The NASA Applied Sciences Disasters Program comprises a multidisciplinary portfolio of research projects. In 2015, the NASA ROSES 2011 project portfolio contained ten projects that were in year two of the full-scale applications development phase. The projects are listed in the table below.
Since Disasters is a unique area of Applied Sciences which also utilizes its project’s applications products to the greatest extent possible to actively support Disaster planning, response and recovery, a secondary portfolio of projects comprising other core capabilities is also maintained. These activities which were covered in 2015 are listed in the table below.
Disaster Assessment and Response
Damage Assessment Map from Interferometric Coherence
Principal Investigator: Sang-Ho Yun
This project develops algorithms to produce reliable damage detection maps of natural disasters using Interferometric Synthetic Aperture Radar (InSAR) coherence, which will guide decision making, disaster assessment, response and recovery activities of international, federal, state and local agencies, including the World Bank and USGS. Our algorithm provides a day-and-night and all-weather synoptic view of damage detection map covering a few thousand square kilometers from imaging radar mounted on a spacecraft/aircraft, enables rapid response, providing decision support information to key partners and stakeholders for timely situational awareness.
Disaster Response and Analysis through Event-Driven Data Delivery (ED3) Technology
Principal Investigator: Sara Graves.
ED3 integrates a disaster data preparedness cyber-infrastructure with end-user/stakeholder decision and situational awareness systems to improve and automate data delivery and provide more timely and better information for disaster preparedness and response. Working with stakeholders, the Event Driven Data Delivery (ED3) technology will be integrated into end-user systems at state, national and international-level disaster responses
Enhancement of the NWS Storm Damage Assessment Toolkit with Earth Remote Sensing Data
Principal Investigators: Gary Jedlovec and Andrew Molthan.
The Damage Assessment Toolkit (DAT) operates on handheld, GPS-equipped devices such as smartphones and tablets, allowing for geo-tagged photography, downloading of satellite imagery, and geospatially referenced satellite products to be used by meteorologists to complete the damage assessment of a given tornado, including path length, width, and maximum intensity. In addition to tornado damage assessment, satellite imagery can be used to evaluate and map damage associated with other severe weather hazards, such as large hail and strong, damaging, straight-line winds.
Earthquakes and Earthquake-induced Tsunamis
Developing Global Building Exposure for Disaster Forecasting, Mitigation, and Response
Principal Investigator: Ronald Eguchi
This multi-year, multi-institutional project addresses the Applied Sciences Program goal of integrating earth science data and information for disaster forecasting, mitigation and response; specifically by delivering EO-derived built environment data and information for use in catastrophe (CAT) models and loss estimation tools. CAT models and loss estimation tools typically use GIS exposure databases to characterize the real-world environment. These datasets are often a source of great uncertainty in the loss estimates, particularly in international events, because the data is incomplete, and sometimes inaccurate and disparate in quality from one region to another. Applying this knowledge within the framework of a Global Exposure Database (GED) will significantly enhance our ability to quantify building exposure, particularly in developing countries and emerging insurance markets. The project team brings together leaders from the insurance industry, as well as from the Global Earthquake Model (GEM) initiative to assess the commercial viability of these products for assessing risk, particularly in developing countries, and to help develop insurance products that more accurately characterize property and casualty exposure. Global insurance products that have a more comprehensive basis for assessing risk and exposure - as from EO-derived data and information assimilated into CAT models and loss estimation tools - will help to transform the way in which we measure, monitor and assess the vulnerability of our communities globally, and in turn, and help encourage the investments needed - especially in the developing world - stimulating economic growth and actions that would lead to a more disaster-resilient world.
GPS-Aided and DART-Ensured Real-time (GADER) Tsunami Early Detection System
Principal Investigator: Tony Song
The GADER project demonstrates and integrates two existing GPS-aided alert system of NASA and tsunami monitoring DART system of NOAA for tsunami early detection. The combination of these two existing real-time systems (NASA and NOAA) offers the best solution for early detection of tsunami hazards and early cancellation of unnecessary false alarms. Nearby ocean-based DART measurements of tsunami height will be assimilated into the system based on the recently tested all-source Green’s function to verify the GPS-aided alert scale. Most of the tsunami victims are local and reduced false alarms and increased reliability will allow more timely and accurate warnings. The combined NASA and NOAA GADER systems will improve near-field early warnings and save lives.
Using real-time GPS/seismic displacements to improve disaster management and decisions pertaining to rapid assessment of structural risk and damage from earthquakes
Principal Investigator: Yehuda Bock
This project applies NASA-funded hazards research to structural health monitoring and damage prognosis of large engineered structures such as bridges, dams and tall buildings, and hospitals with the goal of transferring the technology to targeted end users. It is developing protocols for effective transmission of information from researcher to users such as Caltrans and Offices of Emergency Services to guide preparedness, mitigation and response in order to provide timely data and information to key partners and stakeholders to inform decisions that saves lives, reduce disruption to economies, production loss, and speeds recovery. The project is transferring NASA-funded seismogeodetic methodology developed for real-time earthquake and tsunami early warning systems to targeted end users to save lives and reduce damage to critical infrastructure.
Flood and Inundation
Enhancing Floodplain Management in the Lower Mekong River Basin Using NASA Vegetation and Water Cycle Satellite Observations
Principal Investigator: John Bolten
The existing Soil and Water Assessment Tool (SWAT) used for flood forecasting and floodplain management of the Lower Mekong River Basin is prone to errors due in part to outdated Land Use/ Land Cover (LULC) mapping (i.e., the current map was derived from 1997 data), unrealistic characterization (and lack of in-situ observations) of Available Water Capacity (AWC), flooding extent, and inability to quantify the effects of changes in basin dynamics. This project seeks to enhance regional planning and cooperation for water resources in the Lower Mekong Basin by delivering enhanced and updated products in Soil Moisture, Evapotranspiration (ET), Land Use / Land Cover (LULC), Soil Hydrologic Parameters (SHP), Flooding, and Suspended Sediment to the Mekong River Commission (MRC). The project is also building a customized Graphic Visualization Tool (GVT) to work in concert with the output of the SWAT model parameterized for the Mekong Basin as an adjunct tool of the Mekong River Commission (MRC) Decision Support Framework. Project Mekong is establishing a long-term collaboration between NASA, USGS, educational institutions (Texas A&M, University of South Carolina) and several stakeholders in the Lower Mekong River Basin (LMRB), including the MRC. The project is envisaged to provide improved floodplain modeling, management, and flood mitigation and decision making for the entire LMRB, which could positively impact millions of people who live in the region.
Near Real Time Flood Inundation Prediction and Mapping for the World Food Program, GeoSUR, and USAID/OFDA
Principal Investigator: G. Robert Brackenridge
The goal of this project is to produce specialized products, based on processing NASA orbital sensor data to map floods in near real time, and transfer these products to operations as high-end decision support assets for partners and end users. The planned operational data products include near-real time optical flood mapping, microwave-based river discharge measurements, flood risk maps, and flood extent (inundation) prediction. This fundamental flood mapping product is used extensively by Flood support research and response communities including 3 of our currently active projects.
Real-time Global Flood Analyses and Forecasts Using Satellite Rainfall and Hydrological Models
Principal Investigator: Robert Adler
The Global Flood Monitoring System (GFMS) was developed under NASA ROSES DISASTERS 20008 using TRMM/TMPA precipitation and land Surface and routing models to support Disaster Response Plan. It is a core ASP Disasters capability for flood events. Real-time results at flood.umd.edu. The Global Flood Monitoring System (GFMS) is currently using NASA multi-satellite precipitation data and the Dominant River routing Integrated with VIC Environment (DRIVE) hydrological model system. The next phase of the implemented system will utilize Global Precipitation Measurement (GPM) multi-satellite data, involve Numerical Weather Prediction (NWP) forecast precipitation information to extend the calculations a few days into the future, and involve improved model elements (e.g., high resolution inundation maps, a dam/reservoir module) and improved data displays and access capabilities. NASA-based data sets of precipitation (TRMM and GPM), land surface characteristics (e.g., soil moisture, elevation, land cover), and hydrological and meteorological models will all be used to improve global and regional flood detection/monitoring/forecasting information used as input to decision making processes for use in disaster management, response, preparedness and mitigation activities.
Global Flood and Landslide Monitoring and Forecasting
Principal Investigator: Frederick Policelli
This project is designed to improve reliability of MODIS Flood Map products by addressing intrinsically difficult data challenges presented by false positive elements in the data due to terrain, cloud and other effects. This Project spun-off from earlier work supporting the Adler and Breckinridge Flood projects. The experimental system is running at http://oas.gsfc.nasa.gov/floodmap/ . It is also used substantially as a first-guess field for rapid response to flood by our Near Real Time Flood Inundation Prediction and Mapping project.
A Remote-sensing-based Flood Crop Loss Assessment Service System (RF-CLASS) for Supporting USDA Crop Statistics and Insurance Decision Making
Principal Investigator: Liping Di
The goal of this project is to fully develop, evaluate, and operate a remote-sensing-based flood crop loss assessment service system, the RF-CLASS, for supporting crop statistics and insurance decision-making in two USDA agencies, the National Agricultural Statistics Service (NASS) and Risk Management Agency (RMA). This project will greatly improve the efficiency and effectiveness of flood-related crop decision-making at USDA, shortening by a factor of at least 10 the time needed for the decision makers to obtain decision-support information, and significantly improve the objectiveness and reduce cost for decision-making.
Development of and Integration of a High Resolution 2-D flood Model with Satellite Flood Data
Principal Investigator: Guy Schumann.
Focusing on the cities of Houston and Dallas, and the town of Cuero, the major thrust of this project is to build a high-resolution two-dimensional hydrodynamic model that will simulate the “best” inundation re-analysis of the flood events in locations (including urban settings) where LiDAR floodplain data and gauged rainfall and river flow data are available; and to integrate the model event re-analysis and the satellite flood data to demonstrate the uniqueness of these available multi-temporal and multi-resolution imagery in combination with the model.
Volcanic Eruptions and Effluents
Deformation monitoring of volcanoes in the Caribbean and Latin America using ALOS-PALSAR and Sentinel-1 radar interferometry,
Principal Investigator: Falk Amelung
This project develops ground deformation data from SAR imagery for the volcanoes in Latin America and in the Caribbean and guides local and regional geohazard monitoring agencies in using InSAR data for decision-making and disaster risk reduction. The operational objective is to automatically generate InSAR data in a form that is useful for the monitoring agencies. The long-term science objective is the development of physical models for eruption forecasting including geodetic data. This project will facilitate the use of satellite-based deformation data for volcano monitoring by the geohazard monitoring agencies in the region. The InSAR data will contribute to decision making in crises situations, contribute to disaster risk reduction and ultimately save lives.
Near Real-time Volcanic Cloud Products for Aviation Alerts
Principal Investigator: Nickolay Krotkov.
This project collects and integrates Near Real Time (NRT) and Direct Readout (DR) satellite volcanic cloud imagery with Volcanic Ash (VA) and SO2 models and demonstrates value-enhanced products for the European Support for Aviation Control Service (SACS) and NOAA VA Advisory Centers in Washington and Alaska. These agencies disseminate VA warnings to federal regulators, air navigation service providers, and airlines via the Volcanic Ash Advisory Centers (VAAC’s) Data Distribution System and public web sites. The project in conjunction with Direct Readout Laboratory and NASA’s SNPP Ozone science team supports local very fast delivery (within 20 minutes) processing of OMPS DR volcanic data from multiple daily overpasses at 2 high latitude receiving stations: Sodankyla, Finnish Meteorological Institute and GINA/UAF in Fairbanks, Alaska. More information and downloads of the data in Near-Real-Time are publicly available from the dedicated web sites: http://so2.gsfc.nasa.gov or http://sampo.fmi.fi/volcanic.html .
SAR-VIEWS: SAR Volcano Integrated Early Warning System
Principal Investigator: Franz Meyer
This project strives to overcome the limitations of current operational volcano monitoring systems by developing volcano hazard information from weather and illumination-independent SAR imagery, and adding this information to existing volcano monitoring systems through customized plugins. It accomplishes this by operationally integrating radar remote sensing data into volcano monitoring systems in order to (a) improve performance of existing volcano disaster management systems by adding the 24/7 monitoring capabilities of spaceborne radar and (b) improve ability to forecast, monitor, manage, and mitigate volcanic hazards on population centers and global aviation routes. Currently identified end users of the SARVIEWS service include the volcano monitoring centers, Alaska Volcano Observatory and V-ADAPT (Volcanic Ash Detection, Avoidance and Preparedness for Transportation) Inc., as well as the Alaska Satellite Facility remote sensing data center (ASF NASA DAAC).
UAVSAR Norwegian oil-on-water exercise campaign for advanced SAR- based oil characterization (NORSE2015):
Principal Investigator: Cathleen Jones
This rapid response project established a collaboration with Norwegian researchers at UiT – The Arctic University of Norway and the Norwegian Meteorological Institute to improve the U.S.’s capability for oil spill emergency response. The study advances SAR-based oil slick identification and classification. The collaboration allows NASA to participate in mineral oil release exercises undertaken by the Norwegian Clean Seas Association for Operating Companies in the North Sea, in which validation data can be acquired for algorithm development in oil slick characterization based on slick oil content.