Two billion pixels to check your next ski destination

More exactly: two maps of 934'343'100 pixels!

We [1] have processed 6205 Sentinel-2 images and 593 Landsat-8 images to compute the annual snow cover duration in the Alps and the Pyrenees at 20 m resolution for hydrological years 2016-2017 and 2017-2018. The snow cover duration (or snow persistence) is defined as the total number of days with snow on the ground over a hydrological year (from 01 September and ends on 31 August). We also added the ski runs from the great OpenSnowMap project.


Link: http://osr-cesbio.ups-tlse.fr/echangeswww/majadata/simon/snowMaps.html

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The Khumbu Icefall by Venµs

This is a time-lapse of all clear-sky images captured by Venµs over the Khumbu Icefall near Mount Everest since November 2017 (one year of data, 51 images at 5 m resolution).

Khumbu Icefall by Venµs

Here I used the Level-1C products (i.e. without atmospheric correction) because the Level-2A products are provided at a lower resolution (10 m). Anyway, the atmosphere is rather thin in this area..

To make this animation (without the date annotation to simplify):
1) download
python ./theia_download.py -l 'Nepal' -c VENUS -a config_theia.cfg -d 2017-11-01 -f 2018-12-01 --level 'LEVEL1C'
2) unzip
mkdir -p ../VENUS
parallel unzip -d ../VENUS ::: $(find . -name "VENUS*zip")

3) export as natural color pictures
cd ../VENUS
mkdir -p VIS
parallel gdal_translate -srcwin 4118 3132 1058 770 -of JPEG -b 7 -b 4 -b 3 -scale 0 800 0 255 -ot byte {} VIS/{/.}.jpg ::: $(find . -name VE*[0-9].DBL.TIF)

4) animate with imagemagick
convert -delay 10 VIS/*jpg anim.gif

Snow cover duration from 01 Sep 2017 to 31 Aug 2018 in the Alps and Pyrenees

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We are thinking to distribute a new product that would provide the snow cover duration in the Alps and the Pyrenees over a hydrological year (snow persistence map). Here is a preview of a "beta" version that is only 100 m resolution for now.
 

Snow cover duration from 01 Sep 2017 to 31 Aug 2018. Pixels with less than 60 days were masked out.


 
A few words on the method (see also here): this map is generated after a linear interpolation at the daily time step of every available Theia snow product. Here we have selected 26 Sentinel-2 tiles, which represents 3189 snow maps for the study period, that is 96 billion pixels to process.
 
With the accumulation of Sentinel-2 data it becomes possible to look at interannual variability. Here is for example a visual comparison of the snow cover duration calculated from January to May for three years in the Pyrenees (31TCH):

 Snow cover duration 31TCH

Snow Duration from Jan 01 to May 31 in the Pyrenees (tile 31TCH)

 
We can see longer snow durations for the year 2018, which was an exceptional year!
 

Durée d'enneigement dans les Alpes et les Pyrénées du 01/09/2017 au 31/08/2018

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Nous sommes en train de penser à un nouveau produit qui donnerait la durée d'enneigement dans les Alpes et les Pyrénées au cours de la dernière année hydrologique. Voici ci-dessous un aperçu d'une version "beta" (artisanale) qui n'est qu'à 100 m de résolution pour l'instant.
 

Durée d'enneigement du 01/09/2017 au 31/08/2018.

Durée d'enneigement du 01/09/2017 au 31/08/2018. Les pixels avec moins de 60 jours ont été masqués.


 
Quelques mots sur la méthode (voir aussi ici) : cette carte est générée après une interpolation linéaire au pas de temps journalier de toutes les cartes d'enneigement Theia disponibles. Ici nous avons sélectionné 26 tuiles Sentinel-2, ce qui représente 3189 cartes d'enneigement pour la période considérée, soit 96 milliards de pixels à traiter.
 
Avec l'accumulation des données Sentinel-2 il devient possible de comparer les années entre elles. Voici par exemple une comparaison visuelle de la durée d'enneigement calculée de janvier à mai pour trois années dans les Pyrénées (31TCH):

Durée d'enneigement 31TCH

Durée d'enneigement du 01 janvier au 31 mai sur la tuile 31TCH

 
On voit ressortir des durées d'enneigement élevées pour l'année 2018 qui fut une année exceptionnelle !
 
Un grand merci à Germain Salgues de Magellium pour avoir produit toutes les données rapidement ce qui nous a permis de montrer cette carte au séminaire Theia à Montpellier dès le 18 octobre ! Et merci à Michel Le Page pour la mise en ligne.

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!

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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