Flood detection using radar data Basic principles

Transcription

1 Flood detection using radar data Basic principles André Twele, Sandro Martinis and Jan-Peter Mund German Remote Sensing Data Center (DFD) 1

2 Overview Introduction Basic principles of flood detection using radar data Water appearance in SAR data Sensor-related effects Natural causes Change detection, indundation depth, flood dynamics 2

3 Terminology what are we mapping anyway? water extent = total area covered by water at a certain point in time (derived from e.g. post-disaster data) bright blue and blue normal water level = total area covered by water during average water level conditions (derived from predisaster archive data) blue flood extent = (total) water extent minus normal water level (requires pre- and post-disaster data) bright blue 3

4 Aspects of flood detection using SAR-data Verification topographic maps airborne imagery in-situ data Scale DEM temporal spatial Classification Approach Water / Flood Features wind, roughness vegetation (double bounce) flood type (e.g. flash flood) Sensor Parameters repetition rate spatial resolution object- pixel-based incidence angle supervised/unsupervised thresholding snake/region growing algorithms wavelength polarization 4

5 SAR data for crisis monitoring and disaster response Increasing use of SAR-data in crisis-related applications Weather and illumination-independent Increased awareness and user know-how Data availability (TerraSAR-X, Cosmo-SkyMed, ALOS-PALSAR, Radarsat-2,...) Spatial resolution, repetition rate New application fields 5

6 Recent advances in SAR sensors Spatial resolution Repetition rate Polarizations C-Band Satellites ERS-1/2 / Envisat-ASAR : up to 30m Radarsat-1: up to 8m (fine beam) ERS-1/2 / Envisat-ASAR: 35 days Radarsat-1: 24 days ERS-1/2: VV Envisat-ASAR: Single/Dual-Pol (H/V) Radarsat-1: HH New X-Band Satellites TerraSAR-X: up to 1m (SpotLight) Cosmo-SkyMed: up to 1m (SpotLight) TerraSAR-X: 2-4 days (11 days orbit) Cosmo-SkyMed: 3-24 h (with 4 Satellites) TerraSAR-X: Single/Dual-Pol (H/V) Cosmo-SkyMed: Single/Dual-Pol (H/V) Comparison of Water masks (SW-England Flood, ) Radarsat-1: 12.5m spatial resolution TerraSAR-X: 3m spatial resolution 6

7 Flood detection and TerraSAR-X characteristics SpotLight Mode Resolution: 1 m 1,5 m... 3,5 m Scene Size: 5 km...10 km 10 km [Range Azimuth] StripMap Mode Resolution: 3 m 1,5 m... 3,5 m Scene Size: 100 km 30 km [Range Azimuth] ScanSAR Mode Resolution: Scene Size: 16 m 16 m 100 km 100 km [Range Azimuth] 7

8 TerraSAR-X observation scales StripMap or ScanSAR to provide a first overview (Scale: 1: to 1: ) TerraSAR-X, StripMap, (~3m resolution)

9 TerraSAR-X observation scales Spotlight for detailed damage assessment (Scale: 1: to 1:25.000) Embankment Buildings, vegetation and agricultural areas fully surrounded by waters Embankment breach 1400m Flooded road segments Embankment TerraSAR-X, Spotlight, (~1.75m resolution)

10 Basic Principles of water detection using radar data Separation between land and water through different surface roughness Active reflection Mirror type reflection Nadir Distance Diffuse reflection Water: usually waveless or plane Mirror type reflection low direct reflection to active radar sensor Land: higher surface roughness diffuse indirect reflection sharp edge reflection double bounce effect 10

11 Basic Principles of water detection using radar data Mirror type reflection Diffuse reflection Active reflection Nadir Distance Differentiation of Land and Water according to the incidence angle of radar waves 11

12 Principles of water detection using radar data Water as dark areas with low backscatter Specular reflection Only little backscatter directed back to sensor Nepal, TerraSAR-X, Spotlight, HH,

13 Water appearance sensor-related effects Incidence angle steep vs. shallow Steep incidence angles: more reflection is directed back to the sensor higher backscatter land/water separation more difficult Shallow incidence angles: mainly specular reflection away from sensor water more likely to appear black urban area water Myanmar, Spotlight, , Inc. Angle: 19 Incidence angle depends on satellite orbit and area of interest. In crisis situations, first acquisition might be only possible with a steep incidence angle. Higher Backscatter Lower backscatter 13

14 Water appearance sensor-related effects SAR-Polarization HH vs. VV HH VV-polarization: More sensitive towards roughness at the water surface than HH-polarization increased backscatter land/water separation can become difficult during classification When ordering single-polarization SAR-data for flood mapping, always use HH-polarization! VV Lake Constance (GER), HighRes Spotlight 14

15 Water appearance sensor-related effects SAR-Wavelenght X-band (~3 cm) vs. L-band (~23 cm) TS-X ALOS PALSAR Differences in X- and L-band backscatter, particularly in moist areas and regions with flood waters beneath vegetation 15

16 Water appearance - wind / wave patterns Lake Ammersee (GER) at easterly winds, SM VV, Separation of land surface and water bodies according to surface roughness Problematic cases: Wind induced waves increased surface roughness increased backscatter possible confusion with land surface Bergen (NOR), StripMap, HH, Sea waves: Refraction effects 16

17 Water appearance - wind / wave patterns Mississippi-Delta, SM HH, , open water vs. inland waters 17

18 Objects with low backscatter values Walvis Bay (NAM), HR Spotlight, HH, Separation of land surface and water bodies according to surface roughness Sand dunes Airstrips Potential for misclassification as water bodies / land surface Sand dunes (wave structures) Airstrips (smooth surface) Ships Tokio, Haneda Airport (JAP), SL VV,

19 Objects with low backscatter values Same brightness (DN=16) for water (left) and road (right) Pixel based thresholding problematic! 19

20 Objects with low backscatter values Same brightness (DN=16) for water (left) and canopy shadow (right) Pixel based thresholding problematic! 20

21 Water bodies in mountainous terrain Lake Lucerne (Switzerland), SL VV, Radar shadow Lake Due to side-looking radar geometry no backscatter signal from areas behind mountain slopes (or buildings) Possible confusion of water bodies and radar shadow areas 21

22 Water bodies in urban areas Radar shadow from buildings Discrimination between water and radar shadow difficult Radar ground building water+shadow water spatial resolution: ~1 meter TerraSAR-X SpotLight-Mode - Mexico/Tabasco floods 11/2007 backscatter uncertainty! 22

23 Floods beneath vegetation Arkansas, March / April 2008 Image source: 23

24 White River floods, water beneath forest canopy high backscatter Clarendon (Arkansas, USA), Source: SM HH, Google , Earthspatial resolution 3.5 m double-bounce andre.twele@dlr.de 29/10/

25 Floods and Vegetation Valdivia (Chile), StripMap, Dual-Pol (HHVV),

26 Icing Forggensee (GER), TerraSAR-X, Spotlight Autumn Winter

27 Clouds and Water vapour Rhine falls, Schaffhausen, HS HH, Backscatter increase due to reflection from water drops and water vapour 27

28 Clouds and Water vapour Cloud Radar shadow Myanmar, TS-X ScanSAR, Cumulonimbus cloud, Nasa Backscatter increase due to reflection from water drops and water vapour 28

29 Change detection flood level and normal water level Extraction of pre- and postflood water levels Can be limited by the unavailability of archive data Tewksbury, England, SM HH, Possible workaround for TerraSAR-X StripMap and ScanSAR data: Free Landsat data provided by GLCF (Global Land Cover Facility) 29

30 Seasonality and land use normal water level? Po-Delta, SM HH, , rice cultivation China, SL HH, , rice cultivation knowledge of land use and its seasonal patterns needed! 30

31 Change detection during ongoing flood situations Flood dynamics Mexico/Tabasco floods ( ) StripMap Mode Blue: Differences <-> Black: Water on both dates Flood duration DataTakes with <12h time difference through a synergistic use of different SAR platforms (e.g. TerraSAR-X and COSMO-SkyMed) 31

32 Estimation of flood depths Flood depth: Important parameter in flood damage estimation models Required: High resolution DEM to extract height at the land/water-boundary error classification & correction TIN-interpolation deriviation of flood depths 32

33 Thank you for your attention! 33