Post-Fire Vegetation Regeneration. The Case Study of the "Massif de l'Etoile" Fire.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Here is my query, where are my results? A search log analysis of the EOWeb Geoportal

With the rapid growth of available earth observation data and the rising demand to offer web-based data portals, there is a growing need to offer powerful search capabilities to efficiently locate the data products of interest. Many such web-based data portals have been developed with vastly different search interfaces and capabilities. Up to now, there is no general consensus within the community how such a search interface should look like nor exists a detailed analysis of the user's search behavior when interacting with such a data portal. In this paper we present a detailed analysis of user's search behavior based on a log analysis of a real earth observation data portal and generalize our findings to recommendations for future data portal search frontends to improve the overall user experience and increase the search quality

Automated flood mapping and monitoring using Sentinel-1 data

Flood extent maps derived from Synthetic Aperture Radar (SAR) data can be a key information source for an effective disaster management, helping humanitarian relief organizations and decision makers to obtain spatially-explicit information about inundated areas in a time- and cost-efficient manner. In comparison to manual or semi-automatic flood mapping approaches often utilized in the framework of rapid mapping activities, automatic SAR-based processing chains can substantially reduce the critical time delay between the delivery of satellite data after a crisis event and the subsequent provision of satellite derived crisis information (e.g. the extent of a flood situation) to emergency management authorities. In this work, an automated Sentinel-1 based processing chain designed for flood detection and monitoring in near real-time (NRT) is presented. The work is based upon a TerraSAR-X based flood service which has been adapted to Sentinel-1, including a number of enhancements for improving both robustness and thematic accuracy. Since no user intervention is required at any stage of the flood mapping procedure, the processing chain allows deriving time-critical disaster information in less than 45 minutes after a new dataset is available on the ESA Sentinel Data Hub. Due to the systematic acquisition strategy and high repeat rate of Sentinel-1, the processing chain can be set up as a web-based service which regularly informs users about the current flood conditions in a given area of interest. The processing chain is composed of the following main elements: a) automatic data ingestion through a Python-based script which routinely queries the ESA Sentinel Data Hub for new acquisitions matching user-defined criteria and subsequently downloads them, b) geometric correction and radiometric calibration using the graph processing tool (GPT) of the ESA Sentinel-1 toolbox (S1TBX) which is embedded in the Sentinel Application Platform (SNAP), c) initial classification using an automatic thresholding methodology, d) fuzzy-logic based classification refinement, e) final classification including auxiliary data, and f) dissemination and visualization of the results using a dedicated web client. The thematic accuracy of the processor has been assessed for two test-sites of a flood situation at the border between Greece and Turkey with encouraging accuracies. The accuracy assessment, which was performed separately for both standard polarizations (VV/VH) of the interferometric wide swath (IW) mode of Sentinel-1, further indicates that the thematic accuracy of VV polarization is slightly higher than that of VH polarization under calm wind conditions

Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery

This article presents for the first time the combination of dual-polarimetric C-band Sentinel-1 synthetic aperture radar (SAR) data and quad-polarimetric L-band ALOS-2/PALSAR-2 imagery for mapping of flooded areas with a special focus on flooded vegetation. L-band SAR data is well suited for mapping of flooded vegetation, while C-band enables an accurate extraction open water areas. Polarimetric decomposition-based unsupervised Wishart classification is combined with object-based post-classification refinement and the integration of spatial contextual information and global auxiliary data. In eight different scenarios, focusing on single datasets or fusion of classification results of several ones, respectively, different polarimetric decomposition and classification principles, including the entropy/anisotropy/alpha and the Freeman–Durden–Wishart classification, were investigated. The helix scattering component of the Yamaguchi decomposition, derived from ALOS-2 imagery, showed high suitability to refine the Sentinel-1-based detection of flooded vegetation. A test site at the Evros River (Greek/Turkish border region) was chosen, which was affected by a flooding event that occurred in spring 2015. The validation was based on high spatial resolution optical WorldView-2 imagery acquired with short temporal delay to the SAR data

A Three-class Change Detection Methodology for SAR-data based on Hypothesis Testing and Markov Random Field Modeling

This study presents a new automatic change detection process chain based on bi-temporal co-registered and calibrated Sentinel-1 level-1 Interferometric Wide (IW) Ground Range Detected (GRD) C-band Synthetic Aperture Radar (SAR) intensity imagery. The whole processor contains three main components: Firstly, a prepro- cessing step is used to perform geometrical and radiometrical calibration. Secondly, an automatic coarse detection step is applied based on a statistical hypothesis test to obtain an initial classifcation. Thirdly, a post-classifcation step is introduced to optimise the initial classifcation result in the form of minimising a global energy function de�ned on a Markov Random Field (MRF). In this study, a graph-cut algorithm is applied iteratively to solve the global optimisation problem. At each iteration, the data energy function for the current classification are set by the logarithmic probability density function (PDF). The relevant parameters are estimated by the method of logarithmic cumulants (MoLC). An appendix is presented at the end to explain the formulae used in this study. Experiments are performed using a good event which occurred in 2015 along the coastline of Greece near Kavala region and the Evros River at the border between Greece and Turkey. The proposed method shows a satisfying classifcation result with overall accuracy above 95% and kappa coefcient (�) above 0.87

Combining polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery for mapping of flooded vegetation

This article presents a semi-automated methodology for mapping of flooded areas with a special focus on flooded vegetation based on polarimetric Synthetic Aperture Radar (SAR) data. C-band SAR data is well suited for mapping of open water areas, while L-band enables the extraction of detailed information of flooded vegetation. Here, dual-pol C-band data of Sentinel-1 (S-1) is combined with quad-pol L-band ALOS-2/PALSAR-2 data to enable an accurate mapping of the entire flooded area. The developed proce-dure combines polarimetric decomposition based unsuper-vised Wishart classification with object-based post-classification refinement as well as the integration of spatial contextual information and global auxiliary data. The methodology was tested at the Evros River (Greek/Turkish border region), where a flooding event occurred in spring 2015

Serving continuous and global high resolution satellite data -- an example based on Sentinel-2 data

For the Copernicus Data and Exploitation Platform of Germany (CODE-DE) a full resolution imagery service of Sentinel-2 Level-1C data has been developed. Based on the experiences of this project this paper provides a general overview on how OGC-compliant online access services of large earth observation data sets can be implemented. Different means of implementation are compared through criteria such as the performance of the web services, the storage requirements and the processing power needed as well as the quality and flexibility reached. Considerations like projection, transparency, bit depth, formats and compression play a big role in serving the data in a performant way. This work aims to suggest best practices and to assist on which option to choose for which dedicated use case

Exploring the atmosphere composition with innovative online data analysis services integrating novel level-3 products from Copernicus Sentinel mission

For more than 10 years, the EOC Geoservice (https://geoservice.dlr.de) is operational and provides access to all its hosted data collections and products via the OGC-compliant interfaces WMS and WCS. ISO metadata on data collections and products are exposed via compliant catalogue services. The EOC Geoservice operationally provides access to a multitude of operational EO and EO related produtcts. Among these are specific Sentinel-2-products, value-added land-surface products such as the World Settlement Footprint and a variety of atmosphere-relateted products from GOME-2/MetOp-A/B/C Level-3 as well as innovative Level 3 trace gas, cloud and radiation products derived from Sentinel-5P/TROPOMI observations. The EOC Geoservice faces the problem that it publishes its extremely heterogenious data collections and product though a number of different catalogue services. The challenge for EOC Geoservice is to provide harmonized and standardized metadata for its complete variety of products to support state-of-the-art data discovery. Moreover, state-of-the-art data access to support scientific data analysis by data cube approaches is required. The solution which is implemented by the EOC Geoservice extends the existing OGC Web Service APIs with an additional STAC (SpatioTemporal Asset Cataloguem https://stacspec.org) EO product catalog. This interface allows to easily access published datasets via data cube concepts, supporting direct integration in operational processing environments or into interactive Jupyter notebooks. The aforementioned additional STAC EO product catalog is based on the existing catalog entries in the database. By using templates, they are converted into one of the following five standards: OpenSearch XML EO (to support existing applicatons), OpenSearch JSON, STAC (JSON) and STAC (HMTL). This conversion is performed on-the-fly, all required information for collection and product metadata is stored in an internal Postgres-database. In order to facilitate the uptake of new products (such as individual L3 atmosphere products from S5P/TROPOMI) by the EOC Geoservice, configurable tools for the automated extraction of relevant metadata information from CF-compliant NetCDF files are currently in development. As other software components, this tools is going to be published as open source. For all technologies, the GeoServer software is the technical backbone. Throughout the above mentioned 10-year period, DLR has significantly supported its further-development as open-source software. Its most recent improvements (integration of STAC) have been funded in the framework of the ESA GSTP-project Technologies for the Management of Long EO Data Time Series (LOOSE). Integration of all innovative interfaces into an operational data discovery, access and analysis service (EOC Geoservice and DataCube) for the Copernicus atmospheric composition missions Sentinel-5P, Sentinel-4 and Sentinel-5 is supported by the DLR programmatic project Innovative Produktentwicklung zur Analyse der Atmosphärenzusammensetzung (INPULS)