SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Application of Hyperspectral Data Geo-sciences H. Kaufmann GeoForschungsZentrum Potsdam (GFZ)

SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Introduction Main Application Fields –General Geologic Mapping Lithological/mineralogical - structural –Exporation Geology Alteration mapping –Waist / Abandoned Mines –Geohazards Sudden events – long term processes Expertise at GFZ ( => Dept.1

SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Share of Geologic Research

SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Importance of SWIR range (2.2µm atm. window) Direct identification (of minerals that contain bound or unbound water or C-O bonds) Main methods used –SFF, SAM, SMA, …. Basics Wavelength in microns D = 1 - R min / R c RcRc R min Reflectance [%] kaolinite

SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Example-Identification INDIRECT – field knowledge MS – 6 bands HS – 72 bands av. producer accuracy: 96% DIRECT – spectral features calcite 1 kaolinite 1 dolomite 1 dolomite 2 Fe-sandstone Fe-sandstone 2 dolomite 3 kaolinite 2 dolomite 4 Kaolinite 3 calcite 2 gypsum calcite 3 calcite 4 illite pyroxene arfvedsonite chlorite kaolinite 4 K-Fe class 1 OH-indicated class 3 class 4 Fe-indicated class 7 OH-indicated class 9 OH-indicated class 11 OH-indicated class 13 class 14 OH-indicated class 16 class 17 OH-indicated Fe-indicated av. producer accuracy: 71%

SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Example-Alteration Talus N Har Shen Ramon, Israel Geol. source: Itamar und Baer (1986) Kaolinitic (Argillic) Zone (Q, Fss, Arf + kaolinite) Potassic Zone (Q, Fss, Arf + K-FSS, Fe-oxides) Fresh, unaltered Rock (quartz, feldspars, arfvedsonite) Propylitic Zone (Q,Fss, Arf + Fe-O + chlorite, epidote) Wavelength in Microns % Reflectance rel. to Halon roof rock propylitic zone kaolinitic zone potassic zone Scale 5000 Meters

SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Developments Pure Classification => Identification Lithological/Spectral Maps => Mineral Maps Improved Tool to Identify Influencing Factors Improved Understanding of Processes

SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Discussion Points Mining data mostly classified Geology missing in nat. or EC programs What is a sufficient SNR Up-, downscaling issues Insufficient overlap of knowledge between engineers and users (e.g. nat. scientists) R.S. community not growing

SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Examples kaolinite calcite dolomite wavebands in microns 32 bands of SWIR-range are used for identification process Color composite of three bands of original imagery overlain by calculated signatures N DAIS Data Makhtesh Ramon Israel kaolinite dolomite calcite appr. 1 km

SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5-6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions Example D = 0,62 [Kaolinit] + 4,7 R² = 0, Kaolinite [Weight.-%] Absorption Depth [%] Solving regression for kaolinite: [Kaolinit] = 1,6 · D - 7, Weight % >1 - 3 W. % >3 - 5 W. % >5 - 7 W. % > W. % >10 W. % Sparse vegetation Dense vegetation Water areas Kaolinite content: 1 km N