assessing flood and flood damage using remote ...

3rd International Conference on Water & Flood Management (ICWFM-2011)

ASSESSING FLOOD AND FLOOD DAMAGE USING REMOTE SENSING: A CASE STUDY FROM SUNSARI, NEPAL Kabir Uddin1* and Basanta Shrestha2 1

International Centre for Integrated Mountain Development, GPO Box 3226, Khumaltar, Lalitpur, Kathmandu, Nepal, e-mail: [email protected] 2 International Centre for Integrated Mountain Development, GPO Box 3226, Khumaltar, Lalitpur, Kathmandu, Nepal, e-mail: [email protected] ABSTRACT This paper describe common disaster by flood is possible to monitor and including flood damaged using remote sensing (RS) with combination geographical information systems (GIS) for emergency response and efficient relief work. In Nepal the terai region is main natural hazard prone area and within the terai region Sunsari district numerously affecting by flood. Monsoon session in the Nepal is manly cloudy winter period Landsat 2000 to 2008 images were used to determine river channel migration, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 2008 image for base land cover to assessing the damage and radar images for identification of flood extent area. Object based technique was used for image classification and ArcGIS spatial analysis for necessary statistics calculation. Year of 2000 river was flowing 595.64m from breaching point and 2008 river was flowing enclosed I grain part of the embankment and completion of the Koshi barrage contributed to the breach without warning and create fast-moving flood waves and damaged, agriculture land, homestead. The catastrophic floods have been caused significant damage total flood affected area was 17275 acres and 16 Village Development Committee (VDC) within the Sunsari district. Keywords: Flood, River, Remote Sensing, ASAR, ASTER, Landsat, Segmentation, eCognition 1. INTRODUCTION In the Nepal Terai region is composed of 26 to 32Km wide broad belt of alluvial and fertile plain in the southern part of the Nepal. This belt extends from the westernmost part of the country to the eastern limit and covers about 17% of the total land area. The Tarai region usually identified as a flood prone area of Nepal. Lower part of the Koshi basin is one of major flood affected area. Upper part of Koshi basin river mountainous and hilly and river flow with very high current due to high gradient topography. When it reach to plain territory spreads in large area and starts cutting channel. The Koshi river is well known for breaching the flood control embankment, occurring floods, carrying high sediment loads and capricious behaviour. Riverbank erosion is a natural process, but often human activities can have a significant impact on the rates of morphological change. River channel changes, such as bank erosion, down cutting and bank accretion, are natural processes for an alluvial river (Kummu et al., 2008). Floods are a constant threat to life and property. In the past 30 years, floods have been the most catastrophic natural disaster, affecting about 80 million people per year on average, or half of the total population affected by any natural disaster, causing economic damage of over US$11 million annually around the world (International Federation of Red Cross and Red Crescent Societies, 1998). The number of people affected by riverine floods. As increasing human activity downstream of rivers results in greater

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flood damage, the floods themselves, in turn, are also increasing in size and frequency due to human activities in the upstream section of the river system. (Dutta et al., 2006). Earth observation techniques can contribute toward more accurate causes of flood, flood hazard mapping and they can be used to assess damage to residential properties, infrastructure and agricultural crops. The use of remotely sensed data in identify the trends of river channel migration and as source of input data for determine river behaviour study has been popular in recent years. One of the main characteristics of remote sensing is its capability of generating a large amount of information, frequently and spatially, becoming a powerful tool for monitoring aquatic environments. Remote sensing data has been used to: document water quality (Dekker et al. 2002), estimate water depths (Lyon et al. 1992, Lyon and Hutchinson 1995) and monitor river channel changes and aquatic habitat (Marcus et al. 2003). Object-based analysis of multispectral imagery was introduced early on in the remote sensing literature (Ketting and Landgrebe, 1976); however, the object-based approach has largely been ignored in favour of pixel-based methods, which have been easier to implement (Lobo, 1997). One of the advantages of object-based image analysis is the multitude of additional information that can be derived from image objects versus the amount of information available from individual pixels (Chubey et al. 2006). The objective of this study was using remote sensing and geographic information systems determine the trends of river channel migration to breach the embankment, flood extended area and object base algorithms to produce detailed land cover maps for flood damage assessing.

Figure 1: Location Map of Sunsari District 2. METHODOLOGY 2.1

Study Area

The study area is located in South East part of the Nepal and name Sunsari District (Figure 1). This a very important district of Tarai region and lower part of Koshi river basin. It is a low-lying landscape and elevation of 62m to 1781m (SRTM DEM V4) above mean sea level. The Koshi basin ranges in altitude from 65 metres above sea level in the Terai to over 8,000 metres in the high Himalaya. This is the highest populating density area of eastern Nepal. Most of the land is used for agriculture, housing and horticulture (fruit trees, bamboo and vegetables). Agricultural land use is dominated by rice, mustard, sugarcane, pulses and banana. 70% of the land is cultivable land and 30% is homesteads and homestead forest, roads, and permanent water bodies like rivers, canal and ponds. For flood protection from Koshi river has enclosed embankment.

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2.2

Data Used

Different type satellite images from different sources were used for this study (Table 1). Determine river channel migration Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images, base land cover ASTER image and flood extend area radar images used. In radar image water area normally shown as black. Before flood and during the flood Advanced Synthetic Aperture Radar (ASAR) and Phase Array L-type Synthetic Aperture Radar (PALSAR) image were used determine the difference between flooding area and ordinary water body. The details of data set is given in the following table Table 1: List of satellite imagery

Satellite & Sensor

Season

Date of Acquisition

No. of Scene

ASAR ASAR ASAR ASTER ASTER Landsat5 TM Landsat7 ETM+ Landsat7 ETM+ Landsat7 ETM+ PALSAR

Before flood During the Flood During the Flood Before flood Before flood Before flood Before flood Before flood Before flood During the Flood

2006-07-04 2008-08-31 2008-09-03 2008-03-19 2008-03-26 2004-11-02 2006-11-16 2002-12-23 2000-10-30 2008-08-24

1 1 1 2 1 1 1 1 1 1

2.3

Image analysis

Enumerate the river channel, base land cover map and flood extended area mapping were followed few steps. Image classification was carried out with the object-based image analysis (OBIA) approach using Definiens/ eCognition software. Acquired radar images were not rectified. First radar images were coregistered and then georeferenced into UTM and Zone 45 projection based on generated Ground Control Points (GCP) from ASTER image. After that all images were resample with the nearest neighbor method to a common resolution of 15m. Nearest neighbor resampling has computationally simple and does not alter original pixel values. Resample was chosen to a common resolution so that river of interest would be either equal to or larger than the pixel size. After that consecutively ASTER image were processed Definiens/ eCognition software for objectbased image analysis. Compared with pixel-based methods, this approach has shown better classification results with higher accuracy as it uses both spectral and spatial information (Civco et al 2002; Yoon et al 2004; Harken and Sugumaran 2005; Gao et al 2007). The fundamental step of eCognition image analysis is a segmentation of a scene. Multiresolution segmentations used for object-based image analysis. Multiresolution Segmentation groups areas of similar pixel values into objects. Consequently homogeneous areas result in larger objects, heterogeneous areas in smaller ones.

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Figure 2: Flow Chart showing the steps for assessing the flood damage One of the most important issues in the context of an object oriented classification is the accurate segmentation of the input images. The basic task of segmentation algorithms to delineate image object appropriately. A convenient approach was to run segmentations with different parameters until the result was satisfactory. Shape was given priority during the first-level segmentation process, while color was given priority in the second-level process to get suitable segmentation of the images. For each segment, information on average Normalized Difference Vegetation Index (NDVI), Land and Water Mask, slope etc were derived. This information was used to develop suitable classification algorithms for individual land cover classes. Image objects were linked to class objects and each classification link stored the membership value of the image object to the linked class. Similar way river channel from Landsat images and flood extend was determined from radar images. After the classified data was exported to shape file format for further processing, such as the elimination of areas smaller than the defined minimum mapping units. Centre line of river from generated river channel. Occurred damage statistics was calculated for results and discussion using ArcGIS using overly technique. As in any GIS and remote sensing work, the possible inaccuracy of our results are inherited from both local and positional errors, as a result of employing maps obtained in various times and with various scales. (Kummu et al., 2008). Accuracy assessment was done using GPS and collected point from high resolution images and the land cover at each point was interpreted with the help of field GPS data, IKONOS 4-m multispectral and 1-m panchromatic images, and available field photographs. These were then compared with the land cover map to calculate the error matrix and overall accuracy was 89.6%.

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3. ILLUSTRATIONS 3.1

Results

One of the most serious problems of Koshi river is erosion and siltation in river catchments. This happens to a channel due to morphological adjustments to accommodate the range of flows and sediment loads from upstream. Analysis of a series of Landsat images, between 2000 and 2008, exposed the dynamic nature of the river bank and, including channel migration, movement and It can be seen in Figure 3. The changes river channel from 2000 to 2008 were very high and unstable. The water level changes and fluctuation in the dry season would not cause frequent riverbank erosion compared to the same changes and fluctuations if in the wet season. The river channel migration during the period 2000 to 2008 presented in Table 2. Table 2: Westward channel movement from 2008 to 2008 Northing

2000

2002

2004

2006

2008

30 Km

2262m

2195m

2212m

2214m

2186m

25 Km

2332m

2289m

2290m

2291m

2281m

20 Km

7085m

6182m

5696m

5633m

5633m

15 Km

8434m

8434m

8758m

8900m

8843m

10 Km

12949m

13406m

14122m

14435m

13638m

5 Km

14638m

14638m

14638m

14638m

15150m

0 Km

17839m

19341m

19952m

20588m

21703m

Koshi river embankment and barrage was established accordingly year of 1959 and 1964. Normal river flow was entraped after establishing barrage and due to the permanently protecting the surrounding area from floods, the embankments had changed the morphology of the river, raising the jacketed channel above the level of the surrounding land. Artificial irrigation channel developments blocked the natural drainage and divided the region into a series of enclosed basins. From 2002 to 2008 river has been migrated 375.6m towards the breaching point . Year of 2000 river was flowing 595.64m from breaching point and 2004 it was 596.02 and 2008 it was come up to 304.57m. 2008 River was flowing enclosed I grain part of the embankment and following completion of the Koshi barrage and poor maintained embankment contributed to the breach of August 18, 2008 without warning and create fast-moving flood waves. The floods have been caused significant damage to livelihoods and infrastructure (Table 3) and total flood affected area were 17275.708 acres. Within the overall damaged land cover, 74% agriculture land was damaged. Damages was not limited to the agricultural sector with significant damages also occurred to residential property, businesses and public infrastructure, fish farming pond (Figure 4). The far-western region of the country was cut off from the rest of the country due to damage of the east-west national highway in many places. Many of the feeder roads and embankments were also swept away. Drinking water and electricity supplies, schools and public buildings were collapsed. Many private houses, property, domestic animals and standing crops were washed away. Within the 53 VDC of Sunsari district 16 VDC were affected fully or

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partially. Within the Flood affected 16 VDC Sripurjabdi VDC was affected very much and area was 5349.304 acres.

Figure 3: Channel movement from 2000 to 2008 Table 3: Flood affected area by land cover Land Cover Type

Total Area (Acres)

Flooded Affected Area (Acres)

Flooded Affected Area (%)

Agriculture Bare Area Built Up Area Canal Forest Grass Land Homestead Pond River Shrub Land Total

167065.206 8787.630 1068.306 997.975 53240.722 20092.047 26963.842 328.526 6654.576 9506.339 294705.169

12814.647 1527.555 0 20.992 11.419 1460.353 1328.081 20.486 0 92.175 17275.708

74 9 0 0 0 8 8 0 0 1 100

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Figure 4: Flood Assessed Map 4.

CONCLUSIONS

In conclusion, therefore, it can be seen that past six year river channel migrated to breach the embankment and usually happened flood and create fast-moving flood waves and damages, agriculture land, homestead. Object base remote sensing technique and geographical information systems were used for river channel mapping and morphological changes, flood extends and land cover classification. Object based classification systems allow different type of rules for different classes that can be use for further similar type flood mapping and damage assessing. Monsoon time mainly cloudy weather so radar imagery were suitable for urgent flood extend mapping. This study have shown that the information derived from different imagery can be very valuable to operational users for planning flood-related emergency response measures including river migration to breach the embankment. Natural flood disaster is common and cannot be stop. Use of remote sensing is efficient tools for river morphological changes study to control the flood and assessing the flood damaged for emergency response and efficient relief work. 299


ACKNOWLEDGEMENTS We acknowledged Deo Raj Gurung for support and acquiring satellite images and Rajan Bajracharya for uploading flood map on mountain geoportal of International Centre for Integrated Mountain Development (ICIMOD) and JAXA and ESA for PULSAR and ASAR images.

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