Exploitation of high spatial, spectral and temporal resolution Earth observation imagery for large area land cover estimation

The new generation Earth observation missions such as Sentinel-2 (a twin-satellite initiative prepared by the European Space Agency, ESA, in the frame of the Copernicus programme, previously known as Global Monitoring for Environment and Security or GMES) and Venμs, jointly developed by the French Space Agency (Centre National d’Etudes Spatiales, CNES) and the Israeli Space Agency (ISA), will revolutionize present-day environmental monitoring with the yielding of unseen volumes of data in terms of spectral richness, temporal revisit and spatial resolution. Venμs will deliver images in 12 spectral bands from 412 to 910 nm, a repetitivity of 2 days, and a spatial resolution of 10 m; the twin Sentinel-2 satellites will provide coverage in 13 spectral bands from 443 to 2200 nm, with a repetitivity of 5 days, and spatial resolutions of 10 to 60m. The efficient production of land cover maps based on the exploitation of such volumes of information for large areas is challenging both in terms of processing costs and data variability. In general, conventional methods either make use of supervised approaches (too costly in terms of manual work for large areas), target specialised local models for precise problem areas (not applicable to other terrains or applications), or include complex physical models with inhibitory processing costs. These existent present-day approaches are thus inefficient for the exploitation of the new type of data that the new missions will provide, and a need arises for the implementation of accurate, fast and minimally supervised methods that allow for generalisation to large scale areas with high resolutions. In order to allow for the exploitation of the previously described volumes of data, the objective of this thesis is the conception, design, and validation of a fully automatic approach that allows the estimation of large-area land cover with high spatial, spectral and temporal resolution Earth observation imagery, being generalisable to different landscapes, and offering operational computation times with simulated satellite data sets, in preparation of the coming missions. This approach is based on the integration of data processing algorithms, such as model learning and classification techniques, and land cover background knowledge on ecological and agricultural issues such as vegetation growth impact variables and practices. For example, the new introduction of temperature as a temporal axis for subsequent model learning incorporates an established vegetation growth factor to machine learning techniques for characterization across landscapes. In particular, attention is paid to deal with different issues, such as automation, missing information (determined by satellite tracks, cloud reflectance effects, shadows and snow presence), limited learning and validation data, irregular time sampling (different number of images available for each period and region, and unevenly distributed in time), data variability, and the possibility of working with different data sets and nomenclatures. To this aim, a processing methodology has been devised, automatic and not requiring any date selection or user manual work, and allowing the use of different nomenclatures and data sets. Thus, the developed methodology demonstrates the feasibility of automatic production of large scale land cover maps, dealing with data volume and variability, limited reference data and missing information, and integrating specialised knowledge and different data processing algorithms. This work is framed by the research support programme Terre, Ocean, Surfaces Continentales, Atmosphere (TOSCA) of the Centre National d'Etudes Spatiales, in relation to three research proposals: Production de cartes d’occupation des sols a echelle regionale a partir de donnees SPOT HRV multitemporelles by Jordi Inglada, and Traitement et utilisation de series temporelles d’images LANDSAT pour preparer le traitement et les applications de Venμs et Sentinel-2 and Centre D'ALgorithmie et de VAlidation des Donnees Venμs et Sentinel-2 (CALVADOVS) by Olivier Hagolle. It is dependent of the doctoral school Sciences de l'Univers, de l'Environnement et de l'Espace (SDU2E) of Paul Sabatier University and it has been developed in the laboratory Centre d’Etudes Spatiales de la BIOsphere (CESBIO) in Toulouse, France.