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Application of Hyperspectral Data Geo-sciences H. Kaufmann Geo. Forschungs. Zentrum Potsdam (GFZ) SWOT and Application of Hyperspectral Data Geo-sciences H. Kaufmann Geo. Forschungs. Zentrum 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 – 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 (www. gfz-potsdam. de) => 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 Share of Geologic Research SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5 -6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions

Basics (of minerals that contain bound or unbound water or C-O bonds) • Main Basics (of minerals that contain bound or unbound water or C-O bonds) • Main methods used – SFF, SAM, SMA, …. 100 90 Reflectance [%] • Importance of SWIR range (2. 2µm atm. window) • Direct identification kaolinite Rc 80 70 60 50 Rmin D = 1 - Rmin / Rc 40 2. 05 2. 15 2. 25 2. 35 Wavelength in microns 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 av. producer accuracy: 71% DIRECT Example-Identification INDIRECT – field knowledge MS – 6 bands av. producer accuracy: 71% DIRECT – spectral features 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 calcite 1 kaolinite 1 dolomite 2 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 HS – 72 bands av. producer accuracy: 96% SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5 -6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions

% Reflectance rel. to Halon Example-Alteration N Talus 70 roof rock 80. 0 30 % Reflectance rel. to Halon Example-Alteration N Talus 70 roof rock 80. 0 30 propylitic zone 60. 0 40. 0 kaolinitic zone 15 potassic zone 20. 0 Scale 500 0 Meters Fresh, unaltered Rock (quartz, feldspars, arfvedsonite) Propylitic Zone (Q, Fss, Arf + Fe-O + chlorite, epidote) 0. 0 0. 4 0. 6 0. 8 1. 0 1. 2 1. 4 1. 6 1. 8 Wavelength in Microns 2. 0 2. 2 2. 4 Kaolinitic (Argillic) Zone Har Shen Ramon, Israel (Q, Fss, Arf + kaolinite) Geol. source: Itamar und Baer (1986) Potassic Zone (Q, Fss, Arf + K-FSS, Fe-oxides) 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 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 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 N kaolinite DAIS Data Makhtesh Ramon Israel dolomite calcite 2. 200 kaolinite dolomite Examples N kaolinite DAIS Data Makhtesh Ramon Israel dolomite calcite 2. 200 kaolinite dolomite calcite appr. 1 km 2. 315 2. 330 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 SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5 -6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions

Absorption Depth [%] Example 12 N 8 Kaolinite content: D = 0, 62[Kaolinit] + Absorption Depth [%] Example 12 N 8 Kaolinite content: D = 0, 62[Kaolinit] + 4, 7 R² = 0, 74 4 0 - 1 Weight % >1 - 3 W. % >3 - 5 W. % >5 - 7 W. % >7 - 10 W. % >10 W. % Sparse vegetation Dense vegetation Water areas 0 0 4 8 12 16 Kaolinite [Weight. -%] Solving regression for kaolinite: [Kaolinit] = 1, 6 · D - 7, 6 1 km SWOT and User Needs Workshop, DLR Oberpfaffenhofen, 5 -6 July 2006 HYRESSA - HYperspectral REmote Sensing in Europe specific Support Actions