GRS Capita Selecta GIS in practice. Urban remote sensing at an international research institute. Dr. Wieke Heldens, MSc

Transcription

1 GRS Capita Selecta GIS in practice Urban remote sensing at an international research institute Dr. Wieke Heldens, MSc

2 Content From MGI student to scientist at DLR Remote sensing research as daily work Ideas about the future of GIS and remote sensing

3 From MGI student to scientist at DLR

4 CV Bsc Landscape, planning and design (WUR) Msc Geo-information Science (WUR) Phd Geography, University of Würzburg, Germany 06 Scientific staff, DLR 09 now

5 MGI 04/05 & 05/06 Remote Sensing Geo-information Tools Data Management SDI Spatial modelling Academic Master Cluster Planning Theory Quantitative analysis of land use systems Internship at DLR: imaging spectroscopy Thesis: Spatio-temporal modelling of land cover using Markov theory

6 Current work DLR Earth Observation Center, Dpt. Land Surface Team: Urban areas and land management Design, implementation and demonstration of remote sensing products Operational processing techniques in the context of urban remote sensing

7 Remote sensing research at DLR DLR and the department Land Surface Urban remote sensing research

8 DLR in Germany R&D in Aeronautics, Space, Energy, Transportation, Civil Security At research institutes and facilities at 13 sites With employees (incl. scientific staff, 0 PhD) employees in Oberpfaffenhofen Offices in Brüssel, Paris and Washington Hamburg Bremen Trauen Berlin Braunschweig Dortmund Goettingen Koeln Bonn Neustrelitz Lampoldshausen Stuttgart Weilheim Oberpfaffenhofen DLR site

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10 The Earth Observation Center (EOC) DFD & IMF Core of DLR s remote sensing activities ca. 3 employees working in 2 research institutes and 12 departments at 3 sites ca. employees working in 2 University Departments ca. 2 scientists from more than nations ca. PhD students ca. guest scientists ca. 45% third party funding

11 Three Research Departments at DFD ( staff) Land Suface and Global Change Geo- and biophysical parameters Ecological and Urban mapping Dynamics and Change Detection Civil Crisis Information and Georisks Rapid mapping and early warning Crisis Information Systems Vulnerability and Risk Assessments Atmosphere Climate signal detection in the mesosphere Trace Gases and Aerosols for Air quality and Health applications Solar and bio energy Scientific Geovisualisation Animations Envisioning Science

12 Dpt. Land Surface Applications Allianz Arena Land Surface Processes and Interaction Net primary production imperviousness Englischer Garten Urban Areas and Managed Land Natural Ressources Management soil organic carbon

13 Land Surface Applications: methodology development Monitoring Tools Thematic Processor Development Time Series/ Trend Analysis Global Urban Footprint Twinned object and pixel based classification Change Detection Environmental Information Systems ELVIS Information Extraction SAR Technologies Polarimetry Operational SAR Geocoding Quality Control Atm. Correction Vicarious Calibration Spectral/(spatial) Mixture Analysis Spectroscopy Enhanced Pre-Processing

14 German EO Missions TerraSAR-X and TanDEM-X TerraSAR-X in operation since June 07 Bi-static formation flight with TanDEM-X since October Mission operations and scientific exploitation by DLR institutes in Oberpfaffenhofen Commercial data distribution: Astrium/Infoterra GmbH

15 Tien Shan, China T12:39:09

16 Mission Principle Environmental Mapping and Analysis Program Launch: 17 Spatial Res.: Repetition: m at km swath 5 days at +/- off-nadir DLR Satellite, Principle investigator: GFZ Hyperspectral instrument, >0 channels (4 24 nm) Platform for further operationalization of methods for information retrieval for land surface monitoring

17 Geo-privacy and ethical issues Geo-privacy Usually special conditions for science Use of high res. satellite data of certain missions SatDSiG German Satellity Data Safety Law Cooperation with non-research parties an their data Census data Cadastral data Copyright issues Data Programming code Results Open Access / Open source No ethical issues until now

18 Team: urban areas and land management Global urban footprint Urban micro climate modelling

19 Urban micro climate modelling envi-met - Bruse & Fleer (1998): Simulating surface-plant-air interactions inside urban environments with a three dimensional numerical model. Environmental Modelling and Software, 13,

20 Urban micro climate modelling Input parameters Buildings Vegetation Non-built surfaces Weather conditions Location Roof material (14 different materials) Height Material properties: albedo Material properties: thermal inertia Location Type (deciduous, coniferous, grass) Height Leaf area density (related to leaf area index) Reflectance properties (albedo) Photosynthetic and evapotranspiration properties Location Type (impervious, pervious) Reflectance properties (albedo) Soil properties (hydrological) Temperature Wind speed Date, sun dawn, sun set Color legend HyMap hyperspectral data HRCS height data Literature Weather station or scenario variable

21 Urban micro climate modelling Airborne sensor HyMap 4 m² pixel size µm (125 bands) Recording dates: June 07 Pre-processing: Atmospheric correction Geometric correction HRSC Stereo camera Accuracy: X,Y: 1 m; Z: 0.1 m Recording date:15 September 04 DEM preparation by DLR Berlin Pre-processing: DEM normalization RGB: SWIR, NIR, RED

22 Urban micro climate modelling

23 Urban micro climate modelling 24 hours simulation time Start: 03:00 output time interval: minutes Initial values for Wind speed 3.5 m/s Wind direction 245 Humidity % (rel. Humidity in 2 m) Temperature of atmosphere 287 K Biometeorology boundary parameters

24 Pot. Temperature 2. K Urban micro climate modelling K 291. K K Y (m) 292. K subset 14okt09 15:00: Temperature & wind Humidity x/y cut at z= Pot. Temperature subset 14okt09 15:00: K x/y cut at z= 3 x/y cut at z= K 293. K K % 0. m/s.00 % m/s % % 0. m/s % 294. K.00 % K x/y cut at z= % Y (m) PMV Value % K Y (m) Y (m) 1 Flow v.00 % K 294. K K subset okt09 15: Relative Humidity K 291. K 293. K subset 14okt09 15:00: K Regular block development K Predicted Mean Vote (PMV) Flow v % 0. m/s 0. m/s Flow v 0. m/s 0. m/s 0. m/s 0. m/s 0. m/s 0. m/s <Left foot> 0 X (m) <Left foot> 0 Row house development 01 X (m) Pot. Temperature <Left foot> <Right foot> K X (m) X (m) Flow v 0. m/s 0. m/s x/y cut at z= [??] 3.00 [??] [??].00 % 5.00 [??].00 % 6.00 [??] % 7.00 [??] % Flow v 0. m/s 0. m/s 0. m/s 0. m/s 0. m/s 0. m/s 0. m/s 1.00 m/s 1.00 m/s.00 % % subset1 28sept 09 15:00: subset1 28sept 09 15:00: x/y cut at z= 3 x/y cut at z= 3.00 % % N X (m) Pot. Temperature 1 0 N 1 <Right foot>.00 % K K 293. K K Flow v PMV Value.00 % % % % % % K 0. m/s m/s m/s m/s % 294. K <Right foot> 0. m/s PMV Value % 292. K X (m) <Left foot> K Relative Humidity <Right foot> Y (m) x/y% cut at z= [??] 0. m/s 291. K Y (m) 0.00 [??] % 2. K <Right foot> [??] 0 PMV Value 1 subset sept 09 15: % %N <Right foot> Flow v 0. m/s subset1 28sept 09 15:00: % [??].00 % <Left foot> N <Left foot> 1.00 % 1.00 m/s % K % 0. m/s K.00 % K 294. K <Left foot> Dense block development % 292. K Y (m) Y (m) 1 X (m).00 % K N Relative Humidity K Y (m) 0 Y (m) Y (m) x/y cut at z= 3 N K 0 subset 13 19okt09 15:00: Relative Humidity x/y cut at z= K 1.00 m/s subset 13 19okt09 15:00: x/y cut at z= 3 0. m/s 1.00 m/s subset 13 19okt09 15:00: N 0. m/s 0. m/s 1.00 m/s 1.00 m/s 0. m/s Flow v 0. m/s Flow v % 0. m/s Y (m) Flow v 0. m/s Flow v 0. m/s 0. m/s 0. m/s 0. m/s 0. m/s 0. m/s 1.00 m/s 1.00 m/s 3.00 N <Left foot> X (m) <Left foot> Flow v N m/s 1.00 m/s m/s 0. m/s 0. m/s X (m) <Right foot> <Left foot> 01 X (m) <Right foot> 1 N 0. m/s 0. m/s 0. m/s <Right foot> N

25 Glob Cover 09 Google Earth Global Urban Footprint Accra Dar es Salaam Baghdad Amsterdam 0 m resolution

26 Global Urban Footprint Accra Dar es Salaam Baghdad Amsterdam Global Urban Footprint Google Earth 12 m (resp. 75 m) resolution

27 TanDEM-X Mission Urban Footprint Processor Ground segment Urban Footprint Mosaick classification Mosaicking and post-processing Amplitude (degr.) Texture Urban Footprint Production environment Feature extraction W42 Data base Reference and aux. data DEM

28 Global Urban Footprint Den Helder original backscattering amplitude extracted speckle divergence Urban Footprint

29 Global Urban Footprint Japan: ~0 images original extracted Global backscattering Urban speckle Footprint divergence amplitude

30 Global Urban Footprint Tokio 0 25 Kilometers Global Urban Modis Footprint 0 m 12 m Urban No urban

31 Ideas about the future of GIS and remote sensing

32 Research directions Remote sensing research: Sentinel Satellites: large amount of (free) satellite data Time series analysis Automated processing & processing chains Applied remote sensing Combining RS with secondairy data (statistical, meteorological, census) Decision support system General Programming (C\C++, Python, Java, IDL, ) is important Knowledge about the application also!

33 Urban growth Istanbul

34 Urban Growth Manila Urban spatio-temporal development based on historical optical (Landsat MSS, TM and ETM+) and SAR (ERS, ASAR) data + Global Urban Footprint.

35 Thank you for your attention!