The Namibia Flood Dashboard Satellite Acquisition and Data Availability through the Namibia Flood Dashboard Matt Handy NASA Goddard Space Flight Center NOAA Satellite - April 11, 2013 - College Park MD
The Problem • Severe flooding in Namibia (south-western Africa) with little warning • Existing flood warning models are not very precise - Advance warning would reduce loss of life and property • NASA’s EO-1 mission & research stakeholders want to test the application of Sensor. Webs in a decision support tool for disaster management • United Nations coordinated a partnership between stakeholders The solution: Apply NASA’s technology to create the Namibia Flood Dashboard software to benefit the Namibian population. 2
Introduction • What is a Sensor. Web? • • • Takes disparate data feeds and unites them in cohesive display Used to make analyzing and responding to large datasets easier Area of research to EO-1 team for use in analysis of image products Namibia Flood Dashboard is an example of a Sensor. Web, and represents an extension of a decade of EO-1 research Project Objectives • Aggregate information sources –> better situational awareness / decision making • Integrate and compare data feeds -> enhanced analysis capability • Disseminate information -> wider availability of data products and analysis • Rapid configuration and deployment-> software can be rapidly applied to diverse situations 3
Architecture Cloud – Attached Storage 120+ TB The Cloud “Hot Copy” of EO-1 Data Products OCC MATSU Cloud (Chicago) Flood Dashboard Layers & Data Sources The Flood Dashboard Instance (primary – runs services) The Flood Dashboard Instance (secondary 1 of n – serves data only) Dependent Services (Web Coverage Processing Service) (Virtual Machine) FTP Data Ingest HTTP Proxy Users Internet EO-1 Image Upload 4
Operations Concept NASA / EO-1 Team Department of Hydrology, Namibia Open Cloud Consortium Joint Research Center (JRC) / Global Disaster Alert and Coordination System (GDACS) University of Maryland 5
Tool Overview • Bulletin System (current and archive) • Google Maps/Earth powered geospatial data display • River gauge station graphing and comparison • Automated EO-1 Tasking (plans for more spacecraft) 6
Geospatial Display (The Big Map) 7
EO-1 Observation Request Tasking • Tasking request generated based on alerts • Request either acted upon or not based on priorities • Goal – task multiple satellites for specific observations and bring more sensors into web 8
EO-1 Tasking - Architecture 9
Tool Capabilities • Automated EO-1 Tasking • Rapid delivery of technical information through bulletins • Access to EO-1 Advanced Land Imager (ALI) data products • Access to other satellite data products • Correlation with infrastructure details • Graphing and comparison of river levels • Plans to allow even more powerful comparisons, such as retrieval of satellite products based on ground data comparison 10
River Gauge Stations Start with manual “Yardstick Measurements” 11
Map Layers Infrastructure, MODIS Flood Maps, Flood Classified Images • Establish trust in remote sensing models (ground validated) • Integrate trusted models into decision process • Goal – better, faster decisions 12
Area of Study: Reeds & Flooded Region • Determining flooded area is complicated by vegetation • Satellite sensors may do better job than visual spotchecks • Ground validation used to refine models 13
Future Plans • Enable direct daily uplink of river levels • Add hydrograph to satellite cross-indexing of data products • Add Open Street Map (OSM) layer display to supplement Google Maps / Earth • Add Tropical Rainforest Measuring Mission (TRMM) Precipitation Accumulation Calculator • (Stretch goal) Direct social impact • i. e. – tracking and response to hippo attacks 14
Summary • Dashboards for Sensor. Webs allow flexibility and rapid integration • Cloud technology allows huge datasets, rapid processing, and improved reliability • Sensor. Webs will continue to improve satellite tasking • Global benefit for many remote sensing applications 15
A Last Thought: Our technology makes a big difference in people’s lives! 16
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