modeling digital remote sensing presented by rob snyder

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Modeling Digital Remote Modeling Digital Remote Sensing Sensing Presented by Presented by Rob Snyder Rob Snyder

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Page 1: Modeling Digital Remote Sensing Presented by Rob Snyder

Modeling Digital Remote SensingModeling Digital Remote SensingPresented by Presented by

Rob SnyderRob Snyder

Page 2: Modeling Digital Remote Sensing Presented by Rob Snyder

Scientists use remote sensors to detect different frequencies of electromagnetic radiation.

Page 3: Modeling Digital Remote Sensing Presented by Rob Snyder

http://rst.gsfc.nasa.gov/Intro/Part2_1.html

Page 4: Modeling Digital Remote Sensing Presented by Rob Snyder

Low resolution microwave instruments on satellites in polar orbits detect polar sea ice.

Ice is represented as the color white in each 25 x 25 km pixel on this map. The ice layer is superimposed on a color satellite image. Total polar sea ice extent is calculated by adding the area of all pixels with ice concentrations of at least 15 percent.

Source: http://earthobservatory.nasa.gov/Features/SeaIce/page2.php

Page 5: Modeling Digital Remote Sensing Presented by Rob Snyder

Low-flying aircraft flying carrying gamma radiation detectors fly across snow covered regions.

Page 6: Modeling Digital Remote Sensing Presented by Rob Snyder

Detection of gamma radiation can determine the water content of snow cover on a large scale.

Natural terrestrial gamma radiation is emitted from potassium, uranium, and thorium radioisotopes in the upper eight inches of soil. Snow cover blocks the terrestrial radiation signal in varying degrees.

The difference between radiation measurements made over bare ground and snow covered ground is used to calculate snow water equivalents.

Gamma radiation levels are calibrated using snow samples collected at different sites at ground level.

Page 7: Modeling Digital Remote Sensing Presented by Rob Snyder

NOAA uses gamma ray detection data to produce snow water equivalent maps.

Source: http://www.nohrsc.noaa.gov/nsa/

Maps like this are used to predict river flooding due to rapid snow melt.

Page 8: Modeling Digital Remote Sensing Presented by Rob Snyder

Reflected radio wave data can be used to generate “false color images”

of precipitation and air circulation.

Source: http://www.erh.noaa.gov/box/sigevents/jun01_2011_radarimages.php

NOAA weather radar monitored Western Massachusetts tornadoes on June 1st.

Page 9: Modeling Digital Remote Sensing Presented by Rob Snyder

A variety of remote sensing strategies were used to generate this map of minimum snow and clouds during

a period of maximum of a “green up” period in the Arctic Region.

http://www.arcticatlas.org/maps/themes/cp/

Page 10: Modeling Digital Remote Sensing Presented by Rob Snyder

Students can use a camera to model the process of the remote sensing of:

Snow cover across North AmericaSnow cover across North AmericaSeasonal changes in vegetationSeasonal changes in vegetationMelting sea iceMelting sea iceDamage from a forest fireDamage from a forest fireThe expansion of a cityThe expansion of a city

Page 11: Modeling Digital Remote Sensing Presented by Rob Snyder

Students can use photographs of different colors of paper to model remote.

The photograph can be uploaded onto a computer and analyzed with Analyzing Digital Images (ADI) software.

Page 12: Modeling Digital Remote Sensing Presented by Rob Snyder

A charge coupled-device (CCD) is located inside many digital cameras.

A CCD has three types of detectors that detect light in the red, green, and blue visible

portions of the electromagnetic spectrum.

Page 13: Modeling Digital Remote Sensing Presented by Rob Snyder

ADI software has a “line tool” that draws a line across the different colors in a photograph

.

Page 14: Modeling Digital Remote Sensing Presented by Rob Snyder

ADI software can also produce a graph that reveals changes in intensities of red, green, and blue colors as

the line crossed different colors in the photograph.

Students could choose to work with several colors of paper that have distinctly different RGB signals.

Page 15: Modeling Digital Remote Sensing Presented by Rob Snyder

This group of students chose to use blue, green, and white paper to represent the springtime “green-up” along the shore of an Arctic lake.

Page 16: Modeling Digital Remote Sensing Presented by Rob Snyder

Each group would draw a number of parallel lines across the photograph.

Page 17: Modeling Digital Remote Sensing Presented by Rob Snyder

An ADI graph of RGB intensities along a line can be used to determine the extent of each color along the line.

The total length of the line is multiplied by percentage of the length attributed to each color.

Page 18: Modeling Digital Remote Sensing Presented by Rob Snyder

Each group would compile the data in a format that can be used by another group to reconstruct what was mapped. Compiled data could then be traded among groups.

Page 19: Modeling Digital Remote Sensing Presented by Rob Snyder

A Data Table can be constructed to indicate the distance of each color along the line

Line Color and Distance

Color and Distance

Color and Distance

1 Blue = 20 cm Green = 19 cm White = 8.5 cm

2

etc, etc

This example provides the colors that were used.

Page 20: Modeling Digital Remote Sensing Presented by Rob Snyder

Another data table might only indicate RGB intensities for colors used by a group of students.

Color 1 Red = 38% Green = 36% Blue = 55%

Color 2 Red = 42% Green = 55% Blue = 40%

Color 3 Red = 93% Green = 85% Blue = 85%

How would a group determine what colors were used?

Page 21: Modeling Digital Remote Sensing Presented by Rob Snyder

Students can also design experiments to determine the resolution of a camera as remote sensor.

Is there a third set of RGB squares?

Page 22: Modeling Digital Remote Sensing Presented by Rob Snyder

Resources on the DIGITAL web site include:This PowerPoint.Students documents include strategies

for modeling the remote sensing process and sample data tables.

A teacher’s guide include STEM contexts and applicable national and state learning standards.