Lake Gairdner Satellite Imaging

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Lake Gairdner Satellite Imaging

Note: This content is being updated - this is incorrect as it uses Band 8 instead of Band 10 and 11

This project involves using Landsat 8 images to predict optimal salt lake racing conditions and track layout at Lake Gairdner for the DLRA

Background

For salt lake racing to occur at Lake Gairdner, the lake surface needs to be dry and rock hard. If these conditions are not met Speed Week can be cancelled (rained out) or track conditions cause issues while the event is running. This project looks at satellite image to predict optimal conditions for racing so races happen more often with more runs (and more Vespa records).

We are using Landsat 8 data, which is collected in bands. Below is a table summarising the data and here is some useful NASA info on Landsat 8 Bands. This also has some useful info: Using data from the Landsat 8 TIRS instrument to estimate surface* temperature.

Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)

Band	Wavelength	Useful for mapping
Band 1 – coastal aerosol	0.43-0.45	coastal and aerosol studies
Band 2 – blue	0.45-0.51	Bathymetric mapping, distinguishing soil from vegetation and deciduous from coniferous vegetation
Band 3 - green	0.53-0.59	Emphasizes peak vegetation, which is useful for assessing plant vigor
Band 4 - red	0.64-0.67	Discriminates vegetation slopes
Band 5 - Near Infrared (NIR)	085.-0.88	Emphasizes biomass content and shorelines
Band 6 - Short-wave Infrared (SWIR) 1	1.57-1.65	Discriminates moisture content of soil and vegetation; penetrates thin clouds
Band 7 - Short-wave Infrared (SWIR) 2	2.11-2.29	Improved moisture content of soil and vegetation and thin cloud penetration
Band 8 - Panchromatic	.50-.68	15 meter resolution, sharper image definition
Band 9 – Cirrus	1.36 -1.38	Improved detection of cirrus cloud contamination
Band 10 – TIRS 1	10.60 – 11.19	100 meter resolution, thermal mapping and estimated soil moisture
Band 11 – TIRS 2	11.5-12.51	100 meter resolution, Improved thermal mapping and estimated soil moisture

Getting the Data

Data collected by the instruments onboard the satellite are available to download at no charge from GloVis, EarthExplorer, or via the LandsatLook Viewer.

LandsatLook Viewer

Here is a walk through of how to get a current (could be a week old) view of Lake Gairdner with http:// landsatlook

EarthExplorer

1) Register here https:// earthexplorer

http:// earthexplorer

Processing the Data

The data comes in two sets. "Path" is the flight path of the satellite North/South and "Row" is the bit along that path when the image was taken. Path 99 and 100 at Row 82 have images of Lake Gairdner. In Path 99 Lake Gainrder is in the left of the image and Path 100 it is to the right.

The full datasets are BIG - about ~1G per capture and each band is 110M, with Band 8 being 471M. These cover a region much bigger than the race track so much of the data is not needed.

To make the images a sensible size and to allow images from Path 99 to be compared to Path 100, we crop the images using utilities from GDAL - Geospatial Data Abstraction Library ( http://www.gdal.org/

./gdalwarp -te 580000 -3580000 602000 -3540000 orginal_landsat.TIF cropped_landsat.TIF

I have used this to crop all the images and inserted the image date at the front (year-month-day order)

Band 8 Images: http://files.internetscooter.com/dlr..._condition/B8/

Band 7 images http://files.internetscooter.com/dlr..._condition/B7/

Visualising the Data

The data can be viewed with a standard image viewer and some details can be made out. For example in the image below you can see some light coloured lines which are the race tracks and a group of dots near the middle of the lines which are the pit area. What this image is showing is the land surface temperature. As surface temperature is influenced by the amount of moisture in it, the variations in values can be used to determine moisture content. So the race tracks and pit roads are showing up as a lighter colour because the traffic has reduced the surface moisture and therefore it is slightly hotter than the surrounding salt.

To make better use of the data though we need to visualise it in a way that enhances the contrast difference between good and bad salt.

ParaView

A free tool for visualising data is ParaView here

Some notes on using ParaView:

ParaView is great but it is free so it has a few bugs/quirks
One quirk is that it sees somename.tif and somename.TIF has two different things ("tif" is an image file and it needs to be told "TIF" is also an image file - you'll see this in the instructions below)
The paradigm of ParaView is a pipeline, which can be seen in the "Pipeline Browser" (top left). In other applications you are used to opening up multiple documents and working with them seperately, in ParaView you can make the output of one feed into another. You can also view mutiple things at once and do al sorts of other tricky things

For more info on ParaView see the Help

Opening an Image

Assuming you have downloaded some images, here is how you open them. We'll use the Band 8 image from 2014 Speed Week as an example 20140304_LC80990822014063LGN00_B8.TIF

Step one is to File > Open...

Note:

We need to open "Files of type" = "All files (*)" this is because ParaView is looking for *.tif not *.TIF (yes annoying)
You'll need to expand the file name if you have the whole range of bands. In ParaView file names are similar and only differ by a sequiential number ParaView will group them automatically (this is for 3D image stacks which we aren't using).

Step Two...

Tell ParaView how to open the file by selecting "TIFF Image Files"

Step Three...

You should see the file appear in the pipeline but nothing is seen...

Hit "Apply"

Note: ParaView is designed for very very very large data, so it does not like to process data until it is explicitly told to using the "Apply" or "Update" buttons.

Voila the default view...

Note: The colour given is bad for what we need. The next section will cover mapping the data to colours that help us understand the salt :)

Colour Mapping

Colour mapping is the act of taking the value at a point in the data (the scalar value) and mapping that value it to a coloured pixel. The image above going from Blue (lowest value) to Red (highest value) is not a good colour mapping as the mid-range transition point is white. To a salt lake racer "white is good" but these are actually the middle range vlaues, where-as the darker the red the higher the termperature value (i.e. "red is good", "white is bad" and "blue is worst").

To change this to something more useful we use the Color Map Editor: Using the Color Map Editor in ParaView - The Basics

Better Starting Point

Here is how to get a better image:

Unselect the point symbol - this removes the annoying crosshairs in the middle of the picture
Select the colour scale - this shows how the data value is mapped to the colour (i.e. the colour legend)
Select the auto-update which will automatically apply your changes without you having to hit a button every time
Choose a better preset which will be Grayscale RGB - Black is the lowest value and White is the highest value (and intuitive shades of gray inbetween)

Modifying the Map for a clearer picture

Now we have a better starting point we can now change the map to highlight changes in the areas we are interested in.

To start with the grayscale goes from 0-32000, where 0 is pure black and 32000 is pure white. What we want to do is make the black starting point a lot higher value so that the black of the grayscale region starts where we have a transition from salt condition as judged by eye and experience of the salt. To do this we click on the grey bar in the Colour Map Editor as close to black as possible, this will add a new point. We then drag that point up the scale, the will change the way the grayscale is applied.