Wildfire danger prediction and understanding with deep learning

The authors thank Fabian Gans who provided the instructions to deploy the data cube in a cloud‐optimized format. Publisher Copyright: © 2022 The Authors.Climate change exacerbates the occurence of extreme droughts and heatwaves, increasing the frequency and intensity of large wildfires across the globe. Forecasting wildfire danger and uncovering the drivers behind fire events become central for understanding relevant climate-land surface feedback and aiding wildfire management. In this work, we leverage Deep Learning (DL) to predict the next day's wildfire danger in a fire-prone part of the Eastern Mediterranean and explainable Artificial Intelligence (xAI) to diagnose model attributions. We implement DL models that capture the temporal and spatio-temporal context, generalize well for extreme wildfires, and demonstrate improved performance over the traditional Fire Weather Index. Leveraging xAI, we identify the substantial contribution of wetness-related variables and unveil the temporal focus of the models. The variability of the contribution of the input variables across wildfire events hints into different wildfire mechanisms. The presented methodology paves the way to more robust, accurate, and trustworthy data-driven anticipation of wildfires.publishersversionpublishe

Wildfire monitoring using satellite images, ontologies and linked geospatial data

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

Wildfire monitoring via the integration of remote sensing with innovative information technologies

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, including land use/land cover, administrative boundaries, road and rail network, points of interest, 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 and that have activated Emergency Support Services at a European level in the framework of the operational GMES projects SAFER and LinkER. 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 state-of-the-art Information Technologies that have become available in the framework of the TELEIOS (EC/ICT) project. TELEIOS aims at the development of fully automatic processing chains 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 creation of thematic maps and added-value services. The first objective is achieved with the use of advanced Array Database technologies, such as MonetDB, to enable efficiency in accessing large archives of image data and metadata in a fully transparent way, without worrying for their format, size, and location, as well as efficiency in processing such data using state-of-the-art implementations of image processing algorithms expressed in a high-level Scientific Query Language (SciQL). The product refinement is realized through the application of update operations that incorporate human evidence and human logic, with semantic content extracted from thematic information coming from auxiliary geo-information layers and sources, for reducing considerably the number of false alarms in fire detection, and improving the credibility of the burnt area assessment. The third objective is approached via the combination of the derived fire-products with Linked Geospatial Data, structured accordingly and freely available in the web, using Semantic Web technologies. 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, irrespective of the area of interest and its extent, the observation time period, or the data volume involved, granting the opportunity to combine innovative IT solutions with remote sensing techniques and

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

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

Forecasting magma-chamber rupture at Santorini volcano, Greece

How much magma needs to be added to a shallow magma chamber to cause rupture, dyke injection, and a potential eruption? Models that yield reliable answers to this question are needed in order to facilitate eruption forecasting. Development of a long-lived shallow magma chamber requires periodic influx of magmas from a parental body at depth. This redistribution process does not necessarily cause an eruption but produces a net volume change that can be measured geodetically by inversion techniques. Using continuum-mechanics and fracture-mechanics principles, we calculate the amount of magma contained at shallow depth beneath Santorini volcano, Greece. We demonstrate through structural analysis of dykes exposed within the Santorini caldera, previously published data on the volume of recent eruptions, and geodetic measurements of the 2011–2012 unrest period, that the measured 0.02% increase in volume of Santorini’s shallow magma chamber was associated with magmatic excess pressure increase of around 1.1 MPa. This excess pressure was high enough to bring the chamber roof close to rupture and dyke injection. For volcanoes with known typical extrusion and intrusion (dyke) volumes, the new methodology presented here makes it possible to forecast the conditions for magma-chamber failure and dyke injection at any geodetically well-monitored volcano

Managing big, linked, and open earth-observation data: Using the TELEIOS/LEO software stack

Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the software stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this stack of tools can be used to implement an operational wildfire-monitoring service

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

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

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

Building Virtual Earth Observatories using Ontologies and Linked Geospatial Data

TELEIOS is a European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery of knowledge that can be used in applications. To achieve this, TELEIOS builds on scientific database technologies (array databases, SciQL, data vaults), Semantic Web technologies (stRDF and stSPARQL) and linked geospatial data. In this technical communication we outline the TELEIOS advancements to the state of the art and give an overview of its technical contributions up to today