Chesapeake Bay Mat

Bolden and bay mosaic

NASA Administrator Charles Bolden (center) with Landsat Communication and Public Engagement team member Jeannie Allen, and NASA Head of Communications, David Weaver looking at the Landsat mosaic of the Chesapeake Bay. Picture taken 2014.

The Chesapeake Bay Mat is a 10-foot by 8-foot image of the Chesapeake Bay printed on a durable canvas for crawling and walking on. This unique perspective allows people to see differences in land use (especially between the Delmarva Peninsula and the DC area) in great detail.

Along with the mat we include imagery of various highlights of the bay area using different remote sensing and GIS obtained imagery (near-infrared imagery, road line shapefiles, etc.). This allows the public to hunt for those features and see them from yet another perspective as scientists and data visualizers do.

Spectral Signatures

spectral signature activity display

The spectral signatures activity starts out by talking about what wavelengths are in the visible part of the spectrum. Then we talk about how light is reflected and/or absorbed and what you see is the peak of the visible light that’s reflected. Then we introduce an example spectral signature of a green leaf using all the colors of the rainbow. Five Landsat images are put out along with some sample spectral signatures. Pixels are selected on the Landsat scenes and part of the activity is to match the spectral signatures with them just using the visible portion of the spectrum and ignoring the NIR listed for the moment. Once that is complete then we look at two scenes, the agriculture scene and the water/meltpond scene. The NIR over the water is low because water absorbs NIR and over the agriculture it’s high because the NIR is being reflected. Then we ask the questions, “Why is it useful to see that the NIR is so high over the plants? What does that tell us?” It’s then that the handheld spectrometers are used on the healthy leaves to see what numbers we get for the visible and NIR. After that we use a dying very brown, dry leaf and measure its NIR and compare the two. We see that the NIR has dropped off and we inform them that we can tell the health of vegetation by how much NIR is being reflected and absorbed. In fact we can see an unhealthy plant with NIR before we could see it in the visible wavelengths. If we go even further into the Shortwave Infrared we can start to discern different types of vegetation and we show them a spectral signature going for visible to swir. Holding up the agriculture image they are informed that we ca discern the type of crops that are growing and tell how healthy they are from other crops based upon their spectral signatures. The final example shown is a comparison of the California Station fire in true color (visible wavelengths) versus a false color (visible, NIR and SWIR wavelengths) image. There is a lot of information the false color image gives that the true color one doesn’t such as a much better view of the extent of the burn, cloud penetration, and where the fire skipped areas. They are informed that without using the different wavelengths we wouldn’t be able to see fires or agriculture in this way. That just taken a picture in the visible doesn’t give us the whole story , and we want the whole story and that’s why we looks at Earth and space using all of these different wavelengths.

Landsat Cubes

The Landsat cubes consist of four 4-inch by 4-inch translucent cubes with Landsat scenes acquired of different chunks of the Earth over time to tell a story. The activity is to have the participant select an image they like and have them put the cubes in order chronologically to see how that area has changed. We are able to introduce the importance of temporal resolution, false color imagery (imagery created using EM wavelengths either outside the visible or using the visible wavelengths differently than our eyes perceive them), various applications of remote sensing and Landsat, and an introduction into remote sensing visual interpretations.

Cube images:

Atchafalaya_Bay (190.2 MB, ZIP)

Dubai (212.1 MB, ZIP)

Las_Vegas (247.9 MB, ZIP)

MtStHelens (235.1 MB, ZIP)

Rondonia (262.7 MB, ZIP)

Yellowstone (68.9 MB, ZIP)

Answer key and useful files:

Cube Instructions (37 MB, PDF)

2_5x2_5in_graph_LTSC (193 KB, PDF)

2_5x2_5in_graph_makea (337 KB, PDF)

Landsat in a box*

Bumpy, Wrinkled, Smooth*