Rohstoffe und Fernerkundung -...

55
Rohstoffe und Fernerkundung - geologische Kartierung und Umweltmonitoring mit Satelliten - Natural Resources and Remote Sensing Geological Mapping and Environmental Monitoring using Satellite Data - Christian Fischer

Transcript of Rohstoffe und Fernerkundung -...

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Rohstoffe und Fernerkundung - geologische Kartierung und Umweltmonitoring mit Satelliten -

Natural Resources and Remote Sensing – Geological Mapping and Environmental Monitoring using Satellite Data -

Christian Fischer

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Contents

Short Background – German Aerospace Center

Optical Remote Sensing

Synthetic Aperture Radar - SAR

Data Quality

Mining Life Cycle & Examples

European Activities: Copernicus

Visualization Examples

links

www.DLR.de • Chart 2

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Research Institution Space Agency Project Management Agency

Aeronautics Space Research and Technology TransportEnergy

6900 employees across 31 institutes and facilities

15 sites.involved in Space Researchand Technology

Offices in Brussels, Paris and Washington

Security

DLR - German Aerospace Center

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DLR’s tasks as the National Space Agency

Defining Germanys space planning on behalf of the Federal Government.

Representing German space-related interests in the international arena, in particular at ESA.

Tendering, award and support of space projects in the context of the National Space Program

www.DLR.de • Chart 4

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Employees: Approx. 1600

Research institutes and facilities:- Microwaves and Radar Institute- Institute of Comm. and Navigation- Institute of Atmospheric Physics- Institute of Remote Sensing Techniques- Institute of Robotics and Mechatronics- German Remote Sensing Data Center- Space Operations and Astronaut Training- Galileo Control Center- Flight Experiments

DLR Site Oberpfaffenhofen

Space Segment

Stations

Processing & Archiving Services

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The global station network of DFD

Permanent Neustrelitz (Germany) 3 LSX-band 7.3 m, LS-band 4.0 m, VHF

Oberpfaffenhofen (Germany) X-band 3.6 m, L-band 2.4 m, L-band 4.0 m

Red circles: Existing and planned stations

Blue circles: former station locations

www.DLR.de • Chart 6

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Archive C-AF Oberpfaffenhofen, Data Volume [TByte]

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AIR_RSTerraSAR-XGEMOSSRTMXSAR_2XSAR_1ERS_SARSEASATENV_SCIAENV_MIPASENV-AATSRHyMAPROSISDAISZKIMODISNOAAGOMEMETOP-GOME2MSGMETEOSAT (SOLEMI)METEOSATERS_GOMEERS_ORB

www.DLR.de • Chart 7

DFD - The Data Archive

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www.DLR.de • Chart 8

MODIS ASTER

(Some) Remote Sensing Sensor Systems

Airborne Scanner SAR

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www.DLR.de • Chart 9

Optical Remote Sensing

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Blackbody Radiation, Atmospheric Transmissionand Regions of Operation for EO Remote Sensors

-UV

-Blu

e

-Gre

en

-Red

-IR

Sun’s radiant energy

at 6000 K

Earth’s radiant energy at 300 K

-K b

and

-X b

and

-C b

and

-L b

and

-P b

and

Radar Instruments

Passive microwave

Human eye

Photography Thermal scanners

-Ene

rgy

Optical scanners - ms/xs

-Tra

nsm

issi

on %

-100

[adapted from Lillesand & Kiefer (1994)]Wavelength in Microns

-0.2 0.3 0.6 1.0 2.0 4.0 6.0 10 20 40 60 100 1 mm 1 cm 1 m 10 m 100 m

-0.2 0.3 0.6 1.0 2.0 4.0 6.0 10 20 40 60 100 1 mm 1cm 1 m 10 m 100 m

-0-VIS -NIR -SWIR -MIR -TIR

www.DLR.de • Chart 10

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detailed assessments,monitoring with infrequent coverage

AVIRIS(from 20000 m)

HySpex

HyMap(from 3000 m)

ASTER TM/ETM+

LDCMSPOT ms

SPOT pan ETM pan TM thermal

METEOSAT

AVHRR

MODISreflective

MODISthermal

MERISfull spatialresolution

MERISred. spatialresolution

Large scale assessments,monitoring with frequent coverage

Spatial resolution (GSD in meter)

Num

ber o

f spe

ctra

l ban

ds

1 10 100 1000 10 000

300

100

10

1

EnMAPreflective

IKONOS, World-View pan SAR

WorldView-3

Satellite & Airborne Sensor Systems

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The acquisition of images in hundreds of registered continuous spectral bands such that for each picture element it is possible to derive a complete reflectance spectrum (Goetz 1983).

Hyperspectral vs. multispectral optical data

www.DLR.de • Chart 12

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Topic Approach Bio‐, geochemical & physical Variables

Product

BIOSPHERE

Agriculture – Canopy reflectance models (d)– Crop parameter retrieval based 

on model inversion (i)– Agro‐meteorological production 

model (a)– Data assimilation (I;n)

– Crop type– LAI / APAR– Canopy water; chlorophyll content– Canopy structure– Physical crop condition indicators

– Yield estimation and forecast maps– Crop variability maps– Crop stress maps (nitrogen; water)– Physical crop damage maps

Forestry – Forest reflectance models (i)– Curve fitting based on 

empirical/physical models (i, n)– Linear/non‐linear SMA (d, i, n)– Artificial intelligence, expert 

systems, and case‐based reasoning methods for data fusion and analysis (i, n)

– LAI– Chlorophyll content– Canopy water content– Carbon– Bio‐indicators, (fragmentation, 

canopy chemistry, pigment rations)– Productivity (PAR, fPAR, biomass)

– Forest inventory maps (e.g., forest area, forest type, fragmentation, stem volume

– Productivity maps– Forest carbon maps (reforestation, afforestation, 

deforestation)– Forest condition maps (e.g., health, water stress, fuel 

type)– Expert systems for data fusion of GIS, hyperspectral 

data, and radar data

Coastal Zones &Inland Waters

– RT modelling (a, i)– Neural networks (a, i)– Non‐linear regression (a)– Feature fitting based on 

empirical/physical models (a;i)

– Attenuation coefficient– Pigments (chlorophyll‐a)– Yellow substance (coloured 

fraction of DOC pool)– Suspend mineral/organic matter– Phytoplankton composition

– Trophic state maps– Phytoplankton maps (primary productivity)– Suspended matter maps– Lake habitat maps (submerse and emerse

macrophytes)– Coastal zone habitat maps (tidal zones, wetlands, 

mangroves)– Sediment transport delineation maps

-1) Approach: developed (d); to be adapted (a); to be improved (i); to be newly developed (n).

www.DLR.de • Chart 13

Possible data Products based on RS

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Topic Approach Bio‐, geochemical & physical Variables Product

GEOSPHERE

Soils – Linear/non‐linear SMA (d, i, n)– Feature fitting based on 

physical/empirical models (n, i)– Multiple, non‐linear regress. (a)– Neural networks (i)– Environmental models (water cycle (d, i)– Erosion models (i)– Geostatistical methods (i)

– Soil mineral abundance– Vegetation abundance– Soil parent material type– Dry matter (lignin/cellulose)– Soil condition indices (e.g., 

clay/carbonate ratio)

– Top soil constituents maps (organic matter, minerals, texture classes

– Soil cover/vegetation maps – Soil condition/degradation maps– Degradation trend maps– Land management decision support 

systems– Rain use efficiency maps

Geology – Linear/non‐linear SMA (d, i, n)1)

– Feature fitting based on physical/empirical models (i, n)

– Spectral matching (d, i)– Waveform characterisation (i)– Geostatistical methods (i)– Neural networks (i)– Mineral reflectance model (i)

– Mineral abundance– Lithobionts– Weathering crusts– Opaque constituents– Organic compounds– Pollutants e.g., (PAHs)– Vegetation abundance– Chlorophyll content (heavy metal 

stress)

– Mineral abundance maps– Alteration zone maps– Metamorphic isograd maps– Mining operations maps (e.g., tailings, acid 

mine drainage)– Mine site rehabilitation maps (e.g., 

vegetation, water)– Land slide risk maps– Expert systems for mineral identification

-1) Approach: developed (d); to be adapted (a); to be improved (i); to be newly developed (n).

www.DLR.de • Chart 14

Possible data Products based on RS

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www.DLR.de • Chart 15

Synthetic Aperture Radar - SAR

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-TanDEM-XTerraSAR-X ad-on for

Digital Elevation Measurements

514 km altitude

11 days repeat orbit

Right looking, but rolling to

left looking possible

Transmit & receive in H or V

polarization (single / dual)

-TerraSAR-X

www.DLR.de • Chart 16

TerraSAR-X / TanDEM-X

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-horizontal-baseline

-vertical-baseline

-SH-(asc.)

Helix Orbit of TanDEM-X

-Typical baseline ~ 500 m

www.DLR.de • Chart 17

[Bamler 2008]

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Possible Data Products based on TanDEM-X

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www.DLR.de • Chart 19

Mining Activities & Topographic Changes based on TanDEM-X-Data

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www.DLR.de • Chart 20

Data Quality

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From “Joint Committee for Guides in Metrology” (JCGM) in theGuides to expression of Uncertainties in Measurement (GUM) and theInternational Vocabulary of Metrology (VIM) (www.bipm.org)

Reproducibility : “Closeness of the agreement between the results of measurements of the same measurand carried out under changed conditions of measurement”

Repeatability:“Closeness of the agreement between the results of successive measurements of the same measurand carried out under the same conditions of measurement”

www.DLR.de • Chart 21

Quality Assessment – Terms & Definitions

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Goal:extend quality assessment from L1 to L2 up to L3 products

www.DLR.de • Chart 22

Quality Assessment of Mapping Products

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www.DLR.de • Chart 23

Field Work: Reference Measurements

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www.DLR.de • Chart 24

Mining Life Cycle & Examples

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The mining and extractive industry plays a significant role in the development of many countries all over the world

Most sectors, such as construction, chemicals, automotive, aerospace, machinery and equipment sectors,

that provide in the EU a total value added of € 1´324 billion, and employment for some 30 million people,

depend on unimpaired access to raw materials.

www.DLR.de • Chart 25

Economic & Societal Importance of Minerals

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1kg Gold requires 540,000 kg of material, a large share of which is due to extraction

At continental scale mining wastes represent a high percentage of the total registered waste:

26% in U.S. to which another 16% produced by the primary metals industryis added (EPA, 2004)

20% in Europe (EEA, 2003)

The average tonnage imported or tonnage mining waste ratio increased from 1:4 to 1:16 in the past 25 years

www.DLR.de • Chart 26

Significant Footprint of Mining

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Earth Observation (EO) product levels

[Coetzee 2013]

www.DLR.de • Chart 27

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catchment mining siteregion

Coarse scale regional data, national and EU levels

Complex spatial interactions at ecosystem levels

High resolution data, geo-statistical tools for

spatialization of point data

quantification of measured parameter & environment

geo-chemistry

Intermediate level

water balance’parameter

detailed information on land-cover and

land-use

www.DLR.de • Chart 28

EO Data Collection – Hierarchy of Scales

[Chevrel 2013]

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Conceptual site model(s)

Indicator(s) for each site

Parameter(s) measurable with EO

EO Products

EO

Dat

a A

cqui

sitio

n Project

Resources

-Stakeholder interaction

Detection & Monitoring: Product Development www.DLR.de • Chart 29

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EO in the Mineral Resource Development Cycle (MRDC)

Earth Observation (EO) offers a unique opportunity and varieties of methods to collect spatial information to monitor and assess each phase of the mining cycle:

Spaceborne and airborne

imagery

Ground and airborne geophysics

Geochemistry

In situ measurements

Sensor Monitoring Networks

3D modelling

…[www.mineralsed.ca]

www.DLR.de • Chart 30

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Study Area, Geology and AISA Airborne Data Coverage

AISA, 400-2500nm, 178 bands, ATCOR4 Atmospheric Correction

Average overlap 30%

2 GSD, flown in July 2008

20 AISA flight-lines, 10x20 km

www.DLR.de • Chart 31

[Rogge 2014]

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• Mineral spectra from USGS library

- 25 cm

Background Findingswww.DLR.de • Chart 32

[Rogge 2014]

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AISA Levelled Mosaic (RGB True colour)

Flight lines differences still visible in water.

www.DLR.de • Chart 33

[Rogge 2014]

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AISA Data

EnMAPSimulation2 m AISA (aireborne)

2 km [Rogge 2014]

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Conceptual Site Model

Potential Sources:Pits: dewatering (AMD)Washing plants: effluent (SO4, metals)Dumps: water and air pollutants, dust, slope stabilityUnderground works (fire, AMD, subsidence, sinkhole)

Pathways:Surface water, surface runoffGroundwaterAir

Receptors:Towns, informal settlements, people (gases, dust, drinking water)Wetlands, terrestrial ecosystems, lake (water quality)

www.DLR.de • Chart 35

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SokolovCzech Republic

WitbankSouth Africa

MakmalKyrgyzstan

Czech Republic - Sokolov mining area: area is largely affected by lignite

mining activities: open casts, closed minesand dump sites

acid mine drainage(AMD) and related heavy metal contamination

Kyrgyzstan - Makmal gold deposit: necessity of a regular monitoring of

soil and water on heavy metals content

impact zone around a tailing dump

South Africa - Witbank coalfields: major impact of mining due

to land degradation and water pollution

collapsed abandoned underground mine sites have undergone spontaneous combustion

Exemplary Test Sites

www.DLR.de • Chart 36

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Sokolov – Czech Republic

Acid Mine Drainage (AMD), related heavy metal contamination and influenced vegetation health status

www.DLR.de • Chart 37

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AHS 2011, emissivityHyMap 2010, reflectance

Standardized pre-processing of Airborne Data

www.DLR.de • Chart 38

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Mineral Mapping (VIS-SWIR)

www.DLR.de • Chart 39

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Clay minerals, Goethite

Lignite - weathered Lignite - freshJarosite+ Lignite surface pH model

JarositeJarosite + Goethite

Mineral Mapping (VIS-SWIR)

www.DLR.de • Chart 40

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www.DLR.de • Chart 42

Sulphide-rich materials:

Gold in greenstone belts and the WitwatersrandCoalBase metals and PGMs

Mining in South Africa

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Abandoned Mine Sites – Coal Fires in South Africa

www.DLR.de • Chart 43

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gas fluxes

brightnesstemperature

subsurface measurements

emissivity &temperature

coal fire radiances

www.DLR.de • Chart 44

Coal Fires

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2 km

WorldView-II and FLIR data sets

Temperature Anomaly Mapping

www.DLR.de • Chart 45

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FLIR combined analysis with LIDAR

using the HR LIDAR data to map collapsing structures and alreadyexisting cracks

situation mapping of hot spots,which can serve as a baselinefor future monitoring, incl.possible fire fighting results.

> 35C < 10C

Low alt.

High alt.

Temperature Anomaly Mapping

www.DLR.de • Chart 46

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Gold Mining in KyrgyzstanGold extraction by using cyanide, leaching tailing pond DEM generation bas on WorldView-II stereo data delineation of watershed information and run-off pathways

www.DLR.de • Chart 47

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WorldView-II image draped over WorldView-II DEM]

www.DLR.de • Chart 48

WV-II Digital Elevation Data (DEM)

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www.DLR.de • Chart 49

GIS-based run-off modelling

GIS Analyse to use of the thresholds to carry out an analysis of potentials flooded areas

[Chevrel 2013]

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www.DLR.de • Chart 50

European Activity: Copernicus

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Ground Segment

Services

CollaborativeGround Segment

ContributingMissions

DownstreamServices

StationsProcessing

ArchivingNetworks

Sentinels

LandEmergency

OceansAtmosphere

ClimateCiv. Security

In-SituObservations

www.DLR.de • Chart 51

Space Segment

Copernicus Layout

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2013

Sentinel 1 – SAR imagingAll weather, day/night applications, interferometry

2014

Sentinel 2 – Multispectral imagingLand applications: urban, forest, agriculture,.. continuity of Landsat, SPOT

2014

Sentinel 3 – Ocean and global land monitoring: ocean color, vegetation, sea/land surface temperature, altimetry

2017+

Sentinel 4 – Geostationary atmosphericAtmospheric composition monitoring, transboundary pollution

2015, 2019+

Sentinel 5 – Low-orbit atmosphericAtmospheric composition monitoring (Payload on polar orbiting satellite(S5 Precursor launch in 2014)

Copernicus Missions: Sentinels

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links DLR:

German Remote Sensing Data Center: http://www.dlr.de/eoc/

School Labs: http://www.dlr.de/schoollab/en/desktopdefault.aspx/tabid-1722/3663_read-18208/

European space Agency (ESA):

The Living Planet Programme (ESA): http://www.esa.int/Our_Activities/Observing_the_Earth/The_Living_Planet_Programme

Earth Online (ESA): https://earth.esa.int/web/guest/home(inlc. Software packackes (tooolboxes), eg. BEAM

Data access points:

ESA: https://earth.esa.int/web/guest/data-access

USGS Global Visualization Viewer (GLOVIS): http://glovis.usgs.gov/

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References Bammler, R. (2008): TerrSAR-X – A New Aera of High Resolution Radar Satellites for Earth Observation.

Workshop of Advanced Technologies of Remote Sensing and its Application - The Joint Steering Committee Meeting of Geosciences and Marine Sciences, Beijing, May 25th-28th.

Chevrel, St. (2013): The EO-Miners project. In: EO-MINERS (http:\\www.eo-miner.eu) Final Conference, Versailles, Oct. 11-12, 2013

Coetzee, H. (2013): Acid Miune Drainage in South Africa – Application of EO products. In: EO-MINERS (http:\\www.eo-miner.eu) Final Conference, Versailles, Oct. 11-12, 2013

Lillesand & Kiefer (1994): Remote Sensing and Image Interpretation, 3rd ed. Wiley & Sons. ISBN 0 471 30575 8 (pb).

Rogge, D.M., Rivard, B., Segl, K., Grant, B., Feng, J. (2014): Mapping of NiCu-PGE ore hosting ultramaficrocks using airborne and simulated EnMAP hyperspectral imagery, Nunavik, Canada. In: Remote Sensing of Environment, Vol. 152, pp. 302-317.

Vane, G. & Goetz, A. F. H. (1988): Terrestrial Imaging Spectroscopy. In: Remote Sensing of Environment, Vol. 24, No. 1, pp1-29.

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Vielen Dank für ihre Aufmerksamkeit

Dr.-Ing. Christian Fischer Dt. Zentrum f. Luft- u. RaumfahrtDeutsches FernerkundungsdatenzentrumAbtl. Landoberfläche, Angewandte Spektroskopie

[email protected]