1 / 9

Smoke Characterization from MODIS Imagery

Smoke Characterization from MODIS Imagery. Buck Sharpton Kevin Engle GINA/ION University of Alaska Fairbanks. ION Node. Focus on real-time satellite reception – many passes per day Polar-orbiting satellites NOAA AVHRR MODIS (Terra & Aqua) SeaWiFS* GLI (ADEOS II)

karan
Download Presentation

Smoke Characterization from MODIS Imagery

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Smoke Characterization from MODIS Imagery Buck Sharpton Kevin Engle GINA/ION University of Alaska Fairbanks

  2. ION Node • Focus on real-time satellite reception – many passes per day • Polar-orbiting satellites • NOAA AVHRR • MODIS (Terra & Aqua) • SeaWiFS* • GLI (ADEOS II) • Basic and custom product generation and distribution • AmericaView *Distributed by NASA/GSFC

  3. Early Detection and Characterization of Boreal Wildfires • Operational: • Supply Fire Detection/Management Products to Alaska Fire Service, Tanana Chiefs • Developing Smoke Characterization Products (Smoke Health Index) • Research: • Smoke properties (fuel, fire, environment) • Carbon cycling

  4. Smoke Characterization: Approach • Refine & Extend MODIS AOT Algorithm • For use over bright, variable surfaces • To estimate smoke density, smoke height, particle size distribution. • Validate using field/laboratory instruments • Generate Smoke-Health Index Map • Translate smoke concentration into health risk • Add information on particulates and associated gases and aerosols of concern • Updateable and web-deliverable

  5. Smoke Characterization: Advantages • Real-Time MODIS, AVHRR, GLI reception • Up to 16 passes over station per day (Aqua, Terra) • Identical viewing geometry daily per instrument. • An active fire season each year • Partnerships with Fire Management Agencies • Expertise • Field sites and Laboratory Equipment

  6. Smoke Air Quality Index MODIS Smoke Scene Aerosol reflectances mixed with surface reflectances Band math (normalization) Clear Sky Scene Surface reflectances only Yields Scene with surface reflectances removed Aerosol Optical Thickness Convert AOT to Smoke Density Health-Risk Scene Based on translation schema in wide use by Health Officials

  7. Smoke Air Quality Index Steps: • Test, refine MODIS AOT • Field/laboratory measurements • Theoretical approaches • Convert AOT to Health Risk • Validate • Field monitoring • Client feedback • Deliver • Updateable several times daily • Deliverable through Internet • Graphical, flexible, useful

More Related