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

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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

Department of Physical Geography – Utrecht University

Borgharen in January 1995

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Two extremes 2002 versus 2003:

Full winter bed

&

Hardly room between the groynes

10 years of flooding in NL

Is there an increasing trend?

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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

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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

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

Mmmh

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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

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Landsat TM 30* 30 m

6 may 2000

SPOT XS 20 * 20 m

6 July 1987

IKONOS 1 * 1 m

6 May 2000

Sources for land use & land cover (CORINE, LGN3)

Images available prior to launch IKONOS in 2001

Animation of Borgharen flood (Meuse) in January 1995

• Improved hydraulic resistance estimate (Manning’s n)

• Direct damage assessment due to flooding

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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

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Full resolution IKONOS image Borgharen

Topographic map 1:10.000

IKONOS derived 1:10.000

Buildings, in black derived from

Topographic map and from IKONOS image

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TM_width Green Yellow Withering Vegetation Wavelength (nm) Ref lect ance -0.05 0.05 0.15 0.25 0.35 0.45 0.55 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 4 123 5 7

Traditional spectral-based supervised image classification

1

2

3 band 1

ba

nd 2

Concept of Image Segmentation at Various Hierarchical Levels (eCognition)

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Segmentation parameters

Homogeneity criterion IKONOS-2 bands used

Shape settings Segmentation

and classification level

Land use types

Blue Green Red NIR Scale

parameter Colour

parameter Shape 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

Segmentation approach and parameters of IKONOS image

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

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ground truth 111 112 113 114 115 141 143 132 151 211 212 221 241 331 41 43 50 sum users' accuracy class-map 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 IK ON OS c la ss ifi ca tio n 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

Error matrix IKONOS classification Borgharen

n= 565 samples (field work, topo map, TM image, aerial photo)

image to be evaluated

reference / ground truth

Data Sources for Estimating Manning’s n & Direct Damage:

Land Use Derived from

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Manning derived from CORINE, LGN3, IKONOS

used in flooding

simulation model

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Borgharen flood extent maps derived from various sources

Flood event of January 1995

Model

simulations

Based on

ERS-1 Radar

Satellite

image

(Bristol

University)

Based on

Interpretation

of aerial photo

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

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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

Dam age functions

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0.00 2.00 4.00 6.00 Water depth (m ) D a m a ge f a c

tor w inter w heat

roads industry residential building

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US LOSS CURVES

Structure + Contents

0

20

40

60

80 -2.00

0.00

2.00

4.00

Inundation depth (m ) % d a ma g e SCS FIA USACE NHRC C/B=0.3

International Models for flood damage assessment

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

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Estimated damage map for the 1995 flood of Borgharen

Dark red: high damage rates

Light red: low damage rates

White: no damage/no information

Total estimated

damage of 1995 event

€ 72.0 million

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