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 ( mm) Band 2Visible Green ( mm) Band 3Visible Red ( mm) Band 4Near IR ( mm) Band 5Mid-IR (1.55 – 1.75 mm) Band 6Thermal IR (10.4 – mm) Band 7Mid-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 ( mm) Band 2Visible Green ( mm) Band 3Visible Red ( mm) Band 4Near IR ( mm) Band 5Mid-IR (1.55 – 1.75 mm) Band 6Thermal IR (10.4 – mm) Band 7Mid-IR(2.08 – 2.35 mm) Band 8: 15 x 15 meters Used to “sharpen” multispectral images Band 6: 60 x 60 meters Band 8 Panchromatic ( 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 uses Agriculture, Forestry, and Range Resources Discrimination of vegetative, crop, and timber types, and range vegetation Measurement of crop and timber acreage Estimating crop yields Forest harvest monitoring Determination of range readiness and biomass Assessment of grass & forest fire damage Wildlife habitat assessment Land Use and Mapping Classification of land uses Cartographic mapping and map updating Categorization of land capability Monitoring urban growth Regional planning Mapping of transportation Mapping of land-water boundaries Siting for transportation and transmission routes Flood plain management Geology Mapping of major geologic units Revising geologic maps Recognition of certain rock types Delineation of unconsolidated rocks and soils Mapping recent volcanic surface deposits Mapping landforms Search for surface guides to mineralization Determination of regional structures Water Resources Determination of water boundaries and surface water areas Mapping of floods and flood plains Determination of areal extent of snow and ice Measurement of glacial features Measurement of sediment and turbidity patterns Delineation of irrigated fields Inventory of lakes Estimation snow melt runoff Coastal Resources Determination of turbidity patterns and circulation Mapping shoreline changes Mapping of shoals and shallow areas Mapping of ice for shipping Tracing beach erosion Tracing oil spills and pollutants Environment Monitoring environmental effects of man's activities (lake eutrophication, defoliation, etc...) Mapping and monitoring water pollution Determination of effects of natural disasters Monitoring surface mining and reclamation Assessing drought impact Siting for solid waste disposal Siting 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:

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 not flooded 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 (  Small: search/crewsmeyer_research_qtlv_lo.qtl search/crewsmeyer_research_qtlv_lo.qtl  Large: search/crewsmeyer_research_qtlv_hi.qtl search/crewsmeyer_research_qtlv_hi.qtl