Archives for April 2016

The best of Google Earth for April 2016

April 29, 2016

Google has pushed out new imagery at least four times this month. 1, 2, 3, and a very recent update that we haven’t covered yet!

We found quite a lot of interesting sights in Google Earth imagery this month, including:
* Nyssa, a very large painting by Jorge Rodriguez-Gerada.
* Super Bowl celebrations in Denver Colorado.
* Mississippi flooding.
* A collapsed building due to an earthquake in Taiwan.
* A train crash in Germany.
* Flooding in India, Tanzania and Brazil.
* Demolished buildings in Zaria, Nigeria, indicating a possible cover up of mass killings.

We did a series of posts attempting to estimate the size of the Google Earth database and how much space the different types of imagery require. First we looked at 3D imagery, then different types of 2D imagery, moved on to historical imagery and finally put it all together and came up with a very rough estimate of 3 Petabytes!

We created a map showing the most popular times of year for capturing imagery in different parts of the world. The resulting pattern was very interesting. In the extreme latitudes, there is a lot less imagery captured in the winter because of snow cover and, as GEB reader Chris pointed out, the shorter days and longer shadow make gathering good quality imagery more difficult. In the lower latitudes the situation is reversed, with more imagery gathered in the winter, most likely because there is typically more cloud cover in the summer months.

We had a look at some scratch marks on the floor of the Caspian Sea and compared the view from Landsat imagery and ASTER imagery. We forgot to mention that the area also has DigitalGlobe imagery from 2005, which also shows scratch marks.

We had a look at the Sentinel missions and hope to cover them more in later posts when we have learned how to process the imagery. Thank you to GEB reader ‘mzuehlkeMarco’ for pointing us to a tool called SNAP for processing the imagery.

We had a look at some examples of land reclamation that can be seen taking place in Google Earth imagery.

We had a look at some strange buildings in the desert and decided they are Surface to Air Missile (SAM) sites, which it turns out are remarkably common and easy to identify in imagery once you know what to look for.

At the end of March, Tesla unveiled the Tesla Model 3 and we had a look at Lithium mines around the world, which may supply the necessary Lithium for the Tesla cars.

The Sentinel Missions

April 28, 2016

The satellite Sentinel-1B was launched earlier this week. It is the second satellite of the Sentinel-1 mission, the first of which, Sentinel-1A, has been in orbit since April 2014. Together they operate as a team to cover the whole planet every six days. They are not optical satellites but work with radar, which can operate day or night as well as see through clouds. They follow a polar orbit similar to the one used by the Landsat satellites, whose coverage pattern you can see here. We believe this map showing the deformation after a large earthquake in Chile last year was created using data gathered by Sentinel-1A.

The Sentinel Missions are developed by the European Space Agency (ESA) on behalf of the joint ESA/European Commission initiative GMES (Global Monitoring for Environment and Security). They are in many ways similar to the Landsat missions. The Sentinel-2 mission consists of optical satellites with similar orbits and coverage to the Sentinel-1 satellites. Sentinel-2A was launched in June 2015 and Sentinel-2B is expected to be launched sometime this year and there may be more, 2C and 2D, to be launched in 2021 or later. Sentinel-2A has a resolution of about 10 m per pixel, which is better than Landsat 8, which is 30 m per pixel.

Data from the Sentinel missions can be obtained here. It requires registration, but the Terms and Conditions suggest that anyone may use the data for almost any legal purpose, although correct attribution is required. We tried downloading data from Sentinel-1A, but did not know how to process the data, so all we saw was a completely black image. We then decided to get an image from Sentinel-2A of Mt Egon, Indonesia. It is a volcano which erupted on January 19th, 2016. We chose an image dated January 20th, 2016, which was a 4.4 GB download. Again we didn’t know how to process the imagery and could only see it in black and white and could not see anything of interest around the volcano.

So, it would appear that at the present time, Sentinel data is hard to access for the casual user. However, this article suggests that that may change, with a new plan to analyse the data being gathered and open source the results.

The best we have managed to achieve so far. Copernicus Sentinel data 2016.

If any of our readers knows the easiest way to obtain and process Sentinel data please let us know in the comments.

Scour marks in the Caspian Sea

April 27, 2016

We recently came across this story about scour marks on the floor of the Caspian Sea. It is believed the scour marks are caused by ice that forms in the winter months and is then broken up and blown around scratching the bottom of the shallow sea.

The article states that the featured imagery is from Landsat 8. Landsat 8 imagery is freely available and we have featured it a number of times in the past. We thought it would be interesting to download the images and have a look for ourselves. However, when we used our usual source, the USGS’s Earth Explorer to download a quick preview image, the scratch marks are not visible at all.

Low resolution Landsat 8 image.

So, we decided to download the high quality Landsat imagery, 670 MB download. This includes a number of separate images representing different wavelengths which need to be processed to get a high quality full colour image. Previously, we have used an excellent tool from GeoSage, which at the time was free for non-commercial use. However, that is no-longer the case. So, we decided to see whether or not we could process the imagery with GIMP, an open-source image editing program. We are still learning the best way to do it, so we will not give details at this time, but we were successful and the result is seen below:

High resolution Landsat image processed with GIMP.

We then had a look at ASTER imagery for the same location. ASTER imagery has recently been made freely available and can be easily obtained here. The ASTER imagery looks about the same resolution as the high resolution Landsat imagery, but the ASTER website provides it through a handy KML file and it requires no pre-processing at all. The only disadvantage of ASTER imagery is that there appear to be fewer images of any given location than for Landsat imagery.

ASTER imagery.

The Landsat 8 image is from March 21st, 2016, while the ASTER image is from September 28th, 2015. If you look carefully you can see that the Landsat 8 image has a lot of new scratch marks. There is also an ASTER image available from April 23rd, 2016 which shows almost identical scratch-marks to the Landsat 8 image. So it would appear that the scratch-marks last over multiple seasons.

To see the different images in Google Earth download this KML file. Note that we have cropped the Landsat imagery to make the file size smaller. The ASTER imagery will automatically download from the ASTER site when you view it.

Google Earth preferred seasons for gathering imagery

April 26, 2016

Whenever we look at imagery update maps we notice certain patterns in the updates. One of the factors that causes these patterns is seasonality. We have long known that Google avoids snow cover in imagery where possible. This means that they largely avoid capturing imagery in winter in high latitudes. We thought it would be interesting to use the Google Earth API to see how strong an effect that was. The results were quite interesting and not what you might expect.

We used the Google Earth API to scan the globe and for a given location check all the dates of available historical imagery then calculate which was the most frequent month. We then colour coded the months with an approximately seasonal colour scheme and created a map. Redder colours indicate northern summer and bluer colours indicate northern winter.

To see the map in Google Earth download this KML file

As initially expected, the far north and far south are dominated by imagery captured in the summer months of the respective regions. However, this trend does not hold throughout and there is a reversal in the tropics. This is because of cloud cover. In the tropics, cloud cover roughly follows the sun, with rain mostly occurring in the summer and clear skies occurring in the winter. There are exceptions, such as along the coasts, where rainfall patterns are affected by the ocean, and deserts where there is low cloud cover year round.

Overall, the patterns are quite distinctive and it is clear that when imagery updates occur is strongly affected by cloud cover and snow cover.

Notes:
A lot of aerial imagery in Google Earth does not have a specific date, but rather a date range. The start date is shown in the status bar and the end date on the ‘historical imagery’ timeline. Our map was using the end date. Some aerial imagery, especially the very old imagery, just has a year and this resulted in our algorithm seeing it as ‘December’. This is the reason for the United Kingdom and parts of Europe showing dark blue (December).

When there was more than one maximum, we picked one at random. This is not ideal, but was sufficient for showing general trends.

Mass killings in Zaria, Nigeria

April 25, 2016

In a recent press release by Amnesty International there is a story about a cover up by the Nigerian army of a mass killing of over 350 people in the town of Zaria, Nigeria, in December 2015.

The article mentions a possible mass grave visible in satellite imagery. However, no coordinates are given so we were unable to identify the location in Google Earth. This article includes a screenshot of the location from Google Earth, but again, does not give coordinates. However, it does include a map of a number of buildings that were completely destroyed. Based on the map we were able to locate them in Google Earth as seen below:

Hussainya

Shrine of Ibrahim Zakzaky’s Mother

Fudiyyah Islamic Centre

Darur-Rahma

IMN Recording Studio Film Village

Ibrahim Zakzaky’s Residential Compound

To see the above locations in Google Earth download this KML file. If any of our readers manages to locate the suspected mass grave site please let us know in the comments. A ground level photo suggests it is near some overhead high voltage power lines.

Although the imagery above does not prove that there was a mass killing, nor directly implicate the Nigerian army in a cover up, it can be used to corroborate witness testimonies. We believe that as satellite imagery becomes more frequent and more readily available, it will become increasingly important in cases like this. We would also like to comment that not enough news outlets include coordinates when showing satellite imagery. It is important to allow readers to be able to confirm imagery for themselves by looking it up in Google Earth or other sources.