes a remote sensing scene with harmonised spatial and temporal order of nested entities. SyntEO uses an ontology formulated by domain experts to make their knowledge explicit and machine-readable. Upon that ontology, an artificial data generator composes an abstract scene composition that is used to finally generate the synthetic remote sensing scene by adding texture and to derive the corresponding label. To give an intuitive introduction to SyntEO, we demonstrate the detection of offshore wind farms and their components by using deep learning models that are only trained with synthetic data generated with the new approach. The resulting deep learning models detect offshore wind farms as well as single offshore wind turbines in real-world remote sensing imagery. The underlying data are IW GRD acquisitions of the Sentinel-1 mission in VH polarisation, which lie within a distance of 200 km of the coastline towards the sea. The trained models are used to detect offshore wind farms and turbines for the entire global coastline at a quarterly frequency between 2016 and 2021. The results are validated by assessing their performance on a hand labelled ground truth data set which includes all offshore wind turbines in the North Sea Basin and the East China Sea

Mapping pond aquaculture for the entire coastal zone of Asia using high resolution Sentinel-1 and Sentinel-2 data

Asia is the world’s most important region for aquaculture and generates almost 90 percent of the total production. The farming of fish and shrimp in land-based aquaculture systems expanded mainly along the shorelines of South Asia, Southeast Asia, and East Asia, and is a primary protein source for millions of people. The production of fish and shrimp in freshwater and brackish water ponds in coastal regions of Asia has increased rapidly since the 1990s due to the rising demand for protein-rich foods from a growing (world) population. The aquaculture sector generates income, employment and contributes to food security, has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources and human health. With free and open access to the rapidly growing volume of data from the European Sentinel satellites as well as using machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental-scale. We present a multi-sensor approach which utilizes Earth Observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as a buffer of 200km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at single pond level based on temporal features derived from high spatial resolution SAR and optical satellite acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth observation derived aquaculture dataset to investigate spatial distribution and to identify production hotspots in various administrative units at regional, national, and sub-national scale

A Framework for Multivariate Analysis of Land Surface Dynamics and Driving Variables-A Case Study for Indo-Gangetic River Basins

The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI

The coastline of Vietnam - annual dynamics derived from 35 years of Earth Observation data

Understanding the intricate interplay between alterations in sedimentation patterns and the rising sea levels is of critical significance, particularly for coastal regions at large and, notably, for the vulnerable Mekong Delta. Among the myriad drivers of coastline changes, the repercussions of reservoirs, channel construction, and dredging remain inadequately explored. The construction of reservoirs upstream can drastically alter the sediment flow downstream, reducing the sediment load reaching the delta. Altering natural channels and extensive dredging for navigation and industrial purposes can disrupt sedimentation patterns. Moreover, while the impact of coastal erosion on flooding has been established, it also represents an area of research that remains underexplored. Therefore, the monitoring the coastline of the Mekong Delta is of paramount importance

State of the Vietnamese Coast—Assessing Three Decades (1986 to 2021) of Coastline Dynamics Using the Landsat Archive

Vietnam’s 3260 km coastline is densely populated, experiences rapid urban and economic growth, and faces at the same time a high risk of coastal hazards. Satellite archives provide a free and powerful opportunity for long-term area-wide monitoring of the coastal zone. This paper presents an automated analysis of coastline dynamics from 1986 to 2021 for Vietnam’s entire coastal zone using the Landsat archive. The proposed method is implemented within the cloud-computing platform Google Earth Engine to only involve publicly and globally available datasets and tools. We generated annual coastline composites representing the mean-high water level and extracted sub-pixel coastlines. We further quantified coastline change rates along shore-perpendicular transects, revealing that half of Vietnam’s coast did not experience significant change, while the remaining half is classified as erosional (27.7%) and accretional (27.1%). A hotspot analysis shows that coastal segments with the highest change rates are concentrated in the low-lying deltas of the Mekong River in the south and the Red River in the north. Hotspots with the highest accretion rates of up to +47 m/year are mainly associated with the construction of artificial coastlines, while hotspots with the highest erosion rates of −28 m/year may be related to natural sediment redistribution and human activity

Determining Uncertainty in Estimations of Global Surface Water Extent derived from a Diurnal Earth Observation Time-Series

The availability of fresh water is vital for life on the planet. However, water resources are increasingly affected by changing patterns of climate variables and intensification of human water use and regulatory management. Consequently, focus of many disciplines is directed towards inland water storage dynamics. Satellite remote sensing offers opportunities to monitor global surface water in dense temporal intervals. The DLR-DFD Global WaterPack (GWP) provides daily information on inland surface water on a global level. The dataset has been successfully applied in manifold scientific studies, enabling the investigation of surface water dynamics world-wide. To enhance the usability of the product towards modeling applications, the quantification of inherent uncertainties is essential. As GWP is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, inaccuracies arise due to coarse spatial resolution and interpolation of data gaps. To address these error sources, we quantify interpolation- and classification-based uncertainty in two steps. First, several spatiotemporal time-series characteristics relevant for water body dynamics are considered to determine the probability of interpolated pixels to be covered by water. Second, in case of valid observations, GWP classification probability is derived from relative datapoint (pixel) locations in feature space and subsequently utilized together with previously established temporal information in a linear mixture model. Resulting sub-pixel water fraction estimates facilitate the quantification of observational uncertainty. Performance of water fraction estimates is assessed in 32 regions of interest across the globe by comparison to higher resolution reference data. The ability of temporal layers to approximate unknown pixel states is evaluated for artificial gaps, introduced to the original time-series of four MODIS tiles. Results show that uncertainties can be quantified accurately, revealing more comprehensive and reliable time-series information suitable for modelling applications

Interpreting gaps: a geoarchaeological point of view on the Gravettian record of Ach and Lone valleys (Swabian Jura, SW Germany)

Unlike other Upper Paleolithic industries, Gravettian assemblages from the Swabian Jura are documented solely in the Ach Valley (35-30 Kcal BP). On the other hand, traces of contemporaneous occupations in the nearby Lone Valley are sparse. It is debated whether this gap is due to a phase of human depopulation, or taphonomic issues related with landscape changes. In this paper we present ERT, EC-logging and GPR data showing that in both Ach and Lone valleys sediments and archaeological materials eroded from caves and deposited above river incisions after 37-32 Kcal BP. We argued that the rate of cave erosion was higher after phases of downcutting, when hillside erosion was more intensive. To investigate on the causes responsible for the dearth of Gravettian materials in the Lone Valley we test two alternative hypotheses: i) Gravettian humans occupied less intensively this part of the Swabian Jura. ii) Erosion of cave deposits did not occur at the same time in the two valleys. We conclude that the second hypothesis is most likely. Ages from the Lone Valley show increasing multimillennial gaps between 36 and 18 Kcal BP, while a similar gap is present in the Ach Valley between 28 and 16 Kcal BP. Based on geoarchaeological data from previous studies and presented in this paper, we interpreted these gaps in radiocarbon data as indicating of cave erosion. Furthermore, we argued that the time difference across the two valleys show that the erosion of cave deposits began and terminated earlier in the Lone Valley, resulting in a more intensive removal of Gravettian-aged deposits. The hypothesis that cave erosion was triggered by regional landscape changes seems to be supported by geochronological data from the Danube Valley, which show that terrace formation at the end of the Pleistocene moved westwards throughout southern Germany with a time lag of few millennia.PTDC/HAR-ARQ/27833/2017info:eu-repo/semantics/publishedVersio