The odds to find snow in St. Moritz

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:
0: No-snow
100: Snow
205: Cloud including cloud shadow
254: No data
 
Here is a piece of script to do this:

#!/bin/bash 
# 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)
do
# snow is coded with 100
gdal_calc.py --overwrite -A $f -B snow.tif --type=Byte --calc="B+(A==100)" --outfile=snow.tif
done

# now do the same for clear.tif
# init
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)
do
# 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
done

# 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)!
 

Venµs captured the orange snow in the Pyrenees

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..
Continue reading

Enneigement au 1er avril 2018 dans les Pyrénées

Le 1er avril est le moment privilégié par les hydrologues pour caractériser le potentiel hydrologique du manteau neigeux. Dans le cadre de l'OPCC [1] nous avons compilé différents indicateurs [2] :

 

 

  • 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).

 

https://pbs.twimg.com/media/DZ4_0wkXkAELrmc.jpg

Continue reading

Another validation of CESBIO's 2016 France land-cover map

In this post, a validation of the land-cover map of France produced by CESBIO for the 2016 period was presented. This validation used independent data (that is data collected by different teams and using different procedures than the data used for the classifier training), but the validation procedure consisted in applying classical machine learning metrics which, as described in this other post, have some limitations.

A fully independent validation following a sound protocol is costly and needs skills and expertise that are very specific. SIRS is a company which is specialised in the production of geographic data from satellite or aerial images. Among other things, they are the producers of Corine Land Cover for France and they are also responsible for quality control and validation of other Copernicus Land products.

SIRS has recently performed a validation of the 2016 France land-cover map. The executive summary of the report reads as follows:

This report provides the evaluation results of the CESBIO OSO 2016 10m layer and the CESBIO OSO 2016 20m layer.

The thematic accuracy assessment was conducted in a two-stage process:

  1. An initial blind interpretation in which the validation team did not have knowledge of the product’s thematic classes.
  2. A plausibility analysis was performed on all sample units in disagreement with the production data to consider the following cases:
  • Uncertain code, both producer and operator codes are plausible. Final validation code used is producer code.
  • Error from first validation interpretation. Final validation used is producer code
  • Error from producer. Final validation code used is from first validation interpretation
  • Producer and operator are both wrong. Final Validation code used is a new code from this second interpretation.

Resulting to this two-stage approach, it should be noticed that the plausibility analysis exhibit better results than the blind analysis.

The thematic accuracy assessment was carried out over 1,428 sample units covering France and Corsica.
The final results show that the CESBIO OSO product meet the usually accepted thematic validation requirement, i.e. 85 % in both blind interpretation and plausibility analysis. Indeed, the overall accuracies obtained are 81.4 +/- 3.68% for the blind analysis and 91.7 +/- 1.25% for the plausibility analysis on the CESBIO OSO 10m layer. The analysis on the 20m layer shows us that the overall accuracy for the blind approach is 81.1 +/-3.65% and 88.2 +/-3.15% for the plausibility approach.
Quality checks of the validation points have been made by French experts. It should be noticed that for the blind analysis, the methodology of control was based mostly on Google Earth imagery, no additional thematic source of information that could provide further context was used such as forest stand maps, peatland maps, etc.

These results are very good news for us and for our users. The report also contains interesting recommendations that will help us to improve our algorithms. The full report is available for download.

La neige de Pyeongchang

Connaissiez-vous ce dicton coréen "On n'est jamais trop prudent" ?

 

Voici une série d'images Sentinel-2 près du site olympique de Pyeongchang. On voit de la neige artificielle apparaître dès le mois de novembre !

 

neige Pyeongchang

 

Les images suivantes montrent qu'au cours du mois de janvier de la neige naturelle a finalement recouvert le site...

The operational production of the Theia Snow collection has started

Great news, we can announce that the operational production of the Theia snow collection has started well. It means that maps of the snow cover area are now constantly added to the Theia portal. These maps are automatically generated from Sentinel-2 observations and have a spatial resolution of 20 m. The Snow collection will progressively cover most mountain regions in west Europe, but also the Atlas in Morocco, eastern Canada... The Snow collection can be freely downloaded from http://theia.cnes.fr by any registered user.

 
Today's front page of the Theia website featured this nice example in Sierra de Ancares (western end of the Cantabrian Mountains, Spain). In the southeast, snow was also detected on the Montes Aquilanos, including the small ski resort El Morredero. The image was captured yesterday! It illustrates well the value of multispectral imagery to discriminate the snow cover from the clouds. There is a cloud which looks alike snow but it is actually a valley fog confined by local topography.
 

Theia Sentinel-2 level 2A and snow product in the region de los Ancares, Spain. Image captured by Sentinel-2A on 30 Jan 2018.

Theia Sentinel-2 level 2A and snow product in the region de los Ancares, Spain. Image captured by Sentinel-2A on 30 Jan 2018.


Continue reading

From Multitemp blog to Nature Geoscience

You probably remember Simon Gascoin's story about the Aru glacier avalanches, which started from Simon's observations of the twin avalanches using the Sentinels. It was one of the big buzz pages of the blog in 2016. The first images were published here, then spread out in many scientific websites and the social networks.

 

The same mountain valley in Tibet is shown before and after part of a glacier sheared off on 17 July 2016. Credit: NASA/Joshua Stevens/USGS/ESA

It seems that the story finally made its way to Nature Geoscience, after a large work from many scientists lead by Andreas Kaab.  Congratulations to all the team !

 

So, dear CESBIO colleagues, or remote sensing time series users, it is time to submit your work to this blog as a first step to future publications in Nature ;) !

 

 

Machine learning benchmarking for land cover map production

Land cover map validation is a complex task. If you read French, you can check this post by Vincent Thierion which shows how the 2016 LC map of France produced by CESBIO stands with respect to data sources independent from those used for its production. But this is only one aspect of the validation. A land cover map is a map, and therefore, there are other issues than checking if individual points belong to the correct class. By the way, being sure that the correct class is known, is not so easy neither.

 

In this epoch of machine learning hype 1, it is easy to fall in the trap of thinking that optimising a single metric accounts for all issues in map validation. Typical approaches used in machine learning contests are far from enough for this complex task. Let's have a look at how we proceed at CESBIO when we assess the quality of a LC map produced by classification.
Continue reading