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Today vessel detection from remote sensing images is increasingly becoming a crucial component in maritime surveillance applications. The increasing number of very high and medium resolution (VHR and MR) optical satellites shortens the revisit time as it was never before. This makes the technology especially attractive for a variety of maritime monitoring tasks. Nevertheless, it is quite a challenge to perform object detection on enormous large satellite images that cover several hundreds of square kilometers and derive results under near real time constraints. This thesis presents an end-to-end multiclass vessel detection method from optical satellite images. The proposed workflow covers the complete processing chain and involves rapid image enhancement techniques, the fusion with automatic identification system (AIS) data, and the detection algorithm based on convolutional neural networks (CNN). To train the CNNs, two versions of training datasets were generated. The VHR training dataset was produced from the set of WorldView-[1-3] and GeoEye-1 images and contains about 40 000 of uniquely annotated vessels divided into 14 different classes. The MR training dataset was generated from the set of Landsat-8 images and contains about 14 000 of uniquely annotated vessels of 7 different classes. The algorithms presented are implemented in the form of independent software processors and integrated in an automated processing chain as part of the Earth Observation Maritime Surveillance System (EO-MARISS). The solution developed from the methods presented has proven its usability within different projects and is used and further developed at the ground station of the German Aerospace Center (DLR) in Neustrelitz.

The European Forest Fire Information System (EFFIS) provides timely and reliable information on wildland fires in Europe since 1998. EFFIS supports agencies in charge of the protection of forests within the European Union. The services comprise the daily analysis and publication of weather based fire danger information, as well as the timely delivery of active fire locations and recently occurred burnt areas. As part of Copernicus Emergency Management Service, EFFIS contributes directly to the disaster management in Europe and helps monitoring indicators of the Sustainable Development Goals. The Earth Observation Centre from the German Aerospace Centre (DLR) has been entrusted with modernizing the EFFIS service and with improving the near real time products delivery by shortening timeliness. The new main functionalities comprise analysis, derivation and delivery of burnt areas from Sentinel-3 and MODIS data. In this regard, a methodology has been developed, as well as implemented and tested, to efficiently derive reliable and accurate burnt area perimeters for different optical earth observation satellites. Another main functionality is the active fire and hotspot detection. This element has been updated with the newest available components which improve fire detection and reduces false alarms. The processing chain is fully automated and comprises the pre-processing of satellite data. In addition for burnt areas, it comprises histogram matching, mosaic generation and functions for the tracking growth over time. EFFIS new functionalities are integrated and validated in an operational environment. Results of these functionalities benefit the fire situation monitoring in Europe through faster, more reliable and accurate detections and bring considerable progress towards achieving the SDGs.

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.

The Sea Sate Processor (SSP) was developed for fully automatic processing of high-resolution Synthetic Aperture Radar (SAR) data from TerraSAR-X (TS-X) satellites and implemented into the processing chain for Near Real Time (NRT) services in the DLR Ground Station "Neustrelitz". The NRT chain was organised and tested to provide the processed data to the German Weather Service (DWD) in order to validate the new coastal forecast model CWAM (Coastal WAve Model) in the German Bight of the North Sea with 900 m horizontal resolution. The NRT test-runs, wherein the processed TS-X data were transferred to DWD and then incorporated into forecast products reach the best performance about 10 min for delivery of processed TS-X data to DWD server after scene acquisition. To do this, a new empirical algorithm XWAVE_C (C = coastal) for estimation of significant wave height from X-band satellite-borne SAR data has been designed for coastal applications. The algorithm is based on the spectral analysis of subscenes and the empirical model function yields an estimation of integrated sea state parameters directly from SAR image spectra without transformation into wave spectra. To provide the raster coverage analysis, the SSP intends three steps of recognising and removing the influence of non-sea-state-produced signals in the Wadden Sea areas such as ships, buoys, dry sandbars as well as nonlinear SAR image distortions produced by e.g. short and breaking waves. For the validation, more than 150 TS-X StripMap scene sequences with a coverage of ~30 km × 300 km across the German Bight since 2013 were analysed and compared with in situ Buoy measurements from 6 different locations. On this basis, the SSP autonomous processing of TS-X Stripmap images has been confirmed to have a high accuracy with an error RMSE = 25 cm for the total significant wave height.

The study aims at an optimized prediction of biophysical parameters of winter wheat and the identification of the best explaining spectral bands and vegetation indices from the RapidEye sensor system.

Physically based erosion models have been widely accepted in soil erosion risk assessment in the past years but their application is still restricted due to a large disparity between required model input parameters and data availability. Hyperspectral imaging may offer an alternative to solve that problem providing high spatial and spectral information about earth surface materials. A major problem in image interpretation is the heterogeneous composition of each individual pixel, in the case of agricultural ecosystems a mixture of soil and vegetation signal, but also moisture conditions and illumination have a strong influence. This issue has been addressed with unmixing techniques (vii) which take individual pixel components, endmembers, into account. In this paper, we present a new algorithm which allows the derivation of synthetic spectral endmembers (SY-SEM) of soil. A systematic analysis of lab spectra from more than 100 soil samples measured at different soil moisture and illumination angles revealed stable features independent of soil moisture and illumination in SWIR I and II. Results were confirmed by an analysis of corresponding field spectra. Starting from these stable wavelength regions absolute reflectance values of all spectra were analysed adopting a linear regression analysis. Results showed coefficients of determination (R²) generally above 0.8, in the range of 1000 to 2500nm above 0.95. Based on these results, SY-SEM are derived from self-generated look-up tables containing regression parameters, and atmospherically corrected image data. In contrast to existing approaches, SY-SEM procedure is computed pixelwise over a pre-classified image data set.