Big data in Environmental Remote Sensing Challenges and Chances
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1 Big data in Environmental Remote Sensing Challenges and Chances Th. Udelhoven University of Trier Environmental and Geoinformatics Department 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
2 Definitions Environmental Remote Sensing and Big Earth observing data Definitions Lillesand, T.M., Kiefer, R.W., Chipman, J.W., 2008, Remote Sensing and Image Interpretation, (Wiley: New York) Remote sensing is generally defined as the technology of measuring the characteristics of an object or surface from a distance (Bird, 1991) Big Earth observing data can be defined in terms of volumes, degree of diversity and complexity including streaming of data from presently available and upcoming satellite capabilities, and innovative ground devices -the unpredictable value added derivable from their innovative analyses and fusion (ESA, 2013) 06/02/2013
3 Big data was not an issue of early remote sensing platforms 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
4 Nowadays Big data is an issue in Earth observation... NOAA-6 NOAA-7 NOAA-8 NOAA-11 NOAA-9 NOAA-10 MERIS, MODIS, ASTER NOAA-12 NOAA-14/-16 Envisat EOS Terra EOS Aqua 500 m 5 km SeaWiFS OrbView m SPOT-1 Pan / XS SPOT-2 SPOT-3 SPOT-4 SPOT-5 MSS TM / MSS LANDSAT 1-3 LANDSAT 4-5 ETM LANDSAT 7 Spatial Scale 1-5 m IRS-1A IRS-1B LISS-1 Quickbird Ikonos IRS-1B Pan, LISS-3, WiFS LISS-2 IRS-1C Pan, LISS-3, WiFS Time Scale /12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
5 Multi/hyperspectral remote sensing Radiance (mw cm -2 sr -1 µm -1 ) L Wavelength (µm) L/E g mw cm 2 µm E 0 u. E g Wavelength (µm) 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
6 Multi/hyperspectral remote sensing Examples: Retrieval of Soil organic carbon Sediment concentration Biomethane potential Landuse 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
7 Multi/hypertemporal remote sensing MEDOKADS Mean value Mean value of the annual cycle Amplitude Adaptation of a Fourier polynom (consideration of the annual and the semiannual cycle): 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013 Peaking time
8 Google 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013 Earth
9 Multi/hyperspectral thermal remote sensing Combustion Characterization Chemical Agent Detection & Identification Soil/Surface Contaminants Geology Broadband IR Imaging Courtesy: Air Force Research Institute Anomaly / Hard Target Detection 9 19/12/ Courtesy: US Army Research Lab Plume / Flare
10 SAR Interferometrie and LiDAR Soil moisture 3D-models 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
11 Remote sensing today. Big data ESA distributes earth observation data from ESA EO Missions Third Party Missions (TPMs) ESA Campaigns the GMES Space Component (GSC) sample and auxiliary data from a number of missions and instruments. Data distributed by ESA is available under different data policies and by various access mechanisms Current projects Current projects already launchedlaunched in the near future Artemis Cluster II COROT CryoSat-2 Envisat ERS-2 GIOVE-A GIOVE-B INTEGRAL Mars Express MetOp-A MetOp-B Planck Proba-1 Proba-2 Proba-V Rosetta SOHO SMOS Venus Express XMM-Newton ADM-Aeolus Bepo Columbo Don Quijote ExoMars EXPERT Galileo positioning system Gaia James Webb Space Telescope (with NASA). LISA Pathfinder Swarm Sentinel /12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
12 Remote sensing today. Big data NASA s Earth Science Program comprises a series of satellites, a science component and a data system called the Earth Observing System Data and Information System (EOSDIS) The archive currently (2012) exceeds 7.5 petabytes. 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
13 Overview of GES DAAC Services and Tools Functionality The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) provides Earth science data, information, and services. 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
14 Overview of GES DAAC Services and Tools Functionality Automated Subscriptions to any data in the Archive. MODIS L3 Atmospheric Products Online Visualization and Analysis System (MOVAS) Subsetting Services: These include On-demand Channel and Parameter Subset, On-the-Fly subsetting. MOVAS (MODIS L3 atmospheric data Online Visualization and Analysis System) is designed for quick exploration, analyses, and visualization of MODIS Atmospheres Monthly Level-3 product. NADM (Near-line Archive Data Mining) give Users the capability to upload, test their algorithms and mine data from the GDAAC online cache. WebGIS is an online web software that implements the Open GIS Consortium (OGC) standards for mapping requests and rendering. 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
15 Landsat Search and Download 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
16 Also Earth sciences is evolving... Source: Mark Gahegan, Univ. of Auckland 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
17 The remote sensing process 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
18 The remote sensing process Raw data L f s, t,,,, x, y, z P Sensor calibration Geometric correction IFOV GIS direct atm. diffuse scatte ring Nadir At-senor radiances (W /m 2 sr) Atmospheric correction Reflectance (%) 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
19 Challenges in big Earth data services recently formulated by ESA Establishment of new solutions and practices of big Earth data services Identifying a common ground with respect to regional and global applications and the integration of different data sources Data organisation and provision, and associated costs Development of data intensive and innovative services, with respect to (mass) data processing, integration of additional information from navigation data streams, analytics and correlation of large Earth data sets, and integration across heterogeneous resources. Identification of challenges, barriers, opportunities for such services, and attempt to define a baseline of activity to make the identified scenarios actionable 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
20 Challenge: semi-automated mass processing Example: standardized (pre-)processing of MODIS-data lpdaac.usgs.gov/ 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
21 Challenge: the problem of scale 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
22 Challenge: the problem of scale 19/12/
23 Challenge: the problem of scale 19/12/
24 Challenge: the problem of scale Problem: each Rapideye tile represents a different DOY and thus a different state of phenology that causes uncertainties in land-use classification 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
25 Challenge: the problem of scale Solution: Fusion of temporally high (eg MODIS) and spatially high (eg Landsat) resolution data (mass processing) (STARFM algorithm, Gao et al., 2010) Spatial example of a wetland area (red dot in a) for two time steps in (a) and (d) Landsat, (b) and (e) MODIS, and (c) and (f) STARFM data. 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013 Schmidt et al., 2012
26 Challenge: the problem of scale Time-series of reflectances of the input MODIS(blue) and Landsat (red)imagery (band1). The output STARFM (SFM) images are shown in green. 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013 Schmidt et al., 2012
27 Challenge: Harmonization of data archives Maps of NDVI change between 1999 and 2006 based on statistically significant Sen s slope of 8-km AVHRR GIMMS (A) and 1-km SPOT VGT (B) time series (p < 0.05, MSK test). Inner Mongolia Need to harmonize data archives! 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013 Yin et al., 2012
28 Challenge: Limitation of global/regional ground truth data for classification and quantification Problem: large-area land-use classification with limited number of ground truth data J. Knorn et al. (2009) 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
29 Challenge: Limitation of global/regional ground truth data for classification and quantification Solution: Land cover mapping of large areas using chain classification of neighboring Landsat satellite images Results of two chain classifications (forest in black). Scenes 2 and 5 classified initial scenes; scenes 3 and 7 chain-classified target scenes J. Knorn et al. (2009) 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
30 Challenge: Development of (semi-) automated supervised land use maps 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
31 Challenge: Development of (semi-) automated supervised land use maps Recoded Corine 2000 land-cover map (left) and supervised hierarchival classification (SVM, right) for the period 1999 to 2001 based on monthly AVHRR timeseries Udelhoven, /12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
32 Challenge: Fusion of data that represent different information Merging laser-scanning and hyperspectral reflective data Combination of active and passive RS-data Laser-echo-data are converted to voxels and combined with image data -> Information about biochemical + structural data in forest stands 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013 Buddenbaum et al., (2011)
33 Challenge: Routine data assimilation of RS-data into deterministic models Eg. Assimilation of RapidEye data into a deterministic crop growth model (APSIM) Ensemble of different input parameter sets: - climate: rainfall, temperature - soil: initial water content, nitrogen content, carbon content - management: sowing date APSIM crop growth model Set of ancillary parameters (constant) for the specific crop, varying in time: Car carotenoid content (µg/cm 2 ) Cbrown brown pigment content (arbitrary units) Cw EWT (cm) angl average leaf angle ( ) skyl diffuse/direct radiation (-) hspot hot spot (-) tts solar zenith angle ( ) tto observer zenith angle ( ) psi azimuth ( ) For the observed values at the specific waveband (here: RapidEye), the blue dot is the observed values, m j, at the specific waveband (blue patch) to which it refers. Forecast = Mean of the ensemble of particles, biomass states B t i: i: particle or model run index t: time step index LAI (m 2 /m 2 ) N (-) Psoil (%) Cm (g/cm 2 ) Cab (µg/cm 2 ) PROSAIL radiation transfer model Ensemble of particles, reflectance values rs t i: i: particle or model run index t: time step index Updated forecast = Weighted mean of the ensemble of particles SIS weight w t i computation Observed spectrum 19/12/ UniGR Workshop: Big Data challenges and opportunities 6 December 2013
34 Summary The concept of big Earth observing data is relating to the capability of rapidly and reliably data accessing and consuming This involves continuous performing analysis and visualisation to discover patterns and trends Big data applications are by nature borderless, regional and global: big data centres shall cooperate to make data available The data deluge will make it increasingly difficult to find data of relevance to a given issue; Finding data correlations often requires cross disciplines expertise Mass processing is more and more required. Technical development is envisaged in data automatic acquisition, discovery and aggregation (cubes); intensive visualisation; security of data access, manipulation, transfer; data and algorithms Taking the environment to users, bringing processing to where data are. Physical data aggregation or web processing services approaches; large data holdings associated to commercial hosting, private or public Clouds. Cloud based platforms are proposed as facilitating the production of qualified EO products 19/12/
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