Monitoring vegetation activity in Hungary using Direct Broadcast MODIS data
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1 Monitoring vegetation activity in Hungary using Direct Broadcast MODIS data Anikó Kern1 Contributors: Szilárd Pásztor1, Péter Bognár1, Zoltán Barcza2, Gábor Timár1, János Lichtenberger1, Péter Steinbach3, Gábor Molnár1 and Csaba Ferencz1 1 Department of Geophysics and Space Science, Eötvös Loránd University, Budapest, Hungary 2 Department of Meteorology, Eötvös Loránd University, Budapest, Hungary 3 MTA-ELTE Research Group for Geology, Geophysics and Space science
2 The HRPT - MODIS receiving station at Budapest Foundation: 2002 Location: Eötvös Loránd University, Budapest, Hungary Since 2004: Direct Broadcast MODIS data of satellite Terra and Aqua (+ earlier: data of Chibis and Relec measuring electromagnetic waves of megnetosphere) HUNGARY
3 Automatic processing chain for the DB MODIS data The applied MODIS related software: (1) SeaDAS MODIS Level1DB Software Package (v1.8) (2) MODIS Destripe Direct Broadcast Software (3) IMAPP MODIS Level2 (v3.0) (4) DBCRAS numerical weather prediction software (5) + Nested DBCRAS (6) (7) (8) (9) (10)
4 DBCRAS & NDBCRAS Since
5 Automatic processing chain for the DB MODIS data The applied MODIS related software: (1) SeaDAS MODIS Level1DB Software Package (v1.8) (2) MODIS Destriping Software (3) IMAPP MODIS Level2 (v3.0) (4) DBCRAS numerical weather prediction software (5) Nested DBCRAS (6) IDEA-I air quality forecast (v1.1) (7) (8) (9) (10)
6 IDEA-I Since
7 Automatic processing chain for the DB MODIS data The applied MODIS related software: (1) SeaDAS MODIS Level1DB Software Package (v1.8) (2) MODIS Destriping Software (3) IMAPP MODIS Level2 (v3.0) (4) DBCRAS numerical weather prediction software (5) Nested DBCRAS (6) IDEA-I air quality forecast (v1.1) (7) MOD14 DB software, Identification of fire and thermal anomalies (8) (9) (10)
8 MOD14 DB Identification of thermal anomalies Since Aqua/MODIS
9 Automatic processing chain for the DB MODIS data The applied MODIS related software: (1) SeaDAS MODIS Level1DB Software Package (v1.8) (2) MODIS Destriping Software (3) IMAPP MODIS Level2 (v3.0) (4) DBCRAS numerical weather prediction software (5) Nested DBCRAS (6) IDEA-I air quality forecast (v1.1) (7) MOD14 DB software, Identification of fire and thermal anomalies (8) MODIS True Color software (9) Direct Broadcast Google Earth software (v1.2) (10) Polar2grid & IMAPP MODIS GeoTIFF Web Mapping Service (WMS)
10 Direct Broadcast Google Earth Since
11 IMAPP MODIS GeoTIFF Web Mapping Service Since
12 Automatic processing chain for the DB MODIS data The applied MODIS related software: (1) SeaDAS MODIS Level1DB Software Package (v1.8) (2) MODIS Destriping Software (3) IMAPP MODIS Level2 (v3.0) (4) DBCRAS numerical weather prediction software (5) Nested DBCRAS (6) IDEA-I (v1.1) (7) MOD14 DB software, Identification of fire and thermal anomalies (8) MODIS True Color software (9) Direct Broadcast Google Earth software (v1.2) (10) Polar2grid & IMAPP MODIS GeoTIFF Web Mapping Service (WMS) + DB MODIS Surface Reflectance software
13 Monitoring Vegetation Activity Aim of the research: study the state and behaviour of the vegetation within Hungary based on: MOD13, MOD17 data 10 years long received DB data between Level1b data for downloaded from NASA/Reverb Echo Calculating atmospherically corrected surface reflectances using the DB MODIS Surface Reflectance software package Determining the cloudmask using the IMAPP Level2 software package Calculating country-averaged NDVI values for Hungary
14 Monitoring Vegetation Activity Using the IGBP MODIS land cover classification (included in the MOD12 product) with enhancements by the CORINE land cover 2000 database the main land cover types (croplands, grasslands and forests) were distinguished Using the MOD13 product (EVI & NDVI) comparison with the DB NDVI Using archive meteorological database (daily data with 1/6 grid resolution) the effects of weather on vegetation activity and growth were also investigated
15 Data shown: - all Monitoring Vegetation Activity
16 Monitoring Vegetation Activity Data shown: - only with sensor senith angle lower than 35 - data coverage of the country is higher than 25%
17 Monitoring Vegetation Activity Small cumulus clouds remain after cloudscreening :00 Terra
18 Monitoring Vegetation Activity Small cumulus clouds remain after cloudscreening :00 Terra
19 Monitoring Vegetation Activity Data shown: - only with lower sensor senith angle than 40 - data coverage of the country is higher than 25%
20 Monitoring Vegetation Activity Aqua, :15 Aqua, :25
21 Monitoring Vegetation Activity Unresolved source of errors on corrected reflectances: Cloud shadows Remaining small cumulus clouds Cirrus clouds High atmospehric water vapor content Creating monthly mean NDVI values Correlations between monthly mean DB NDVI & MOD13 NDVI (12 years): Croplands Grasslands Forests Bias: RMSE: R 2 :
22 2D plots of mean NDVI for Hungary DB data Entire Hungary Croplands Julian day Julian day Grasslands Forests Julian day Julian day
23 D plots of mean NDVI for Hungary MOD13 Entire Hungary Grasslands Julian day Croplands Can we find quantitative relationship between NDVI Julian day Julian day and the environmental variables? Forests Julian day
24 Monitoring Vegetation Activity Relationship between the mean temperature of the period May-October and the land cover specific mean NDVI values of the period May-October for Hungary based on DB NDVI data All: R 2 = 0.78 Agriculture: R 2 = 0.80 Grass: R 2 = 0.79 p = p = p = Forest: R 2 = 0.40 p = NDVI Temperature [ C]
25 Monitoring Vegetation Activity Relationship between the precipitation sum of the period May-October and the land cover specific mean NDVI values of the period May-October for Hungary based on DB NDVI data All: R 2 = 0.48 p = Agriculture: R 2 = 0.46 p = Grass: R 2 = 0.55 p = Forest: R 2 = 0.36 p = NDVI Precipitation [mm]
26 Monitoring Vegetation Activity Relationship between the precipitation sum of three consecutive months and the NDVI anomaly of the period s last month separately for the different land cover types for June-August Croplands Grasslands Forests R 2 = 0.76 p = R 2 = 0.78 p = R 2 = 0.80 p = NDVI anomaly NDVI anomaly NDVI anomaly Total precipitation [mm] Total precipitation [mm] Total precipitation [mm]
27 Crop yield estimations based on DB NDVI data Lowest yearly precipitation Heat wave in July in Hungary Highest yearly precipitation Heat wave in Europe Based on the crop yield data of Hungarian Statistical Office
28 Thanks to KATHY STRABALA LIAM E. GUMLEY AND THE IMAPP-TEAM NASA & EOSDIS FOR THE MOD12, MOD13 HUNGARIAN SCIENTIFIC RESEARCH FUND (OTKA PD & K )
29 2D plots of mean NDVI, EVI and GPP for Hungary
30 Precipitation conditions in Hungary ( ) The monthly precipitation amounts and its anomaly The monthly precipitation amounts The average values during the 15 years Precipitation anomaly
31 Temperature conditions in Hungary ( ) The monthly maximum, mean and minimum temperatures and the anomaly of the mean temperature The monthly maximum temperature The monthly mean temperature The monthly minimum temperature The range of the monthly maximum, mean and minimum temperatures during the 15 years Mean temperature anomaly
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