River Flood Damage Assessment using IKONOS images, Segmentation Algorithms & Flood Simulation Models

Transcription

1 River Flood Damage Assessment using IKONOS images, Segmentation Algorithms & Flood Simulation Models Steven M. de Jong & Raymond Sluiter Utrecht University Corné van der Sande Netherlands Earth Observation Ad de Roo JRC, European Commission, Italië Borgharen in January 1995 Dam 1

2 Two extremes 2002 versus 2003: Full winter bed & Hardly room between the groynes 10 years of flooding in NL Is there an increasing trend? NRC 12 Oct

3 Recurrence time of peak discharge Borgharen 1993 & 1995 floods What to expect in the future...?? Analysis of discharge over the years: - yearly peaks in black - 15 yr average in red - trend in blue 3

4 Joint JRC UU project: EC JRC overall objectives: 1) Develop 'numerical' simulation tool for flooding in Europe: LISFLOOD 2) Apply it to larger catchments such as: Rhine, Meuse, Oder, Severn, Elbe, Styre, Tisza, Gard Mmmh 3) To evaluate the consequences of environmental measures: buffer basins, afforestation, wider banks etc. 4) To increase flood forecast time Our UU/JRC sub-objectives: 5) Quick assessment of damage, assessable in money, typically after 2 or 3 days after flooding on the basis of: IKONOS satellite imagery, Dutch LGN cover maps & EU-CORINE 6) To refine hydraulic roughness maps (Manning) for LISFLOOD 4

5 Simulation of the 1995 Meuse Flood Event using LISFLOOD for the floodplain of Borgharen Requirements (transnational): - Reliable rainfall data (temporal, spatial) in entire Meuse catchment - Accurate DEM & channel characteristics - Hydraulic roughness (Manning s n) - Initial (moisture) conditions - Land use, land cover: CORINE, LGN3, Earth observation -etc. Floodplain DEM derived from laser altimetry 5

6 Sources for land use & land cover (CORINE, LGN3) Images available prior to launch IKONOS in 2001 Landsat TM 30* 30 m 6 may 2000 SPOT XS 20 * 20 m 6 July 1987 IKONOS 1 * 1 m 6 May 2000 Animation of Borgharen flood (Meuse) in January 1995 Improved hydraulic resistance estimate (Manning s n) Direct damage assessment due to flooding 6

7 Reliable land cover maps are essential for: Damage estimates based Hydraulic resistance estimates on land cover objects based on look up tables of land cover and water depth IKONOS image Data acquisition: 6 May 2000; hr Spatial resolution: 1 meter pan-sharpened Spectral bands: Blue nm Red nm Green nm Near infrared nm at 11 Bits Orbit around the earth: 682 km sun-synchronous Map projection UTM Lambert WGS84 7

8 Full resolution IKONOS image Borgharen Buildings, in black derived from Topographic map and from IKONOS image Topographic map 1: IKONOS derived 1: IKONOS image 1:

9 Traditional spectral-based supervised image classification 0.55 Withering Vegetation Reflectance Wavelength (nm) TM_width Green Yellow band band 1 Concept of Image Segmentation at Various Hierarchical Levels (ecognition) Pixel level Small objects Medium objects Large objects ecognition 9

10 Segmentation approach and parameters of IKONOS image Segmentation Land use types Segmentation parameters and classification IKONOS-2 bands used Scale Homogeneity criterion level parameter Colour Shape Shape settings Blue Green Red NIR parameter parameter smoothness compactness Level 1 All yes yes yes yes Level 2 Buildings no yes yes yes Level 3 Roads no yes yes yes Level 4 Agriculture, water, large buildings and roads no no yes yes Nearest neighbour classification through the various levels e.g. forest at level 2; building at level 4 Results are very good Main disadvantage: algorithms are black box for the user IKONOS based land cover map 6 May

11 Error matrix IKONOS classification Borgharen reference / ground truth image to be evaluated IKONOS classification ground truth users' class-map sumaccuracy accuracy Residential building Garden Grass in built-up area Pavement/other urban Water side Road Railroad Sand deposit area Industrial company Pasture Winter wheat Nursery Fallow Natural vegetation Deciduous forest Mixed forest Water sum producers' accuracy Overall accuracy 0.74 KHAT accuracy 0.70 n= 565 samples (field work, topo map, TM image, aerial photo) Data Sources for Estimating Manning s n & Direct Damage: Land Use Derived from EU-CORINE IKONOS LGN3 11

12 Manning derived from CORINE, LGN3, IKONOS used in flooding simulation model Resulting computed flooded area & water depth 12

13 Borgharen flood extent maps derived from various sources Based on Interpretation of aerial photo Based on ERS-1 Radar Satellite image (Bristol University) Model simulations Flood event of January 1995 Theory of flood damage assessment (Vrisou van Eck, 2001; Kok, 2001 ; USACE, 1996; Penning-Roswell, 1994) Direct damage: loss of means, recovery damage Indirect damage business interruption, environmental damage, cleaning costs, evacuation costs Flood factors controlling damage: water depth, velocity, duration, sediment concentration & size wave/wind action, pollution load, water rise during flood onset Economic & social variables Infra structure properties Warning time before flooding 13

14 Damage assessment functions proposed by Delft Hydraulics (WL) S the total damage [ ] α i (h) damage factor of damage category i, depending on water depth (h) h water depth (m) n id (h) number of units in category i with flooding depth h [-], S i max maximum damage per unit in category i [ ], m number of categories [-]. Source: Vis et al, Int Journal of River Basin Management vol.1 (1), pp Damage functions Damage factor w inter w heat roads industry residential building Water depth (m) 14

15 International Models for flood damage assessment US LOSS CURVES Structure + Contents 80 % damage Inundation depth (m) SCS FIA USACE NHRC C/B=0.3 SCS: Soil Conservation Service FEMA: Federal Emergency Management Agency USACE: US Army Corps of Engineers NHRC: Natural Hazards Research Centre (Australia) Estimated flood (direct) damage maps CORINE: 95.2 m LGN3: 83.7m IKONOS: 72.0 m Indication by insurance company: 80m 15

16 Estimated damage map for the 1995 flood of Borgharen Total estimated damage of 1995 event 72.0 million Dark red: high damage rates Light red: low damage rates White: no damage/no information Damage estimate by insurance company (1 year after event) insurance companies are very reluctant to provide financial data Source: Kok et al., 2000, Risk of Flooding and Insurance in the Netherlands Proc. The Second International Symposium on Flood Defence (ISFD 2002) Beijing, September 10-13,

17 Plans for flood mitigation: - wider river banks - deeper river banks - vegetation to slow down flow - elevated dikes at locations Conclusions: High resolution earth observation imagery contributes considerably to fast damage assessment after flooding, typical 3 to 4 days Hydraulic resistance factor for flooding models, retrieved from HiRes earth observation images, improve flood simulations Thank you for your attention 17