IGARSS 2011<br />	International Geosience and Remote Sensing Symposium<br />MODELLING COASTAL PROCESSES BY MEANS OF INNOVA...
OUTLINE OF THE PRESENTATION<br /><ul><li> Research frame of coastal remote sensing studies in Theseus;
multiscale  approach;
 in situ data for remote data characterization;
 satellite imagery data fusion;
 results.</li></li></ul><li>Research frame of the Theseus EU project<br />Human activities imapcts<br />Dynamic coastal pr...
MODELLING<br />Biochemistry<br />LANDSCAPE<br />SEASCAPE<br />COASTAL PROXY INDEX<br />
Feedback from geomorphological and sedimentology indicators<br />Biological indicators selection and quantification within...
Scientific Hypotesis<br />i) Reflectance signature and biophysical parameters are related to each other via morphology<br ...
Data Sources: CAT1 Proposal C1P.7963<br />LANDSAT, CHRIS PROBA, ASTER <br />SAR, LiDAR<br />IN SITU (vegetation pattern di...
Studysites<br />South Po delta and foce Bevano (Ravenna), Italy<br />Plymouth and Erme  saltmarsh, UK<br />Erme saltmarsh<...
Methodology<br />1) Change detection NDVI analisystohighlightinter-annualchangeofvegetation<br />2) Change detection fract...
CHANGE DETECTION ANALYSIS: Po delta and Wetlands of Comacchio valley<br />calculated NDVI <br />Δ image <br />NDVI image d...
METHODOLOGY SMA<br />Endmemberscollection<br />SpectralMixtureAnalysis classifies individual mixed pixels according to the...
Pincipal Component Analysis<br />PCA<br />ENDMEMBER COLLECTION<br />
Unmixing with three pure endmebers<br />Hyperspectralendmembers fractions map<br />Red: anthropic/sand<br />Green: vegetat...
In Situ vegetation and morphometric field validation <br />A<br />B<br />N<br />
METHODOLOGY SBAS<br />Use of a large number of radar images to reduce the various noise components of DInSARinterferograms...
 reference pixel is chosen by the operator
 displacement maps of each interferogram are combined together using the SVD technique to estimate ground displacement at ...
DataAssimilation<br />NDVI change analysis; <br />mean ground velocity map<br />vegetation fraction change analysis using ...
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Taramelli_al_IGARSS_2011.pptx

  1. 1. IGARSS 2011<br /> International Geosience and Remote Sensing Symposium<br />MODELLING COASTAL PROCESSES BY MEANS OF INNOVATIVE INTEGRATION OF REMOTE SENSING TIME SERIES ANALYSIS<br />Taramelli A.1, Valentini E.1, Dejana M.1, Zucca F.2, Mandrone S.1<br />1ISPRA –InstituteforEnvironmentalProtection and Research<br />Rome– Italy<br />2Universita degli Studi di Pavia<br />
  2. 2. OUTLINE OF THE PRESENTATION<br /><ul><li> Research frame of coastal remote sensing studies in Theseus;
  3. 3. multiscale approach;
  4. 4. in situ data for remote data characterization;
  5. 5. satellite imagery data fusion;
  6. 6. results.</li></li></ul><li>Research frame of the Theseus EU project<br />Human activities imapcts<br />Dynamic coastal processes<br />Management<br />Critical situation<br />Cappucci S., Scarcella D., Rossi L., Taramelli A., 2009, Dredging and downdriftnourishment at Marina di Carrara: sediment management and ICZM,Ocean and Coastal Management, 2011<br />
  7. 7. MODELLING<br />Biochemistry<br />LANDSCAPE<br />SEASCAPE<br />COASTAL PROXY INDEX<br />
  8. 8. Feedback from geomorphological and sedimentology indicators<br />Biological indicators selection and quantification within habitats (both emerged and submerged)<br />Upscaling<br />Land/sea form system pattern<br />Habitat selection: landscape scale definition<br />Downscal i ng<br />Multisensor data collection (Satellite and field survey) <br />Selection of nearshore extent in each area of interest<br />
  9. 9. Scientific Hypotesis<br />i) Reflectance signature and biophysical parameters are related to each other via morphology<br />ii) Morphological and SAR data are able to enhance hyperspectral interpretation<br />III) Integration of different methods (Spectral Mixture Analysis, Change Detection and SBAS) to process SAR, hyperspectral and in situ data in two different ecosystems enhance our ability to detect coastal trend <br />
  10. 10. Data Sources: CAT1 Proposal C1P.7963<br />LANDSAT, CHRIS PROBA, ASTER <br />SAR, LiDAR<br />IN SITU (vegetation pattern distribution and morphological parameters)<br />
  11. 11. Studysites<br />South Po delta and foce Bevano (Ravenna), Italy<br />Plymouth and Erme saltmarsh, UK<br />Erme saltmarsh<br />Foce Bevano<br />
  12. 12. Methodology<br />1) Change detection NDVI analisystohighlightinter-annualchangeofvegetation<br />2) Change detection fractionsmapanalisysto integrate inter and intra-annualchange detection mapsofvegetationusing SMA (Small, 2001; Taramelli and Melelli, 2009)<br />3) Interferometric Low resolution SBAS Approach(Berardinoet al., 2002)to produce deformation and spatiotemporalvariationsmaps and Lidar morphometricanalisys(Taramelliet al., 2010)<br />4) Fieldvalidation and data integrationfortimeseriesanalisys<br />
  13. 13. CHANGE DETECTION ANALYSIS: Po delta and Wetlands of Comacchio valley<br />calculated NDVI <br />Δ image <br />NDVI image data 2001-08-26, path 192 row 29<br />NDVI (2001) – NDVI (1991)<br />Landsat 7 ETM+ data 2001-08-26 path 192 row 29<br />No change<br />Loss<br />Gain<br />change detection NDVI from 1991 up to 2010 with a focus on twodecades 1991-2001 and 2001-2010<br />
  14. 14. METHODOLOGY SMA<br />Endmemberscollection<br />SpectralMixtureAnalysis classifies individual mixed pixels according to the distribution of spectrally pure end member fractions and provides a tool for discrimination and classification of surface topography.<br />Spectral characteristics of the surface topography show significant differences between the spectral reflectance of different land-form surfaces. <br />A mixing space can be thought of as a coordinate system in which reflectance spectra are represented as linear mixtures of spectrally pure endmember spectra<br />Water<br />Wet <br />Soil<br />Vegetation<br />
  15. 15. Pincipal Component Analysis<br />PCA<br />ENDMEMBER COLLECTION<br />
  16. 16. Unmixing with three pure endmebers<br />Hyperspectralendmembers fractions map<br />Red: anthropic/sand<br />Green: vegetation<br />Yellow: substrate/soil<br />SpectralMixtureAnalysis (SMA) classifies individual mixed pixels according to the distribution of spectrally pure end member fractions.<br />R: antropic/sand<br />G: vegetation<br />B: substrate/soil<br />
  17. 17. In Situ vegetation and morphometric field validation <br />A<br />B<br />N<br />
  18. 18. METHODOLOGY SBAS<br />Use of a large number of radar images to reduce the various noise components of DInSARinterferograms:- maximum temporal and orbital separations are defined between the images of each DInSARinterferogram<br /><ul><li> all possible interferograms are generated and unwrapped
  19. 19. reference pixel is chosen by the operator
  20. 20. displacement maps of each interferogram are combined together using the SVD technique to estimate ground displacement at each image date, filtering at the same time the short term atmospheric contribution</li></li></ul><li>Deformation <br />time-series<br />+<br />Atmospheric artifacts and orbital ramps<br />SVD based interferograms inversion<br />&<br />Topography artifacts estimation and removal<br />Interferogram phase unwrapping<br />Low Resolution SBAS Approach<br />Co-registered SAR images<br />Deformation time-series (2)<br />SAR images pairs selection<br />&<br />Interferograms generation<br />Space-time filtering<br />Atmospheric artifacts <br />and orbital ramps<br />Residual Topography<br />Subsidence Rate (1)<br />
  21. 21. DataAssimilation<br />NDVI change analysis; <br />mean ground velocity map<br />vegetation fraction change analysis using fraction map.<br />
  22. 22. Results<br />Gain (green)<br />No change (white)<br />Loss (red)<br />Statistics within the difference image of the fraction maps and within the difference image of the NDVI<br />Ground in central and upper part moved away from satellite with rates up to 10 cm/y<br />Taramelli A., Valentini E., Dejana M., Zucca F., Mandrone S., 2011, MODELLING COASTAL PROCESSES BY MEANS OF INNOVATIVE INTEGRATION OF REMOTE SENSING TIME SERIES ANALYSIS, IGARSS 2011 International Geosience and Remote Sensing Symposium. <br />
  23. 23. Conclusion<br /><ul><li>To investigate Remote Sensingrole in landscape pattern evolution in coastaldynamics a combined SMA, NDVI and Topographic SAR characterizationmustbeusedtoenhancelandform Proxy indexidentification.
  24. 24. Fractionsmapofcoastalmorphologycouldbeusedtopoint out the parameters via hyperspectralendmembers, todetermine the extenttowhichoptical data can distinguishimportantcoastalland cover and the costallandsurfacepropertiesthatinfluence the mass and energyfluxestobemodeledwithin amprphometric SBAS temporalevolutionanalysis. </li></li></ul><li>ACKWNOLEDGMENT:<br />- THESEUS (FP7– ENV2009-RTD) Task.1.6 Investigationofcoastalareasmorpho-dynamicsusing high resolutionmultispectralimages and SAR processing; <br />- ESA Category1Proposal (id7963) “Modellinguncertainties in coastalprocessesbymeansof innovative integrationof remote sensingsystems”;<br />- MERMAID (FP7– OCEAN2011-RTD) Task Investigationof Marine areasmorpho-dynamicsusing high resolutionmultispectralimages and SAR processing; <br />
  25. 25. Thankyouforyourattention!<br />andrea.taramelli@isprambiente.it<br />

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