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The fires of Qayyara, Iraq, with Landsat and Sentinel imagery

We recently came across this interesting article on Bellingcat which is about the use of environmental damage as a weapon of war in Iraq. As the Islamic State (IS) is being pushed back, they are setting light to oil wells, pouring oil on the streets and in trenches and setting light to it and also setting light to other industrial products such as sulphur.

The article features some Landsat imagery showing the plumes of smoke. Landsat imagery is freely available, and we have in the past created a KML file that can make animations with Landsat imagery. If you download the KML file and view the animation for the area around Mosul, Iraq, you can clearly see the smoke in the last few images. Also of note, you can see some smoke from a previous event in an image from August 2014 to the north west of Mosul. We have also created animations using Sentinel 2 imagery, which you can download here.

The animations above are created using low resolution thumbnails provided on Amazon Web Services (AWS). To see some high resolution imagery, we downloaded the most recent Landsat-8 image and processed it with GIMP using a method similar to the one described here and here.

The result can be seen below:


Landsat 8 image from October 20th, 2016. Note the sulphur fire marked with an arrow. The black smoke is from oil fires.

For comparison, we also downloaded a Sentinel 2 image from September 14th 2016.


Sentinel 2 image from September 14th 2016. This is before the sulphur fire was started. Copernicus Sentinel Data, 2016.

See the above images in Google Earth, download this KML file.

The Bellingcat article also features an image of the sulphur fire from Planet Labs.

Landsat and Sentinel-2 data now on Google Cloud

Google has just announced the release of Landsat and Sentinel-2 data on the Google Cloud. Landsat and Sentinel-2 data are public datasets of satellite imagery from earth observation satellites. The Landsat data is from a joint program between US Geological Society (USGS) and NASA and the Sentinel-2 data is from the European Space Agency’s Copernicus program.

Google has long had the datasets in Google Earth Engine, but accessing it required a Google Earth Engine account, which had certain restrictions on usage. The Google cloud version appears to be without restrictions. The data itself is public data and you can do almost anything you like with it although proper attribution may be required.

More about the individual datasets and how to access them can be found here: General instructions, Landsat, Sentinel-2.

The imagery is provided as unprocessed tiles for each of the optical bands that each satellite provides. To see the images in colour requires processing. The easiest way to do this is with commercial tools from GeoSage. Alternatively, see this post for instructions on how to process Sentinel-2 imagery using GIMP. Landsat data can be processed in a similar way, although it an some extra steps are required to get the best resolution.

The resolution of Sentinel-2 imagery is 10 m per pixel and Landsat-8 is 15 m per pixel after pan-sharpening. Do not expect to see the kind of detail we are used to in Google Earth.

Sadly, Google has not provided thumbnails with the data.

Amazon provided Landsat data via its cloud infrastructure Amazon Web Services (AWS) in 2015 and more recently added Sentinel-2 data. We did a series of posts featuring KML tools to allow you to preview the latest images and make animations from provided thumbnails. The Landsat data on AWS is somewhat limited compared to the Google Cloud offering. Google is providing all Landsat images from Landsats 4,5,6,7 and 8 from 1982 to present. AWS only has Landsat 8 data and even that is only complete data for 2015 and select images from 2013 and 2014.

The image below is a Sentinel-2 image downloaded from the Google Cloud and processed with GIMP. It shows a small part of the Soberanes Fire, California, on September 12th, 2016. To see it in Google Earth (and covering a larger area) download this KML file


Copernicus Sentinel data, 2016.

Cloudy places are hard to photograph from space

In yesterday’s post we talked about Landsat imagery and how in some locations there are only a few cloud-free images per year. That was not entirely accurate, as there are some places where it is practically impossible to find a cloud-free Landsat image.

We were recently exploring the north-western area of Colombia (looking for circular islands) and noticed that the imagery there is of particularly poor quality. Most of Choco Province, Colombia, has no high resolution satellite imagery and instead uses the global mosaic created from Landsat imagery. To make the global mosaic, Google took Landsat imagery captured over multiple years and searched for cloud-free pixels to use in the final mosaic. However, when we looked at the region with our Landsat animations KML we found 49 images captured over the last three years, but they all had significant cloud cover. The very best image is listed as having 17% cloud cover and the next best has 31% cloud cover and the rest are much worse. Add to this the fact that the clouds tend to form around particular geographical features and there are some places that never have cloud free pixels. The result is particularly poor imagery in some places.


Some areas look like abstract art.


We are not sure whether the above effect is due to just cloud cover, seasonal changes in the water levels or the fact that the water colour changes over the seasons.

For an understanding of which parts of the globe have the most cloud cover, see this animation from NASA. Note that it is not images of clouds, but rather maps of the average amount of cloud cover over a month. We could not find a version for longer periods to find out which locations have near-permanent cloud cover.

Another place with a similar problem – year round cloud cover – is the rainforest belt of Central Africa. In some places, Google has had to use Landsat 7 imagery, which we can see because of its characteristic stripes due to a faulty component on the Landsat 7 satellite.


Landsat 7 stripes.

Animating Landsat imagery in Google Earth

[ Update: We have updated the KML file with more stable animations and the ability to zoom in and out and pan. See this post for more details. ]

Last week we created a KML file to easily preview the latest Landsat imagery. The data comes from Amazon Web Services (AWS), which hosts a large amount of Landsat 8 imagery and also includes some handy thumbnail images. So, we have now created a KML file that automatically creates animations from the thumbnail images.

Yesterday we had a look at the coverage of Landsat 8 imagery, but we focused on how recent the imagery is. Today’s KML file instead colour codes tiles based on how many images are available. As we noted yesterday, the AWS archive is Landsat 8 imagery only and does not include all the imagery. It turns out that most of the world includes a fairly comprehensive set of imagery from 2015 onward, whereas the US (excluding Alaska) has imagery going back to 2013. The result is that most locations outside the US have about 44 scenes whereas the US typically has nearly twice as many, at about 77 scenes. Note that these figures increase with time as the data is live and a new scene is added to each tile approximately every 16 days.

To see the animations, download this KML file. Click on any coloured tile for an animation of that location. Depending on your internet speed, it may take a short while to load all the images. If you have slow internet or just want to get a quick preview of the animation, then download this KML file which uses smaller, much lower resolution thumbnails.

There are three sliders. The first slider shows the progress of the animation and allows you to manually switch between images. Doing so stops the animation, which you can restart with a button. The second slider lets you adjust the speed of the animation. The third slider allows you to filter the images by removing any with over a specified level of cloud cover.

AWS provides two thumbnails for each scene, one quite small one that we used for our ‘recent images’ KML and one much larger. We have used the larger one even though it is too large to comfortably show at full resolution in Google Earth. To see the animation running at full size, click the link at the top right of the animation window.

Remember that Landsat imagery is very low resolution (about 30m per pixel) and these are just thumbnails, which are even lower resolution than that. So expect to only see very large scale changes. Look for changes in seasons, ice cover changes near the poles, lakes shrinking or growing, and even sand dunes moving.

A major problem is the large amount of cloud cover in the images. If you select only scenes with minimal cloud cover you end up with only a few images to work with. This highlights just how difficult it is for commercial providers to get good imagery.

One problem we had was that the images are not captured from exactly the same angle, so there was a significant amount of shaking in the animations. We have tried to fix this by reading the latitude and longitude metadata included with the scenes and moving the individual images appropriately. It is still not perfect as a browser cannot position an image at sub-pixel resolution.

Let us know in the comments if you find anything particularly interesting. Ideally tell us the path and row.

Here is an interesting tile in Chad that shows some fire scars.

Landsat imagery coverage

Last week we looked at Sentinel imagery coverage and today we are doing the same for Landsat imagery.

First, note that we are discussing Landsat 8 imagery only, as Landsat 7 imagery is not currently available on Amazon Web Services (AWS), which is the source of our information. In addition, we have excluded Landsat imagery captured at night as we do not have the appropriate Row/Path data for night time imagery – although the imagery itself is available on AWS. Also, this page says that although all scenes from 2015 onwards are available, only a selection of cloud-free scenes from 2013 and 2014 are included.

You can see the colour coded coverage map in Google Earth with this KML file. Click on any square to see the date the last image was captured and how old that image is in days. Note that this KML is not dynamic and is based on the data as it was on AWS on August 20th, 2016.

For imagery captured in the last 20 days, we have used this colour range: with red for the most recent imagery and green as the oldest. Anything older than 20 days we have coloured white.

Having a look at the order in which strips are captured, we get a pattern like this:
1   10   3   12   5   14   7   16   9   2   11   4   13   6   15   8
which then repeats after the 16th day. See this post to see an animation of the Landsat orbit.

The imagery of Antarctica is mostly several months old, because it is too dark to capture good imagery over the winter months. There is also a horizontal stripe in the Atlantic and Pacific from February/March 2015 and a vertical stripe from May 2015. We do not know why they were captured, or why the oceans in general have never been captured.

Also of note is that some scenes are recorded with a slightly different scene identifier. Most of the scene identifiers start with the letters LC8, whereas these images start with either LO8 or LT8. There are only a few scenes with these different identifiers. According to this page this means that they include only data from the Operational Land Imager (OLI) or only from the Thermal Infrared Sensor (TIRS), whereas most Landsat imagery includes data from both sensors. We have included separate folders in the KML to show the coverage for the different cases. This page explains what each letter in a Landsat scene identifier means.

Our next project with this data is to create animations with the thumbnails.