Remote Sensing Data Currently Available for Vector-borne Disease Modeling Penny Masuoka Uniformed Services University NASA Goddard Space Flight Center Katalin Kovacs Science Systems and Applications Inc. NASA Goddard Space Flight Center

Overview of Presentation Multispectral Imagery – what’s available and what to order Data Products - Weather Products - Vegetation Indices - Digital Elevation Models - Land Cover Example Imagery and Data Products: MODIS

Low resolution Greater than 200 meter pixels Country or continental scale studies Medium resolution 10 – 100 meter pixels Regional studies High resolution Less than 10 meter pixels Local studies Which Images to Use?

Low Resolution - MODIS Image of Nile Delta - 1 km pixels

Pyramids Medium Resolution Cairo, Egypt Subset of Landsat Thematic Mapper False Color IR 30 meter pixels

High Resolution IKONOS image of the pyramids 1 meter pixels

The Resolution Trade Off As spatial resolution increases: Spectral resolution (number of bands) decreases File size increases Cost increases Lesson: Use the lowest resolution data that will get the job done. How do you decide which resolution?

Example: Estimating Mosquito Habitat Area in Korea Landsat – 30 meter pixels IKONOS – 4 m pixels Estimate of Habitat Area: 5,712,291 m2 Estimate of Habitat Area: 5,909,175 m2 Source: David Claborn and Penny Masuoka

Either Landsat or IKONOS images could be used to estimate mosquito habitat in Korea, but to cover a 100 by 100 km. area: Sensor Cost Landsat ETM+ $600 ($600 per 185 x 185 km. scene) IKONOS $403,000 ($40.30 per sq. km.*) *Price varies depending on location. ($27 per sq. km. in Central America) DoD buyers must purchase through the National Geospatial-Intelligence Agency ( and cost may be higher, but any imagery that they already have is free.

Current High Resolution Multispectral Sensors SatelliteBandsResolutionTo Order IKONOSBlue, Green, Red, NIR, Pan 4 m. (multi) 1 m. (Pan) QuickBird 2Blue, Green, Red, NIR, Pan m. (multi) cm. (Pan) Orbview 3Blue, Green, Red, NIR, Pan 4 m. (multi) 1 m. (Pan) Still in check-out phase. Products will be available from SPOT 5 HRV-IR Green, Red, NIR, MIR 5 m. (pan) 10 m (multi) 20 m (MIR)

A Few Current Medium Resolution Multispectral Sensors SatelliteBandsResolutionTo Order Landsat Enhanced Thematic Mapper + 7 multispectral, Pan m (multi) 15 m (Pan) /EarthExplorer/ EO-1 Advanced Land Imager 9 multispectral, Pan 30 m (multi) 10 m (Pan) /EarthExplorer/ SPOT-4Green, Red, NIR, MIR, Pan 20 m. (multi) 10 m. (Pan) Terra ASTERGreen, Red, NIR, 6 SWIR, 5 TIR meters v/imswww/pub/imswelcom e/index.html IRS-P6 LISS3 Green, Red, NIR, MIR 24 metershttp:// om/products/irs/index.htm

A Few Current Low Resolution Multispectral Sensors SatelliteBandsResolutionTo Order Terra MODIS Aqua MODIS 36 bands250–1000 mhttp://redhook.gsfc.nasa.gov/i mswww/pub/imswelcome/ind ex.html SPOT 4 and 5 Vegetation Blue, Green, Red, NIR, MIR 1000 mwww.spot.com ENVISAT-1 AATSR Green, Red, NIR, SWIR, 3 TIR 1000 metershttp://envisat.esa.int/dataprod ucts/availability/ NOAA AVHRRRed, NIR, 3 TIR 1100 metershttp://edcdaac.usgs.gov/1KM/ 1kmhomepage.html or arthExplorer/

What we didn’t cover: Radar Future Satellites Others: BILSAT,CBERS CCD, CBERS IR-MSS, CBERS WFI, ENVISAT MERIS, EROS A, ERS ATSR, IRS MOS, IRS WiFS, IRS OCM, Priroda/Mir MOMS, Priroda/Mir MSU-SK, Resurs OI N2 MSU- SK, etc. Find out more about these and others on this NASA website:

Data Products Derived from Remote Sensing Data: Weather Products (Precipitation, Temperature, Humidity) Vegetation Indices Digital Elevation Models Land Cover

Weather Products Uses: Can monitor conditions that affect vector-borne disease outbreaks. Source: NCEP/NCAR Reanalysis data (derived from ground stations, satellite data and weather models): Total precipitation January - March.Total precipitation July - September.Average temperature January - March.Average temperature July - September. Source: Andrew Au, NASA

Vegetation Indices Uses: A high vegetation index indicates wet conditions that can be associated with a disease outbreak (such as Rift Valley Fever). Sources: MODIS vegetation indices: AVHRR Normalized Difference Vegetation Index (NDVI):

Digital Elevation Models (DEM) Uses: Elevation can be a limiting factor in the location of a disease. Sources: NASA Shuttle Radar Topography Mission GTOPO30 Global Land One-Kilometer Base Elevation (GLOBE) Low High

Land Cover Uses: Land cover maps can be used to define vector habitat areas. Source: Land cover map

Bartonellosis occurs in agricultural areas at elevations between 1800 and 3400 meters. Weather data could be added to this model to decide when and where to spray pesticides. ElevationLand Cover Areas at Risk for Bartonellosis Example of Using Data Products: Bartonellosis in Peru

MODerate-resolution Imaging Spectroradiometer –Spatial resolution: 250m (2 bands), 500m (5 bands), 1km (29 bands) –36 spectral bands (Visible-IR) –Is on Terra and Aqua spacecraft –40 science products with daily volume of 880GB per instrument –Available for free on the internet Example: MODIS

MODIS Products MODIS Ocean Land Atmosphere Products: MOD36 Ocean Color MOD28 SST … Products: MOD04 Aerosols MOD05 Water Vapor MOD06 Cloud MOD35 Cloud Mas … Products: MOD09 Reflectance MOD12 Snow Cover MOD13 Vegetation MOD14 Thermal Anomaly … To learn more go to:

MOD09 image Smoke over the Eastern US Source:

MODIS Data products come in different Spatial Resolution 250 m 500 m 1000 m 4 km 5 km 5 min 0.05 deg 0.25 deg Etc… Temporal Resolution Versions daily 8-day composites 16-day composites 96-day composites Etc… Version3 v003 Version4 v004 To learn more go to: ~But most products do NOT come with all these resolutions and versions~

… and Each MODIS Data product has several data layers (some have 2 and some have 13 or more) For Example the MOD13 500m product has 11 bands: 1 500m 16-day NDVI 2 500m 16-day EVI 3 500m 16-day NDVI Quality 4 500m 16-day EVI Quality 5 500m 16-day Red Reflectance 6 500m 16-day NIR Reflectance 7 500m 16-day Blue Reflectance 8 500m 16-day MIR Reflectance 9 500m 16-day average view zenith angle m 16-day average sun zenith angle m 16-day average relative azimuth angle

What’s in a Name? MOD13A1.A h11v hdf Product short name Spatial Resolution (in general) A2=1km A1=500m Q1=250m Acquisition date year day of year format SIN tile location horizontal and vertical version Hi! I’m Bob. Terra MODIS product names start with MOD Aqua MODIS product names start with MYD

What are SIN and ISIN GRIDs? Source: Robert Wolfe

To order data go to: To learn how to order data: Confused? Data products Data Sets Data Granules Tiles SIN ISIN Bins Try the Frequently Asked Questions Page: Layers Bands v003 v004 search order

Once you find the data, you will need to Free HDF tools: Free Projection tool: Get it on your computer  use FTP or read it in from media (8mm tape, CD, DVD) View it  use a free HDF tool or an image processing software Reproject it  use the free MODIS Reprojection Tool or GIS software

At this point you may be saying: Help!!! I don’t know how to process remote sensing data! NASA may be able to help…

Example Customized MODIS Products for Federal Agencies: MODIS Rapid Response System History –Summer 2000, MODIS images used by US Forest Service in fighting fires in Montana/Idaho –2001 Rapid Response system produced automated, near real time MODIS images for monitoring fires and other natural hazards –Products are posted on the web within 3-6 hours from acquisition Applications and Customers –US Forest Service - active fire locations –USDA Foreign Agricultural Service – monitoring crop stress and drought –Department of Defense – mission planning/monitoring weather and dust in Afghanistan and Iraq –Environmental Protection Agency – monitoring hazardous material sites, Iraq war (smoke)

Example MODIS Rapid Response Product Fires in Australia Source: Feb 4, 2003

Discussion There are many remote sensing images and products currently available. Use of the images/products requires a great deal of expertise. Partnerships between remote sensing scientists, modelers, entomologists, etc. will be necessary to create effective decision support systems for disease prevention.

Thanks to the following people for helpful suggestions on this talk: Robert Venezia Timi Vann Brian Zeichner Richard Wells