My Google Map Blog

Last Saturday, on 17th June, a landslide in Greenland caused a tidal wave, killing four people and injuring nine in the community of Nuugaatsiaq. Two other communities, Igdlorssuit and Viaqornat, were apparently affected. Read more about it on the Landslide Blog.

We thought it would be interesting to see the area in Google Earth using Sentinel-2 imagery. We downloaded the Sentinel-2 image from 19th June, 2017 and imported it into Google Earth:

The relative positions of the landslide and Nuugaatsiaq.Copernicus Sentinel data, 2017.

The distance between the landslide and the village of Nuugaatsiaq is about 30 km. Igdlorssuit is about 60km from the landslide site and Viaqornat just over 100 km.

The region as seen in Google Earth imagery.

The village of Nuugaatsiaq as seen in a DigitalGlobe image from 2012.

Here is a YouTube video showing the Tsunami arriving at Nuugaatsiaq:

To see the relevant section of the Sentinel-2 image in Google Earth, download this KML file
.

The post Landslide and Tsunami in Greenland appeared first on Google Earth Blog.

We love Landsat and Sentinel-2 imagery for their easy accessibility and global coverage, but they are rather low resolution at 10 m per pixel for Sentinel-2 and 15 m per pixel for Landsat. Commercial satellite imaging company Planet, now covers the globe with greater regularity and higher resolution (typically about 3 m per pixel) and for the US state of California, releases the imagery under creative commons licence within a couple of weeks of capture. We recently came across a large landslide that occurred along the Californian coast in an area known as Big Sur.

We were able to find it in Planet’s tool ‘Planet Explorer’ for browsing their imagery. You need to sign up to view daily imagery, but signup is easy and free.

The Big Sur Landslide as seen in Planet imagery.

Once you have signed up you can try going here to see the location in Planet Explorer. Try comparing before and after images with the built in ‘compare’ feature.

The post A Landslide in California with Planet Imagery appeared first on Google Earth Blog.

In March this year we had a look at landslides in Kyrgyzstan and noted just how frequent landslides there appear to be.

Just a month after that post, two more major landslides occurred to the southeast of the region we looked at before. On April 24th a very large landslide engulfed the village of Kurbu-Tash, burying 11 houses, a school, a kindergarten, a mosque and a medical facility. Luckily, nobody appears to have been harmed in that event. Then on April 29th, a much smaller but more deadly landslide, killed 24 people in the village of Ayu. For more on both landslides, including ground level photos and video, see The Landslide Blog (1 2 3).

You can see before and after images using Landsat imagery on NASA’s Earth Observatory website. However, the latest Landsat image was not available on Amazon Web Services at the time of writing, so we instead got a Sentinel-2 image of the location. Here is a ‘before and after’ of the Kurbu-Tash event.

Left: CNES / Airbus image from Google Earth. Right: Sentinel-2 image dated May 19th, 2017.

The landslide flowed south to north, burying buildings near the end of its run. The total length of the landslide is around 5 km. Also note the small lake that has formed uphill from the landslide. This is known as a ‘landslide dam’, a topic we covered last year. Such dams can be potentially catastrophic if enough water builds up, overflows and suddenly erodes the dam.

The Ayu landslide is much harder to spot. In fact, we can see at least five other landslides nearby, some of which were larger.

This report states that prior to the Ayu landslide, the Osh region has had at least 25 landslides so far this year, killing six people.

For the locations above, including image overlays using portions of the Sentinel-2 image and the locations of the many landslides we found, download this KML file

The post The Kurbu-Tash and Ayu landslides in Kyrgyzstan appeared first on Google Earth Blog.

According to Wikipedia, during the pre-dawn hours of 1 April 2017, locally heavy rain triggered flash flooding and landslides in the city of Mocoa, Putumayo, Colombia, killing at least 316 people, injuring 332, and leaving 103 others missing. Technically, the tragedy was not a landslide but rather a mud-flow triggered by many landslides. For some analysis of the cause of the tragedy see the Landslide Blog. It was predicted in 2014.

DigitalGlobe, as part of its Open Data program, has recently released satellite imagery of the location. Although we knew about the event soon after it happened, we had not expected to see any imagery as Mocoa is in a region that has near constant cloud cover, making it difficult to photograph.

DigitalGlobe provides the imagery divided up into squares of 1Gb files with no compression. So even a completely black square that is off the edge of the main image is a 1Gb download! There is a preview of the whole image, but we still ended up downloading almost all the squares to find the right ones as there are no previews of the individual squares.

Mocoa, Colombia, as seen in DigitalGlobe imagery.

Keep in mind that what we are seeing is the debris left behind from the flood. The actual flood waters probably covered some of the houses.

We didn’t download the ‘before’ images from DigitalGlobe so we haven’t done a ‘before and after’. The imagery in Google Earth is so old that such a comparison is not very informative as the town had expanded considerably since the last Google Earth image.

We have cropped and compressed the part of the imagery showing the town of Mocoa and created an image overlay for you to view in Google Earth.

The post The Mocoa Landslide in DigitalGlobe imagery appeared first on Google Earth Blog.

According to Wikipedia, during the pre-dawn hours of 1 April 2017, locally heavy rain triggered flash flooding and landslides in the city of Mocoa, Putumayo, Colombia, killing at least 316 people, injuring 332, and leaving 103 others missing. Technically, the tragedy was not a landslide but rather a mud-flow triggered by many landslides. For some analysis of the cause of the tragedy see the Landslide Blog. It was predicted in 2014.

DigitalGlobe, as part of its Open Data program, has recently released satellite imagery of the location. Although we knew about the event soon after it happened we had not expected to see any imagery as Mocoa is in a region that has near constant cloud cover making it difficult to photograph.

DigitalGlobe provides the imagery divided up into squares of 1Gb files with no compression. So even a completely black square that is off the edge of the main image is a 1Gb download! There is a preview of the whole image, but we still ended up downloading almost all the squares to find the right ones as there are no previews of the individual squares.

Mocoa, Columbia, as seen in DigitalGlobe imagery.

Keep in mind that what we are seeing is the debris left behind from the flood. The actual flood waters probably covered some of the houses.

We didn’t download the ‘before’ images from DigitalGlobe so we haven’t done a ‘before and after’. The imagery in Google Earth is so old that such a comparison is not very informative as the town had expanded considerably since the last Google Earth image.

We have cropped and compressed the part of the imagery showing the town of Mocoa and created an image overlay for you to view in Google Earth.

The post The Mocoa Landslide in DigitalGlobe imagery appeared first on Google Earth Blog.

On April 27th, 2016, a landslide occurred in Kyrgyzstan and was captured on video as you can see below. The Landslide Blog also wrote about it here and here. We have been keeping an eye on the location and Google has recently updated the imagery.

Before and after of the landslide in the video.

Having looked around the area it is clear that the region is very susceptible to landslides, with evidence of past landslides almost anywhere you look. Unfortunately, there is not a lot of imagery and most of the landslides took place before the earliest image. We did find one more that took place in 2016, although it is not quite covered by the latest image.

Before and after of another landslide that occurred in 2016.

Apart from the immediate danger from landslides, there is also the phenomenon of landslide dams, examples of which we have looked at before. A landslide dam occurs when the landslide blocks a river, creating a lake behind it and a catastrophic flood may occur when the dam gives way. We had a look around the region and found several cases where there probably was a small landslide dam and there is significant risk of such disasters in the future.

This landslide blocked the river, which has since carved a channel through the debris.

In the picture above, the slope has been slipping for many years and may never have caused a landslide dam, but the risk is clearly significant as a dam would result in the flooding of the nearby houses and its collapse could cause flooding downstream. Just a little further upstream it looks as if part of the town is slowly sliding into the river:

Also note the smaller landslide on the opposite bank which could potentially have created a landslide dam.

To find the above locations in Google Earth, download this KML file. We have marked some of the more notable landslides that we found, but there are many more in the region.

According to this video, a significant cause of the landslides is deforestation, followed by uncontrolled grazing. Trees and other plant cover helps to stabilize slopes.

The post Kyrgyzstan landslides in Google Earth appeared first on Google Earth Blog.

On 8th August, 2016, a containing dam failed at the Xiangjiang Wanji Aluminium plant in Luoyang, Henan Province in China. About 2 million cubic meters of red mud was released, spreading out over 2 kilometres and burying parts of a village in the process. Luckily, according to this article, no one was killed or injured.

Before and after of the mudslide.

Before and after closeup of some of the houses that were buried .

Find the location in Google Earth with this KML file.

We learned about the above disaster via the Landslide Blog.

Similar stories we have covered in the past include another tailings dam failure in Brazil – the Bento Rodrigues disaster, the collapse of a dam containing construction waste in Shenzhen, China, and a major landslide in the Bingham Canyon mine in the US.

The post Google Earth Imagery – Tailings Dam Collapse appeared first on Google Earth Blog.

On November 14, 2016, the South Island of New Zealand experienced a 7.8 magnitude earthquake named the Kaikoura Earthquake after the town of Kaikoura near the quake’s epicentre. The affected region is mountainous with steep slopes and the earthquake resulted in a large number of landslides, including creating some landslide dams (a topic we have covered in the past).

The Landslide Blog has done a number of posts on the Kaikoura landslides (1, 2, 3 and 4). It also mentions this article, which shows a map of the locations of the landslides so far identified using Sentinel 2 imagery.

We thought it would be interesting to examine the sentinel 2 Imagery in Google Earth. The image in question has quite a lot of cloud cover, but in the gaps between the clouds we can see the scars of a large number of landslides. It must be noted that landslides appear to be common in the region, with many landslide scars being visible in older imagery, too. Here are a couple of ‘before and afters’ showing just how many landslides there were in some places.

After image: Copernicus Sentinel data, 2016.

After image: Copernicus Sentinel data, 2016.

We processed the Sentinel 2 imagery using GeoSage’s Spectral Discovery.

To explore the Sentinel 2 imagery for yourself using Google Earth download this KML file

The post The Kaikoura Earthquake Landslides appeared first on Google Earth Blog.

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.

Cyclone Roanu was, according to Wikipedia, a relatively weak tropical cyclone that, nevertheless, caused severe flooding in Sri Lanka and Bangladesh. In addition, it caused a number of large landslides in Sri Lanka. The only imagery of the event so far in Google Earth is two patches of imagery of Sri Lanka: an image of the capital, Colombo, showing flooding and a set of images further inland showing a landslide.

The images were captured soon after the cyclone so they are rather cloudy and the light is poor.

Flooding in Colombo

Flooding in Colombo.

Much of the landslide is covered in cloud.

 
Before and after of the tail end of the landslide.

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

The post Cylcone Roanu: Landslide and Floods appeared first on Google Earth Blog.