BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.2019.foss4g.org//77BJYC BEGIN:VTIMEZONE TZID:Europe/Bucharest BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:EET TZOFFSETFROM:+0300 TZOFFSETTO:+0200 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:EEST TZOFFSETFROM:+0200 TZOFFSETTO:+0300 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-bucharest-77BJYC@talks.2019.foss4g.org DTSTART;TZID=Europe/Bucharest:20190828T170000 DTEND;TZID=Europe/Bucharest:20190828T172000 DESCRIPTION:\n
\n\nA number of climate chan ge research projects discover dependencies between dynamics of vegetation indexes and dynamics of meteorological parameters\, which make possible es timation and monitoring of growing season parameters using remote sensing data. In our study\, we use Normalized Difference Water Index (NDWI) that can be derived automatically from the daily satellite imagery collected by MODIS sensor. The NDWI indicates amount of liquid water in plant tissue\, and then reflects change of vegetation growing conditions and particularl y growing season change.
\nTo ensure monitoring of growing season parameters we elaborated an automated software complex that incorporates desktop Geographic Information System (GIS) software (QGIS w as used)\, geospatial database and complex of computational tools. The GIS is used as an infrastructure element for operating and visualization purp oses\, while the database together with computational tools enable storage and multipurpose processing of meteorological and remote sensing data. Th e meteorological data is collected for the past period of 130 years and ND WI data for the 20 years. Developed complex is tested on the example of Re public of Komi (Northern part of European Russia) that is covered by Taiga and Tundra natural zones and impacted by different climate forming factor s.
\nCurrently we describe architecture of the elab orated complex and design of data processing chains. Elaborated complex en sure automation of downloading raw remote sensing data and reprocessing it into gridded NDWI maps. In this context\, it can be underlined that daily collected MODIS imagery can be discovered as big geospatial data\, due to this we were needed to resolve a number of optimization tasks to implemen t its processing. Subsequently\, NDWI data is used to produce gridded map series that reflects time and spatial dynamics of growing season character istics. Produced data have a special significance for areas with sparse me teorological network.
\n\nKeywords: GIS\, R emote Sensing Data\, Climate Change\, Growing Season\, Vegetation Indexes\ , MODIS\, NDWI.
\n\n DTSTAMP:20240328T204813Z LOCATION:Coralle Room SUMMARY:Automated GIS-based Complex Developed for the Long-term monitoring of Growing Season Parameters Using Remote Sensing Data - Deleted User\, Ev geny Panidi\, V. Yu. Tsepelev URL:https://talks.2019.foss4g.org/bucharest/talk/77BJYC/ END:VEVENT END:VCALENDAR