India water crisis: Sentinel-1 detects surface water, ForEarth app restitutes and shares water stresses

In the framework of an open call Science4society funded by ESA, researchers at CESBIO have implemented a surface water detection algorithm from radar data Sentinel-1 on a cloud computing system. An API developed by Geomatys and JeoBrowser, two IT companies in France, is used to send the resulting surface water masks to a smartphone App named ForEarth, which display the surface water fluctuation in time for Indian regions.

The beta version is available on PlayStore and displays statistics and water masks for the Hyderabad region, Telangana, India.

The Hyderabad area has been processed. rest of India will be processed soon

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Near real time detection of deforestation in French Guiana

Marie Ballère started in October 2018 a Ph.D. funded by WWF and CNES. The aim of her Ph.D. is to characterize animal habitats in tropical forest using radar and optical data. The first results on near real time forest disturbances assessment using radar Sentinel-1 data in French Guiana were shown at the ESA Living Planet Symposium 2019 in Milano, and they are striking !

 

The near real time forest disturbances detection method used by Marie has been described in Bouvet et al. (2018) and successfully tested over a test site in Peru. Classical methods are based on the hypothesis that the radar backscatter decreases when disturbances occur. However, the backscatter does not necessarily decrease, because rainfalls and/or trees remaining on the ground for example, lead to an increase of radar backscatter.

 

To get around this problem, the method from Bouvet et al. (2018) is based on the detection of radar shadowing. Shadowing occurs in radar images because of the particular side-looking viewing geometry of radar systems. A shadow in a radar image is an area that cannot be reached by any radar pulse. Shadows created by trees at the borders between forest and non-forest areas can be observed in high-resolution radar images (Figure 1), depending on the viewing direction. Shadows that appear are characterized by a sudden drop of backscatter in the radar time series. Thanks to the purely geometrical nature of the shadowing effects, this decrease of backscatter is expected to be persistent over time. New shadows should consequently remain visible for a long time and are easily detectable when dense time series of radar data, such as Sentinel-1 time series, are available.

Figure 1 Illustration of the SAR shadowing effect at the border between forests and deforested areas

 

This method has been tested over various sites in South American, African and Asian tropical forests for three years now and significantly improved. Marie Ballère participated to the improvement of the method, applied it over the whole French Guiana using Sentinel-1 data acquired from 2016 to 2018, and validated the resulting maps. Slashing deforestation (farming method that involves the cutting and burning of trees) detection has been validated using 94 reference data (surface area of 48.2 ha) kindly shared by Pierre Joubert and Eloise Grebic from the Parc Amazonien de Guyane. Producer and user accuracies related to disturbed forests reached 83% and 99% respectively. Gold mining detection has been validated using 36 reference data (surface area of 76 ha), leading to producer and user accuracies of 86% and 99% respectively.

 

In addition, we compared our results with the deforestation patches detected in the University of Maryland (UMD) Global Land Analysis and Discovery (GLAD) Forest Alert dataset (Hansen et al., 2016), a Landsat-based humid tropical forest disturbance alert system over the tropics (http://glad.geog.umd.edu/alerts). Producer accuracies of 24% and 44% were found for slashing deforestation and gold mining respectively. A small area showing the comparison between the CESBIO and UMD-GLAD methods is shown in the maps in Figure 2.

Figure 2 Comparison between the CESBIO and UMD-GLAD near real time forest disturbances detection methods in French Guiana in 2018. Gold mining reference plots are shown in green. Producer accuracies of 86% and 44% were found using the CESBIO and UMD-GLAD respectively

Figure 3 shows the number of disturbed areas detected per month using the CESBIO and UMD-GLAD methods (note that disturbed plots that were not detected using the UMD-GLAD method were not taken into account). Slashing deforestation, occurring mainly during the dry season, was detected timely using both methods. However, because clouds hamper the GLAD optical-based forest disturbances detection during the rainy season, gold mining occurring all year long was detected 72±58 days in advance using the CESBIO method.

Figure 3 Slashing deforestation, occurring mainly during the dry season, was detected timely using CESBIO and UMD-GLAD methods. Gold mining occurring all year long was detected 72±58 days in advance using the CESBIO method

Figure 4 Forest disturbances detection using the CESBIO method in French Guiana from 2016 to 2018

The CESBIO method has been applied over the whole French Guiana for the years 2016, 2017 and 2018 (Figure 4). The deforestation rates were found to be -0.7%, -0.5% and -0.5% respectively, relatively to French Guyana area.

 

A lot of exciting research can now be performed based on these results (e.g. for understanding the causes related to the spatial and temporal evolution of disturbances patterns). In addition, Sentinel-1 and Sentinel-2 data are being currently used by Marie to identify the drivers of deforestation.

References:
  • Bouvet, A., Mermoz, S., Ballère, M., Koleck, T., & Le Toan, T. (2018). Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series. Remote Sensing, 10(8), 1250.
  • Hansen, M. C., Krylov, A., Tyukavina, A., Potapov, P. V., Turubanova, S., Zutta, B., ... & Moore, R. (2016). Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11(3), 034008.

Apport des images radar et optiques pour la cartographie des surfaces irriguées

(English version below)

Dans le cadre du projet Simult’eau (partenaires : Arvalis, CACG, Chambres d’Agriculture du Tarn et des Hautes-Pyrénées, financement CASDAR) nous avons testé l’apport d’une utilisation combinée des images radar et optiques pour la cartographie des surfaces irriguées (maïs et soja) dans le Sud-Ouest de la France. Les résultats publiés dans Remote Sensing (https://www.mdpi.com/2072-4292/11/2/118) ont révélé que l’utilisation d’images radar Sentinel-1 combinées aux images optiques (Landsat-8) permettait de détecter les surfaces irriguées plus précocément qu’avec les images optiques seules. En effet ces dernières sont souvent perturbées par la présence de nuages qui rendent la détection impossible à certaines périodes de l'année. Ce résultat, qui doit être confirmé par des études complémentaires (autres lieux et autres dates), est très encourageant. Il ouvre de nouvelles perspectives pour une gestion "optimisée" des ressources en eau notamment pour des organismes tels que la CACG (Compagnie d'Aménagement des Coteaux de Gascogne) ou les Organismes de Gestion Collective de l’eau (OUGC). Les cartes produites sont en libre accès sur le site Theia: http://www.theia-land.fr/fr/ces-surfaces-irriguees.


Ces recherches se poursuivent actuellement dans le cadre de la thèse de
Yann Pageot financée par le CNES, l'Agence de l'Eau Adour-Garonne et la CACG.
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S1-Tiling, on demand ortho-rectification of Sentinel-1 images on Sentinel-2 grid

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Sentinel-1 is currently the only system to provide SAR images regularly on all lands on the planet. Access to these time series of images opens an extraordinary range of applications.
In order to meet the needs of a large number of users, including our needs, we have created an automatic processing chain to generate "Analysis Ready" time series for a very large number of applications. Sentinel-1 data is ortho-rectified on the Sentinel-2 grid to promote joint use of both missions.

 

 

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4 thèses en cours à Toulouse pour étudier les forêts tempérées par télédétection

Le pôle toulousain de recherche publique en télédétection est surtout connu sur la thématique forestière grâce à la mission BIOMASS, qui sera lancée vers 2021 et qui est portée par le CESBio, mais qui concerne surtout les forêts tropicales. L’objectif de cet article est de présenter les travaux en cours sur les forêts tempérées, dans le contexte de la France métropolitaine, qui sont portées en synergie par l’UMR Dynafor (collègues INRA, Ensat et EI Purpan) et par l’UMR CESBio. En effet, 4 thèses sont actuellement en cours dont 2 qui seront soutenues fin 2019. Le point commun à ces 4 thèses comme aux travaux qui les ont précédées est l’utilisation de séries temporelles, d’abord basse résolution (Modis), puis, depuis 2015, en haute résolution spatiale avec Sentinel 1 et 2 (‘S1’ et ‘S2’).

 

Différence de phenologie entre chênes

Figure 1. Différences de phénologie entre espèces de chênes.

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S1Tiling : ortho-rectification à la demande des données Sentinel-1 sur la grille Sentinel-2

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​Sentinel-1 est actuellement le seul système à fournir des images SAR régulièrement sur toutes les terres de la planète. L'accès à ces séries temporelles d'images ouvre un champ d'application hors du commun.

Afin de répondre aux besoins d'un grand nombre d'utilisateurs, dont les nôtres, nous avons créé une chaîne de traitement automatique permettant de générer des séries temporelles "prêtes à l'emploi" pour un très grand nombre d'applications. Les données Sentinel-1 sont ortho-rectifiées sur la grille Sentinel-2 pour favoriser l'usage conjoint des deux missions.

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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Speed-up downloads from PEPS S2 mirror site with peps_download.py

The French Sentinel mirror site, PEPS, has a very clever data management facility. All the products are stored on tapes, with a capacity of several PB, and there is some sort of cache made of disks. The products accessed recently are on disks, while the other products stay on tapes. The storage costs and also power consumption are therefore largely optimized.

 

The drawback is that before accessing a file on tape, some time is needed to get the tape, and read the file on tapes. This can take something like 2 to 10 minutes. My little tool, peps_download.py was designed when most of the products were on disks, and it was quite slow to download products on tapes. As I am not a patient person, I have tried to speed it up, and it works well, thanks to good advise from CNES peps  colleagues (Christophe Taillan and Erwann Poupart).

 

The previous version was working like that :

Make catalog request

For all product in the request result :

- while product is not downloaded

 - try to download the product

 - if still on tape, wait for 2 minutes

As a result, for each product on tape, it was necessary to wait for 2 to 10 minutes.

 
Now, it works like that

Make catalog request

For all products on tape in the request result

- ask to read it on disks

While (still some products to download):

- Redo catalog request

- Download products on disk

- If some products are not on disk yet

 - wait for 2 minutes

 
On my computer, it used to take more that 12 hours to download 2 years of Sentinel-2 data for a given tile. It has now been reduced to less that 3 hours (but my computer is on CNES network). I hope you will have similar results !

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 ;) !