Detecting geolocation errors in glacier outlines with Sentinel-2 snow cover duration maps

Two years ago I posted an animation of the snow cover area evolution near Zermatt, Switzerland from Sentinel-2 L2A data processed by LIS.

From this time series of snow maps I generated a snow cover duration map and
added the glacier outlines from the Randolph Glacier Inventory 5.0.

Colors: Snow cover duration between 01 Sep 2016 to 31 Aug 2017 (in days). Black line: Glacier outline from RGI.



I was satisfied by the overall good agreement between the areas with a high snow cover duration and the glacier outlines. However by looking more closely at the small isolated glacier in the eastern part I noticed a mismatch between both datasets..
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Les dépôts d'avalanche sur le glacier du Miage vus par le produit neige Theia

Récemment j'ai extrait la carte de la durée de l'enneigement faite à partir des produits neige Theia pour la Direction Départementale des Territoires de Haute-Savoie (DDT 74). Pour cela j'ai traité quatre tuiles Sentinel-2 ce qui couvre une bonne partie des Alpes du Nord.

Durée de l'enneigement du 01 septembre 2017 au 31 aout 2018 calculée à partir des produits Theai

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En inspectant cette image mon œil a été attiré par des anomalies positives sur la langue rocheuse du glacier du Miage au sud du Mont-Blanc (cliquer sur l'image pour agrandir).

Ces surfaces qui restent enneigées plus longtemps que leur voisinage sont des dépôts d'avalanche bien visibles sur Google Earth. Je n'ai pas trouvé la date de l'image utilisée par Google Earth mais on remarque la bonne correspondance entre les dépôts de cette photo instantanée et le produit de durée d'enneigement (un peu comme dans ce post historique) !


Note

Comme pour les Rocheuses Canadiennes, j'ai d'abord cherché la limite haute de l'altitude des zones de forêts pour ne garder que les zones ouvertes d'altitude où le produit Theia est le plus robuste. Ce qui m'a permis de conclure qu'en première approximation on peut conserver les zones situées au-dessus de 2000 m.

Densité du couvert forestier dans les tuiles T31TGL, T31TGM, T32TLR et T32TLS par tranche d'altitude d'après le produit Copernicus Tree Cover Density 2015 à 100 m de résolution. Pour chaque case, le trait rouge central indique la médiane, et les bords inférieur et supérieur indiquent respectivement les 25ème et 75ème percentiles. Les boites à moustaches s'étendent jusqu'aux points de données les plus extrêmes non considérés comme des valeurs aberrantes, et les valeurs aberrantes sont tracées individuellement à l'aide du symbole «+».

Snow cover duration in the Canadian Rockies from Sentinel-2 observations

Recently I generated one year of snow maps from Sentinel-2 in the Canadian Rockies for a talented colleague who is working on the numerical simulation of the snow cover at high-resolution with an exciting new hydrological model. The area is not covered by Theia, hence I used Start_Maja to generate the L2A products and then LIS to generate the snow masks on the CNES supercomputer (thanks!).

Study area (four Sentinel-2 tiles)

This area is quite challenging for snow optical remote sensing: the terrain is steep and there are a lot of forests. After processing this area, I also found some unexpected issues in the LIS processor, which need to be fixed, like turbid rivers detected as snow, or wildfires smoke detected as snow. I tried to use Pekel's water mask to remove the rivers and lakes pixels but there are some glaciers that are misclassified as water in this product hence I simply masked out areas below 2000 m. This eliminates most of the water surfaces but not all the forests, hence I used Hansen's global forest product to mask out pixels with a tree cover density larger than 50%.

Peter Lougheed Provincial Park


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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.

Synthèses de la durée de l'enneigement au 1er avril dans les Pyrénées

Le 1er avril est la date souvent utilisée par les hydrologues pour caractériser le stock de neige disponible avant la saison de fonte. A partir des produits neige Theia, j'ai calculé la durée d'enneigement par pixel de 20 m dans les Pyrénées depuis le début de l'année hydrologique (le 1er septembre 2018) jusqu'au 1er avril 2019.

Durée de l'enneigement entre le 01 Septembre 2018 et le 01 Avril 2019


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