BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Services Publics de Wallonie//Geoportail 1.0//EN METHOD:PUBLISH X-WR-CALNAME:Géoportail de la Wallonie X-WR-TIMEZONE:Romance Standard Time BEGIN:VTIMEZONE TZID:Romance Standard Time X-LIC-LOCATION:Romance Standard Time BEGIN:STANDARD DTSTART:16011028T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010325T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT DTSTART:20170518T123000Z DTEND:20170518T150000Z UID:881f7ab6-d75c-47af-b57f-717e798004ab CREATED:20170515T103515Z DESCRIPTION:Summary - Mapping annual cropland over large areas with high resolution satellite image time series \nWith human population growth, biofuel development and climate change, the food supply system is subject to increasing pressures. In this context, timely and dependable information on crop production becomes crucial for market stability and food security. In spite of advances in satellite systems and data processing, there is a disconnect between operational cropland mapping and the state-of-the-art. This thesis seeks to bridge this gap by capitalizing on available land cover maps and by optimizing the satellite inputs. First, priority areas are identified for all countries to strategically allocate future efforts. Second, methods are proposed to enable regular cropland mapping over large areas in the absence of in situ calibration data. The combination of calibration data selected from land cover maps and spectral-temporal features derived from the satellite image time series yields spatially consistent results with an accuracy that varies depending on the landscape. The features are stable over time and reduce the intra-class variability which improves generalization. Finally, the spatial resolution requirements can be anticipated by quantifying the fragmentation of the agricultural landscapes. It allows to adjust the resolution to cope with the ever-increasing data volumes and to select appropriate imagery for reliable area estimation. These are essential building blocks towards an operational cropland mapping system and an improved global agriculture monitoring. \n \nhttp://www.uclouvain.be/fr/agenda/sst \n X-ALT-DESC;FMTTYPE=text/html:Summary - Mapping annual cropland over large areas with high resolution satellite image time series \nWith human population growth, biofuel development and climate change, the food supply system is subject to increasing pressures. In this context, timely and dependable information on crop production becomes crucial for market stability and food security. In spite of advances in satellite systems and data processing, there is a disconnect between operational cropland mapping and the state-of-the-art. This thesis seeks to bridge this gap by capitalizing on available land cover maps and by optimizing the satellite inputs. First, priority areas are identified for all countries to strategically allocate future efforts. Second, methods are proposed to enable regular cropland mapping over large areas in the absence of in situ calibration data. The combination of calibration data selected from land cover maps and spectral-temporal features derived from the satellite image time series yields spatially consistent results with an accuracy that varies depending on the landscape. The features are stable over time and reduce the intra-class variability which improves generalization. Finally, the spatial resolution requirements can be anticipated by quantifying the fragmentation of the agricultural landscapes. It allows to adjust the resolution to cope with the ever-increasing data volumes and to select appropriate imagery for reliable area estimation. These are essential building blocks towards an operational cropland mapping system and an improved global agriculture monitoring. \n \nhttp://www.uclouvain.be/fr/agenda/sst \n LAST-MODIFIED:20170517T061520Z LOCATION:Auditoire croix du Sud,1 place croix du sud, 1348 Louvain La Neuve, Belgium SEQUENCE:0 STATUS:CONFIRMED SUMMARY:Seminars on large scale satellite remote sensing for agriculture & PhD Dissertations TRANSP:OPAQUE END:VEVENT END:VCALENDAR