Project Portfolio

NASA Disasters Program 2019 ROSES Grant Awardees 

NASA Research Opportunities in Space and Earth Science (ROSES) proposal requests solicit basic and applied research to support the work of NASA’s Science Mission Directorate (SMD). A number of individual program elements — each with its own due dates and topics — cover a wide range of disciplines in space and Earth sciences that are supported by SMD. 

Awards to non-governmental organizations are made primarily as grants or cooperative agreements and occasionally as contracts, as the nature of the work and/or program requirements dictate. Awards to government labs are made as inter- or intra-agency transfers. 

The typical period of performance for an award is three years, but some programs may allow up to five years, and others specify shorter periods. Organizations of every type, domestic and foreign, government and private, for profit and not-for-profit, may submit proposals without restriction on teaming arrangements.

Below appear the awardees for ROSES grants made in 2019 by NASA’s Disaster Program.

Using Space-Borne Remote Sensing to Assess Hail Storm Risk 

Principal Investigator: Kristopher M. Bedka

Much of the world is impacted by severe thunderstorms, but whether they become disasters depends upon resilience: the capacity to prepare, mitigate, respond, and recover. Hail is the costliest severe weather hazard for the insurance industry, generating ~70% of severe convective storm losses in 2017 due to damage to assets such as homes, businesses, agriculture, and infrastructure. Most insurance companies do not reserve enough capital to cover catastrophes, so they acquire reinsurance. The reinsurance industry uses catastrophe models (CatModels) to statistically estimate risk to an insurer’s portfolio.

This project will create a framework for developing continental to global hail climatologies and CatModels based on NASA satellite data and capabilities. This is a collaboration between NASA’s Langley Research Center and NASA’s Marshall Space Flight Center, Willis Towers Watson (WTW) reinsurance, and partners in Brazil and Argentina. This project seeks to mitigate hail disasters over South America by aiding development of new satellite-based severe storm nowcasting tools by regional partners and developing climatologies to improve societal understanding of hail frequency. 

Work with WTW will improve socioeconomic resilience through development of new CatModels. Southern Brazil, Uruguay, Paraguay, and Argentina feature some of the most intense thunderstorms on Earth. South America is a developing insurance market of interest to WTW clients and is similar to other regions routinely impacted by hail that do not have comprehensive hail reporting or radars to assess hailstorm frequency.

Historical low Earth orbit and geostationary Earth orbit imagery and reanalysis data will be used to detect hailstorms and estimate their severity. Global lightning mapper data will be used to further improve hailstorm analyses. We will mature land surface imaging satellite methods for identifying hail damage to aid disaster mapping. Few satellite-based severe storm nowcasting methods have been developed to serve South America. The project team will demonstrate nowcasting capabilities with NASA datasets and provide training to regional partners to help them adopt these methods to improve warnings and mitigate disasters.

Advancing Access to Global Flood Modeling and Alerting 

Principal Investigator: Dr. Margaret T. Glasscoe

Obtaining highly reliable information about flooding events on a global scale currently requires the manual review and integration of multiple sources. There is a great variety of data, each part of which may or may not be relevant for a particular scenario, and each with different access mechanisms. Given that floods are both the most deadly and most costly natural hazards, this project will integrate flood inundation information from multiple sources into the DisasterAWARE (All-hazard Warnings, Analysis, and Risk Evaluation) platform, providing a single source of global information on floods that is supported by a common, normalized data model. End users will no longer be required to extract and merge data from multiple sources by hand as this will be done automatically by the middleware.

By using a model-of-models approach, which will include innovative new interferometric synthetic aperture radar. Using SAR-based sources as well as existing third-party sources, we will furthermore create a repository of flood information that will potentially be greater than the sum of its parts, providing higher levels of confidence and supplemental information than any single source. Our model-of-models approach will apply recent innovations in machine learning methods to create a unified picture that will become progressively better over time as more data become available.

Furthermore, by integrating with DisasterAWARE, the project will create a situational awareness tool that will specifically identify flood events and push this information to end users through various mechanisms. Conversely, end users will be able to register for notifications about events detected by the system in areas of interest. This will streamline the delivery of data and remove the requirements for navigating through multiple sources. End users will be presented with the data of interest immediately.

Enhanced Forecasting of Weather, Fire Behavior and Smoke Impact for Improved Wildland Fire Decision Making

Principal Investigator: Kyle Hilburn

The United States has entered a new era of increasing wildfire frequency and intensity, which has culminated in a number of devastating wildfire seasons over the past decade. This fire-prone landscape is also more densely settled and developed than in previous years, resulting in steeply rising fire-suppression costs. Although fire plays a crucial ecosystem role, its prevention can often lead to excessive fuel accumulation and catastrophic fires that are difficult to manage. 

There is a significant need for management decisions based on a multifaceted analysis of risks and benefits associated with wildfires and prescribed burns. Better and more advanced decision-support tools that integrate satellite/aerial remote sensing with economic data, with a coupled fire, weather, fuel and smoke modeling framework are therefore necessary.

The primary goal of this project is to significantly reduce the risks associated with wildland fires and their management through the development and deployment of new decision support and situational awareness tools. These new tools will focus on rendering the spatial and temporal variability of weather and fuel conditions, in addition to the two-way interactions between fire behavior, local weather, and smoke, which do not currently exist in operational fire management. The project aims to improve situational awareness and support decisions, especially for wildland fire incidents that are impacted by significant weather variability. Such incidents are difficult to handle with the current system, which utilizes hourly weather station data often located miles away from the fire.

Through a seamless integration of weather data, surface fuel moisture observations, satellite fire detections, operational numerical weather prediction models and a state-of-the-art high-resolution, coupled fire-atmosphere smoke modeling, the project will deploy novel integrated decision-support tools that designed to reduce the risk associated with wildfire management. The new system will provide a 3-D spatial representation of essential elements that affect fire behavior, including dead-fuel moisture, fire spread and smoke.

Identifying Critical Infrastructure Exposure for Disaster Forecasting, Mitigation and Response

Principal Investigator: Charles K. Huyck

Cities are complex systems with interconnected “lifeline networks” enabled by critical infrastructure, which can be severely damaged or destroyed in the aftermath of a natural disasters. Following Hurricanes Maria and Katrina and the Tōhoku earthquake and tsunami, for example, damage to critical systems resulted in cascading effects that severely impeded recovery and crippled regional economies. Geographic information system data gives the location of critical infrastructure (CI) and can be used to identify and mitigate damage, but in many cases the locations of key components are unmapped or unshared, particularly in developing countries.

Without pinpointing the physical location of key assets, it is not possible to identify where the regional risk from infrastructure disruption can lead to cascading damage that, in some cases, could significantly reverse progress in developing countries. This project builds on previous work to expand the ability to model the catastrophic impacts of infrastructure disruption by providing a foundation for CI exposure development in concert with remote-sensing Earth observations made via satellite. 

The project team will begin its work in India, and expand to developing countries globally, prioritizing based on end-user requirements. As with buildings, identifying lifeline networks is a data-fusion process requiring collection of existing datasets and use of segmentation and edge-detection algorithms. Data will be delivered openly and globally to developing countries and all those interested in risk, as well as integrated into commercial products for global risk identification and management. 

Development of Predictive Models to Improve Landslide Disaster Risk Reduction and Response 

Principal Investigator: Dr. Dalia Bach Kirschbaum

Landslides globally cause loss of life and lasting damage to critical infrastructure. A major rainfall or earthquake can trigger tens of thousands of landslides, compounding losses from damage to transportation networks that inhibit disaster response, resulting in cascading effects such as flooding and debris hazards. Despite their ubiquitous nature in many natural disaster scenarios, there is little integration of pervasive landslide impacts throughout the complete landslide disaster life cycle, including preparation, recovery and mitigation. 

This proposal supports key decision-making and resilience-building capabilities related to landsliding for a wide range of stakeholder partners, as well as publicly served data and models that will be available through partner websites. Our team will advance landslide forecasting using predictive models, satellite data and ground observation, including evaluation of landslide risk based on the hazard model outputs combined with exposure and vulnerability data. We address the impact of widespread landsliding triggered by disaster events, including tropical cyclones and earthquakes, where landslides are a significant secondary hazard interrelated with the effects of strong ground shaking and flooding. Importantly, we seek to cover a range of spatial and temporal scales that are relevant to our stakeholder decision making and response needs through an integrative approach of empirical and mechanical modeling. 

Given the dynamic nature of the proposed suite of tools, we anticipate that engagement in Disaster Program teams will provide new opportunities to integrate our efforts with other teams during natural disaster events that include multiple types of hazards, of which landslides play a pivotal role. We rely significantly on NASA data and directly incorporate multiple sensor types, platforms and spatiotemporal scales to model susceptibility, hazard and risk, and also incorporate cascading effects of landslides on other disaster phenomenon. 

Over the duration of this award, the proposing team will contribute nowcasts, forecasts, real-time updates on evolving hazards, and post-event data collection in support of rescue and recovery efforts and longer-term model improvement/validation. The stakeholder partners participating in this effort will directly contribute to and co-develop the products and tools to ensure a seamless transition and uptake within their decision- making systems. These efforts will provide improved situational awareness, disaster risk reduction, response and resilience of landslide hazards relevant to both scientific and stakeholder communities.

Day-Night Monitoring of Volcanic Sulphur Dioxide and Ash for Aviation Avoidance at Northern Polar Latitudes 

Principal Investigator: Dr. Nickolay A. Krotkov

The dangers that volcanic ash clouds pose on inflight air traffic safety can lead to prolonged flight cancellations that have a ripple effect on the airline industry’s economy and personal travel. Low-latency satellite observations provide crucial information for rerouting air traffic around volcanic clouds. During a previous Applied Sciences Program project our team established partnerships between the Geographic Information Network of Alaska, NASA’s Direct Readout Laboratory, NASA’s ozone processing team and the Finnish Meteorological Institute so as to locally process ultraviolet-based (UV) direct-readout (DR), satellite-observed volcanic ash and sulphur dioxide (SO2) data for distribution to a number of end users. 

The UV-based monitoring is unavailable at night or under low-light conditions, so users have requested that we expand the DR monitoring capabilities to better serve the ever-increasing number of flights that operate at night or fly over polar regions. Here, we will address this critical need by developing DR volcanic ash and SO2 products based on thermal infrared data acquired by several satellite-borne instruments. 

We will also critically improve and extend our DR-UV products to monitor smoke plumes from forest fires. These new products will be evaluated and assessed by several organizations, including the National Weather Service and the Alaska Volcanic Ash Advisory Center. Our collaborators from the Finnish Meteorological Institute will evaluate the DR plume products for use in the northern Atlantic, Norwegian, Barents and Kara Seas, and distribute them to European users. 

The combined use of ground-based stations and multiple satellite platforms, each with several overpasses over the polar region, will provide low-latency coverage of all high-latitude volcanoes in the Northern Hemisphere.

Enlisting Satellite Data to Modernize Local Tsunami Early Warning 

Principal Investigator: Dr. Diego Melgar

Compared to other natural hazards such as hurricanes or forest fires that annually propagate, large tsunamis are infrequent. As a result, over the last 50 years as digital geophysical instrumentation has matured, local tsunami warning systems that alert the coastlines immediately adjacent to a large event have not been a priority of national or international monitoring agencies. The incidence of return periods of large events are usually measured in many decades to centuries. Thus, local warning systems do not exist in the majority of countries located along subduction zones, including the United States. However, recent events in Indonesia, Chile, and Japan, have shown that despite their comparative rarity, tsunamis can lead to substantial casualties, potentially tallied in the tens to hundreds of thousands of lives, as well as to the total economic collapse of the affected regions.

Compounding the problem are steady increases in population in tsunami-prone areas over the last 25 years. Because evacuation start time is the most important variable in tsunami mortality rates, rapid tsunami information systems that forecast intensities at the local level in the first 5 minutes are essential in providing actionable information to emergency responders and decision makers to order evacuations in the affected regions as quickly as possible. All of these elements make the development of a rapid and accurate local tsunami warning methodology, and its implementation, a pressing issue which we propose to help solve in this work. 

The use of Global Navigation Satellite System (GNSS) displacement data in the near-field is a paradigm shifting technology thanks to its ability to track the motions of large earthquakes without going off-scale. Real-time, high-data rate GNSS networks are currently operational in many countries around the Pacific Rim. These networks were originally installed to measure long-term tectonic motions, and over time, were upgraded with higher sample rate receivers and robust telemetry. Because of this, these networks are primed to both record long-term tectonic motions and strong ground motions from nearby great earthquakes, often times with greater spatial density than complementary seismic networks. 

We will modernize near-field (local) operational tsunami forecasting and early warning through the addition of GNSS-derived earthquake source products and the seamless connection to already existing NOAA tsunami modeling codes. Extensive testing, both online and offline, will be performed using historical and synthetic earthquake datasets. These tests will help to guide modifications in the software and familiarize practitioners with the strengths and limitations of the different codes. Finally, using lessons learned, we will work with partners in Chile and New Zealand to guide them through the process of an operational GNSS-enabled tsunami early warning system.

Integrating Synthetic Aperture Radar Data for Improved Resilience and Response to Weather-Related Disasters

Principal Investigator: Dr. Franz Meyer

Weather-related hazards are ubiquitous in the United States, including: 1) hurricane storm surges impacting coastal areas, 2) rapid snow melt and heavy rainfall causing basin-scale flooding, 3) severe weather leading to flash floods and tornadoes, and 4) seasonal freeze and thaw of rivers that may lead to ice jams. Each of these hazards affects human settlements ranging from major cities to rural areas and has the potential to significantly impact agricultural productivity. In each setting, end-user partners engaged in disaster management need access to data-processing tools helpful in mapping past and current disasters to capture their impacts. Analysis of past events supports risk mitigation by understanding what has already occurred and how to alleviate those impacts in the future.

Similarly, having capabilities to generate the same products in a response setting means that lessons learned from risk analysis will carry forward to event response. Synthetic aperture radar (SAR) data are particularly useful for these activities due to their all-weather, 24/7 monitoring capabilities. However, complex processing and high computational costs associated with SAR require the development of approaches that streamline product generation.

To meet this need, this project will develop a cloud-based automatic data analysis toolbox for the processing of SAR data into value-added products that address the mapping of meteorological and hydrological disasters, such as heavy rainfall and flooding, as well as related cascading hazards, such as landslides and levee instability. The integration of these products into end-user decision-making workflows will improve capacity in the use of SAR in response situations. 

Furthermore, the SAR analysis tools will assist in preparing for and mitigating risk by allowing users to process image time series gathered from NASA Distributed Active Archive Centers or through their purchasing of commercial data. To ensure adoption of the developed technology by end users, we will partner with several organizations interested in managing and monitoring meteorological and hydrological disasters. Cost-sharing partners include the United States Department of Agriculture Foreign Agricultural Service and the National Weather Service Alaska-Pacific River Forecast Center. Beyond these cost-sharing partners, we will also work with the Federal Emergency Management Agency and private industry representative DuPont, who will both participate in the project but are legally prevented from providing cost share or letters of support.

Development and Implementation of Remote Sensing Techniques for Oil Spill Monitoring and Storm Damage Assessment

Principal Investigator: Francis Monaldo

The National Oceanic and Atmospheric Administration (NOAA) is responsible for monitoring coastal U.S. waters for accidental and deliberate oil spills, as well as for the emergency response and environmental assessment and restoration following such events. In addition, in the aftermath of severe storms, NOAA assesses the status of and potential damage to offshore platforms and pipelines, which are potential sources of significant oil leaks and marine debris. All-weather and high-resolution data from the enlarging set of spaceborne synthetic aperture radars (SARs) and high-resolution optical satellites offer an important opportunity for NOAA to exploit remote sensing for automated oil spill response and remediation and post-storm offshore infrastructure assessment.

Our work supports the hurricane/tropical cyclone primary hazard scenario with cascading risks, focusing on semi-automated oil-spill characterization that can direct response to areas with more oil; health status assessment of offshore platforms, especially small uncrewed ones; and identification of marine debris. Marine technological hazards are also addressed. 

Our work is intended to: 

  • Develop and mature automated oil spill detection and thickness estimates from SAR and optical imagery, based on focused field testing combined with in situ oil sampling, and incorporation of new sensors; 
  • Improve post-storm assessment of offshore oil and gas production facilities and marine debris; and
  • Implement new algorithms and databases in a semi-automatic system that NOAA uses operationally to detect and assess oil spills and post-storm offshore damage and debris. 

These products will be delivered through NOAA to enable rapid situational assessment and an optimized deployment of rescue and oil remediation and recovery resources.

We anticipate SAR instrumentation will provide a greater quantity of open data and more frequent coastal ocean imaging. For operational use, the data must be rapidly processed into products delivered to analysts, first responders, and people planning recovery and restoration efforts. It is critical that NOAA develop a validated assessment of the capabilities of the newer high-resolution sensors under different environmental conditions and for different oil properties in order to use them operationally for oil spill remediation. Combined oil extent and thickness products using all available data, both SAR and optical imagery, can provide significant improvements in both latency and reliability.

For post-storm assessment of the marine environment, there is also a need for a validated detection database of offshore platforms as a function of wind speed and sensor mode and type to support existing algorithms for the detection of offshore platforms and ships from remote sensing data. In a post-storm environment, this database will be used to assess whether the platform has actually moved or been destroyed or is simply undetectable in the given meteorological conditions. Similar well-understood relationships are needed between marine debris object size and detectability.

Using Spaceborne Synthetic Aperture Radar to Rapidly Map Global Damage

Principal Investigator: Dr. Sang-Ho Yun

Rapid mapping of natural disasters is of financial and humanitarian importance. Radar is an ideal tool for this, as it sees through clouds and can image day and night. The number of spaceborne synthetic aperture radar (SAR) sensors is increasing: anywhere on Earth, the first SAR satellite will overpass the affected area in less than 12 hours of a disaster occurrence.

Our team has seven years of experience using SAR data for rapid post-disaster mapping following earthquakes, hurricanes, volcanic eruptions, and wildfires, from proof-of-concept for the 2011 Christchurch earthquake in New Zealand to a functioning prototype for the 2018 Fuego volcano eruption in Guatemala. We have learned lessons on how we can better respond to future events. 

In the course of our work, we will address these, and complete automation of a system that will rapidly produce, validate, and deliver damage proxy maps — DPMs are maps derived from SAR data — and ground deformation maps to responding agencies around the world. These DPMs can indicate building damage and surface change due to natural disasters. By maturing the system, we aim to deliver these maps within 24 hours of data acquisition.

We will first focus on building and automating the DPM and tools for three SAR missions. This work opens the potential to explore additional tools such as translation of DPMs into a building damage ratio (that is, the cost of damage repair), and a post-disaster construction monitoring tool to support post-disaster needs assessments and recovery efforts. We will host tutorial webinars for disaster application response and recovery team partners and end-users. Then we will build product delivery pipelines collaborating with end-users. The toolbox will be designed to scale with more advanced algorithms and new missions as they deploy.


NASA Disasters Program Previous Project Portfolio

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. 

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 this table.

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.

These activities which were covered in 2015 are listed in this table.


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 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 . 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: or .

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).

Oil Spill

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.