Dozens of landslides after the 2018 Hokkaido earthquake

An earthquake with magnitude 6.7 occurred on 06 Sep 2018 in Hokkaido, Japan, killing at least 17 people and leaving nearly 3 million households in Hokkaido without electricity.
 
The quake came two days after typhoon Jebi, "the strongest storm of 25 years".
 
Below is an image comparison near Atsuma dam lake in the south of Hokkaido, before and after the earthquake, showing multiple landslides in the forest areas. Both images were acquired by Sentinel-2 and are shown as false color composites of bands 11-8-2. Click here to view a larger version.
 

 
The heavy rainfalls brought by typhoon Jebi probably explain why the earthquake triggered so many landslides. I would like to count the landslides in this area, but the Sep 15 image is not yet available in my favorite image processing engine. I will check later.
 


 
Links:
- in the AGU landslide blog
- Google published a crisis map but it seems that the imagery is not publicly available
 
Thanks to Laurent Longuevergne for letting me know about this!

A seamless and cloudless Sentinel-2 image of France in July 2018

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UPDATE: resolution improved to 20m !
 
I had to tinker a couple of days with Theia's Sentinel-2 monthly syntheses, in order to produce the mosaic below. For that, I used:

  • monthly syntheses of Sentinel-2 L3A products delivered by Theia in July 2018; this products are made from Level2A products, corrected from atmospheric effects, and provided with a good cloud mask, thanks to MAJA processor. Such L3A products will be delivered every month.
  • a script developed by Simon Gascoin
  • good advice from Simon and Michel Le Page
  • and Gdal, (Thanks Gdal !)

This mosaic best resolution is 20m. It is already requiring 8 GB. We could have provided it at 10m resolution, but it would have required 32 GB and several dozens of hours of computation.


See it full screen

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Sentinel-2 captured a jökulhlaup in Afghanistan

In the Landslide blog Dave Petley has analyzed Planet images of the Pashgor debris flow in Afghanistan (here and here). Here I used two Sentinel-2 images (before and after the event) to show the path of the debris flow from the high mountain area to the Panjshir Valley. Sentinel-2 images have a lower spatial resolution than Planet images but they have a larger swath and the near-infrared channel is useful to highlight the water-rich surfaces (dark blue) and the vegetation (red). Also, Sentinel-2 images are free to use for everyone.

According to the experts this event can be called a jökulhlaup since it was due to the abrupt collapse of a supraglacial lake, i.e. a lake formed on the surface of a glacier, in this case a debris-covered glacier. The debris flow (a mix of water and debris) has traveled 13 km from the source to the deposit area where it has dammed the Panjshir river.

Belgium at 10 m resolution in July 2018

Using the new L3A product in Theia it is possible to make nice cloud-free mosaics from Sentinel-2 imagery. Here is an example for Belgium and the script to do it in your terminal.

 

Click here to view in full screen.
 


# download L3A images over Belgium
python theia_download.py -l 'Belgium' -d 2018-07-01 -f 2018-07-31 --level LEVEL3A -a config_theia.cfg
# unzip using GNU parallel
parallel unzip ::: SENTINEL2X_201807*zip
# make a mosaic of each band
# WARNING only works if all images have the same projection otherwise an extra step is required with gdalwarp
parallel gdalbuildvrt {}.vrt SENTINEL2X_201807*/*{}*.tif ::: B2 B3 B4
# stack the band mosaics
gdalbuildvrt -separate B432.vrt B4.vrt B3.vrt B2.vrt
# export as a RGB image at full resolution
gdal_translate -ot Byte -scale 0 2000 B432.vrt B432.tif
# Optionally clip the image using the polygon of the Belgium borders

gdalwarp -dstnodata 0 -q -cutline Belgium.kml -crop_to_cutline B432.tif B432_Belgium.tif
# make a tiled map to display in a browser
gdal2tiles.py -z 6-12 B432_Belgium.tif

 

NB) I used this command to generate the file Belgium.kml from the Eurostat Countries datasets:


ogr2ogr -f KML Belgium.kml -where "NAME_ENGL='Belgium'" CNTR_RG_01M_2016_4326.shp

Whitewhashing the Plastic Sea near Almería

Almería province in Spain is "one of the most recognisable spots on the planet from the lens of a passing satellite. The roofs of tens of thousands of closely packed plastic greenhouses form a blanket of mirrored light beaming into space." (The Guardian).

True color image Sentinel-2 on 22 Aug 2018

Greenhouses in Almería are typically made with transparent plastic to increase the air temperature near the crops. This enables to boost the yield and to harvest earlier than in open field. However, in summer, the temperature increases too much and must be reduced to maintain more suitable conditions for plant growth. Natural ventilation is generally not sufficient to evacuate the heat during sunny days. Therefore, the farmers cover the roofs of the greenhouses with white painting to reduce the incoming solar radiation (Baille et al. 2001). This operation is called blanqueo in Spanish.

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Sentinel-2 sees a persistent degradation of forests near the Gorges du Verdon, after 2017 drought

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Coming back from our stay in the Alps, we stayed one day near the Verdon Gorges, the European Grand Canyon (I know there is a difference in scale with the American one). Anyway, the landscape was gorgeous as you can seen on the panorama below.

Panorama from "Sublime point" (and modest)

 

 

But don't worry, I will not tell you about all my holidays adventures, I keep that for my close colleagues at lunch time, and it seems they have had enough of it with the two past days.

 

Let's go to the point, I also noticed quite a number of brown pine trees, which surprised me as the spring and summer had been rather wet for the region so far. I asked some locals who told me it was due to the severed drought that happened last year in Provence, which happened during summer and fall 2017 and damaged mainly the pine forest, particularly where the soil root zone is thin due to the presence of rock.

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Xe-Namnoy lake dam failure

[This post was written by Simon Gascoin and Sylvain Ferrant]
 

The Lao News Agency reported that the Xe-Pian Xe-Namnoy dam collapsed on Monday causing catastrophic flash floods. "The disaster has claimed several human lives [and] left hundreds of people missing," the agency reported. Construction of the Xe-Pian Xe-Namnoy dam began in 2013. Commercial operations were expected to begin in 2018. The animation below shows the water filling using a time series of Sentinel-1 images (IW orthorectifed VV only descending orbits).
 
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Snow conditions in southern Africa ski resorts

When I present the potential of Sentinel-2 for snow science, I often tell that the spatial resolution of Sentinel-2 is sufficient to detect snow at the scale of the ski runs. Because a picture is worth a thousand words, here is the Sentinel-2 view of the only two ski resorts in southern Africa on July 11.

Sentinel-2 true color composites on 11 July 2018

The snow on these ski slopes is artificial but this region can get quite a lot of snow!

[Venµs news] Distribution of Level2A has started

You might have noticed the apparition of the first Venµs L2A products on Theia web site within the real time production, since last Friday.  A first global processing will start this summer, to provide you with the data acquired from November until now. There will be probably further reprocessings to benefit from the fine tuning of all the parameters and to propagate the further evolution of Level 1 improvement.

Even if it took us a few months to check the software and set the parameters up, what took us very long... was waiting for the level 1 validation and calibration phase. As you know, our colleagues from CNES did a great work to rescue the Venµs raw data which were full of surprises. They started to provide us with calibrated products in April only, and that's when we started the validation.

 

We were quite happy with the first results, as our processor MAJA did not show any bug, and the first images looked good.  But the first validation results were quite poor, with undetected thin clouds, with biases in the estimates of atmospheric properties (Aerosol, water vapour), as well as biases in reflectances (with a lot of negative values). We then started iterating tests on the parameters, and after several iterations we corrected several errors in the parameters (Venµs band numbers are different from those of Sentinel-2, and in a couple of cases, I forgot to change them:( ), and we tuned better all the thresholds. Among those, we had to change the calibration of band 910 band by 6% (this band is hard to calibrate in flight due to the presence of water vapour and is also affected by some newly discovered stray light).

 

 

The following table compares the results we had initially, on the left, and the results obtained after tuning the parameters, on the right. Of course, what we distribute is on the right ! We will of course need to increase the number of validation points, and we expect that the low level stray light in band 910 that was discovered during the commissioning phase and is not yet corrected will introduce some site related bias in the water vapour estimates. We will therefore need a reprocessing after this defect has been fixed, if the Level 1 team finds a way to fix it. And finally, we have still some issues to solve with the shadows mask which can often be quite poor.

 

 

Before tuning After Tuning

RGB Quicklook with cloud mask contour

RGB Quicklook with cloud mask contour

Water vapour in g/cm2 compared with Aeronet

Water vapour in g/cm2 compared with Aeronet

Aerosol Optical Thickness compared with Aeronet (sorry for the scale different from that on the right)

Aerosol Optical Thickness compared with Aeronet (sorry for the scale different from that on the left)