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

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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

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Architecture

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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

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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)

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Geospatial Display(The Big Map)

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EO-1 Observation Request Tasking

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• 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

EO-1 Tasking - Architecture

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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

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River Gauge StationsStart with manual “Yardstick Measurements”

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Map LayersInfrastructure, MODIS Flood Maps, Flood Classified Images

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• Establish trust in remote sensing models (ground validated)

• Integrate trusted models into decision process

• Goal – better, faster decisions

Area of Study:Reeds & Flooded Region

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• 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

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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

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A Last Thought:Our technology makes a big difference in people’s lives!

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