This paper introduces a novel geospatial identification approach to distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Carried out within the framework of the World Bank’s Country Disaster Risk Profiles (CDRP) project initiative - currently being implemented in Central America - global applicability and easy transferability is considered crucial. Therefore global spatial datasets are used throughout in the setup of the disaggregated property stock exposure model, one of the key elements for subsequent disaster risk and loss estimation. After initial urban-rural classification at a 1km grid level, predominantly residential areas need to be identified as opposed to areas of mixed use in order to spatially link accordingly compiled property stock information (e.g. from global tabular databases such as PAGER-STR). Impervious Surface Area (ISA) data based on remotely sensed nighttime lights from the DMSP-OLS sensor are used as proxy to identify areas of peak human activity. Intense lighting in that context is associated with a high likelihood of commercial and/or industrial presence, commonly clustered in certain parts of a city (such as central business districts and or peripheral commercial zones). Areas of low light intensity, in turn, can be considered more likely residential. Several light intensity threshold are tested for Cuenca City, Ecuador, in order to best match the situation on the ground, where local-level cadastral land use data show a 75-25 distribution ratio of residential vs. mixed use. Results will be presented first-hand in this paper and future work will be addressed highlighting the relevance of remote sensing data for top-down modeling approaches at wide spatial scale.

The seismic vulnerability analysis of urban environments is an operational issue that concerns the comprehensive knowledge of both building structural features and soils geophysical parameters, especially when considering areas that are prone to hydrogeological and seismic disasters. The protection of such environments, together with the population growth and the urbanization processes, requires a multi-disciplinary approach aiming at providing both an effective assessment of urban resources and synthetic parameters for managing post crisis events, restoration activities and search & rescue operations. Within such a framework, airborne Light Detection and Ranging (LiDAR) and Hyperspectral sensors have demonstrated to be powerful remote sensing instruments, whose jointly use allow providing meaningful parameters to describe both the topographic settings of urbanized areas and the buildings properties, in terms of geometrical, spectral and structural features. Based on this rationale, in this study, the operational benefits obtained by combining airborne LiDAR and Hyperspectral measurements are provided to support the seismic vulnerability assessment of urban seismic areas. The digital elevation model as well as the building height and the shape of the observed area are gathered by using airborne LiDAR measurements. Spectral and structural information of urban buildings are provided through the supervised classification of IMSpectorV10E VNIR (wavelength range between 400 and 1000nm subdivided into 503 bands) measurements acquired by the IPERGEO sensor. The objective is to combine the different products provided by LiDAR and Hyperspectral image processing analysis within a Geographic Information System (GIS) platform, to evaluate the intrinsic properties of buildings (e.g. perimeter, covered area, height and type of roofs) together with the topographic features of the surrounding area (e.g. the surface height and slope) for providing synthetic parameters and thematic maps useful for seismic assessment and mitigation purposes, such as: (i) the identification of steep slope areas, (ii) the analysis of building roof typology for supporting the evaluation of structural load conditions, (iii) the detection of critical structures (e.g. asbestos buildings), (iv) the identification of primary roads (in terms of escape or access routes) for supporting search and rescue operations, (v) the analysis of main road conditions after building collapses. Meaningful experimental results, gathered for the historical center of Cosenza city (Italy), allow demonstrating the benefits of the proposed approach for both seismic assessment and mitigation purposes.

The present work is supported and funded by Ministero dell'Università, dell'Istruzione e della Ricerca (MIUR) under the project PON01-02710 "MASSIMO" - "Monitoraggio in Area Sismica di SIstemi MOnumentali".

In the context of the project OpSSERVE - Optical Satellite Services for EMSA (European Maritime Safety Agency) – the European Space Imaging (EUSI) and the German Aerospace Center (DLR) established for the first time a fully operational and near real-time service to detect vessels and maritime activities with optical satellite imagery . The service was implemented in 2013 and contribute to maritime situation awareness, e.g. in order to reduce the risk of maritime accidents, marine pollution and the loss of human life at sea.

Since its activation in 2007, EMSA’s CleanSeaNet (CSN) system provides pollution detection services in support of maritime surveillance and decision making for all participating European member states based on SAR satellite data. The main advantage of SAR data is their independence of weather conditions (cloud coverage), but the identification of small vessels is rather difficult. The use of very high resolution (VHR) optical data provides valuable information to identify small vessels.

From the VHR optical imagery, actionable information products are created in an automatic processing chain including image pre-processing, data transcription, automatic vessel detection, GUI based interactive vessel and activity detection and finally the delivery of standardized products to EMSA. The service provides access to globally collected satellite imagery and receives data and derived products in near real-time (1 or 3 hours). In OpSSERVE, five satellite missions are available: WorldView-1, WorldView-2, GeoEye-1, IKONOS and EROS-B. The poster will present the experiences of the operational service and give an overview on future aspects and possible applications.

Physical modelling is usually a complicated way to estimate soil moisture content, while machine learning algorithms have the potential to retrieve information from remote sensing data. In this study, the neural network, one of the most common machine learning algorithms, was used to map soil moisture from active microwave and optical data in combination. The study area was set in the middle stream of Heihe River Basin in China, from where Landsat and Envisat ASAR data were acquired in July 2008. The neural networks were trained with ground truth data and input parameters extracted from remote sensing data including bands information, Normalized Difference Vegetation Index (NDVI), Brightness Index (BI), the dual polarizations (HH and VV) and the ratio (HH/VV). Compared to an existing output of an empirical model with purely Envisat ASAR data in the same area (with R²=0.71), this study showed a slightly better correlation between the measured and estimated soil moisture (R²=0.75). It also revealed that the model with multi-source data had a better performance than the one with only a single source data, and that the selection of input parameters and the number of hidden layers and nodes can also affect the model's accuracy. Finally, the verified model was applied to the whole study area, and it was showed that this method has operational potential for estimating soil moisture under vegetated area in the middle stream of Heihe River Basin.

Forest cover dynamics and disturbance can be tracked using a pixel based time-series analysis of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) backscatter and coherence data. In particular, derived features from pixel trajectories in time can be a powerful tool to map changes in tropical forest, where deforestation and forest degradation occur driven by a series of processes such as fire, selective logging, subsistence agriculture and complete clearance of forest due to large scale deforestation. The research presents results from tropical forest environments in Cameroon, Republic of Congo and Indonesia.  Several SAR data with different frequency and resolution were tested including ENVISAT ASAR, ALOS PALSAR and TanDEM-X. Furthermore, the analysis was undertaken on both TanDEM-X backscatter and coherence at HH polarization. Multi-temporal coherence was employed due to its sensitivity to the upper canopy volume, which causes decorrelation as a function of the amount of vegetation (e.g. disturbance event).

A pixel trajectory is defined as a set of values of all resolution elements (backscatter or coherence) at the same row and column position in the stack of images. The stack is generated by multi-resolution analysis (MRA) at a number of spatial resolutions, enabling analysis in the combined time and space domains. Analysis of the trajectories over an area by means of a set of parameters (features) that characterize its time evolution can give insight on the nature and changes of landcover. The following set of trajectory features was computed: running ratios with respect to a baseline year, linear fitting (trend), coefficient of determination (goodness of fit), dispersion around trend, maximum change relative to mean (swing) and statistics of first derivative (variance, kurtosis). These features are designed to detect in each pixel trajectory the presence of a linear trend, the stationary of the distribution around the linear regression, the occurrence of intermittent events, and the dynamic range of the changes.