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Mis à jour : il y a 1 heure 48 min

Sun-synchronous orbits with Google Earth

il y a 9 heures 59 min

After our recent posts on rainbow plane offsets and the list of imaging satellites we thought it would be interesting to see what a sun-synchronous orbit actually looks like in Google Earth. We have previously written a post about sun-synchronous orbits and why most imaging satellites use them, but we only showed an approximate single orbit of the earth assuming the earth was not rotating. In reality, the earth rotates on its axis as well as going around the sun. A sun synchronous orbit is designed to drift slowly so as to keep in sync with the earth’s orbit around the sun. All this starts to get complicated when you want to plot an actual orbit. But we believe we have succeeded.

What the orbit of WorldView-3 looks like.

We used the equations from Wikipedia, which allow us to use the altitude and period of a given satellite to work out its orbit. For simplicity we start at latitude zero and longitude zero. We show the orbit for a period of approximately 24 hours. Some satellites, such as Landsat 8, have their orbits arranged so that they repeat the same path on a regular basis. Others do not.

All sun-synchronous orbits look very similar, with the differences in altitude being hardly noticeable. The most obvious difference is in the period, which affects the spacing of the orbits.

Red: Landsat 8 orbit. White: WorldView-3 orbit.

WorldView-3 has an altitude of 617 km and a period of 97 minutes.
Landsat 8 has an altitude of 703 km and a period of 98.8 minutes.

The diagonals from the north east to south west ( / ) are on the daylight side of the earth and the diagonals from south east to north west ( \ ) are on the night side.

To see the above orbits in Google Earth download this KML file. Alternatively, you can create your own orbit by entering the altitude and period below:


Sun-synchronous orbit creator.

Altitude: km

Period: minutes

Create orbit

The post Sun-synchronous orbits with Google Earth appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Imaging satellite list

lun 25-07-2016

Last week we mentioned that it would be useful to have a table of imaging satellites with some of the specifications of most interest to users of Google Earth. We couldn’t find any site with a single list in table form, so we decided to create our own.

The information comes from three main sources: Wikipedia, DigitalGlobe’s website and Sat Imaging Corporation’s website. We make no guarantees about the accuracy of this data.

.satelliteList th{ font-weight:bold; padding:4px; } .satelliteList td{ padding:4px; } .satelliteList{ font-size:12px; } .future{ color:#2980B9; } .old{ color:red; } h2{font-size:1rem} Satellite Company Panchromatic
Resolution Multispectral
Resolution Launch
Date Decommissioned Altitude GeoEye-1 DigitalGlobe 46cm 1.64m 2008-09-06 681 / 770 WorldView-1 DigitalGlobe 50cm – 2007-09-18 496 WorldView-2 DigitalGlobe 46cm 1.85m 2009-10-08 770 WorldView-3 DigitalGlobe 31cm 1.24m 2014-08-13 614 WorldView-4 DigitalGlobe 31cm 1.24m 2016-09 617 IKONOS DigitalGlobe 80cm 3.2m 1999-09-24 2015-04 681 QuickBird DigitalGlobe 55cm 2.16m 2001-10-18 2014-12-17 400 Pleiades-1A CNES 50cm 2m 2011-12-17 695 Pleiades-1B CNES 50cm 2m 2012-12-02 695 KOMPSAT-3A KARI 55cm 2.2m 2015-03-25 543 KOMPSAT-3 KARI 70cm 2.8m 2012-05-17 533 Gaofen-2 CAST 80cm 3.2m 2014-08-19 631 TripleSat (3 satellites) SSTL 80cm 3.2m 2015-07-10 SkySat-1 Terra Bella 90cm 2.0m 2013-11-21 450 SkySat-2 Terra Bella 90cm 2.0m 2014-07-08 450 SPOT-1 CNES 10m 20m 1986-02-22 1990-12-31 832 SPOT-2 CNES 10m 20m 1990-01-22 2009-07-01 832 SPOT-3 CNES 10m 20m 1993-09-26 1997-11-14 832 SPOT-4 CNES 10m 20m 1998-03-24 2013-07-01 830 SPOT-5 CNES 2.5m 10m 2002-05-04 2015-03-31 832 SPOT-6 CNES 1.5m 6.0m 2012-09-09 832 SPOT-7 CNES 1.5m 6.0m 2014-06-30 832 Landsat 1 USGS 1972-07-23 1978-01-06 Landsat 2 USGS 1975-01-22 1982-02-25 Landsat 3 USGS 1978-03-05 1983-03-31 Landsat 4 USGS 1982-07-16 1993-12-14 Landsat 5 USGS 1984-03-01 2013-06-05 Landsat 6 USGS 1993-10-05 1993-10-05 Landsat 7 USGS 15m 30m 1999-04-15 702 Landsat 8 USGS 15m 30m 2013-02-11 702 RapidEye (5 satellites) Planet Labs 5m 630 Doves (many satellites) Planet Labs approx 400 ASTER Japan METI 15m 15m 1999-12-18 705 Sentinel 2A ESA 10m 10m 2016-06-23 768 Sentinel 2B ESA 10m 10m 2016 768

Red: No longer active.
Blue: Not yet launched. Notes on the above data:

Most of the satellites capture imagery in high resolution in grayscale (panchromatic) and lower resolution in each of the three primary colours and the images are combined to produce a high resolution colour image.

Orbits are typically not perfectly round and the earth itself is not perfectly spherical, so altitudes listed are averages. GeoEye-1 apparently had its orbit changed to a higher orbit in 2013.

Details not listed.

As far as we know, all the satellites can capture imagery in various infra-red bands that are typically lower resolution than the colour bands. There is significant variation between satellites as to which infrared bands they can capture.

There are differences in how the satellites capture imagery, such as whether they use a single sensor, a row of sensors or a sensor array.

Most satellites capture imagery in strips, which have a specific ‘swath width’ for a given satellite.

The best quality image is usually captured straight down from the satellite (nadir) but most satellites are capable of rotating so as to be able to capture imagery to either side of their orbit. How fast they can rotate varies by satellite.

Most satellites have a limit on how much imagery they can capture at a time. This is due to data storage and communication limitations.

Satellites vary as to how accurately they can report exactly where they were and what direction they were pointing when they took the photo.

The mass of the satellites varies considerably. In some cases the ‘satellite’ listed is actually an instrument on a larger satellite (ASTER for example).

Each satellite crosses the equator at a specific time.

Satellite design.

The various different requirements for satellites are
1. Resolution.
2. Acquisition speed. (How quickly a satellite can image a given area when required such as after a natural disaster).
3. Repeat acquisition. (The frequency with which it can image a given location).
4. Area covered. (Higher resolution typically means smaller coverage footprints, so lower resolution can be an advantage at times).
5. Consistency. (This is important for long term environmental monitoring).
6. Global coverage. (The ability to image the whole globe on a regular basis).
7. Price.

Commercial satellites tend to be more interested in 1. to 4., whereas government programs are more interested in 5. and 6. DigitalGlobe’s satellites are mostly expensive high resolution satellites whereas Planet Labs has sacrificed resolution in order to be able to make large numbers of cheap satellites, which allows them to cover more area quicker and repeatedly. The Landsat, SPOT and Sentinel satellites are designed for long term, regular, consistent coverage for environmental monitoring. The Sentinel satellites are essentially a continuation of the SPOT satellites with their orbits chosen to match.

Where does Google Earth imagery come from?

The main suppliers of satellite imagery to Google Earth at the current point in time are Digital Globe and CNES/Astrium, so almost all satellite imagery comes from their satellites. The low resolution global mosaic is Landsat 7 / 8 imagery, which is also used to fill in the gaps where there is no higher resolution imagery. Imagery from the SPOT satellites is also used for filling in gaps and can be recognised from the copyright notice “CNES/Spot Image”.

South America has a lot of imagery dated 1970. We are not sure which satellite it comes from, but we guess it may be from one of the early Landsat satellites and the date is only approximate.

Landsat, ASTER and Sentinel imagery is free to the public and can be downloaded and viewed in Google Earth.

Google Earth includes aerial imagery (captured from aircraft not satellites) from a number of sources.

Landsat 8. Image from Wikimedia commons.

The post Imaging satellite list appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Rainbow plane offsets

ven 22-07-2016

When we had a look at the ‘rainbow effect’ of planes in flight we mentioned that the offsets of the different images were a result of both the plane’s movement and the movement of the satellite taking the photos. We thought it would be worth having a look at that in more detail.

We know that most imaging satellites follow a sun-synchronous orbit. It is fairly easy to approximate that orbit relative to a plane in flight by drawing a line from the location of the plane to form a tangent on the right hand side of the North pole with the circle of latitude 82° N. For increased accuracy we will try to follow the tail of the plane.

In the image above, the satellite was travelling north to south in the direction of the red line although not necessarily directly overhead.

The satellite captured 4 photos: one high resolution grey scale image which we see at (1), and then after a short delay, blue, red and green images in quick succession which we see overlapping at (3).

If the plane was completely stationary, we would have expected the rainbow images to appear at (2) due to parallax and the motion of the satellite. If the satellite was stationary and the plane moving, then we would expect to see them at (4).

Using Google Earth’s measuring tools the distance from (1) to (2) is about 40 m. The distance from (2) to (3) is about 70 m.

This is enough information such that if we knew which satellite took the image, and how long it pauses between the monochrome and colour images, we could work out the approximate altitude and velocity of the plane. Alternatively, if we knew the altitude and velocity of the plane, we could, work out which satellites could have taken the image.

One useful fact is that all the possible satellites have very similar velocities which we can approximate at 7.5 km/s.

Wikipedia suggests that a typical plane cruises at 878–926 km/h at an altitude of around 12km and that a much higher altitude is not possible.

So, if we start by guessing the planes altitude at 12 km, its velocity at 900 km/h then we get the satellite altitude at about 550 km. Now we look through this list to try and find a matching satellite from DigitalGlobe – keeping in mind the image was captured in 2012 so satellites launched after that date must be excluded. Our best guess is that the image was probably captured by a satellite in a slightly lower orbit such as World-View-1 at an altitude of 496 Km and to make our calculations match up, the plane was probably at a slightly lower altitude of 11 km above sea level. (The ground at this location is 1.3 km above sea level.)

The time between the the monochrome and colour images is about 0.27 seconds.

If any of our readers knows of a reference with satellite orbit data in tabular form for a wide variety of imaging satellites please let us know in the comments.

We also came across this image:

The images of the plane appear to have been sheared and offset slightly in a horizontal direction, but the image of the ground does not seem to have been affected. We don’t know how this happened. Do our readers have any suggestions? One of the images in our earlier post on the ‘rainbow effect’ also includes an plane which seems to have a double tail which may be a related effect.

To find the above planes in Google Earth download this KML file.

We found the above planes using the Google Earth Community aircraft in flight list.

The post Rainbow plane offsets appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Watching sand dunes move with Google Earth

jeu 21-07-2016

We recently got the idea of trying to see if we could see sand dunes moving using Google Earth historical imagery. The biggest problem is that for the best results we needed frequent satellite imagery over as long a time period as possible, but most deserts have very little satellite imagery. Google Earth imagery tends to focus on populated areas, so we looked for towns that have sand dunes on the outskirts. We started with Nouakchott, the capital of Mauritania. We had come across this article, which suggests that Nouakchott may be slowly obliterated by creeping sand dunes. But what we found, in the places we looked, was that the opposite was the case – the city was slowly taking over the sand dunes.

We recommend watching the videos full screen.

We tried a little to the east of Nouakchott and, since the image jumps around quite a lot due to poor image alignment in Google Earth, we cannot definitively say which way the dunes are moving, if at all. They do change shape quite considerably in the first few frames:

Next we tried Namibia and chose some sand dunes just east of Lüderitz. This time there is no doubt that the dunes are moving northwards.

We also looked at sand dunes east of Oranjemund, also in Namibia. There isn’t much imagery and it gets updated in sections, but the overall movement is still clear. If it wasn’t for the town of Oranjemund staying in one place you might think the sand was stationary and the imagery was just being moved.

Still in Namibia, we go to Walvis Bay and here we can see dunes slowly moving north-west.

You can find some more videos we made of dunes near Dubai, UAE, that were less successful here and here.

You can download the Google Earth tours used to create the above videos.

To create the above videos we used our advanced Google Earth historical imagery tour maker and Google Earth Pro’s built-in Movie Maker.

We presume that how fast sand dunes move depends on many factors, including wind speeds, dune size and grain size.

The post Watching sand dunes move with Google Earth appeared first on Google Earth Blog.

Catégories: Sites Anglophones

How often is Google Earth imagery updated: Europe

mer 20-07-2016

We recently had a look at the frequency of imagery updates for the continental US. Today we are doing a similar analysis for Europe.

As with the US, Europe has a lot of aerial imagery gathered by the various countries or local companies and later obtained by Google. Also, as with the US, Google has been gathering its own aerial imagery in recent years. In general, most Western European countries have good aerial imagery coverage with the exception of Ireland and the northern parts of Norway, Sweden and Finland. Meanwhile most Eastern European countries have only satellite imagery. This does tend to result in Eastern Europe seeing more regular updates, although it is lower quality imagery.

Keep in mind that actual imagery is smaller than the areas shown and the maps show large sections in the sea that are due to a bug in the way the Google Earth API reports imagery.

Speed in milliseconds per image:

The are no clearly discernible patterns in the yearly data and we estimate complete coverage to be taking about 5 to seven years, which is generally poorer than the US, which is every three years. The UK is an exception, with noticeably poorer coverage in recent years.

2016 has seen very few updates in Western Europe, but we believe that is because of the seasonality of aerial imagery. We can probably expect to see significant additions in the coming months as aerial imagery gathered in the summer starts to be added. Eastern Europe’s satellite imagery is processed faster and is less restricted to the summer months.

To see the data in Google Earth download this KML file.


The post How often is Google Earth imagery updated: Europe appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Street View Tour Maker by Steven Ho

mar 19-07-2016

Steven Ho, whose excellent work we have covered many times in the past, has recently produced a tool for creating Street View tours. The tool and details on how to use it can be found on his blog here. The instructions for how to use it are a little hard to follow as English is not Steven’s first language, but he provides a number of examples so if you wish to use the tool, it shouldn’t be too difficult to figure out how to use it.

In addition to a number of samples produced with the tool provided in the above post, he has also used it to create a tour of Kumamoto Castle, Japan which was severely damaged in the Kumamoto Earthquakes in April, 2016. See part of that tour in the YouTube video below and read more about it on his blog.

The tours he has created can be played back in Google Earth, but due to a bug in Google Earth they do not always enter Street View automatically. If this happens he suggests pausing the tour and manually entering Street View by dropping the yellow man on the map before continuing.

The post Street View Tour Maker by Steven Ho appeared first on Google Earth Blog.

Catégories: Sites Anglophones

Sheep View

lun 18-07-2016

Google Street View coverage has been increasing at a steady pace for the last few years, with new countries being added every few months. There are, however, still many places that do not have Street View and feel left out. One such place is the Faroe Islands, which are situated north of Scotland about halfway between Norway and Iceland. A group of Faroe Islanders decided to start a campaign #WeWantGoogleStreetView but rather than simply wait for Street View they started capturing their own Street View with the help of other residents of the Faroe Islands – the sheep. Read more about it here. Also see their website SheepView360 and the YouTube video below:

On the SheepView360 website there are some 360° YouTube videos where you can look around while watching the video or pause it and look around as if it is Street View. This makes us wonder whether such features will one day make it into Street View. Would 360° videos improve the Street View experience or not work that well?

We only managed to find one Sheep View image in Street View, but it is a new project, so we expect many more will follow.

See it in Street View

In some places, the reason Google has not collected Street View has to do with governments not allowing it for various reasons. Germany put a halt to Street View collection over privacy concerns (some parts of Germany do have Street View) and India has also so far refused to allow it on a large scale (some Street View exists) over security concerns. Submissions by individuals however do not face the same legal obstacles so there is nothing stopping you from adding to Street View in those countries.

Currently, user submitted photos do not work in Google Earth’s Street View so there is no way to see Sheep View in Google Earth. Hopefully Google is working on a fix for this.

The post Sheep View appeared first on Google Earth Blog.

Catégories: Sites Anglophones

More about processing Sentinel imagery

ven 15-07-2016

We recently had a look at how to process Sentinel imagery using GIMP. GEB reader ‘DJ’ asked in the comments if the geodata supplied with the imagery can be used to automatically align the imagery in Google Earth, rather than the manual method we had suggested. So we decided to investigate. We had initially thought the geodata could easily be extracted from a file called metadata.xml that is supplied on AWS with the imagery, but it turns out that although that file does contain the geodata it is not the straightforward latitude and longitude of the images. Instead, the coordinates are supplied in the Universal Transverse Mercator coordinate system. There is also a lot of other information, such as the angle of the sun relative to the ground at any given point and the angle relative to the ground with which the satellite camera was viewing it.

In addition to the metadata.xml file, it turns out the ‘.jp2’ files also contain geodata in Geography Markup Language (GML), again using UTM for the coordinates. If you open the ‘.jp2’ files in a text editor you can see the GML data. There is also a file called tileInfo.json that again contains the coordinates in UTM format.

We decided it wasn’t worth the effort of trying to convert the UTM coordinates into latitude and longitude, as the KML file for determining tile codes already has all the information we need. So we made this JavaScript tool that accepts a tile code and creates a KML Ground Overlay with the correct coordinates and you can then open its properties in Google Earth and select the Sentinel image you have created with GIMP. Note that the corners will be correct, but we found that the imagery in other parts of the image may not line up exactly with Google Earth imagery. We believe GeoSage’s Spectral Transformer for Sentinel-2 Imagery is able to stretch the image using the information in metadata.xml for a more accurate result, but we could not confirm this as our trial licence has expired. For most casual uses, however, our method should be good enough.

Also of note is that the AWS files include a file called preview.jpg that is well worth checking before anything else, as you may find there is too much cloud cover, or the image doesn’t cover the part of the tile you are interested in, etc.

It is important to note that the Sentinel imagery has a resolution of 10 m per pixel, which is better than Landsat imagery but not as good as the high resolution satellite and aerial imagery available for most locations in Google Earth. So the main use of Sentinel imagery is for observing large scale events that are not yet visible in Google Earth. It is also good enough to see developments like road construction, deforestation or mining in areas where Google Earth has no recent imagery.

False Colour
It is also relatively easy to create false colour images. To do this, just download extra bands and substitute them when combining in GIMP. For example, one of the more popular false colour combinations is to use the near-infrared band B08 as Red, the Red band B04 as Green and the Green band B03 as Blue. This is a good combination for seeing fire scars. We tested it on the area around Lake Erskine, California the site of the largest, most destructive wildfire of the 2016 California wildfire season.

Copernicus Sentinel data, 2016.
1. The scar from the Erskine fire (blackened area).
2. and 3. show two other small fire scars.

Note that the other bands are lower resolution. See this page for details on the different bands.

Here is the same area done with band B8A, B11 and B12:

The post More about processing Sentinel imagery appeared first on Google Earth Blog.

Catégories: Sites Anglophones

China’s South–North Water Transfer Project

jeu 14-07-2016

We recently came across China’s South–North Water Transfer Project. China has more water in the south than the north and water shortages are becoming a problem in the north. The South–North Water Transfer Project uses canals to move water from large rivers in the south to northern provinces. The project consists of three main routes:

  • The Eastern Route that makes use of China’s ancient Grand Canal for some of its length and started operating in December 2013.
  • The Central Route that started operating in December 2014.
  • The Western Route that is still very much in the early planning stages.

With $79 billion already spent on the project by 2014 it is one of the most expensive engineering projects in the world.

We thought it was such a large project that a map of the routes would be easy to find, but surprisingly enough, the Wikipedia page is somewhat out of date and we couldn’t find any maps showing the completed routes. The best we could find were rough sketches drawn before the completion of the Eastern and Central routes.

We thought it would be interesting to trace the routes in Google Earth. We found the Central route without difficulty and it is easy to follow over its length of over a thousand kilometres. It is an enormous project, having to cross over or under major rivers along its route. It also goes through mountains with tunnels often several kilometres long.

The Central Route starts from the Danjiangkou Reservoir, which had to be raised by 13 metres, displacing some settlements along its shores.
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Before and after of a settlement that had to be relocated because of the rising waters.

To get an idea of the scale of the project, this YouTube video shows some of the construction of the Central Route.

The Eastern Route was much harder to trace, as the area has a lot of waterways, including rivers and canals used for shipping and it is not easy to figure out which ones are also being used to move water to the north.

We also came across complicated junctions, such as this one:

.junction{ width:676px; height:530px; background: url(""); } .junction:hover { background: url(""); }

According to Wikipedia the tunnel under the Yellow River consists of two 9.3 m diameter horizontal tunnels, positioned 70 m under the riverbed.

The tunnel under the Yellow River is nearly 5 km long.

You can see below the routes we were able to map out. We are not certain we have got all the Eastern Route sections correct and there are some gaps. The yellow sections are not navigable and are therefore definitely intended for moving water. The orange section is not part of the project, but is a section of the route of the Grand Canal, which extends further south than the water transfer project.

To see the routes in Google Earth download this KML file.

jQuery(document).ready(function() {jQuery(function(){jQuery('.sliders').each(function(i){jQuery(this).beforeAfter({imagePath: '/js/utils/',showFullLinks : false});});});});

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Catégories: Sites Anglophones

Processing Sentinel imagery with GIMP

mer 13-07-2016

Sentinel imagery can be thought of as Europe’s equivalent of Landsat imagery. It is freely available just like Landsat imagery, but higher resolution. Today we are having a look at how to process it in order to view it in Google Earth with the help of GIMP.

Before we begin, if you intend to work with Sentinel imagery a lot, then first have a look at GeoSage’s Spectral Transformer for Sentinel-2 Imagery, as that makes the process extremely easy and adds some additional features that we simply cannot accomplish with GIMP. The only downside is it is not a free product.

In addition, the European Space Agency (ESA), provides a free tool called SNAP for processing Sentinel imagery, but we have not yet managed to figure out how to use it to get imagery into Google Earth.

Obtaining the imagery
The best way we have found for getting Sentinel imagery is from Amazon Web Services (AWS). The first step is to find out what the tile code is for the area you are interested in. To do this, download this KML file, which shows the tiles and tile codes. In our case we were interested in a landslide that occurred near Glacier Bay, Alaska on 28th June, 2016. This turned out to be tile 08VLL. The next step is to go to this page on AWS and find the data for your chosen tile. So in our case, select ‘8’ which represents the ’08’ in the tile name, next select ‘V’ and finally ‘LL’. Then you choose the date you are interested in, in year – month – day order. Imagery is captured about once a week, but it can vary by location. In our case, the only image so far captured after the event of interest was captured on July 11th, 2016. Finally click on the ‘0’ as there is typically only one image for a given day. You should now see a list of files available, and for a standard colour image you only need B02.jp2, B03.jp2 and B04.jp2. Download them by clicking on the links. Each one is about 85 MB.

The imagery can also be obtained here, which provides the imagery in a format suitable for use with SNAP, but the downloads are typically 5 to 6 GB as they include a large area and all the colour bands.

Converting to jpg
The Sentinel imagery is provided in a format known as JPEG 2000 with file extension “.jp2”. Although the JPEG 2000 standard was created in 2000, it hasn’t been very popular and not many programs support it. We believe GIMP has partial support, but it was not able to open the Sentinel imagery. So, we used a free image viewing program called Irfanview to do the conversion. Simply open the files in Irfanview then save them again as “.jpg”. Other free converters exist such as OpenJPEG and ImageMagick, both of which are command line converters.

Combining the colour bands
The next step is to open all three images in GIMP – open one first, then add the others as layers by dragging them into the ‘layers’ pane. To combine them into a single image, select Colors->Components->Compose. Choose RGB as the colour model and select B04 as the red channel, B03 as the green channel and B02 as the blue channel. This will open a new GIMP window with the three layers combined into a single image. It may still look a bit colourless at this stage. Now select Colors->Levels. In the popup window click the ‘auto’ button, then click ‘OK’. The colours should now look a lot better.

At this point our image looked like this:

Glacier Bay, Alaska, Copernicus Sentinel data, 2016.

Note that the image doesn’t fill the whole square and it is actually only part of a much larger image. However, even this piece is larger than we actually want. So, we cropped the image to the area we were interested in, then exported it as a “.jpg”.

Importing into Google Earth
When you use GeoSage’s Spectral Transformer for Sentinel-2 Imagery, as mentioned earlier, the resulting image contains the geographical coordinates and it can simply be drag and dropped into Google Earth Pro. However, our method above does not include any geolocation information, so it must be manually positioned. Open Google Earth, navigate to the approximate location the image was captured then add an image overlay. In the image overlay properties select the file previously created with GIMP. Now adjust the transparency slider (found just below where you selected the image) to about half way, so you can see both the image you are adding and the Google Earth imagery behind it. The default settings allow you to rotate and adjust the size of the image overlay, but force it to remain a rectangle. However, our Sentinel image is typically not exactly rectangular, so go to the ‘location’ tab in the overlay’s properties window and click ‘Convert to LatLngQuad’. This changes the way you adjust the overlay so that you can now move each corner individually. It can be a little difficult to get it just right, but patience usually pays off in the end. Moving each corner adjusts the whole image and puts out of alignment parts that had already been aligned. You need to look for easily recognisable features as close as possible to each corner then match up the overlay with the Google Earth imagery at each corner in turn and repeat several times until they all match. Once you are done positioning it, put the transparency slider back to the right, so that the overlay is no-longer see-through.

Once aligned, this is what our image looked like in Google Earth:

Glacier Bay, Alaska, Copernicus Sentinel data, 2016.

Zooming in to the location of the Landslide:

Landslide near Glacier Bay, Alaska, Copernicus Sentinel data, 2016.

We can also use Google Earth’s measuring tools to find that the area affect by the landslide is about 10 km in length.

To see the above image in Google Earth download this KML file. To get an idea of the size of the event, look at the northern edge of the overlay. There are two cruise ships visible, one in the Google Earth imagery and one in the Sentinel image. They look tiny in comparison to the landslide. If the landslide had gone into the water it could have caused a catastrophic tsunami.

The post Processing Sentinel imagery with GIMP appeared first on Google Earth Blog.

Catégories: Sites Anglophones

The “backspace key” in Google Earth

mar 12-07-2016

We recently accidentally pressed the Backspace key in Google Earth and discovered that it takes you back to the previous ‘view’ and even pops up a little help window to tell you what it did and also suggests Ctrl-Backspace has the opposite effect i.e. if you had pressed backspace and gone back to a previous view, Ctrl-Backspace will take you ‘forward’ to the view you just came from.

The interesting thing is that we couldn’t find this keyboard shortcut in any lists of keyboard shortcuts for Google Earth.

We did some experimenting and found that Google Earth remembers everything you have looked at in a given session. A ‘view’ counts as when the earth is stationary in the viewing pane. So, if you move around and zoom in and out stopping between each movement then Google Earth records each pause as a ‘view’. Pressing Backspace repeatedly takes you back through all the things you have looked at. Then if you don’t click anything, you can use Ctrl-Backspace to take you forwards again through the same sequence.

We found that in ‘historical imagery’ it is able to remember what date you were looking at. However, Ctrl-Backspace does not. This is a pity, as it would have made switching back and forth between dates in ‘historical imagery’ much easier. It also does not automatically switch between ‘historical imagery’ and normal view. In addition, it doesn’t remember other settings, such as what layers were selected, etc.

We closed the little help window telling us about the functionality and could not get it back without editing the registry:
HKEY_CURRENT_USER\SOFTWARE\Google\Google Earth Plus\UndoViewNotificationShown=true

For it to work, it requires the ‘map’ area to be selected. So if, for example, you click something on the tool bar, such as switching to ‘historical imagery’, backspace will no longer work until you click on the map again. Also, if you have a placemark selected, backspace will offer to delete the placemark rather than the above functionality.

If any of our readers knows any other shortcuts not found on this list please let us know in the comments.

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Catégories: Sites Anglophones

How often is Google Earth imagery updated: The continental US

lun 11-07-2016

We are often asked how often Google updates the imagery in Google Earth. The answer depends on where you live and can be anywhere from once a week to never. For much of the world, there are certain hotspots that get fairly regular updates and other places that have no high resolution imagery whatsoever. To get an idea of where the hotspots are, see our posts on historical imagery density.

The continental US is a special case as it has complete coverage with aerial imagery that is updated over time in large patches that are not concentrated around particular points of interest. The US government has had various aerial imagery gathering programs in the past whose imagery Google has since acquired and put into Google Earth. These appear to have often been done state by state, or at least the imagery Google acquired often follows state boundaries. More recently, however, Google has had its own imagery collection program. So, we decided to see whether or not they are managing to cover the whole of the US and just how long that takes. We used the Google Earth API to map out imagery of the continental US for the last 20 years.

Speed in milliseconds per image:

And if you wish to see the data in Google Earth, download this KML file.

Keep in mind that the areas shown are slightly larger than the actual imagery.

We believe Google started gathering imagery around 2012 and the change in patterns is noticeable in the above animation. Instead of following state boundaries, Google appears to gather imagery in what appears to be a random pattern. Analysing the data from 2012 onwards, we believe Google is covering the whole country roughly every three years. There is just one little area in Nevada that seems to have been missed.

We must keep in mind that if you are using Google Maps when there is 3D imagery available, that is what is shown, and that is not included in our analysis here, as the dates and extents of 3D imagery is not available via the Google Earth API.


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Multi-coloured snow

ven 08-07-2016

Last week Google gave the Google Earth global mosaic a makeover. When we were having a look at some of the minor issues with the new mosaic we noted some odd patterns in Chandeleur Sound, Louisiana. We have since found what we believe are related patterns in other parts of the world where there is permanent snow cover.



The effect occurs as you zoom in and Google Earth is transitioning between the global mosaic created from Landsat imagery, to higher resolution imagery. We believe that it is a glitch in the algorithm used to do the blending between the two imagery sets. The most obvious cases are rectangular sections with very bright colours, but we believe the yellowish tint to the imagery in the right hand side of the screenshot below is also related. The tint goes away as you zoom in further (as do the brighter coloured patches).


In Iceland, there are patches of yellow:


Chandeleur Sound, Louisiana.

To find the locations above in Google Earth download this KML file.

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Street View extended coverage and problems: Bangladesh and Mongolia

jeu 07-07-2016

It’s been a couple of months since Kyrgyzstan got Street View, so we thought we would have a look at the changes since then. As you can see below, the main additions are a near doubling of area covered in Bangladesh and Mongolia.

Street View changes between May 9th, 2016 and July 7th, 2016. Red: new, Blue: previously existing. Large version.

However, we found that as of this writing, it is impossible to actually view the new Mongolia Street View. We tried with both Google Earth and Google Maps, both of which show the blue lines, but when you drop the ‘yellow man’ on the map, Google Earth just takes you to ‘ground level view’ and Google Maps does nothing. The older Mongolian Street View works, as does the fresh Bangladeshi Street View.

We also came across a great example of what to do if you just can’t wait for the Street View car. Notice the trail of blue dots in the screenshot below?

Google Maps user Wahidur Rahman has travelled along the road by bus and every so often captured a panoramic photo and uploaded it to Street View. It was overcast, so the lighting was poor and the imagery isn’t as high resolution as typical Street View, but it is certainly better than nothing.

See in Google Maps

However, Google Earth does not currently show user-contributed Street View, so the above imagery can only be viewed in Google Maps. It used to work in the past, but appears to have been broken when Google merged Google Views with Street View.

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Why “Google Jupiter” would have to be different

mer 06-07-2016

The big astronomy news this week is the arrival of NASA’s Juno probe at Jupiter-orbit after a five year flight. Of course, for us Google Earth fans, the first question is ‘we have Google Earth, Google Moon and Google Mars, why not Google Jupiter?’.

The problem, however, with Google Jupiter, is that what we see of Jupiter, is actually its clouds and not its surface. The reason why Google Earth does not show the clouds by default and Google goes to great lengths to remove them from imagery is because they are ever changing, and the same applies to Jupiter’s clouds. There are some long term, large scale structures in the clouds such as the famous Great Red Spot, which is actually a large storm system, but even that changes shape and position over time. Even if we had the technology to see through the clouds, the surface is believed to be mostly liquid hydrogen and would again not have any static features. So, a Google Jupiter would serve almost no useful mapping purpose unless it included features not found in Google Earth.

The above observations apply to all the giant planets: Gas Giants Jupiter and Saturn, and Ice Giants Uranus and Neptune.

One possibility would be to have a Google Jupiter that kept updating, but for this we would need regular imagery of the planet. To get an idea of what this would look like, see the animation below. To see it in Google Earth, download the original animation it is based on found in this post created by Google Earth Community member ‘barnabu’ back in 2006.

Speed in milliseconds per image:

Of course there are other rocky planets like Mercury, which has been mapped, which would benefit from a Google Mercury, and there are many rocky moons, with Jupiter alone having 51 that are more than 10 km in diameter. There are some moons such as Saturn’s Titan which have such thick atmospheres that they will be difficult to map, but Titan does have surface features that almost certainly will be mapped eventually.


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Catégories: Sites Anglophones

Making use of the Google Earth API

mar 05-07-2016

Although the Google Earth API was officially deprecated back in December 2014, Google chose not to shut it down on schedule in December 2015 and have instead kept it running. Although you should not design websites around it as most browsers no-longer support the technology it is based on (NPAPI), it can still be a very useful tool for particular purposes.

Today we are sharing a tool based on the Google Earth API that we developed for our own purposes, but think others might find useful. If you have a collection of placemarks and want to know whether or not Google Earth has relevant imagery, rather than switching to ‘historical imagery’ then checking them one by one in Google Earth, this tool can do much of the work for you.

As an example of what it might be useful for, we took this page on Wikipedia that lists the locations of all the tornados in the United States from January to March 2016. We used the provided coordinates to create placemarks in Google Earth. We then used the tool to generate a new KML which shows which placemarks have imagery from 2016.

Placemarks with imagery from 2016 are highlighted in green.

It is not perfect in this particular situation as it does not tell us whether the imagery in question was captured before or after the particular tornado. To achieve that, we would have to find a way to include the tornado date in the original KML file. But it does considerably cut down on the number of placemarks we should check if we want to find signs of tornado damage. You can get the above KML file here. Keep in mind that many of the tornados were very week and did almost no damage.

In order to use this tool, you need to open this page in Firefox which, as of this writing, still supports the Google Earth API. The first time you open the page, you must click the link that says ‘Activate Google Earth’, then choose ‘Allow and Remember’ in the popup. Then refresh the page.

Next, select the KML file containing the placemarks you want to use, and enter a date in the space provided and click “Get Dates”. The tool will check the latest imagery date for each location and when complete will download a KML file which you can view in Google Earth. Note that each placemark takes two seconds because we find that the Google Earth API is a bit unreliable if rushed. The resulting KML file includes all placemarks from the original file and puts the date of the most recent imagery in the placemark description. In addition, it colour-codes the placemarks yellow and green depending on whether or not the latest imagery is before or after the date provided below.


KML file:

Date: (yyyy/mm/dd)

Get Dates

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Catégories: Sites Anglophones

Google Earth weather layer broken again

lun 04-07-2016

Over the last few weeks we have had several emails from GEB readers saying that the weather information in Google Earth is not accurate. We have compared the temperatures shown in Google Earth with those shown on, which is listed as the source of the information in the Google Earth popups, and we have found that the figures do not match and in some cases there are significant differences. Last year the weather layers had a similar problem, with the weather data simply not being updated. In that case it was obvious that the issue was a communication problem between Google Earth and the source of the information, as the dates shown in the popups were not being updated, indicating that the data was old. This time, however, the dates shown in the popups are current, but the actual figures are not changing. We checked some locations and although the date shown changes quite regularly the figures displayed in the popups do not. Only the ‘conditions and forecast’ layer is affected as far as we can tell. We verified by comparisons to various websites that the cloud and radar maps are reasonably current.

Despite the name, Snowville, Utah, is actually quite hot this time of year, yet Google Earth gives it 37°F / 3°C. gives its temperatures in the 55°F – 90°F range, so the issue is not one of time of day.

We checked locations on several different continents and the issue seems to be universal.

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Catégories: Sites Anglophones

Animating the Aral Sea

ven 01-07-2016

Earlier this week Google released an update to the global mosaic that Google Earth shows when zoomed out. In our post where we were having a deeper look at the mosaic, we mentioned that it would be interesting to try and put a date on the imagery by looking at inland lakes or seas that are known to be growing or shrinking over time. So, we decided to have a go at doing this.

We chose what is possibly the largest and best known example of this – the Aral Sea. According to Wikipedia it is technically a lake and the ‘Sea’ in the name is a reference to the sea of islands that used to inhabit it. Formerly one of the four largest lakes in the world, it had shrunk to just 10% of its former size by 2007. So, it seemed like an excellent candidate for our project.

We had a look through historical imagery, and clearly we were not the first to think of this, as Google has kindly provided historical images of just this location starting in 1973. Below, you can see an animation made with Google Earth historical imagery and the final frame is the new global mosaic.

Speed in milliseconds per image:

Next, we had a look at Google Earth Engine’s ‘timelapse’ feature. Just go to this site, move the map to the location of the Aral Sea and start the animation. Again, it looked fairly encouraging, except for the fact that it seemed to experience a regrowth in 2010. Google Earth Engine’s timelapse feature is made in a similar way to the method used for creating the global mosaic. Many Landsat images from a particular year are combined to create an image that is as cloud free as possible. The difference with the global mosaic is that there is no need to restrict the search for cloud free imagery to a single year. Earth Engine’s ‘timelapse’ feature currently ends in 2012 – which is when Google first released a global mosaic for Google Earth. Let’s hope that with this new global mosaic, they will consider updating the ‘timelapse’ feature too.

So, the next step was to download Landsat imagery of the region for more recent dates. We stuck to Landsat 8 imagery, which begins in 2013. We also selected only images with less than 50% cloud cover. The result can be seen in the YouTube video below:

As you can see above, the Aral Sea is not consistently shrinking over time, but varies quite considerably over time. Whether this was also the case in previous years, we don’t know, as the Google Earth Engine timelapse only does one image per year and that image is a compound image from multiple images from the year. Clearly the Aral Sea has shrunk considerably and is still, on average, shrinking, but it is not so easy to judge the exact time the imagery in the global mosaic was captured. Compounding this is the fact that there is ice on the shores in the winter months, which changes the appearance of the shoreline. However, by carefully looking at the south western branch of the lake, which appears to be shrinking fairly consistently, we believe the Google Earth global mosaic of that part, most closely matches the size of the lake in early 2015. But, the area as a whole is a compound of multiple images and we are fairly sure we can even detect Landsat 7 stripes in the central region between the two lakes. Keep in mind that the Landsat 7 satellite is still operating, so this does not mean that the imagery used is old.

Finally, below is an animation combining both Google Earth historical imagery and the Landsat imagery. We are not sharing the Landsat imagery as a download because it is rather large (90Mb).

In the above video, the dates shown in the timeline are only approximate for the Landsat images, as there were often no corresponding historical imagery dates to match. The correct dates for the Landsat imagery are shown in the first video.


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Catégories: Sites Anglophones

The best of Google Earth for June 2016

jeu 30-06-2016

The biggest news this month is that Google Earth got a makeover, with Google updating the global mosaic shown when zoomed out. Overall, we like it a lot, but nothing is perfect, so we had a look at some of the minor issues it still has.

There were several imagery updates in the month and we had a look at some of the interesting sights to be found in the imagery, including:

We also discovered an image in the Sahara Desert from Google’s Terra Bella. The image has since been removed. Presumably Google were just testing something. It would be nice if they do decide to use Terra Bella imagery to fill in the gaps in Google Earth’s high resolution imagery.

We had a look at a new tool by GeoSage called Spectral Transformer for Sentinel-2 Imagery which, as the name suggests, is for processing imagery from the Sentinel program.

We noted that Google had released some imagery of Rio de Janeiro, Brazil, that was captured in 2013 and we talked about the reasons why it sometimes takes Google so long to release 3D imagery.

We had a look at an archaeological find in Petra, Jordan, that was found by Dr. Sarah Parcak with the aid of satellite imagery.

We had a look at a story about a Kraken being spotted in Google Earth imagery – which turned out to be a rock.

We had a look at how to make desktop backgrounds with Google Earth imagery (Google Earth Pro makes it easy).

We came across a story on Bellingcat that mentioned that Google is continuing to update historical imagery in Ukraine, despite it being essentially censored since July 2015. We discussed the issue as well as having a look at some of the locations relevant to the Bellingcat story.

We had a look at Sun-synchronous orbit, the orbit configuration used by most imaging satellites.

We provided a Google Earth API based tool for making historical imagery animations and also gave some tips for making good animations.

We had a look around Rio de Janeiro and the developments in preparation for the upcoming Olympics. We also had a look at an oil refinery there and animated the oil tanks showing the floating roofs rising and falling over time, depending on oil stocks.

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Catégories: Sites Anglophones

The Google Earth new global mosaic: a deeper look

mer 29-06-2016

Google recently refreshed Google Earth’s global mosaic with newer, sharper imagery. So far, we like it very much and think it is definitely an improvement. However, we will have a look around and see if we can find any flaws or interesting aspects to the new imagery.

Landsat 7 stripes

We already pointed out yesterday that although Landsat 8 imagery was used in the new mosaic, it is not entirely free of Landsat 7 imagery with its characteristic stripes. They typically show up in hard to photograph places, such as those that have near year round snow cover or cloud cover, but we think we even saw some in the Sahara.


There are a few locations where non-Landsat imagery has been included in the mosaic. This includes a number of islands, such as Svalbard and the islands in the South China Sea.
Below we can see a particularly noticeable strip across Smith Island, which is part of the Andaman and Nicobar Islands in the Indian Ocean. The image is actually a DigitalGlobe image from 2011 which disappears as you zoom in.

We believe the reason for this is that there simply aren’t any good quality, snow-free and cloud-free Landsat images of the locations in question. Islands, it would appear, are cloud magnets.


Overall, the contrast in the imagery is noticeably higher and features you may have never noticed before stand out. Lakes generally seem to be greener or browner than before.


It is important to note that the ocean floors are actually a different data set and have not, as far as we know, been updated at this time. However, they did receive a significant update in January this year. We have come across some oddities in the margin between land and sea. For example, along the coast of Vietnam there is a thin border of brown where the Landsat mosaic ends, but between that and the sea floor data is some other imagery which includes some clouds.

We saw this same effect in a number of other locations around the world.

We also found that if you zoom in on Chandler Sound, which is part of the Mississippi delta in the Gulf of Mexico, Google Earth shows this strange pattern:

We are not sure if this has anything to do with the global mosaic update.

Dating the imagery

The imagery is a mosaic collected from parts of images from the Landsat archive going back many years, so it is impossible to put a date on the whole mosaic. However, there are specific places where it is possible to determine the approximate date of the imagery used. The best locations to do this would be large lakes or inland seas that are shrinking or growing over time. We haven’t yet done this for any such lakes, but we did check the Nansen Ice-shelf in Antarctica and determined that the imagery there has not been changed from the previous mosaic. The imagery is from 2003 as we determined when watching the ice sheet crack. We also checked Bento Rodrigues in Brazil and are fairly sure that the imagery is from before the disaster that took place there in December last year


Landsat imagery has a resolution of only about 30m per pixel and we suggested yesterday that Google consider using Sentinel imagery, which is higher resolution. However, after some consideration we have realised that for the global mosaic, the important factors are consistent colouring and good global coverage. As you zoom in, Google Earth transitions to higher resolution imagery where available so greater resolution of the global mosaic is not necessary. It is, however, the case that there are some parts of the world where no higher resolution imagery exists and the Landsat imagery is used even when you zoom in and only for these locations does Google need to seek alternative sources. For much of the globe they have already used medium resolution imagery from Spot Image. For more on what image sets are used where, see our series on Google Maps API Maximum Zoom.

To see the locations featured in this post in Google Earth download this KML file.

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Catégories: Sites Anglophones