rohstoffe und fernerkundung -...
TRANSCRIPT
Rohstoffe und Fernerkundung - geologische Kartierung und Umweltmonitoring mit Satelliten -
Natural Resources and Remote Sensing – Geological Mapping and Environmental Monitoring using Satellite Data -
Christian Fischer
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
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
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
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
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
Archive C-AF Oberpfaffenhofen, Data Volume [TByte]
0
20
40
60
80
100
120
140
160
180
1996
.06
1996
.12
1997
.06
1997
.12
1998
.06
1998
.12
1999
.06
1999
.12
2000
.06
2000
.12
2001
.06
2001
.12
2002
.06
2002
.12
2003
.06
2003
.12
2004
.06
2004
.12
2005
.06
2005
.12
2006
.06
2006
.12
2007
.06
2007
.12
2008
.06
AIR_RSTerraSAR-XGEMOSSRTMXSAR_2XSAR_1ERS_SARSEASATENV_SCIAENV_MIPASENV-AATSRHyMAPROSISDAISZKIMODISNOAAGOMEMETOP-GOME2MSGMETEOSAT (SOLEMI)METEOSATERS_GOMEERS_ORB
www.DLR.de • Chart 7
DFD - The Data Archive
www.DLR.de • Chart 8
MODIS ASTER
(Some) Remote Sensing Sensor Systems
Airborne Scanner SAR
www.DLR.de • Chart 9
Optical Remote Sensing
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
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
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
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
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
www.DLR.de • Chart 15
Synthetic Aperture Radar - SAR
-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
-horizontal-baseline
-vertical-baseline
-SH-(asc.)
Helix Orbit of TanDEM-X
-Typical baseline ~ 500 m
www.DLR.de • Chart 17
[Bamler 2008]
Possible Data Products based on TanDEM-X
www.DLR.de • Chart 19
Mining Activities & Topographic Changes based on TanDEM-X-Data
www.DLR.de • Chart 20
Data Quality
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
Goal:extend quality assessment from L1 to L2 up to L3 products
www.DLR.de • Chart 22
Quality Assessment of Mapping Products
www.DLR.de • Chart 23
Field Work: Reference Measurements
www.DLR.de • Chart 24
Mining Life Cycle & Examples
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
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
Earth Observation (EO) product levels
[Coetzee 2013]
www.DLR.de • Chart 27
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]
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
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
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]
• Mineral spectra from USGS library
- 25 cm
Background Findingswww.DLR.de • Chart 32
[Rogge 2014]
AISA Levelled Mosaic (RGB True colour)
Flight lines differences still visible in water.
www.DLR.de • Chart 33
[Rogge 2014]
AISA Data
EnMAPSimulation2 m AISA (aireborne)
2 km [Rogge 2014]
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
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
Sokolov – Czech Republic
Acid Mine Drainage (AMD), related heavy metal contamination and influenced vegetation health status
www.DLR.de • Chart 37
AHS 2011, emissivityHyMap 2010, reflectance
Standardized pre-processing of Airborne Data
www.DLR.de • Chart 38
Mineral Mapping (VIS-SWIR)
www.DLR.de • Chart 39
Clay minerals, Goethite
Lignite - weathered Lignite - freshJarosite+ Lignite surface pH model
JarositeJarosite + Goethite
Mineral Mapping (VIS-SWIR)
www.DLR.de • Chart 40
www.DLR.de • Chart 42
Sulphide-rich materials:
Gold in greenstone belts and the WitwatersrandCoalBase metals and PGMs
Mining in South Africa
Abandoned Mine Sites – Coal Fires in South Africa
www.DLR.de • Chart 43
gas fluxes
brightnesstemperature
subsurface measurements
emissivity &temperature
coal fire radiances
www.DLR.de • Chart 44
Coal Fires
2 km
WorldView-II and FLIR data sets
Temperature Anomaly Mapping
www.DLR.de • Chart 45
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
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
WorldView-II image draped over WorldView-II DEM]
www.DLR.de • Chart 48
WV-II Digital Elevation Data (DEM)
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]
www.DLR.de • Chart 50
European Activity: Copernicus
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
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
www.DLR.de • Chart 52
www.DLR.de • Chart 53
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/
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.
www.DLR.de • Chart 54
www.DLR.de • Chart 55
Vielen Dank für ihre Aufmerksamkeit
Dr.-Ing. Christian Fischer Dt. Zentrum f. Luft- u. RaumfahrtDeutsches FernerkundungsdatenzentrumAbtl. Landoberfläche, Angewandte Spektroskopie