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Remote Sensing in Natural Hazard Monitoring Bruce H. Ramsay*, William Y. Tseng* , George Stephen*, Robert Fennimore*, and Fernando Sotelo** *NOAA/NESDIS, Washington, D.C. 20233, USA **Litton PRC, GOES Data Distribution System Support Contractor, Washington, D.C. 20233, USA Table of Contents

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Remote sensing in natural hazard monitoring l.jpg

Remote Sensing in Natural Hazard Monitoring

Bruce H. Ramsay*, William Y. Tseng* , George Stephen*, Robert Fennimore*, and Fernando Sotelo**

*NOAA/NESDIS, Washington, D.C. 20233, USA

**Litton PRC, GOES Data Distribution System Support Contractor, Washington, D.C. 20233, USA


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Table of Contents

  • Introduction

    • OSEI Purpose

    • Support Policies

    • Defining a Natural Hazard/Significant Event

  • The Process

    • Identification of Significant Events

    • Notification Protocols

    • The Acquisition of Imagery

    • The Analysis of Imagery

    • The Production of Imagery

    • Quality Control


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Table of Contents (Cont’d)

  • Storage and Dissemination of Data

    • Website Control and File Management

    • The Daily Report

  • Recent Event Supports and Examples


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Introduction

  • OSEI Purpose

    • To produce and analyze remotely sensed imagery, in near real-time, from satellite sensors, for the identification and monitoring of short-term natural and anthropogenic hazards.

  • Imagery available in NOAA: NOAA-AVHRR, GOES, GMS, DMSP-SSM/I, TOMS-EP, METEOSAT, RADARSAT SAR, and SeaWiFS

  • Support Policies

    • Operational support

    • All locations in the world

    • Near real-time and quick turn around after an event


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Introduction (Cont’d)

  • Defining a Natural Hazard/Significant Event

    • “Significant” depends upon one’s point of view.

    • “Significant Events” are defined not just in context with situations which produce spectacular remotely-sensed images, but also with those that have a severe impact to lives and property.

    • Typical Significant Events: Fires, Smoke, Volcanic Eruptions and Ash Plumes, Tropical Cyclones, Floods, Oil Spills, Snow Cover or Ice, Dust Storms, Tornado Outbreaks, Storms, and Red Tides.


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The Process

  • Identify Significant Events

  • Define Significance and Classify the “Significance Threshold (ST)” from ST0 (lowest) to ST3 (highest).

  • Notify the OSEI Team Members

  • Acquire, Analyze and Produce Imagery

  • Quality Control

  • Store, Disseminate and Archive Data

  • Daily Operational Significant Event Support Report


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Recent Event Supports and Examples

  • Fires: Southeast Asia, Australia, Brazil, Canada, Central America, Guyana, Indonesia, Mexico, the Philippines, and the United States

  • Dust: Africa, East Asia, MidEast and the Pacific Ocean

  • Floods: Afghanistan, Argentina, Kenya, Pakistan, Peru, Somalia, and the United States

  • Snow Cover or Ice: The Antarctic, and the Arctic, Canada, and the United States


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Recent Event Supports and Examples (Cont’d)

  • Storms, Tornado, or Tropical Cyclones: Canada, Europe, Japan, India, Russia and Georgia, the Philippines, the United States, etc.

  • Smoke: SE Asia, Japan, South America, Italy, and the United States

  • Volcanic Eruptions: Hawaii, Soufriere Hills, Kamchatka Peninsula (near Bering Sea), Montserrat, Pavlof, and El Arenal (in Costa Rica)

  • Oil Spills: Coastal oceans off Pakistan, Japan, and Paraguay

  • Red Tides: Hong Kong


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Future Plans

  • Implement 7X24 support.

  • Implement “list serve” capability.

  • Develop automate data acquisition/event identification/classification system - Hazard Mapping System (HMS).

  • Develop a simple and more friendly image processing software.


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Discussion and Conclusion

  • All imagery supports are emphasized on the operational objective, rather than on the scientific purpose.

  • Since the turn-around time of imagery support for an event is about three hours, a detailed study of the imagery is impossible. Therefore, a bias in the event location and reality is inevitable.


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Discussion and Conclusion (Cont’d)

  • Ground truth data are usually not available when the imagery is issued and disseminated to the customer.

  • The imagery support, however, has still been proved to be a very useful, economical, and effective method for identifying and monitoring larger-scaled events.


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