50 research outputs found

    Real-Time Wildfire Monitoring Using Scientific Database and Linked Data Technologies

    Get PDF
    We present a real-time wildfire monitoring service that exploits satellite images and linked geospatial data to detect hotspots and monitor the evolution of fire fronts. The service makes heavy use of scientific database technologies (array databases, SciQL, data vaults) and linked data technologies (ontologies, linked geospatial data, stSPARQL) and is implemented on top of MonetDB and Strabon. The service is now operational at the National Observatory of Athens and has been used during the previous summer by emergency managers monitoring wildfires in Greece

    Operational Wildfire Monitoring and Disaster Management Support Using State-of-the-art EO and Information Technologies

    Get PDF
    Fires have been one of the main driving forces in the evolution of plants and ecosystems, determining the current structure and composition of the Landscapes. However, significant alterations in the fire regime have occurred in the recent decades, primarily as a result of socioeconomic changes, increasing dramatically the catastrophic impacts of wildfires as it is reflected in the increase during the 20th century of both, number of fires and the annual area burnt. Therefore, the establishment of a permanent robust fire monitoring system is of paramount importance to implement an effective environmental management policy. Such an integrated system has been developed in the Institute for Space Applications and Remote Sensing of the National Observatory of Athens (ISARS/NOA). Volumes of Earth Observation images of different spectral and spatial resolutions are being processed on a systematic basis to derive thematic products that cover a wide spectrum of applications during and after wildfire crisis, from fire detection and fire-front propagation monitoring, to damage assessment in the inflicted areas. The processed satellite imagery is combined with auxiliary geo-information layers and meteorological data to generate and validate added-value fire-related products. The service portfolio has become available to institutional End Users with a mandate to act on natural disasters in the framework of the operational GMES projects SAFER and LinkER addressing fire emergency response and emergency support needs for the entire European Union. Towards the goal of delivering integrated services for fire monitoring and management, ISARS/NOA employs observational capacities which include the operation of MSG/SEVIRI and NOAA/AVHRR receiving stations, NOA’s in-situ monitoring networks for capturing meteorological parameters to generate weather forecasts, and datasets originating from the European Space Agency and third party satellite operators. The qualified operational activity of ISARS/NOA in the domain of wildfires management is highly enhanced by the integra

    Wildfire monitoring using satellite images, ontologies and linked geospatial data

    Get PDF
    Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation data archives have been constantly increasing in size in the last few years, and have become a valuable source of data for many scientific and application domains. When Earth Observation data is coupled with other data sources many pioneering applications can be developed. In this paper we show how Earth Observation data, ontologies, and linked geospatial data can be combined for the development of a wildfire monitoring service that goes beyond applications currently deployed in various Earth Observation data centers. The service has been developed in the context of European project TELEIOS that faces the challenges of extracting knowledge from Earth Observation data head-on, capturing this knowledge by semantic annotation encoded using Earth Observation ontologies, and combining these annotations with linked geospatial data to allow the development of interesting applications

    Operational wildfire monitoring and disaster management support using state-of-the-art EO and Information Technologies

    Get PDF
    textabstractFires have been one of the main driving forces in the evolution of plants and ecosystems, determining the current structure and composition of the Landscapes. However, significant alterations in the fire regime have occurred in the recent decades, primarily as a result of socioeconomic changes, increasing dramatically the catastrophic impacts of wildfires as it is reflected in the increase during the 20th century of both, number of fires and the annual area burnt. Therefore, the establishment of a permanent robust fire monitoring system is of paramount importance to implement an effective environmental management policy. Such an integrated system has been developed in the Institute for Space Applications and Remote Sensing of the National Observatory of Athens (ISARS/NOA). Volumes of Earth Observation images of different spectral and spatial resolutions are being processed on a systematic basis to derive thematic products that cover a wide spectrum of applications during and after wildfire crisis, from fire detection and fire-front propagation monitoring, to damage assessment in the inflicted areas. The processed satellite imagery is combined with auxiliary geo-information layers and meteorological data to generate and validate added-value fire-related products. The service portfolio has become available to institutional End Users with a mandate to act on natural disasters in the framework of the operational GMES projects SAFER and LinkER addressing fire emergency response and emergency support needs for the entire European Union. Towards the goal of delivering integrated services for fire monitoring and management, ISARS/NOA employs observational capacities which include the operation of MSG/SEVIRI and NOAA/AVHRR receiving stations, NOA’s in-situ monitoring networks for capturing meteorological parameters to generate weather forecasts, and datasets originating from the European Space Agency and third party satellite operators. The qualified operational activity of ISARS/NOA in the domain of wildfires management is highly enhanced by the integration of innovative Information Technologies that have become available in the framework of the TELEIOS (EC/ICT) project. Through this activity a fully automatic processing chain has been developed reliant on, a) the effective storing and management of the large amount of EO and GIS data, b) the post-processing refinement of the fire products using semantics, and c) the timely creation of fire extent and damage thematic maps. These technologies are built on top of a robust and modular computational environment, to facilitate several wildfire applications to run efficiently, such as real-time fire detection, fire-front propagation monitoring, rapid burnt area mapping, after crisis detailed burnt scar mapping, and time series analysis of burnt areas. The approach adopted allows ISARS/NOA to routinely serve requests from the end-user community, such as Civil Protection and Forestry Services, irrespective of the location and size of the area of interest, the observation time period, or the size of data volume involved, granting the opportunity to combine innovative IT solutions with remote sensing techniques and algorithms for wildfire monitoring and management

    Developing a wildfire surveillance algorithm for geostationary satellites

    Get PDF
    Wildfire surveillance is an important aspect of effective wildfire management, requiring near continuous observations to detect and monitor fires. Geostationary satellites have the potential to meet this challenge, capturing full disk images every 10 to 30 minutes at ground sample distances down to 500 m for some sensors. However, the MIR (Middle Infrared) and TIR (Thermal Infrared) channels on geostationary satellite sensors have a coarse ground sample distance of 2-4 km. Currently, fire detection algorithms depend on these channels to detect thermal anomalies. The coarse spatial resolution in the MIR and TIR channels limits the application of geostationary satellite for wildfire surveillance. This thesis looks to fully exploit the potential of geostationary satellites for wildfire surveillance through a multi-spatial and multi-temporal approach. The first research question in this thesis, develops and tests an algorithm to improve the wildfire surveillance capabilities of the geostationary satellites. The new algorithm utilises the MIR, NIR and visible channels, linking them to biophysical processes on the ground. The MIR channel is used to detect thermal anomalies, the NIR channel is used to detect changes in vegetation cover, and the visible channel detects smoke from the fire. By combining these detections, or observations, fire surveillance can be achieved at the highest ground sampling resolution available (typically in the visible wavelength channels). Initial algorithm development and testing were conducted on the Advanced Himawari Imager (AHI) sensor onboard the Himawari-8 satellite. The MIR, NIR and RED channels on AHI have 2 km, 1 km and 500 m ground sampling distances respectively, enabling the new algorithm to detect 2 km thermal anomalies and 500 m fire-line pixels. Fire-line pixels is a new product designed to Adetect the trailing edge of the fire. Quantifiable methods for assessing algorithm performance in geostationary satellites are dicult to apply due to their high temporal resolution and lack of concurrent in-situ information. The second research question investigates methods for assessing the performance by considering the near continuous temporal sampling of geostationary satellites and the higher spatial ground sampling resolution a↵orded from LEO (Low Earth Orbiting) satellite observations. The study examines di↵erent evaluation methods and suggests a three-step process to provide the optimum performance evaluation for geostationary wildfire surveillance products, inter-compared with LEO satellite-based thermal anomaly detections. Algorithm performance is further evaluated in research question three using the intercomparison method developed in research question 2 and applied to case study fires over Northern Australia. Subsequently, the algorithm is evaluated using an annual dataset (2016) comprising of nine study areas across Australia (totalling 360.000 km2) stratified by tree canopy cover. The algorithm reported an omission error of 27 % at 2 km ground resolution when compared to VIIRS (Visible Infrared Imaging Radiometer Suite) hotspots over the nine study grids. In Northern Australia, the algorithm detected fires up to three hours before LEO observations due to the high temporal frequency of observations. Furthermore, in comparison to MODIS (Moderate Resolution Imaging Spectroradiometer) hotspots, there was a 73 % chance of detecting fire activity at the location of the MODIS hotspot, before the MODIS overpass. The algorithm also demonstrated a 40 % detection probability for fires less than 14 ha over Northern Australian woodlands. The fire-line pixels with a ground sampling distance of 500 m demonstrated a 25 % commission error when compared to VIIRS hotspots over the nine study grids. Over Northern Australia, this figure was 7 % inter-compared to Landsat-8 burnt scars. The fourth research question applied the developed algorithm to the SEVIRI (Spinning Enhanced Visible and Infrared Image) sensor onboard the European Meteosat Second Generation (MSG) satellite. SEVIRI has an operational fire product (FIR (Active Fire Monitoring)) which provides 3 km ground resolution hotspots using the MIR and TIR channels. The algorithm initially developed for AHI was modified to work with SEVIRI 3 km MIR channel and the High-Resolution Visible (HRV) channel (1 km). An inter-comparison of the modified algorithm with FIR products showed a 28 % and 16 % improvement in commission and omission errors respectively over a large case study fire in Portugal. The modified algorithm also improved the SEVIRI wildfire surveillance ground sampling resolution to 1 km taking advantage of the HRV channel. The algorithm developed in this study demonstrates a novel approach to utilise geostationary satellites for wildfire surveillance with improved spatial resolution. Compared to the 2 km thermal anomaly hotspots derived through existing algorithms for AHI, the new algorithm provides 2 km thermal anomaly detections and 500 m fire-line pixels with performance comparable to that of medium resolution LEO satellites. Near-real time implementation of the algorithm has the potential to provide high temporal fire surveillance capabilities. The fire-line pixels from the algorithm could also be used to derive fire behaviour parameters such as heading and speed, providing an essential tool for wildfire surveillance in remote parts of Australia and other areas, where resources can only be deployed for a hand full of high-risk fires

    The Portuguese Large Wildfire Spread database (PT-FireSprd)

    Get PDF
    Wildfire behaviour depends on complex interactions between fuels, topography, and weather over a wide range of scales, being important for fire research and management applications. To allow for significant progress towards better fire management, the operational and research communities require detailed open data on observed wildfire behaviour. Here, we present the Portuguese Large Wildfire Spread database (PT-FireSprd) that includes the reconstruction of the spread of 80 large wildfires that occurred in Portugal between 2015 and 2021. It includes a detailed set of fire behaviour descriptors, such as rate of spread (ROS), fire growth rate (FGR), and fire radiative energy (FRE). The wildfires were reconstructed by converging evidence from complementary data sources, such as satellite imagery and products, airborne and ground data collected by fire personnel, and official fire data and information in external reports. We then implemented a digraph-based algorithm to estimate the fire behaviour descriptors and combined it with the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) fire radiative power estimates. A total of 1197 ROS and FGR estimates were calculated along with 609 FRE estimates. The extreme fires of 2017 were responsible for the maximum observed values of ROS (8900 m h−1) and FGR (4400 ha h−1). Combining both descriptors, we describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. Analysis of the database showed that burned extent is mostly determined by FGR rather than by ROS. Finally, we explored a practical example to show how the PT-FireSprd database can be used to study the dynamics of individual wildfires and to build robust case studies for training and capacity building. The PT-FireSprd is the first open-access fire progression and behaviour database in Mediterranean Europe, dramatically expanding the extant information. Updating the PT-FireSprd database will require a continuous joint effort by researchers and fire personnel. PT-FireSprd data are publicly available through https://doi.org/10.5281/zenodo.7495506 (Benali et al., 2022) and have large potential to improve current knowledge on wildfire behaviour and to support better decision making.</p

    EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds

    Get PDF
    The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986, last access: 5 November 2021). The alert products developed by the EUNADICS-AV EWS, i.e. near-real-time (NRT) observations, email notifications and netCDF (Network Common Data Form) alert data products (called NCAP files), have shown significant interest in using selective detection of natural airborne hazards from polar-orbiting satellites. The combination of several sensors inside a single global system demonstrates the advantage of using a triggered approach to obtain selective detection from observations, which cannot initially discriminate the different aerosol types. Satellite products from hyperspectral ultraviolet–visible (UV–vis) and infrared (IR) sensors (e.g. TROPOMI – TROPOspheric Monitoring Instrument – and IASI – Infrared Atmospheric Sounding Interferometer) and a broadband geostationary imager (Spinning Enhanced Visible and InfraRed Imager; SEVIRI) and retrievals from ground-based networks (e.g. EARLINET – European Aerosol Research Lidar Network, E-PROFILE and the regional network from volcano observatories) are combined by our system to create tailored alert products (e.g. selective ash detection, SO2 column and plume height, dust cloud, and smoke from wildfires). A total of 23 different alert products are implemented, using 1 geostationary and 13 polar-orbiting satellite platforms, 3 external existing service, and 2 EU and 2 regional ground-based networks. This allows for the identification and the tracking of extreme events. The EUNADICS-AV EWS has also shown the need to implement a future relay of radiological data (gamma dose rate and radionuclides concentrations in ground-level air) in the case of a nuclear accident. This highlights the interest of operating early warnings with the use of a homogenised dataset. For the four types of airborne hazard, the EUNADICS-AV EWS has demonstrated its capability to provide NRT alert data products to trigger data assimilation and dispersion modelling providing forecasts and inverse modelling for source term estimate. Not all of our alert data products (NCAP files) are publicly disseminated. Access to our alert products is currently restricted to key users (i.e. Volcanic Ash Advisory Centres, national meteorological services, the World Meteorological Organization, governments, volcano observatories and research collaborators), as these are considered pre-decisional products. On the other hand, thanks to the EUNADICS-AV–SACS (Support to Aviation Control Service) web interface (https://sacs.aeronomie.be, last access: 5 November 2021), the main part of the satellite observations used by the EUNADICS-AV EWS is shown in NRT, with public email notification of volcanic emission and delivery of tailored images and NCAP files. All of the ATM stakeholders (e.g. pilots, airlines and passengers) can access these alert products through this free channel.Peer ReviewedArticle escrit per 46 autors/es: Hugues Brenot Nicolas Theys Lieven Clarisse Jeroen van Gent Daniel Hurtmans Sophie Vandenbussche Nikolaos Papagiannopoulos Lucia Mona Timo Virtanen Andreas Uppstu Mikhail Sofiev Luca Bugliaro Margarita Vázquez-Navarro Pascal Hedelt Michelle Maree Parks Sara Barsotti Mauro Coltelli William Moreland Simona Scollo Giuseppe Salerno Delia Arnold-Arias Marcus Hirtl Tuomas Peltonen Juhani Lahtinen Klaus Sievers Florian Lipok Rolf Rüfenacht Alexander Haefele Maxime Hervo Saskia Wagenaar Wim Som de Cerff Jos de Laat Arnoud Apituley Piet Stammes Quentin Laffineur Andy Delcloo Robertson Lennart Carl-Herbert Rokitansky Arturo Vargas Markus Kerschbaum Christian Resch Raimund Zopp Matthieu Plu 1 Vincent-Henri Peuch Michel van Roozendael Gerhard WotawaPostprint (author's final draft

    Advances in Remote Sensing and GIS applications in Forest Fire Management: from local to global assessments

    Get PDF
    This report contains the proceedings of the 8th International Workshop of the European Association of Remote Sensing Laboratories (EARSeL) Special Interest Group on Forest Fires, that took place in Stresa, (Italy) on 20-21 October 2011. The main subject of the workshop was the operational use of remote sensing in forest fire management and different spatial scales were addressed, from local to regional and from national to global. Topics of the workshops were also grouped according to the fire management stage considered for the application of remote sensing techniques, addressing pre fire, during fire or post fire conditions.JRC.H.7-Land management and natural hazard
    corecore