Remote Sensing with Multispectral Scanners

Multispectral scanners

First developed in early 1970’s

Why use?

Concept: Gather data from very specific wavelengths

A passive sensor

Platform: Airplane or more usually a satellite

Sensitivity: Visible to Thermal IR (.4 – 12 m)

What is a Multispectral Scanner?

A group of electronic sensors that respond to specific wavelengths of ER

Multiple sensors collect data for different wavelengths

The sensors collect data across a wide swath of the earth’s surface

Multispectral Scanners: Resolution

Ground resolution of a scanner:

The size of an area on the ground that is imaged by one sensor at one moment (pixel)

First satellite borne scanner – LANDSAT 1:

1 pixel = 79 x 79 meters

Today’s satellites?

Smallest dimension of an object must be at least 2 times the system’s ground

resolution in order for it to be detectable

Multi-spectral Scanners: LANDSAT

Seven LANDSAT missions, starting in 1972

Originally developed by NASA, now under USGS

LANDSAT 5 and LANDSAT 7 still in use

A Closer Look: LANDSAT 7 Spatial coverage:

Orbits the earth from pole to pole,

Recording data for 185-km swath below it

“sun-synchronous orbit”: Always pass over each location on earth at the same time of day

Multispectral Scanner called “Thematic Mapper Plus” (TM+)

A closer look: LANDSAT 7

Bands:

•Band 1 Visible Blue (.45-.52 mm)

•Band 2 Visible Green (.52-.60 mm)

•Band 3 Visible Red (.63-.69 mm)

•Band 4 Near IR (.76-.90 mm)

•Band 5 Mid-IR (1.55 – 1.75 mm)

•Band 6 Thermal IR (10.4 – 12.08 mm)

•Band 7 Mid-IR (2.08 – 2.35 mm)

Snow and cloud cover

Mineral & rock types

Thermal mapping

LANDSAT 7: Resolution Resolution: 30 x 30 meters, except..

•Band 1 Visible Blue (.45-.52 mm)

•Band 2 Visible Green (.52-.60 mm)

•Band 3 Visible Red (.63-.69 mm)

•Band 4 Near IR (.76-.90 mm)

•Band 5 Mid-IR (1.55 – 1.75 mm)

•Band 6 Thermal IR (10.4 – 12.08 mm)

•Band 7 Mid-IR (2.08 – 2.35 mm)

Band 8: 15 x 15 metersUsed to “sharpen” multispectral images

Band 6:60 x 60 meters

•Band 8 Panchromatic (.45-.90 mm)

Some additional satellite-based multispectral scanners

SPOT

IKONOS

Quickbird

Advantages of Scanner vs. Aerial Photography

Better spectral resolution

Records energy values as numbers

Data is transmitted to ground & can be processed immediately

Target

Converting Scanner Data to Images

LANDSAT-- May 11, 2002

Converting Sensor Data to Images:

Image enhancement

Converting sensor data to images:

Overlay of several bands: Morro Bay, CA

Visible blue Visible red Visible green

Converting sensor data to images:

Overlay of several bands: S. Calif fires

Composed of 3 bands:

3 - visible red

5 – mid-IR

7 – mid-IR

Interpreting Scanner Data: Classification

Goal: to assign all pixels in an image to particular categories or themes

Spectral signatures: Group pixels with similar spectral

signatures Use statistical analysis

End product?

Land Cover map of the Phoenix Area, 1998

Classification by W. Stefanov, ASU Dept of Geological Sciences

What are the advantages of a classified image like this, as compared to a traditional map?

LANDSAT imagery usesAgriculture, Forestry, and Range Resources Discrimination of vegetative, crop, and timber types, and range vegetationMeasurement of crop and timber acreageEstimating crop yieldsForest harvest monitoringDetermination of range readiness and biomassAssessment of grass & forest fire damageWildlife habitat assessment

Land Use and MappingClassification of land usesCartographic mapping and map updatingCategorization of land capabilityMonitoring urban growthRegional planningMapping of transportationMapping of land-water boundariesSiting for transportation and transmission routesFlood plain management

GeologyMapping of major geologic unitsRevising geologic mapsRecognition of certain rock typesDelineation of unconsolidated rocks and soilsMapping recent volcanic surface depositsMapping landformsSearch for surface guides to mineralizationDetermination of regional structures

Water ResourcesDetermination of water boundaries and surface water areasMapping of floods and flood plainsDetermination of areal extent of snow and iceMeasurement of glacial featuresMeasurement of sediment and turbidity patternsDelineation of irrigated fieldsInventory of lakesEstimation snow melt runoff

Coastal ResourcesDetermination of turbidity patterns and circulationMapping shoreline changesMapping of shoals and shallow areasMapping of ice for shippingTracing beach erosionTracing oil spills and pollutants

EnvironmentMonitoring environmental effects of man's activities (lake eutrophication, defoliation, etc . . .) Mapping and monitoring water pollutionDetermination of effects of natural disastersMonitoring surface mining and reclamationAssessing drought impactSiting for solid waste disposalSiting for power plans and other industries

Case study: St. Charles, Missouri in the 1993 flood

Missouri River flood of 1993

To receive aid, local agencies needed to quickly delineate area flooded

Compare conditions using Landsat images from:

Unflooded (July 1988) and

Flooded (July 19, 1993) dates

The region

Current (sort of) Space-shuttle composite

Blue areas: floodplains

Green areas: hills

The analysis

1. Which LANDSAT bands best help solve the problem ?

2. How to mathematically merge the bands to get information

Which band clearly separates land from water?

- Band 4: 0.76-0.9

The analysis

Average each pair of pixels (1988 and 1993)

Results:

• Very low reflectance in permanent water

• High reflectance in permanent land

• In between for ’93 flooded areas

Problem: industrial areas

The analysis

To differentiate flooded areas from industrial sites:

Add in a band where this contrast is easy to see

Band 6 – thermal infrared

The analysis

flooded

notflooded

river

In conclusion …

Scanner-collected remote sensing information’s potential lies in

Ability to collect precise spectral ranges

Digital format that makes it possible to manipulate data of interest

Cool RS Resources

Yool-RS&ValleyFever Dr. Stephen Yool from University of Arizona uses

remote sensing to study Valley Fever Video and PowerPoint

Super links for remote sensing capabilities: Dr. Kelley Crews-Meyer (http://

www.esi.utexas.edu/outreach/video/crewsmeyer.html) Small:

http://www.esi.utexas.edu/outreach/video/crewsmeyer/videos/research/crewsmeyer_research_qtlv_lo.qtl

Large: http://www.esi.utexas.edu/outreach/video/crewsmeyer/videos/research/crewsmeyer_research_qtlv_hi.qtl