The landsat imagery, however, does not quite cover the whole globe. The oceans are excluded, as well as the poles, but most curiously is the fact that recently the country of Moldova has got its own set of imagery.
The imagery over Moldova stands out, as it has not been blended into the rest of the imagery.
We are not certain when it was changed, but it was apparently after we took the screenshot used in this post in July.
The imagery shows the copyright message Cnes/SPOT Image. We do not know why only Moldova was changed and we can’t think of any reason for it. It is only low resolution imagery and only visible when zoomed out. When you zoom in most of the country has higher resolution satellite imagery supplied by DigitalGlobe and CNES/Astrum.
If any of our readers knows what the reason is, please let us know in the comments.
Google has not released any ‘imagery updates’ maps since the release of the ‘Voyager’ layers at the end of June, so it is not easy to find new imagery. However, we have come across two sets of imagery related to flooding that we thought worth sharing.
The first one is an image of a zoo in Tbilisi, Georgia. When we first saw the story in the news of a number of zoo animals escaping during a flood, we found the location in Google Earth and saved a Placemark. Since then, we have been checking back regularly to see if it gets updated – and it has.
Tbilisi Zoo, Georgia, captured on June 18th, 2015, about 4 days after the flooding.
The second set of imagery is of Austin and Houston, Texas, captured between May 27th and June 1st, 2015. Austin, Houston and surrounding areas experienced heavy flooding a few days before the imagery was captured. However, the earlier imagery is too cloudy to see much and the flooding appears to have mostly dispersed by the later imagery. However, there is evidence of flood damage especially along the Blanco River southwest of Austin.
There is another bridge washed away further downstream and a number of houses have been damaged or washed away.
On Lake Travis, north of Austin, there are floating houses that are obviously designed to be able to move to some extent depending on water levels. However, some houses have been washed downstream due to the flooding.
Those purple roofed houses in midstream come from a location further upstream. See the KML file at the end of this post for the exact location.
We were unable to find any obvious signs of flood damage in Houston, Texas. If you find any please let us know in the comments.
When Landsat 8 imagery was made available on Amazon AWS our first question was: ‘how easy will it be to view it in Google Earth?’. This is our experience trying to answer that question.
The Landsat 8 satellite has a number of different cameras that capture imagery in different wavelengths. For details of the various sensors see this article. What we want to do is try to get a true colour image into Google Earth, which involves combining the three colour bands (2,3 and 4) and the panchromatic band (band 8) to provide extra detail (a technique known as pansharpening).
The first step is to decide which part of the globe you wish to look at. Landsat 8 covers almost the whole globe every 16 days. The imagery is arranged into strips and rows and you need to determine the strip and row of your point of interest. We used the USGS Global Visualization Viewer for this purpose. It requires the Java plugin, which Chrome says will soon be unsupported. An alternative is USGS Earth Explorer. Both websites also help you find what Landsat imagery is available for a given point of interest. Look for one with either minimal cloud cover or as close as possible to the date you are interested in. We chose an image of the Cape Town region captured on July 1st, 2015. Take note of the scene ID, which in our case was LC81750832015182LGN00.
After downloading and installing the GeoSage’s Spectral Transformer for Landsat-8 (GUI), we ran it and opened the text file that was downloaded earlier. However, when we tried to run it, it could not read the TIF files as it doesn’t recognize the compression used. So we had to open and re-save each of the images to remove the compression. We managed to do this with the free Gimp image editing software. Having done that, Spectral Transformer worked without a hitch and produced a full colour pan-sharpened and stretched image.
All that remains is to put it in Google Earth as an image overlay and position it appropriately. The instructions for Spectral Transformer suggest that the output image is a GeoTIF and contains the necessary positioning information, but we were unable to get that to work, so we did it manually.
The result is an image that, apart from a little cloud cover, looks nearly identical to the Google Earth imagery – which of course makes sense because, as we mentioned yesterday, Google Earth at this zoom level shows an image that was created from Landsat imagery.
A Landsat 8 image imported into Google Earth looks almost identical to the default imagery.
Closer inspection reveals slight differences in crop patterns and water levels in a lake, as the image is of a different date than the Google Earth imagery. If you zoom in any further Google Earth switches to Digital Globe imagery.
Left:Imported Landsat 8 image. Right: Google Earth default image (also from Landsat).
The resolution of Landsat 8 imagery is only 15m for the Panchromatic band and even worse for the colour bands. You can make out larger ships and the football stadium, but individual houses or cars cannot be distinguished.
So, in conclusion, it is relatively easy to get Landsat imagery into Google Earth, but nevertheless probably not worth the effort unless you have a specific reason for doing so. If there was a particular event that was visible at 15m resolution (large wildfires or volcanic eruptions, for example) that happened to coincide with a Landsat 8 pass of that location, then it might be worth it. It could also be useful for looking at large scale vegetation cover changes over time, but if you want to compare more than one or two images, then consider using Google Earth Engine, which not only provides a wealth of tools and computing power specially designed for analysing Landsat imagery, but you might find someone has already done the analysis you are interested in. Another possible reason for getting Landsat imagery into Google Earth is that there may be interesting things to find in the non-visible colour bands that are not part of the current Google Earth imagery.
The image created by Spectral Transformer was 173MB, but we have compressed it with jpeg compression down to 23MB so that you can try it out if you wish, using this KML file.
Yesterday we looked to Google Earth’s new layer that shows satellite and aerial imagery updates. However, it is important to note that Google has continued to add new imagery to Google Earth since that layer was added and they have not yet updated the layer nor updated their map.
So, we thought we would have a look at some of the more interesting imagery we have found so far.
On the 12th of May, 2015, an Amtrak train derailed in Philadelphia, USA killing 8 people and injuring over 200. The train was going in excess of the speed limit for the track, but it appears that it is not yet known why that was the case. For more, see the Wikipedia page.
The image is a bit cloudy, but it was captured the day following the accident, and you can see the rail cars. Check older imagery to see the layout of the tracks in the area.
On May 18th, 2015 a landslide tore through a valley near Salgar, Colombia. The older imagery for the area is black & white and not very good quality, but if you compare various spots along the river, you can see that the river has carved out a much larger channel than was there before, taking houses and trees along with it.
We also spotted some imagery of Lynchburg, Virginia captured on May 4th and 6th, 2015. The second is a black & white image, strongly suggesting that there was something of interest around those dates. Can any of our readers identify what it was?
Thank you to GEB reader Sladys for identifying the reason for the black & white image of Paris that we mentioned in our new layer suggestion post last week. It shows the Quarterfinals in the French Open at Roland Garros albeit half covered in cloud.
To view all the locations mentioned in this post in Google Earth download this KML file