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.
Le conseil départemental des Alpes de Haute-Provence a mis en ligne le tracé des 6 500 km de sentiers aménagés et balisés sur l’ensemble de son territoire. Ces sentiers sont visibles sur le joli site www.rando-alpes-haute-provence.fr qui fonctionne avec le logiciel Geotrek.
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!
To celebrate the 10'000th snow product in Theia, here is the latest snow map over the Vicdessos area in the french Pyrenees near Andorra. The snow is in blue and the clouds are in white! Pan and explore the map below..
See full screen
Big up to the Muscate team!
On June 23 we will celebrate the third anniversary of Sentinel-2A in orbit. With three years of data we can start looking at the inter-annual variability of biophysical variables, like.. (random example), the snow cover.
This is what I attempted to do for the Theia workshop. I downloaded all available snow cover products from Theia over the Central Pyrenees (tile 31TCH) and I generated additional snow maps from the Theia Landsat-8 level-2A products using let-it-snow processor. Landsat-8 images enable to increase the frequency of observations when only Sentinel-2A was operational between 2015 to 2017.
I resampled the Landsat-8 snow maps to the same reference grid as Sentinel-2 at 20 m resolution using the nearest neighbor method. I cropped all snow maps to the intersection of the Sentinel-2 tile (green polygon) and Landsat-8 tile (red polygon).
When there was a snow map from Sentinel-2 (S2) and Landsat-8 (L8) on the same day, I merged them into a composite using a simple pixel-based rule:
Si vous avez vu le JT du 26 mars dernier, vous savez que le lac de Serre-Ponçon est à un niveau exceptionnellement bas en ce moment .
Extrait du journal 12/13 de France 3 du 26/03/2018
Did you know that the St. Moritz Casino is the highest in Switzerland? If you like gambling, I have a little game for you: what are the odds to find snow near St. Moritz?
Fortunately, I just finished the processing of 218 Sentinel-2 dates from 2015-Dec-04 to 2018-Apr-10 of tile 32TNS with our let-it-snow processor. I did this off-line production for a colleague because, as of today, Theia only distributes the snow products after July 2017 in this region of Switzerland (see the available products here).
A quick way to check the output is to compute a snow cover probability map: that is, for each pixel, the number of times that snow was observed divided by the number of times that the snow could be observed.
To compute this map we just need to know that the Theia snow products (LIS_SEB.TIF raster files) are coded as follows:
205: Cloud including cloud shadow
254: No data
Here is a piece of script to do this:
# initialize snow.tif with zeros
# store in Byte because we have less than 255 dates
f0=$(find . -name LIS_SEB.TIF | head -1)
gdal_calc.py --overwrite -A $f0 --type=Byte --calc=A*0 --outfile=snow.tif
# accumulate snow pixels in snow.tif
for f in $(find . -name LIS_SEB.TIF)
# snow is coded with 100
gdal_calc.py --overwrite -A $f -B snow.tif --type=Byte --calc="B+(A==100)" --outfile=snow.tif
# now do the same for clear.tif
gdal_calc.py --overwrite -A $f0 --type=Byte --calc=A*0 --outfile=clear.tif
# accumulate clear pixels in clear.tif
for f in $(find . -name LIS_SEB.TIF)
# only snow and no snow are coded with values lower than 101
gdal_calc.py --overwrite -A $f -B clear.tif --type=Byte --calc="B+(A<101)" --outfile=clear.tif
# Finally compute the snow probability in % (100.0* makes the calculation in float)
gdal_calc.py -A snow.tif -B clear.tif --type=Byte --calc="(100.0*A)/B" --outfile=snowProba.tif
This is the output:
The images are scaled from 0 (black) to 100 (white). The units are number of days for snow and clear, percentage for snowProba.
From which you can map the odds to find snow near St. Moritz (click on the image to animate)!
Theia just published the first Venµs images today, including a beautiful view of the Pyrenees. Once you have dezipped/untared/unzipped the files you can make a true color composite using the command:
gdal_translate -b 7 -b 4 -b 3 -scale 0 300 0 255 -ot byte VE_VM01_VSC_PDTIMG_L1VALD_ES_LTERA_20180419.DBL.TIF myColorCompo.tif
I tend to focus on the snow so I stretched the colors between reflectances 0-1000 instead of 0-300:
gdal_translate -b 7 -b 4 -b 3 -scale 0 1000 0 255 -ot byte VE_VM01_VSC_PDTIMG_L1VALD_ES_LTERA_20180419.DBL.TIF mySnowColorCompo.tif
First, I was a bit puzzled by the orange shade in the northern part of the image. We inspected carefully the image with Olivier because at this stage radiometric calibration issues are still possible..
Le 1er avril est le moment privilégié par les hydrologues pour caractériser le potentiel hydrologique du manteau neigeux. Dans le cadre de l'OPCC  nous avons compilé différents indicateurs  :
- L'équivalent en eau du manteau neigeux dans les sous-bassins pyrénéens du bassin de l'Ebre est calculé par la Confederación Hidrográfica del Ebro (agence de bassin) à partir d'observations MODIS, des données météorologiques, et un modèle de type "degré-jour" (la fonte est proportionnelle à la température de l'air).
The ESA website featured this Sentinel-2 image of a large and thick plume of dust off Libya (click here to view in the Sentinel-Hub)
Contains modified Copernicus Sentinel data (2018), processed by ESA, CC BY-SA 3.0 IGO