Earth observation data is readily available at the time of a crisis through a variety of channels, including the International Charter for Space and Major Disasters. The accessibility and use of geospatial information from government sources ensures that decision makers and other stakeholders get an accurate common operational picture of critical scenarios before, during, and after disasters.
In July 2018, GEO worked closely with the United Nations committee of experts for Global Geospatial Information Management (UN-GGIM) and UN Member States to provide input to the UN Resolution on Geospatial Information and Services for Disasters, which was adopted by the UN Economic and Social Council. The Resolution guides stakeholders in the management of geospatial information and services through better governance and policies, awareness raising and capacity building, data management, common infrastructure and services, and resource mobilization.
GEO also contributed to the updated 2018 Strategic Framework on Geospatial Information and Services for Disasters, providing information on Earth observation requirements and use cases to the UN-GGIM Working Group on Disasters, in close collaboration with UNDRR.
Geohazard Supersites and Natural Laboratories (GSNL) is a GEO Initiative composed of the Committee on Earth Observation Satellites (CEOS), space agencies, in situ data providers, including monitoring institutes, agencies and consortia managing data infrastructures, and engagement with the international scientific community.
Since its inception in 2010, GSNL has promoted rapid and effective uptake of scientific results for in Disaster Risk Reduction (DRR). The focus of the initiative is areas with important scientific problems and high risk levels, including Supersites and the Natural Laboratories. Two recent case studies illustrate the value of analysis ready GSNL data for disaster response.
The supersite data and resources are open for the international scientific community and this stimulates collaboration, knowledge transfer, capacity building, and the generation of new scientific results, which are shared within the community. These research results, which are relevant to geohazard assessment, are then communicated by a Supersite Coordinator to end users and decision makers, through national institutional channels.
In 2019, researchers used high resolution InSAR data, shared by the Marmara Permanent GSNL Supersite, to analyse a seismic slip occurring along Turkey’s North Anatolian Fault. The 1,600 kilometre slip feature separating the Eurasian and Arabian plates has produced seven large (magnitude >7) earthquakes since 1939. The most recent event, the 1999 magnitude-7.4 Izmit earthquake that occurred roughly 100 kilometres east of Istanbul killed more than 20,000 people.
Using 307 images acquired by the Sentinel-1 and TerraSAR-X satellites, the researchers examined deformation and changes in the direction of motion across the central segment of the 1999 rupture for the period spanning 2011 to 2017. The results indicated this segment continued to creep nearly two decades after the earthquake. The team also presented evidence for a transient “creep burst” in November 2016 that corresponded to 1.7 years of average creep in just three weeks.
Collectively, these findings indicate that post-seismic slip along the North Anatolian Fault is more complex than has previously been suggested. This study offers new insight into long-term, post-seismic deformation following a major earthquake along one of Earth’s most active strike-slip faults.
Without the joint interpretation of satellite and in situ data, organized by GSNL, this study could not be made. This initiative provides innovative methods for earthquake hazard assessment and improvement of our shared knowledge. These observations are important for decision makers to be prepared for emergency management, via rapid generation of critical information relevant to the co-seismic deformation event, using pre- and post-event imagery.
In 2018, the Hawaii Supersite was impacted by a crisis at Kilauea volcano, where a lava flow destroyed over 700 homes. Starting in late May, the Kilauea caldera began to rapidly subside. In two months, parts of the ground had dropped by 400 meters, a level rarely observed in a volcano eruption.
High resolution InSAR monitoring with COSMO-SkyMed and TerraSAR X images granted through the GSNL Supersite provided unprecedented views of the collapse and have been critical for understanding how the subsidence was evolving over time. This data, along with high temporal resolution global positioning system (GPS) and seismic data, were analysed by the Hawaiian Volcano Observatory and the scientific community, generating information which guided the disaster response.
This information was of crucial importance to the Hawaii County Civil Defense, which is responsible for emergency response operations, including evacuations. The Supersite data collected by international space agencies form a valuable source of information to understand the scientific aspects of this unprecedented eruptive event at Kilauea, and can contribute to volcanic hazard assessments for similar volcanoes around the world.
Sequence of Sentinel-1 interferograms spanning the initiation of the lower East Rift Zone dike intrusion (top), onset of lower East Rift Zone eruption and M6.9 south flank earthquake (middle), and co-eruption, post-earthquake time periods (bottom).
Map of Kilauea’s lower East Rift Zone generated by the Hawaiian Volcano Observatory on August 14, 2018. Purple areas indicate regions of past lava flows (in 1840, 1955, and 1960), while red areas are from 2018. Blue lines indicate topographic paths of steepest descent, which were used to forecast initial flow paths. Black lines are roads.
Kīlauea Volcano, on the Island of Hawaiʻi. Rift zones radiate to the east (ERZ=East Rift Zone) and southwest (SWRZ=Southwest Rift Zone) from the summit caldera. Vents in the vicinity of Puʻu ʻŌʻō have been actively erupting lava (noted by red areas) since 1983, and a lava lake has been present at the summit since 2008.
In 2017, over 126 Gigabytes (GB) of data collected from eight satellites operated by AOGEO countries was shared with Mexico during the Central Mexico earthquake to assist with disaster response and recovery. The 2017 Puebla earthquake killed an estimated 370 people with more than 6,000 injured.
Previously, a similar amount of data was rapidly shared with New Zealand, Australia and other countries affected by the 2016 New Zealand earthquake.
Under the coordination of AOGEO, a new mode of international disaster emergency cooperation is being established. This data sharing for disasters is expected to supplement other international disaster cooperation mechanisms, and has already proven valuable in several cases over the last few years.
The Global Wildfire Information System is a joint initiative of the GEO and the Copernicus Emergency Management Services (EMS) Work Programmes. (GWIS ) brings existing information sources together at regional and national levels to provide a comprehensive view, and for evaluation of fire regimes and fire effects at global level.
GWIS builds on the ongoing activities of the European Forest Fire Information System (EFFIS), the Global Observation of Forest Cover- Global Observation of Land Dynamics (GOFC-GOLD) Fire Implementation Team (GOFC Fire IT), and the associated Regional Networks, complementing existing activities that are ongoing around the world with respect to wildfire information gathering. The development of GWIS is supported by the partner organizations and space agencies. NASA has made financial support for GWIS available through its ROSES programme.
Access to worldwide information on wildfires is available through the GWIS viewer.
During summer 2019, a heatwave spread across Europe, and high temperatures resulted in July 2019 being declared one of the hottest months on historical record. GWIS was monitoring active wildfires and their impacts across northern Europe, Russia, Canada, Brazil and in regions of the Arctic.
The Current Situation Viewer provides users near real-time updates in the online portal with information on the Fire Danger Forecast. It also provides the status of wildfire effects in the online Rapid Damage Assessment including information on Black Carbon, Methane, Carbon Dioxide, Carbon Monoxide, Sulphur Dioxide, Nitrogen Oxides, Organic Carbon, Particulate Matter and other gases or substances in the atmosphere.
Record rainfall and flooding in mid-March 2019 in Iran forced evacuations of thousands of people, causing hundreds of millions of dollars of damage, and led to substantial loss of life. Iran's Mehr News Agency reported that 1,900 cities and villages were flooded, while CNN reported that more than 140 rivers burst their banks, 409 landslides were reported, 78 roads were blocked, and 84 bridges affected. Rapid access to high resolution satellite imagery was important for government agencies and first responders to assess damage, and prioritise and plan their response operations.
Responding to requests from the UN Economic and Social Commission for Asia and the Pacific (UNESCAP) and the Iranian Earthquake Engineering Association (IEEA), China GEO activated its Disaster Data Response (CDDR) mechanism to provide high resolution satellite imagery in support of disaster response planning.
Following the activation of the CDDR for Iran, four contributors developed satellite observation plans for flooded areas in Aq Qala, Darvazeh Quran and Pol Dokhtar, and provided high resolution Jilin-1 sp06 satellite images to support disaster response.
UNESCAP confirmed that the data was then provided to Iran's National Disaster Management Organization (NDMO) and the Iranian Red Crescent as the key disaster management operational bodies. The data was also used for flood analysis in cooperation with UN-Habitat and the Saman Paydar Insurance Risk Management Institute (SPRMI).
On 25 January 2019, the tailings dam to the Córrego do Feijão mine burst near Brumadinho, Minas Gerais, Brazil, resulting in hundreds of lives lost and extensive environmental and economic damage. Following the devastating Brazilian dam collapse, Chinese satellite data products were provided to support Brazilian disaster response as part of CDDR.
“[The ChinaGEOSS Disaster Data Response Mechanism] is complementary to the International Charter Space and Major Disasters,” explained Professor Li Gouging, coordinator of ChinaGEOSS Data Sharing Network and co-chair of CODATA’s task group on Linked Open Data for Global Disaster Risk Research, “We are able to mobilize high resolution satellite resources operated by both government institutes and commercial sectors for international emergency response and make the data openly available to the public afterwards.”
Just two days after the dam burst, the ChinaGEO Secretariat - based in the National Remote Sensing Center of China - trigged the ChinaGEO Disaster Data Response (CDDR) mechanism to collect high resolution satellite data of the impacted area in. High resolution optical images obtained by SuperView-1 were used for planning field operations and for tactical planning by the search and rescue team, and supported Brazil’s disaster response efforts.
Between 28-30 January 2019, various Chinese optical satellite data products were provided to Brazil’s National Center of Risk and Disaster Management (Centro Nacional de Gerenciamento de Riscos e Desastres - CENAD). A 2m resolution image (GaoFen-1 satellite) provided an overview for planning, while a 0.92m resolution image (JiLin-1 satellite) provided river pollution information and a 0.5m resolution image (SuperView-1 satellite) assisted with assessments of building damage.
The United States Geological Survey (USGS), in close collaboration with GEO Associate Esri, produced several standardized, rigorous, high spatial resolution global data layers with considerable potential utility for disaster applications. These products have been commissioned by GEO and many of these resources are a first to characterize earth surface features at finer spatial and thematic resolution.
Published in 2017, the World Hammond Landforms product was developed by classifying 250 m global DEM data produced by USGS into 16 landform types, including several subclasses of plains, hills, and mountains. This product emphasizes regional terrain expressions and is a useful resource for identifying areas and types of relief based on analysis of slope, elevation, and ruggedness.
This data is useful for identifying candidate areas of vulnerability to landslides and flooding, including both downstream flooding form landform-determined “water towers,” and in low elevation coastal areas subject to tidal surges. Data on landforms plays a useful role in all natural science fields of study and planning disciplines.
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In 2019, a freely available booklet made a major contribution to understanding the physical and ecological geography of the Earth. A New Map of Global Islands publication maps hundreds of thousands of islands in greater detail than previous efforts. The islands range in size from continental mainlands to tiny islets smaller than a fraction of a square kilometer. The work was done at a very fine spatial resolution, and the shorelines and islands were mapped from hundreds of 2014 Landsat images.
The publication also describes an elegantly simple tool, the Global Island Explorer, which was designed as a window for users of the data. The Global Island Explorer is an online visualization and query tool, which allows anyone with an internet connection to explore any of the world’s islands in an easy to use app. Further opportunities are also available for developing machine learning and AI approaches to updating the vector shoreline.
As a new product from USGS and Esri at 30 m spatial resolution, it is the finest global shoreline product available in the public domain. Research and development opportunities are abundant with respect to reconciling the new vector shoreline with existing Digital Elevation Models (DEM) with an aim to improving the accuracy and spatial resolution of DEMs.
A devastating flood disaster occurred in Sri Lanka in late May 2017, leaving over 300 people dead or missing. Since further flood damage was anticipated, The University of Tokyo (U-Tokyo) and the International Centre for Water Hazard and Risk Management (ICHARM) developed a prototype of flood hazard information system to provide real-time flood monitoring and forecasting for Sri Lanka using the Data Integration and Analysis System (DIAS) developed by Japan.
DIAS/U-Tokyo/ICHARM provided support for the post disaster assessment using the integrated flood monitoring and forecasting system. DIAS is an advanced GEOSS-compliant e-infrastructure component that addresses the challenges of a large increase in the volume of Earth observation data by developing a core system for data integration and analysis.
The system integrates ground and satellite precipitation data, rainfall forecasting data, results of flood inundation analysis and forecasting. This system provided the information in real time to the related organizations of the Sri Lankan government.
Since further flood damage was anticipated during the rainy season after the heavy flood in May 2017, there was an urgent need to acquire the latest information for preventing recurring disasters and assisting the country in emergency response and post disaster restoration.