1

Flood Monitoring Using Multi-Temporal Radarsat-1

Images

Z. Damla Uça Avcı1, Barı Göral

1, Ayda Akkartal

1, Filiz Sunar

2

1Istanbul Technical University,

Center for Satellite Communications and Remote Sensing,

34469 Maslak Istanbul, Turkey

damla@cscsr.itu.edu.tr, baris@cscsr.itu.edu.tr, ayda@cscsr.itu.edu.tr

2Istanbul Technical University, Civil Engineering Faculty,

Remote Sensing Division, 34469 Maslak Istanbul, Turkey

fsunar@ins.itu.edu.tr

Abstract

Turkey is affected by many natural and manmade hazards, mainly by earthquakes,

floods, fires and landslides that cause considerable damage to towns, roads and agriculture

with a high loss of life. In general floods, as being the most devastating natural hazard in the

world, are caused by heavy rain, snowmelt, or dam failures.

Recently, the Maritsa River, known as the Meric River in Turkey and Evros River in

Greece, which forms the border between the two countries, has experienced severe flooding.

Thousands of homes and agricultural lands have been flooded and damaged. Continuous

monitoring of flooding aids effective recovery and environmental management. Satellite data,

with its synoptic and regular coverage, offer effective and efficient ways to provide

information on phenomenon changes that occur in land use/cover type. The all-weather

capability of synthetic aperture radar imagery acquired from sensors such as RADARSAT-1

provides data over large areas whenever flood information is required.

In this study, the flooded areas occurred in March 2006 in the Maritsa River, were

evaluated with three multitemporal (before and after the event) Radarsat-1 images. The

evaluation of the results related to temporal changes in the river and damage assessments

were outlined.

1. Introduction Floods, causing damage to agricultural crops, roads, and the loss of human lives, are one of

the most common disasters in the world accounting for 40% of all natural disasters

worldwide. (Beyhuni et. al. 2006)

Flood effects can be local, impacting a village or fields around, or very large, affecting

entire river basins and multiple cities. One of the biggest problems during these emergencies

is to obtain an overall view of the phenomenon, with a clear idea of the extent of the flooded

area, and, to predict the likely developments. Many of the world’s urban centers are in low-

lying areas subject to flooding, and rapid identification and response to flooded areas is

essential to avoid turning an environmental phenomenon into a potentially grave disaster.

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(ESA, 2006) Traditional methods of flood mapping are based on ground surveys and aerial

observations, but when the phenomenon is widespread, such methods are time consuming and

expensive; furthermore timely aerial observations can be impossible due to prohibitive

weather conditions. An alternative option is offered by satellite remote sensing (RS)

technology. (Brivio et. al. 2002)

In recent decades optical data acquired by sensors onboard spacecraft have been used

in many studies to map inundated areas over regions characterized by very different

conditions in climate, morphology and land use. Bad weather conditions during and after

these kind of events can represent a strong constraint to the utilization of optical remotely

sensed data. For this reason, optical sensors are generally used to assess inundated fields only

some days after the event, either by recognition of fluvial sediments left on the land (Rosso,

C. 1995) or by the detection of vegetation stress (Michener & Houhoulis 1997). On the

contrary, spaceborne radar systems, because of their exclusive cloud penetration capacity,

offer a primary tool for real-time assessment of flooded areas.

Difficulties in the interpretation / classification of the acquired signal are the main

disadvantages of the use of radar sensors, due to the influence of complex ground and system

variables. To improve this situation, multi-temporal techniques based on detection of changes

between radar images acquired before and after the inundation event are usually

recommended. (Wang et. al. 1995) Moreover if a high temporal resolution data set is

available, change detection analysis can be used to produce a good flood evolution map.

(Oberstadler et. al. 1995)

Especially after the floods, more precise assessment of the damaged area is needed by

local authorities and the insurance companies to cover the damages.

RADARSAT-1 data, downlinked by the ITU-CSCRS Ground Receiving Station are

being used for monitoring the effects of the winter floods in the main rivers in Turkey. One of

the main rivers Maritsa river is facing flooding every year and this event can only be

monitored by SAR data since the cloud cover during the event.

2. Study Area and Data Used The Maritsa sub-basin, including Arda, Tundja and Ergene tributaries, is one of the major

river systems located in the eastern Balkans, with a total length of 550 km and a total

catchment area of 39,000 km . About 66% belongs to Bulgaria, 28% to Turkey and 6% to

Greece. A small section of the northern branch of the river runs entirely in Turkey, the lower

course of the Maritsa forms part of the Bulgarian-Greek border and most of the Greek-

Turkish border. (INWEB 2006)

The mean discharge of the river at the mouth is about 1,610 m / s. The water is mainly

used for irrigation and water supply of cities and villages. (Dartmouth Flood Observatory,

2006)

The delta area (about 150 km ) at south part of the river, is a very important ecological

site, especially being a site for seasonally immigration of the birds, protected under the

RAMSAR convention. (Dartmouth Flood Observatory, 2006) And 2369 hectares are being

under protection since 1991, by Republic of Ministry of Environment and Forestry. (Lee &

Lee 2003)

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Northern part of the delta is chosen as the study area in order to observe the flood

affects on the main city Edirne, and also to watch over the region where three countries

borders intersect. (26° 9´ 51´´ E - 26° 47´ 44´´ E and 42° 1´ 41´´ N - 42° 20´ 21´´N, covering

approximately 50*50 sq. km.) (Figure 1)

Figure 1. Study area.

The region around Maritsa experiences flood disaster almost each year. As seen from

the Dartmouth Flood Observatory measurements in Figure 2, springtime flooding is common

along the river.

Figure 2. Flooding activity in Maritsa region from 2002 to 2006.

The causes of the floods are mainly heavy rain and snowmelt. The details about the

last flood events occurred in 2005 and 2006 are summarized in Table 1.

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Table 1. The main 2005 and 2006 floods on Maritsa.

Beginning

Date

Ending

DateCause

Affected

Region

(sq km)

Severity

Class

*

Flood

Magnitude

**

17 Feb 2005 24 Mar 2005 heavy rain + snowmelt 7610 2 6.3

2 Jan 2006 20 Jan 2006 snowmelt 4600 1 2

9 Mar 2006 25 Mar 2006 rain + snowmelt 19590 1 4.0

* Class 1: large flood events: significant damage to structures or agriculture; fatalities; and/or 1-2 decades-long

reported interval since the last similar event, Class 2: very large events: greater than 20 yr but less than 100 year

recurrence interval, and/or a local recurrence interval of at 10-20 yr., Class 3: Extreme events: with an estimated

recurrence interval greater than 100 years

** Flood Magnitude = ln(duration) * severity class * sqrt (affected region) / 100

As can be seen from Table 1, the floods of 2006 have been the worst, although

flooding is being observed very often in the area.

The recent main flooding began with heavy rainfall and snowmelt resulting to rise the

waters of the Evros and its tributaries, particularly the Arda River in Bulgaria. Such intensive

heavy rainfall brought a vast amount of property damage to rice fields and to residential areas

in Turkey, Greece and Bulgaria. (BBC 2006) The heavy snowfalls caused floods in Bulgaria

and also caused its three dams to overflow in the Arda basin and flooded into the Maritsa.

Several artificial lakes along the Arda overflowed, and flooded. 19590 sq km. area is affected

by this flood. (Wikipedia 2006) In Turkish part, the city of Edirne was flooded and many

farms, many villages and houses were flooded. The effect of the flood in Edirne is shown in

Figure 3 with photographs taken before and after the event.

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Figure 3. Before and after photographs of flood in Edirne.

As a satellite data, the multitemporal RADARSAT-1 data were used (Table 2).

Table 2. The characteristics of the radar data used.

SatelliteAcquisition

Date

Beam

Mode

Spatial

Resolution

(m)

BandWavelength

(cm)Polarisation

Coverage

(sq km.)

13.08.2005 Fine 1

17.03.2006 Fine 1

27.03.2006 Fine 5

CRADARSAT-1 505.3 HH8

3. Methology The fundamental characteristic recorded on a radar image is the backscattering coefficient,

which may vary from surface to surface. The strength of the returned signal from the surface

is influenced by the combination of both system and ground parameters. These parameters are

the average surface roughness and soil dielectric properties. Horizontal smooth surfaces, such

as water bodies, reflect nearly all incident radiation away and the weak return signal is

represented by dark tonality on radar images. (Brivio et. al. 2002) With distinctive radar

responses of water surfaces, synthetic aperture radar (SAR) data are becoming a valuable tool

to analyze water related investigations. (Lee & Lee, 2003)

However, SAR data are subject to speckle, a multiplicative random noise that

considerably reduces the interpretability of the images and limits classification techniques,

and SAR images have to be filtered in order to increase the signal-to-noise ratio. (Brivio et. al.

2002) For this purpose, various filters such as Mean, Median, Frost, Lee and Gamma-Map

were tested with different window sizes and over two passes on the data set. The selected

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filtering was done using a 3*3 median filter first, and then the second iteration using a 5*5

mean filter.

In this study, SAR images acquired at three different dates were co-registered to form

a color composite data set. (Figure 4) The dry image (13 August 2005) is chosen as the base

image for the registration process. The flood (17 March 2006) and after flood (27 March

2006) images were registered with ± 2.4 and 3.2 rms. error by using 21 and 16 points

respectively.

Figure 4. The multi-temporal Radarsat-1 data set (R: 13 August 2005, G: 17 March 2006, B: 27 March 2006).

In the analysis, object-oriented approach is preferred as an image processing method

since it combines the spectral information with spatial information as shape, size, texture and

neighborhood relations to increase the classification abilities.

The methodical principles of object-oriented image processing consist of two basic

domains: the segmentation and the classification i) Segmentation which aims at producing

basic processing units for object oriented image analysis (so called image objects or

segments). The resulting image objects are the image units instead of pixels and have

attributes as not only spectral statistics but also shape information, relations to neighbouring

objects and texture ii) Classification which is based on the class-hierarchy. The class-

hierarchy contains the classification rules to which the image will be classified. Class

description is defined and a network of hierarchy is structured by relations of sub and super

objects through levels for each class.

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In this study, the Definiens Professional Earth software (Ver. 5.0) is used in the

analysis.

4. Analysis and Results

4.1 Segmentation

As a first step, image objects are extracted by segmentation in different scales and different

composition of homogeneity criteria, which represents the image information in different

spatial resolutions. The multi-scale analysis of data was conducted at three levels. The

segmentation parameters are given in Table 3 and the segmentation output of the same region

is shown in figure 5.

The level-3 segmentation was applied to create main objects for a general

discrimination as land and water body areas. However, in level-1, very small image objects

are formed to conduct more detailed discrimination of water body features. As an

intermediate segmentation, in level 2 which is main output level, the classification process

was interfered by using both sub and super object properties from all levels to get a better

classification result.

Table 3. The segmentation parameters used in the analysis.

Level 3 0 , 1 , 0 200

Level 2 1 , 1 , 1 50

Level 1 1 , 1 , 1 10

Seg. Level

Layer Weights

(13.08.2005,

17.03.2006,

27.03.2006)

Seg. Scale

Homogenity Criterion

Shape-ColorSmoothness-

Compactness

0.2 - 0.8

0.3 - 0.7

0.2 - 0.8

0.9 - 0.1

0.9 - 0.1

0.9 - 0.1

(a) (b) (c)

Figure 5. The segmentation results for (a) Level 3, (b) Level 2, (c) Level 1.

4.2 Classification

The classification had a structure based on 3 levels, mainly to produce the level 2 as the

output. The classification result (level 2) was maintained by using class definitions both from

newly described and inherited relations. Operations like combination and extraction from sub

and super objects were also applied to take the advantage of all levels.

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The classification hierarchy is shown in figure 6.

Figure 6. Scheme of the class hierarchy used.

The main class groups are the land, river bed and flooded areas. Flooded areas are

grouped in two sub-classes. One for the regions that are under water on the day that flooding

occurred, and the other for the regions those remain under water in the image taken 10 days

after. The discrimination is achieved by operations through the hierarchical network, like

subtracting the “land under water” classes from the general land class and assigning them to

flood group, and subtracting the “flood” and “river bed” classes from the water class as sub-

classes.

The classification results in three levels for a zoomed area are given in figure 7.

Consequently, resultant classification is not as general as level 3, and not noisy as level 1

classification. The classification result for the whole image is shown in figure 8.

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(a) (b) (c)

Figure 7. (a) Level 1 classification, (b) Level 3 classification, (c) Level 2 classification (main output).

Figure 8. Classification image.

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4.3 Accuracy Assessment

A confusion or error matrix is considered to be the most widely well known and used

accuracy assessment method. (Foody 2002) A simple cross-tabulation of the mapped class

label against that observed in the ground or reference data, provides an obvious foundation for

accuracy assessment. (Campbell 1996)

The final classification overall accuracy was achieved as 84 % and the KIA (Kappa

Index of Agreement or Kappa Coefficient) as 77 %. (Figure 9)

Figure 9. Classification Error Matrix.

As can be seen, the “flooded areas” were mixed with “wet after flood class” mostly.

5. Conclusion Flood disasters account for about a third of all natural catastrophes throughout the world (by

number and economic losses) and are responsible for more than half of the fatalities. (Gerhard

2006) Remote sensing and satellite data are effective tools for hazard related applications like

flooding that need a synoptic view when the field is on emergency status and it is hard to

make ground surveying. Radar systems are more advantageous in data providing and

observation of floodplains than optic systems, since flood occurs mostly on cloudy days.

In this study, the effectiveness of radar images for flood applications is shown with

multi-temporal radar data. Also object oriented image processing technique is used, which is a

promising technique that enables to use many additional parameters of image objects with

their spectral properties. Especially, segmentation and classification in a hierarchical manner

provides an effective classification. As shown in this study, different regions affected by

flooding can easily be extracted and the accuracy obtained (84 %) was satisfactory.

In near future, as a further study, the complementary use of optic and radar data for

flood applications will be analyzed and improved by different processing methods including

object oriented image processing techniques.

6. References

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WIKIPEDIA, 2006, Maritsa, http://en.wikipedia.org/wiki/maritsa, 04 August 2006