Real time production of land cover maps without terrain data of the current time period.

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With the new availability of repetitive image time series after atmospheric correction over France from the Theia Land Data Centre, it is now possible to imagine the automatic production of land cover maps continuously with the availability of new images.

 

In the framework of the SYRHIUS project, a prototype was developed at CESBIO to assess the results of this kind of classification method at the scale of a medium scale catchment. The study zone is the Fresquel catchment (937 km2), close to the famous medieval city of Carcassonne. The main crops present in this catchment are cereals, sunflower and vineyards, and also some corn and rapeseed.
A supervised classification is used, based on Support Vector Machines, but for which the learning data base is not derived from terrain surveys held during the time period to process, as in the classical supervised methods. The learning data base which is used is created from previous years observations and from terrains data acquired in the past. Such a method has the advantage of needing no terrain data on the present period, knowing that these data often come too late to allow a real time processing, but it requires a very large data volume from several years. In case of a time period with an exceptional climate, errors might arise if the training data base does not contain the necessary information to recognise the crops.


View of the real time land cover processor

 

To test this approach, we used the Common Agriculture Policy plot data base, for years 2011 and 2012, for the Fresquel catchment, along with LANDSAT5/7 time series, which allow a time evolution of reflectances for the plots in the data base. Both data sources were used to create the learning data base. which was then use to classify the data of 2013, 2014 and 2015 for the Fresquel catchment.

 

THEIA LANDSAT8 Level 2A (corrected from atmospheric effects and provided with a cloud mask) are used as input or the processor. Due to the late availability of the Commpon Agriculture Policy data base, we are not able to provide validation figures, but previous campaigns provided Kappa in the 0.65-0.7 range for Midi Pyrénées region.
Of course, at the beginning of the crop season, the available information is not complete and the accuracy might be reduced. For that reason, the nomenclature and the number of classes evolves with the number of available LANDSAT dates. Three key dates are used : end of March, end of July and end of year. For each of the dates a new land cover map is computed with an increased detail level, as shown in next figure.

 

Three land cover maps are produces along the year, first one (S1) in March, Second one in July (S2), and the last one at the end of the year with an increasing number of classes.

We will however stress the fact that steady observations are necessary, and that on certain years, the cloud cover might degrade the quality of the results, as in the case of spring 2013, for which the LANDSAT observations only started in April. In 2013, some parts of the Area where only observed 3 times along the whole year. The results at the beginning of season are quite bad, but they enhance along the year. For the subsequent years, results are better and should further enhance with the availability of Sentinel-2 and its far better observation frequency.

The SIRHYUS project

The SIRHYUS project aims at developping and setting operationnal services related to managing water resources thanks to the integration, assimilaton and valorisation of satellite earth observation  : Veolia Environnement Recherche&Innovations, Veolia Eau, EDF, G2C environnement, Acri ST, l’UMR TETIS-IRSTEA, le CNES, VERI et le CESBIO. It was funded by the 12th Fonds Unique Interministériel, by the ministry in charge of water  and by the Provence and Languedoc-Roussillon, and the aeronautics and space foundation.

The aim is to provide new services, based on the know how of experience companies. In this framework, CESBIO implemented or enhances methods for 4 products : snow cover, land cover, evaop-transpiration and soil water content. In the future, these products will be applied to Sentinel-2. In this framework, two posts will be published on this blog : tis one, and a second related to evapotranspiration estimates in this same catchment.

 

 

 

Yoann Moreau et Isabelle Soleihavoup

Posted under: In English, Landsat, Sentinel-2

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