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The Namibia Flood Dashboard Satellite Acquisition and Data Availability through the Namibia Flood Dashboard
Matt HandyNASA 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 SensorWebs in a decision support tool for disaster management
•United Nations coordinated a partnership between stakeholders
2
Introduction
•What is a SensorWeb?• 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 SensorWeb, 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
4
The Cloud
OCC MATSU Cloud (Chicago)
The Flood Dashboard Instance (primary – runs services)
(Virtual Machine)
Cloud – Attached Storage 120+ TB
Flood Dashboard Layers & Data Sources
“Hot Copy” of EO-1 Data Products
The Flood Dashboard Instance (secondary 1 of n – serves data only)
(Virtual Machine)
Dependent Services (Web Coverage Processing Service)
(Virtual Machine)
FTP Data Ingest
HTTP Proxy InternetUsers
EO-1 Image Upload
Operations Concept
5
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
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
EO-1 Observation Request Tasking
8
• 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
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
Map LayersInfrastructure, MODIS Flood Maps, Flood Classified Images
12
• Establish trust in remote sensing models (ground validated)
• Integrate trusted models into decision process
• Goal – better, faster decisions
Area of Study:Reeds & Flooded Region
13
• Determining flooded area is complicated by vegetation
• Satellite sensors may do better job than visual spot-checks
• Ground validation used to refine models
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 SensorWebs allow flexibility and rapid integration
• Cloud technology allows huge datasets, rapid processing, and improved reliability
• SensorWebs will continue to improve satellite tasking
• Global benefit for many remote sensing applications
15