MAJA and LIS selected for the next Pan European high resolution snow and ice monitoring service

The European Environment Agency (EEA) has selected a consortium led by Magellium to implement the next Pan European high resolution snow and ice monitoring of the Copernicus Land Monitoring Service.

EEA 34 countries and corresponding Sentinel-2 tiles

This future service aims to monitor three variables at 20 m resolution over the tiles displayed above:

  1. Fractional Snow Cover (FSC)
  2. Permanent Snow Line (PSL)
  3. River and Lake Ice (RLI)

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Sen2Agri final version was just published

The Sen2Agri system was just released in version 2.0. As the project is now terminated and will not receive more funding from ESA, V2_0 is the final version, apart maybe for a few bug fixes.

 

Sen2Agri is a very complete ground segment that automates download and processing of Sentinel-2 data to obtain multi-temporal syntheses, crop/non crop, and crop type masks as well as biophysical variables and phenological indicators. It was designed to be able to process the Sentinel-2 data over whole countries.

 

The Sen2AGri system has been a huge success. I receive an email each time MACCS/MAJA is downloaded, and I just counted 651 downloads after sorting them out the double download,

The new version includes the most recent version of MAJA (3.3) for atmospheric correction and cloud detection (but without using the CAMS data, which are not always easy to access), an early version of WASP to make the composites, an early version of Iota2 to obtain crop types, a processor to obtain the crop/non Crop mask, and a processor to compute biophysical variables inspired by the works of F.Baret and M.Weiss at INRA.

 

The consortium, funded by ESA, was lead by Sophie Bontemps et Pierre Defourny at the Université Catholique de Louvain. The system was developed by CS-SI France and Romania, and most of the methods were designed in CESBIO.

 

 

 

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.

Combined exploitation of VENμS, Sentinel-2 and Landsat-8: the spectral bands

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The combined use of VENμS, Sentinel-2 and Landsat-8 data can increase the likelihood of obtaining cloud-free images or may allow detailed tracking of rapidly evolving phenomena.

In order to facilitate this combination, the table below summarizes the correspondences between the spectral bands of the instruments. VENμS does not have a spectral band in the middle infrared.

The figure below shows the spectral bands of VENμS and Sentinel-2 in the 400 to 1000 nm range. The SWIR bands of Sentinel-2 are not included.The table below shows the usual band combinations


The figure below makes it possible to assess the degree of similarity of the spectral responses of these usual bands.

The detailed spectral responses of each instrument are available via the following web pages:

VENµS

http://www.cesbio.ups-tlse.fr/multitemp/?page_id=14229

SENTINEL-2

https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/document-library/-/asset_publisher/Wk0TKajiISaR/content/sentinel-2a-spectral-responses

LANDSAT

https://landsat.usgs.gov/spectral-characteristics-viewer

https://landsat.usgs.gov/landsat/spectral_viewer/bands/Ball_BA_RSR.xlsx

 

 

 

Exploitation combinée de VENµS, Sentinel-2 and Landsat-8 : les bandes spectrales

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L’utilisation combinée des données de VENµS, Sentinel-2 et Landsat-8 peut permettre d’augmenter la probabilité d’obtenir des images sans nuage ou de suivre de manière détaillée des phénomènes à évolution rapide.

Afin de faciliter cette combinaison, le tableau ci-dessous présente de manière résumée les correspondances entre les bandes spectrales des instruments. VENµS ne comporte pas de bande spectrale dans le moyen infrarouge.

La figure ci-dessous présente les bandes spectrales de VENµS et Sentinel-2 dans le domaine 400 à 1000 nm. Les bandes SWIR de Sentinel-2 ne sont incluses.Le tableau ci-dessous présente les combinaisons de bandes usuelles

La figure ci-après permet d'apprécier le degré de similarité des réponses spectrales de ces bandes usuelles.

Les réponses spectrales détaillées de chaque instrument sont disponibles via les pages web suivantes :

VENµS

http://www.cesbio.ups-tlse.fr/multitemp/?page_id=14229

SENTINEL-2

https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/document-library/-/asset_publisher/Wk0TKajiISaR/content/sentinel-2a-spectral-responses

LANDSAT

https://landsat.usgs.gov/spectral-characteristics-viewer

https://landsat.usgs.gov/landsat/spectral_viewer/bands/Ball_BA_RSR.xlsx

 

 

 

Our blog's audience in 2018

A seventh year begins for the "Séries temporelles" blog, and as usual, it is an opportunity to review its audience, and to get a little self-satisfaction. We usually publish this post early January, but it seems there was no January this year (or was I too busy ?). The blog is still receiving more visits every year, with a sharp growth this year : +35% of visits ...

Blog traffic from December 2012 to January 2019

Blog traffic from December 2012 to January 2019 (the trends were computed using the Theil–Sen estimator), computed by Simon Gascoin.

So, if we look at the trends on the plots above, the audience growth is remarkably linear, but if we sum-up everything per year, we see a sharp increase. The cause is that outlier in the top-right corner or each graph above, related to a big buzz in Japan for SImon's article about Xe-Namnoy lake dam failure in July, that flooded several villages, and killed too many people.

 

2013 2014 2015 2016 2017 2018
Number of visits 13985 22928 34723 47773 57692 79243
Number of viewed pages 30922 46940 66947 89555 105846 131846

 

French visitors only counted for 25% of visits, much less than the other years. Japan ranked second with 16%,  United States ranked third, followed by European countries (UK, Germany, Italy, Spain) and by India, Canada and Morocco.

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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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Sentinel-2 Level3A time series (July, August, September 2018)

If you are not afraid to spend too much time while you have urgent things to do, you may have a look to the mosaic of Sentinel-2 monthly syntheses for September over France. You may access to each monthly synthesis using the following links :

 
Or you may also use the viewer below to compare with the previous months and see how France became brown in September :
 

See it full screen

The monthly syntheses are produced using the WASP processor, which is described here.

By comparing the various syntheses, you will see the evolution of the landscape, generally much brownler in September, but this representation will also help you spot the composite artefacts. These are not very numerous, but you will see them :

  • on some web browsers (firefox V58), geometrical differences appear even at a low resolution. Other browsers and versions do not have this defect. It is really not due to Sentinel-2 or Theia products
  • above water and snow (we must work on this defect)
  • where clouds have covered a place during the whole month of July or August. These pixels are flagged as invalid in the products (but not on the mosaic).
  • where clouds or shadows were not properly detected by MAJA
  • at the edges of Sentinel-2 swath. For the first time, in october, a swath edge is clearly visible near Cambrai. The area must have been quite cloudy, and we observe here a greener part on the right, observed later in October, that the browner part on the left. The only way to correct this kind of atefact while keeping a physical meaning to the reflectances, would be to improve Sentinel-2 revisit time
  • some tile edges in July, due to the fact that Level 3A products were not all generated for the 15th of July, but for dates between the 8th and the 26th. This has been corrected for the next months