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
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 2003 2
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
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
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
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
Reliable land cover maps are essential for: 1. 2. 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; 10.31 hr Spatial resolution: 1 meter pan-sharpened Spectral bands: Blue 450-530 nm Red 520-610 nm Green 640-720 nm Near infrared 770-880 nm at 11 Bits Orbit around the earth: 682 km sun-synchronous Map projection UTM Lambert WGS84 7
Full resolution IKONOS image Borgharen Buildings, in black derived from Topographic map and from IKONOS image Topographic map 1:10.000 IKONOS derived 1:10.000 IKONOS image 1:10.000 8
Traditional spectral-based supervised image classification 0.55 Withering Vegetation 0.45 0.35 Reflectance 0.25 0.15 0.05 1 2 3 4 5 7-0.05 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 Wavelength (nm) TM_width Green Yellow band 2 2 1 3 band 1 Concept of Image Segmentation at Various Hierarchical Levels (ecognition) Pixel level Small objects Medium objects Large objects ecognition 9
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 5 0.7 0.3 0.9 0.1 Level 2 Buildings no yes yes yes 10 0.5 0.5 0.9 0.1 Level 3 Roads no yes yes yes 30 0.5 0.5 0.9 0.1 Level 4 Agriculture, water, large buildings and roads no no yes yes 100 0.9 0.1 0.9 0.1 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 2000 10
Error matrix IKONOS classification Borgharen reference / ground truth image to be evaluated IKONOS classification ground truth users' class-map 111 112 113 114 115 141 143 132 151 211 212 221 241 331 41 43 50 sumaccuracy accuracy Residential building 111 18 0 0 3 0 3 0 0 0 0 2 0 0 0 0 0 0 26 0.69 0.44 Garden 112 0 10 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 11 0.91 0.56 Grass in built-up area 113 0 1 12 1 0 1 0 0 0 0 0 0 0 0 1 0 0 16 0.75 0.63 Pavement/other urban 114 4 1 0 39 0 1 0 1 7 0 0 0 1 2 2 0 0 58 0.67 0.57 Water side 115 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 2 0.50 0.25 Road 141 11 0 2 23 1 36 0 1 3 0 0 0 1 4 0 0 0 82 0.44 0.41 Railroad 143 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 4 1.00 1.00 Sand deposit area 132 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 7 1.00 0.50 Industrial company 151 0 0 0 10 0 0 0 5 17 0 0 0 0 0 0 0 0 32 0.53 0.40 Pasture 211 0 5 1 2 0 0 0 0 0 123 0 1 1 22 8 0 0 163 0.75 0.74 Winter wheat 212 0 0 0 0 0 0 0 0 0 1 37 0 0 0 0 0 0 38 0.97 0.80 Nursery 221 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 5 1.00 0.83 Fallow 241 0 0 0 0 0 0 0 0 0 1 0 0 42 0 0 0 0 43 0.98 0.89 Natural vegetation 331 0 0 0 0 0 0 0 0 0 0 3 0 0 4 0 0 0 7 0.57 0.10 Deciduous forest 41 0 0 0 0 1 0 0 0 0 2 3 0 0 3 23 1 0 33 0.70 0.52 Mixed forest 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 6 1.00 0.86 Water 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 32 32 1.00 1.00 sum 33 17 15 78 3 41 4 14 27 127 45 6 46 36 34 7 32 565 producers' accuracy 0.55 0.59 0.80 0.50 0.33 0.88 1.00 0.5 0.63 0.97 0.82 0.83 0.91 0.11 0.68 0.86 1.00 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
Manning derived from CORINE, LGN3, IKONOS used in flooding simulation model Resulting computed flooded area & water depth 12
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
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.33-40 Damage functions Damage factor 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 0.00 2.00 4.00 6.00 w inter w heat roads industry residential building Water depth (m) 14
International Models for flood damage assessment US LOSS CURVES Structure + Contents 80 % damage 60 40 20 0-2.00 0.00 2.00 4.00 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
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, 2002 16
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