Combining automated satellite based flood mapping with exposure mapping for flood risk assessment and management

Transcription

1 DLR.de Chart 1 > United Nations/Germany Expert Meeting > A. Twele and F. Hummel > Combining automated satellite based flood mapping with exposure mapping for flood risk assessment and management André Twele & Franz Hummel, German Aerospace Center (DLR) United Nations/Germany Expert Meeting Space Based Information for Flood and Drought Risk Reduction, 5 6 June 2014

2 Flood mapping from semi automatic tools to fully automatic services ZKI activations (since 2003): regions and disaster types ( 2

3 Flood mapping from semi automatic tools to fully automatic services Earth Observation Center ENVI/IDL: - Automatic split-based thresholding - Pixel-based ecognition Developer: - Automatic and semi-automatic - Segment-based (multi-scale) Martinis, S., Twele, A. & Voigt, S., IEEE TGRS, 49 (1) Martinis, S. & Twele, A., Remote Sensing, 2 (9) Martinis, S., Twele, A., Voigt, S., NHESS, (9) 3

4 DLR/ZKI flood monitoring services Automated EO based processing chains and services Current focus: Sentinel 1 4

5 Linking both scales Flooding in Albania TerraSAR X acquisition (16:32) MODIS acquisition (10:33) TerraSAR X ordering (09:32) Alert 30 km TerraSAR X SC (20/03/2013) TerraSAR X floodmask 5

6 MODIS Delivery Server Alert TerraSAR-X Delivery Server Earth Observation Center Ftp pull Ftp pull Modis data (L0) Georeferencing TerraSAR-X data MODIS metadata Computation of reflectances TerraSAR-X metadata Adapted global DEM Reprojection Global DEM Adapted global DEM Projection GIM (optional) Slope, Aspect Radiometric calibration to σ0 Reference water mask Slope Layer stacking Global DEM Adapted or computed GIM Rescaling MODIS flood service Adaption / computation AuxData Index computation (EVI, LSWI, DVEL, NDVI) Integration of auxiliary data Cloud shadow modeling Classification result (Flood, Non-Flood, Cloud, Mixture, Standing/Receding Water) Adapted reference water mask Preprocessed MODIS data (Band 1,2,3,4,6) Thresholding of indices/spectral bands Region growing Separation flood / cloud shadow Footprints Statistical computations Reference water mask Adaption / computation AuxData Separation reference water / flood Automatic thresholding Definition of fuzzy sets Classification result (Flood, Non-Flood, Standing/ Receding Water) Adapted reference water mask Removing layover / shadow by GIM Image tiling/tile selection Defuzzification Filtering Preprocessed TerraSAR-X data Fuzzy region growing Footprints TerraSAR-X flood service DB (Vector data) Files (Raster data) t-1 DB (Vector data) Files (Raster data) WMS WFS Geoserver Website Alert WMS WFS Geoserver Website Martinis, S., A. Twele, C. Strobl, J. Kersten, and E. Stein A multi scale flood monitoring system based on fully automatic MODIS and TerraSAR X processing chains. Remote Sensing 6

7 MODIS Delivery Server TerraSAR-X Delivery Server Automatic data ingestion when new satellite data is Ftp pull Ftp pull available (e.g. through LANCE MODIS, TerraSAR X Modis data TerraSAR-X (L0) data Georeferencing delivery server, Sentinel Collaborative Ground Segment) MODIS TerraSAR-X Adapted metadata Computation of metadata global DEM Reprojection reflectances Alert Earth Observation Center MODIS flood service Global DEM Adapted global DEM Adapted or Reference water mask Slope Global DEM computed Rescaling Layer stacking reprojection to lat/lon) and adaption/computation of GIM Adapted Adapted Adaption / computation AuxData reference Statistical Reference auxiliary data (e.g. DEM information, reference water) water mask computations water mask Filtering water mask Preprocessed MODIS data (Band 1,2,3,4,6) Projection Index computation Thresholding of Automatic Image tiling/tile Thematic analysis with a high degree of robustness and (EVI, LSWI, DVEL, NDVI) indices/spectral bands thresholding selection Cloud shadow Separation flood / Separation reference Removing layover / modeling cloud shadow water / flood shadow by GIM Fuzzy region growing independent from different environmental conditionsand Classification result (Flood, Non-Flood, Cloud, Mixture, Standing/Receding Water) Footprints GIM (optional) Adaption / computation AuxData Classification result (Flood, Non-Flood, Standing/ Receding Water) Slope, Aspect Footprints Radiometric calibration to σ0 Automatic data preprocessing (radiometric calibration, Preprocessed TerraSAR-X data transferability Integration of auxiliary (e.g. automatic land/water Definition of fuzzy threshold Region growing Defuzzification data sets derviation). A high thematic accuracy needs to be assured satellite acquisition parameters. TerraSAR-X flood service DB (Vector Files (Raster DB (Vector Files (Raster data) data) data) data) t-1 Storage of derived information in a geodatabase and dissemination of flood products via an interactive Geoserver Geoserver webclient WMS WFS Website Alert WMS WFS Website Martinis, S., A. Twele, C. Strobl, J. Kersten, and E. Stein A multi scale flood monitoring system based on fully automatic MODIS and TerraSAR X processing chains. Remote Sensing 7

8 Example: MODIS Flood Service Visualization of the most recent MODIS data, derived flood extents, and further classes (e.g. clouds/snow/ice) 8

9 Example: MODIS Flood Service Definition of areas of interest and warning levels 9

10 Example: MODIS Flood Service Monitoring & Alerting Service: Automatic alert notifications by SMS or according to user defined parameters/aois 10

11 Earth Observation Center Example: TerraSAR X flood service TerraSAR X StripMap, , Bosnia and Herzegovina/Croatia 11

12 Earth Observation Center Example: TerraSAR X flood service TerraSAR X StripMap, , Bosnia and Herzegovina/Croatia 12

13 Earth Observation Center Outlook: Sentinel 1 flood service Sentinel 1 IWS Namibia, Zambezi river 13

14 Earth Observation Center Outlook: Sentinel 1 flood service Sentinel 1 IWS Namibia, Zambezi river 14

15 Outlook: Sentinel 1 flood service Sentinel 1 IWS, Map produced by Copernicus Emergency Management Service (EMS) 15

16 Outlook: Sentinel 1 flood service Result of DLR/ZKI s fully automated Sentinel 1 processing chain (early prototype) Sentinel 1 IWS, Map produced by Copernicus Emergency Management Service (EMS) 16

17 Application cases of flood services Flood Services (MODIS, TerraSAR-X, Sentinel-1, etc.) Early Warning/Monitoring (using daily acquired data) Combination with ancillary geodata: rapid loss estimation, exposure mapping, risk assessment Alert Detailed NRT flood assessment (using HR/VHR SAR data) Base layer for subsequent rapid mapping products 17

18 Quantifying Risk Risk = ƒ { Hazard, Exposure, Vulnerability } Potentially damaging event - Likelihood - Magnitude - Duration - Speed - Spatial extent Objects potentially adversely affected - People - Property - Infrastructure Present attribute that decribes possible future harm - Physical - Social - Economic Sentinel-3 Sentinel-1

19 Manila, 1975 Manila, 1990 Manila, 2000 Manila, 2010

20 Mapping of urban areas from TerraSAR-X data (3m), Nairobi, Kenya 20

21 City District Block Building

22 Flood extent 22

23 Building Number Percentage Sum ,00% <100 m² 7 4,5% 100 m² <250 m² 17 10,9% 250 m² <500 m² ,2% >500 m² 10 6,4% 23

24 Rapid Flood Loss Estimation Earth Observation Center Sentinel-3 Sentinel-1 + Building inventory / Population information 24

25 Thank you very much for your attention! 25