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Flood Hazard Assessment for the Construction of Flood Hazard Map and Land
Development Priority Map Using NOAA/AVHRR Data and GIS - A Case Study in Bangladesh
Md. Monirul Islam
Kimiteru Sado
Department of Civil Engineering, Kitami Institute of Technology,
165 Koen-cho, Kitami 090-8507, Japan
Md. Monirul Islam
Kimiteru Sado
Department of Civil Engineering, Kitami Institute of Technology,
165 Koen-cho, Kitami 090-8507, Japan
ABSTRACT
Bangladesh suffered damage on account of the most catastrophic floods of 1987, 1988 and 1998, resulting in untold suffering of the people. This paper demonstrates the technique to develop a flood hazard map and a land development priority map for countermeasure against flood damage. To create the final products, flood hazard map and land development priority map, National Oceanographic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data for the flood events of 1988, 1995 and 1998 were incorporated with Geographical Information System data of physiographic divisions, geologic divisions, land cover classification, elevation height, drainage network, administrative districts and population density. Flood-affected frequency and flood depth categories were estimated using NOAA AVHRR images. Special attention was paid to population density for the construction of the land development priority map, because highly dense populated areas represent the highly important urban and industrial areas of Bangladesh. The land development priority map offers a new opportunity for flood risk management, planning, design and operation of flood control measure in Bangladesh, and should be useful in assigning priorities for the development of at-risk-areas.
INTRODUCTION
There have been many destructive floods in Bangladesh, including very severe floods of 1987, 1988 and 1998. The 1988 flood set a new record for flooded area, while 1998 flood was unprecedented with its long duration. Different Governmental organizations such as Bangladesh University of Engineering and Technology, Space Research and Remote Sensing Organization, and the Bangladesh Water Development Board normally investigate the flood conditions in Bangladesh. After the big flood of 1987, a Japanese inquiry commission investigated the disaster from the aspect of meteorology, geomorphology, hydrology, river engineering and sociology in order to be able to plan structural and non-structural countermeasures (Muramoto 1988; Oya 1993). In 1988, Bangladesh experienced one of the worst floods in living memory, which resulted in a total cost to the national economy of approximately $2 billion, and it was estimated that 45 million people were directly affected (Brammer 1990). The official death toll was put at 2379; the number of houses affected was 12.8 million, including 3.8 million totally destroyed; and crop damage was 7.54 million ha (Sado & Islam 1997). In addition, the damage to the infrastructure was enormous. Therefore, flood management is necessary not only for saving lives, but also for safety of crops and infrastructure. Then a study team comprising a core group of international consultants provided by the United Nations Development Program (UNDP), the Asian Development Bank, the European Economic Community, and the World Bank (Bangladesh government and UNDP 1989; World Bank 1989) and local experts investigated the flood damages and its remedy. In the course of its work, the team consulted various donor country missions to Bangladesh. Different donor countries have made extensive investments in the development of flood control works.
Although various flood control and management measures have been adopted and some flood control works have been done by extensive investment of the donor countries, the 1998 and 2003 severe floods proved that floods and damages are not decreasing. The flood damage potential is increasing due to the possible causes of climate change, urban concentration in the three river basins, encroaching of settlements into flood prone areas, and overreliance on the safety provided by flood control works such as levees, reservoirs, and so on (Kundzewich & Takeuchi, 1999). Bangladesh lies in the downstream area of the three river basins of the Ganges, Brahmaputra and Meghna as shown in Fig. 1. The three mighty rivers enter Bangladesh from India through northwest, north, and northeast of the country, respectively. High magnitude floods strike on a regular basis in these river basins in Bangladesh, India and the peninsular area (Bhattacharyya 1997; Kale & Pramod 1997; Kundzewich & Takeuchi 1999; Muramoto 1988; Rahman 1996; Islam & Sado 2000c) because of the passage of tropical depressions and cyclone storms during the monsoon season. Bangladesh has limited control over the Ganges, Brahmaputra and Meghna Rivers. For adequate and timely flood forecasting, Bangladesh depends on information from surrounding countries. The frequently occurring floods are very costly in terms of human life and economic loss. Therefore, the ability to estimate damages associated with the flood events is very important and is necessary for the evaluation of future alternate flood control policies. In a roundtable discussion following the 1998 flood event, experts from different fields recommended the need for flood hazard maps for proper planning and management for future flood disasters (Center for Alternatives 1998; Nishat 1998). In our previous studies (Islam and Sado 2000a,b,c), we have developed flood hazard maps using only the 1988 event. Therefore, in this study we focus on the 1988 and 1998 severe floods and 1995 medium flood.
Fig. 1. Whole are of Bangladesh selected for study
The capacity of the European Remote Sensing Satellite (ERS) and Landsat series of satellites to assess flooding has been well documented (Hallberg et al. 1973; Rango & Salomonson 1973) as well as some works already done using NOAA AVHRR data (Wiesnet et al. 1974; Hue et al. 1985a,b,c; Ali & Quadir 1987; Islam & Sado 2000a,b,c). NOAA AVHRR data are found to be very useful for monitoring large surface phenomena, such as floods, in the fields throughout the world on local, regional, and international scales. The results of analysis of NOAA AVHRR data, from the series of NOAA satellite 10 and 12, are used for the flood hazard assessment in Bangladesh in this paper. There are no available land development priority maps in Bangladesh incorporating flood hazard assessment and population density.
The objective of this study is to utilize Remote Sensing (RS) technique with the available geographic information system (GIS) data to construct a set of GIS data, a flood hazard map, and land development priority map to help the responsible authorities develop, design, and operate flood control infrastructure and prepared aid and relief operations for high-risk areas during future floods. The GIS plays a major role in flood control technique, and the integration of this data in a spatial database is crucial --especially for a development country. The role of GIS as a tool to enable the visualization and analysis of inundation with RS for flood hazard assessment, and the development of a map for land development on priority basis, is obviously important. Consequently, the paper presents a unique use of GIS and RS to delineate flood prone areas, and shows how to determine the relative sensitivity of the individual pixel of flood prone areas within Bangladesh. This enhances the capability in Bangladesh to utilize GIS for water resources planning and management, and may help implement or act as the basis for a hydrological decision support system to ascertain critical locations. This technique can be used from a local or regional scale to a global scale.
BASIC DATA
The three NOAA AVHRR images covering the whole area of Bangladesh and parts of neighboring countries were collected from NOAA. The three images of September 18, 1988; October 31, 1995; and September 18, 1998 were used for the flood of 1988, 1995 and 1998, respectively. These three images from three flood events of 1988, 1995 and 1998 have been found to be of suitable quality, with less cloud amount, and fairly close to the combined peak flow of the three mighty rivers among the available less-cloud-covered images.
Correction is needed to avoid geometric distortion from a distorted image. To establish the relationship between the image coordinate system and the geographic coordinate system positions of several ground control points (GCPs) were chosen on the satellite images. GCPs were selected from points easily identified on the satellite images and on the topographic map of Bangladesh. Geometric corrections were carried out until the root-man square errors were less than one pixel. Both the lengths of one pixel and of one line for NOAA AVHRR images represent 1.1 km on the ground surface. This is the ground resolution of NOAA AVHRR data. After the geometric correction the whole area of Bangladesh was extracted by using a vector layer which was prepared from the geographic map of Bangladesh.
Digital elevation data, physiographic divisions, geological divisions, administrative districts, drainage network and population density data were prepared in a common coordinate system for Bangladesh. Each of GIS and RS image yields 118736 pixels on a display monitor, and each pixel covers 1.1km ´ 1.1km on ground surface after geo-coding. Finally, all digital GIS data were incorporated with geometrically corrected NOAA AVHRR data within a GIS approach.
ESTIMATION OF FLOODED AREA
Estimation of Flooded Area
Although each image for a particular date during the flood permitted mapping of all water bodies present at the time of data acquisition, only three NOAA AVHRR image data of September 18, 1988; October 31, 1995; and September 18, 1998 were used to estimate the flood effects of 1988, 1995 and 1998, respectively.
In our previous studies, we used three NOAA AVHRR image data for the event of 1988 (Islam & Sado 2000a,b,c). To differentiate between water and non-water, Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering of unsupervised classification was performed. ISODATA clustering is an iterative non-hierarchical clustering that use minimum spectral distance to assign a cluster for each candidate pixel. Initially all pixels were classified into several categories, and then these categories were divided into three classes of water, non-water and cloud. After the interpretation of cloud cover pixels, the three categories were divided into two categories of water and non-water. Cloudy skies are expected during the flood, and the NOAA AVHRR cannot receive the radiance from a cloud-covered ground surface. The presence of cloud over the damaged areas after an event limits the usefulness of this data, and difficulty arises with the interpretation of whether a given area beneath the clouds is dry or covered by water.
Therefore, low cloud covered images (cloud coverage for the images taken September 18, 1988; October 31, 1995; and September 18, 1998 were 15.00, 9.08 and 13.07%, respectively) were employed to estimate flooded area, flood affected frequency and floodwater depth category. The cloud-covered pixels were interpreted by using the algorithm that was developed for the recovery of cloud-covered pixels as water or non-water. The details of the algorithm have been described by Islam & Sado (2000b).
Estimated flooded areas for September 18, 1988; October 31, 1995; and September 18, 1998 are 34.74, 27.71 and 36.08%, respectively, when the drainage network map was superimposed onto the flood season images. The flooded area was estimated after subtracting the normal water area (river, lake, pond, etc.) from total inundated area; it was then converted to percentage of land area (non-water area in dry season) of the whole country. Our previous study showed that the flooded area for September 18, 1988 was 40.00% when the dry season image of January 20, 1988 was superimposed onto the September 18, 1988 flood season image. Moreover, the total flooded area estimated by different methods ranged from 47% to 50% where three different images of September 18 and 24 and October 8, 1988 were considered within one event of 1988 (Islam & Sado 2000b,c). The flooded area decreased due to the consideration of drainage network map instead of dry season’s classification image (two classes of water and non-water). Because the dry season image of 1988 indicated the normal water area was only 5.32% whereas drainage map showed the normal water area as 15.15%. Our previous study included error pixels (water area in dry season and non-water area in flood season, 4.57%). Those did not appear in this study, due to superimposing of drainage network map instead of dry season’s classified image. Some studies indicated that flood extent surpassed the previous record and the flood of 1988 covered nearly 60% of the country (Bangladesh Government & UNDP 1989; Rasid & Pramanik 1990). According to the local organization, the flood of 1998 also affected more than 50% of the country. The flood effect of 1998 is still under investigation.
Fig. 2. Concept for flood-affected frequency
Fig. 3. Map of flood-affected frequency using the three events of 1988, 1995 and 1998 floods
CONSIDERED HYDRALIC FACTORS
Flood-affected Frequency
The flood-affected frequency was estimated by using the images of September 18, 1988; October 31, 1995; and September 18, 1998. The flood-affected frequency is determined for each pixel as the ratio of the number of NOAA images within the three flood events of 1988, 1995 and 1998, showing inundation to the total number of cloud free NOAA images available near the peak flood time. The concept of the different degree of flood-affected frequency is shown in Fig. 2. The inundated area that did not appear in any of the above-mentioned three images was considered to be a non-hazard area, and that which appeared in a single image was considered to be a low hazard area. The common inundated area that appeared in two images (MH in Fig. 2) and that which appeared in all three images (HH in Fig. 2) were considered to be medium and high hazard areas, respectively. The flood-affected frequency for the area flooded for the three events was categorized as non-hazard, low, medium and high, according to their hit number of the floods, as shown in Fig. 3. Fig. 3 shows that high flood affected areas are Sylhet trough, flood plain areas of the Meghna and Brahmaputra Rivers and Faridpur trough.
Fig. 4. Map of flood depth for 18 September 1988
Flood Depth
Although flood depth determining from remote sensing imagery is very difficult, maximum likelihood method of supervised classification was used to determine flood depth categories for the individual pixel. Flood depths were classified as shallow, medium and deep by using the images of September 18, 1988; October 31, 1995; and September 18, 1998. Training areas of shallow, medium and deep floods were selected on each image, according to the visual interpretation of difference in color and gray scales for different categories of depth for supervised classification. These were interpreted after superimposing the NOAA images onto a digital elevation image of Bangladesh. The results of different categories of flood depth were examined by the estimated average albedo of the pixels with the same flood depth category.
Generally, deeper waters show lower albedo; the results of albedo estimation show good agreement with these results (Islam & Sado 2000a,c). The ranking for flood depth was categorized as no-flooding, shallow, medium and deep flooding. Three flood depth maps were constructed for three flood events of 1988, 1995 and 1998 by using the above-mentioned images, respectively. Different categories of flood depth for 1988 image are shown in Fig. 4. Fig. 4 shows that Sylhet trough is affected by very deep flooding, with some scattered areas across the Brahmaputra and Meghna Rivers. Medium flooding appear in Sylhet trough; floodplain areas of the Meghna, Ganges and Brahmaputra Rivers; and the lower part of the Ganges River, including Faridpur trough. The flood-affected frequency map (Fig. 3) shows the maximum areas affected by high flooding.
FLOOD HAZARD ASSESSMENT
Best Combination of Thematic Data for a Flood Hazard Map
The concept of two and three dimensional ranking matrix in multiplication mode for different combination of thematic maps is shown in Fig. 5. The best combination of thematic data for flood hazard map was examined among the ten combinations (4C2=6 and 4C3=4) of the thematic data; physiography, geology and land cover classification, for flood-affected frequency and flood depth using a ranking matrix for three dimensional multiplication mode. The selection technique for the best combination of GIS data for flood hazard maps created for flood depth and flood-affected frequency was described in detail in authors previous paper (Islam and Sado, 2000a). Summation of diagonal elements for the possible combinations, 4C2 and 4C3, are shown in Table 1. Possible combinations show that the summations of diagonal elements ranges from 37% to 66%. The combination which shows the maximum total pixels for diagonal elements is considered as the best –in this case, the combination of physiography, geology and land cover. The combination of physiography, and geology is the second best, whilst that of land cover and elevation is the worst. Therefore, a flood hazard map may be derived from land cover classification, physiographic and geological divisions using either flood-affected frequency or flood depth.
In this study, flood hazard assessments were undertaken using land cover, physiographic and geologic features and drainage network data. Land cover classification was carried out using dry season’s NOAA AVHRR image of January 20, 1988. Flood depth and floodaffected frequency were used as hydraulic parameters of the floods.
Hazard Rank Assessment through Land Cover, Physiograpy and Geology Flood hazard ranks were estimated based on a weighted score for land cover, physiographical and geological data for each pixel of the land area of Bangladesh. A weighted score was estimated by
where A, B, C and D represent the occupied area percentage by nonhazardous area, and low, medium, respectively, when flood-affected frequency was considered to be a hydraulic factor, for each category of GIS component. Included are land cover categories (9 categories), physiographic divisions (31 divisions), and geologic divisions (28 divisions) (Islam & Sado 2000a,b). Similarly, A, B, C and D represent the occupied area percentage by nonflooded area, shallow, medium and deep flooding, respectively, for each category of above-mentioned GIS components, when flood depth was considered to be a hydraulic factor.
The coefficients of 0.0, 1.0, 3.0 and 5.0 for A-D in Eq. (1) were used to describe the weight for the flood damage. The top upper half of Table 2 shows the acquired area percentage by nonhazardous, low, medium and high damage areas only for the land cover categories for flood affected-frequency while the lower half of the Table 2 shows the acquired area percentage of nonflooded, shallow, medium and deep flooding only for land cover classification for flood depth of 18 September 1988, with calculated weighted score and hazard rank (HR). Points for the categories of land cover were estimated on the basis of linear interpolation between 0 and 100, where 0 corresponds to the lowest (0), and 100 to the highest (230.21 in the top half of Table 2) weighted score. To quantify the flood hazard, the three rankings for flood damage (HR 1~3) were obtained from the allocated point. Hazard ranks were fixed according to the corresponding value of the points. Points 0-33 corresponded to hazard rank 1, 33-66 correspond to rank 2, and 66-100 correspond to rank 3, as shown in Table 2. Hazard ranks were determined using the same algorithm for geologic divisions and physiographic divisions, using flood-affected frequency.
Fig. 5. Concept of the ranking matrix
Table 1 Summation of diagonal elements of square
matrixes for determining the best combination of
thematic data for flood hazard map.
Four different categories of flood depth (no-flooding, shallow, medium and deep depth) were estimated independently for the images of September 18, 1988; October 31, 1995; and September 18, 1998. Hazard ranks were determined for each event using the above-mentioned algorithm for physiographic divisions, geologic divisions, and land cover categories by using the flood depth.
Hazard Map
Flood hazard maps were constructed by considering the interactive effect of flood-affected frequency and flood depth on the land cover categories, physiographic and geological divisions.
Flood Hazard Map using Flood-affected Frequency as Hydraulic Factor
The flood-affected frequency map (Fig. 3) was constructed by using the images of September 18, 1988; October 31, 1995; and 18 September 1998, consisted of four classes --non-hazardous, low, medium and high damaged areas. Before considering the interactive effect of land cover categories, physiographic divisions and geologic divisions on the flood-affected frequency, each hazard map consisted of three ranks (HR 1-3), which were only developed by land cover categories or physiographic divisions or geologic divisions. Hazard ranks were considered from 1 to 27 after combining the hazard rank of land cover categories (HR 1- 3), physiographic divisions (HR 1-3) and geologic divisions (HR 1-3) simultaneously, using the ranking matrix of three-dimensional multiplication mode (Fig. 5). A model was considered for the assessment of the flood hazard. The schematic concept of the model is shown in Fig 6.
Fig. 6. Schematic concept of model
Flood Hazard Map using Flood Depth as Hydraulic Factor
Three flood depth maps were constructed by using three images of September 18, 1988; October 31, 1995; and September 18, 1998, respectively, which consisted of four classes --non-flooded area, shallow, medium and deep flood. Therefore, initially three different hazard maps were developed for three different flood depth maps by considering the interactive effect of land cover categories, physiographic divisions and geologic divisions on the flood depth, using the ranking matrix of three-dimensional multiplication mode (Fig. 6). Each hazard map consisted of hazard ranks ranging from 1 to 27. Table 3(a) and (b) show the comparison among the hazard maps of 1988 to those of 1995 and 1998, when only flood depths were considered independently. The rows in these tables present the area percentage occupied by the hazard ranks of the hazard map for the 1988 flood, while columns represent the area percentage occupied by the hazard ranks of the hazard maps developed by 1995 and 1998 flood, respectively. The tables show that flood hazard map developed for the flood depth of 1988 exhibits the deviation of the marginal distribution toward higher ranks among these three hazard maps. Therefore, the hazard map of 1988 event for the flood depth was selected as a hazard map for flood depth among the three hazard maps, because the histogram of the hazard map of 1988 shows many frequencies over higher hazard ranks among the three hazard maps. Design and development purposes for flood countermeasures considering higher hazard ranks provide a higher factor of safety.
Table 2. Flood hazard ranks for land cover categories
Considering the Interactive Effect of Flood-affected Frequency and Flood Depth
Two flood hazard maps were developed by flood-affected frequency and flood depth, respectively, considering the flood hazard rank ranging from 1 to 27. Table 4 shows comparison between the area occupied by the same hazard rank, when the flood hazard maps were developed by flood depth and floodaffected frequency independently. The columns of the table show the area percentage occupied by the hazard rank of the hazard map developed by using flood depth, while rows of the table present the area percentage occupied by the hazard rank of the hazard map developed by using flood-affected frequency. In these two hazard maps, 56.31% areas exhibited the same hazard ranks and 43.69% were different, as shown in Table 4. Therefore, the authors need to integrate the flood hazard maps developed by considering flood-affected frequency and floodwater depth. Comparing between the hazard maps of flood-affected frequency and flood depth, the higher rank for a pixel was assigned for that pixel for the new developed hazard map. As a result, the newly developed hazard map represents the higher rank between the two ranks of two hazard maps for each pixel. Finally, more pixels were occupied by higher ranks of this hazard map. The flood hazard map developed by flood-affected frequency shows the deviation of the marginal distribution toward higher ranks between these two hazard maps. Consequently, it is to be said that the flood-affected frequency comparatively dominates higher hazard ranks for flood hazard map and land development priority map. Watercourses were not included to the developed flood hazard map. Therefore, the drainage map was overlaid onto the hazard map, and final hazard map with watercourses is shown in Fig. 7. Ten hazard ranks, 1, 2, 3, 4, 6, 8, 9, 12, 18, 27, ranging from 1 to 27, are shown, because the ranking matrix of threedimensional multiplication mode in Fig. 6 was used. Flood plain area of the Meghna River including Sylhet trough and floodplain area of the Brahmaputra River fall into highest flood hazard areas; these have a hazard rank of 27, and these areas also capture high flood-affected frequency (Fig 3) as well as deep or medium flood depth (Fig. 4). Flood plain area of the Ganges River, Faridpur trough and lower southwest part (tidal area) except for mangrove areas fall into higher hazard areas; hazard rank 12 and 18. Southeast lower part (eastern hill), northwest upper part and west part of lower flood plain of Ganges River show low hazard rank; hazard rank 1, 2, 3 and 4.
Table 3. Examination of the results of Flood Hazard Maps Developed by Floodwater Depth
Table 4. Comparison between the hazard maps developed by flood-affected frequency and flood water depth
LAND DEVELOPMENT PRIORITY MAP FOR FLOOD COUNTERMEASURE
The major cities of Bangladesh are extremely highly populated. The planning of river works for flood countermeasure should be undertaken by considering the economical effects of the infrastructure and the importance of the concerned areas. Therefore, using flood hazard map and population density map, a land development priority map was developed. Digital population data was prepared using the population map of Bangladesh. Urban and industrial areas show highly dense population, while agricultural low lands and agricultural flat plains show low-density population. According to the population density, the digital population data was categorized into five zones. Areas that show a population density 1 to 500 per square kilometer were considered as zone 1, similarly, areas that show a population density 501 to 1000, 1001 to2500, 2501 to 4000 and over 4000 were considered as zone 2, zone 3, zone 4 and zone 5, respectively. Hazard ranks of flood hazard map were categorized into five groups. Hazard ranks 1, 2 and 3 are grouped as 1. Similarly, 4 and 6 are group 2; 8 and 9 are group 3; 12 and 18 are group 4, and 27 is group 5.
Fig. 7. Flood hazard map
This new hazard-grouped map was incorporated with digital population categories map and finally, using ranking matrix of two-dimensional multiplication mode, a land development priority map was developed. Fig. 8 shows the land development priority map for flood countermeasures. The land development priority score (PS) range from 1 to 25. Higher score indicate that higher priority must be given for the development for flood countermeasure. Therefore, the highest score 25 shows the first priority (priority rank, PR=1) for the development, and score 1 shows the last priority for the land development. Therefore, land development priority rank (HR) ranges from 1 to 14 for the PS 25 to 1. A priority rank 1 indicates first priority for the development and then 2, 3, 4 and so on. This development priority map shows the priority score on the basis of pixel. Comparing between the hazard map (Fig. 7) and the development priority map (Fig. 8), it is understood that some high hazardous areas do not show the high score for the development. The northeast part of the Meghna River and southwest lower parts of Bangladesh show high hazard ranks, whereas the development map shows the low score for the development of the same areas. Some parts of Dhaka and Narayanganj districts show the higher development score due to the high-density population with high hazard area. Study has shown that Dhaka, the capital city of Bangladesh, was highly affected during the 1988 flood (Sado & Islam 1997).
Fig. 8. Land development priority map
The hazard ranks for the administrative districts were estimated by using the mean value of the pixels belonging to the particular administrative district using the following equation
where ni =number of pixels occupied by ith rank of hazard map for each administrative district; and hi= value of ith rank. Similarly, the development score rank for the administrative districts were estimated by using the mean value of the pixels belonging to the particular administrative district using the following equation
where ni =number of pixels occupied by ith rank of development priority map for each administrative district; pi =value of ith rank. A comparison between the flood hazard ranks and development priority score is shown in Table 5. Comparing the hazard ranks and development priority score, it is found that some higher hazardous districts, Jamalpur, Netrokona and Sunamganj, do not show the higher score for development, because those areas are comparatively low-density populated areas. On the other hand, some lower hazard districts, Munshiganj, Dhaka and Chandpur, show a higher development score due to the highdense population, compared to the above-mentioned districts.
Table 5. Development rank (DR) for administrative districts and the comparison between the
ranks of land development priority map and hazard map for administrative districts
CONCLUSIONS
Flood hazard assessment can be performed using NOAA AVHRR data with physiographic, geological, elevation, administrative district and drainage network data. Flood-affected frequency and flood depth are essential components for the evaluation of flood hazard. In this study, categories of flood affected frequency and flood depth were estimated using NOAA satellite data. Flood hazard rank assessment was undertaken on the basis of land cover classification, physiographic divisions, geological divisions and administrative districts. The summarized results can be concluded as follows:
Flood hazard assessments were undertaken and a new flood hazard map for Bangladesh was developed by using the flood events of 1988, 1995 and 1998; considering the interactive effect of flood-affected frequency and flood depth, those were estimated from NOAA AVHRR images of September 18, 1988; October 31, 1995; and September 18, 1998.
The floods hazard maps based on each pixel and each administrative district represent the magnitude of flood damage for each pixel and each administrative district, respectively. This type of map helps the responsible authorities to better comprehend the inundation characteristics of the floodplains.
The land development priority map for flood countermeasure was based on each pixel and each administrative district. Although flood hazard rank for some urban areas are comparatively less than the hazard for some rural areas, development should be undertaken for those urban areas (higher dense populated area) on a reasonable priority basis.
The results described in this study should provide helpful information about flood risk management and should be useful in assigning priority for the development of very high risk areas for flood control planning, and the construction and development of flood countermeasures. In addition, this study may have considerable management implications for emergency preparedness, including aid and relief operation in high risk areas in the future. Finally, these types of flood hazard and land development priority map in digital form can be used as a database to be shared among the various government and non-government agencies responsible for the construction and development of flood defence.
REFERENCES
Bangladesh suffered damage on account of the most catastrophic floods of 1987, 1988 and 1998, resulting in untold suffering of the people. This paper demonstrates the technique to develop a flood hazard map and a land development priority map for countermeasure against flood damage. To create the final products, flood hazard map and land development priority map, National Oceanographic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data for the flood events of 1988, 1995 and 1998 were incorporated with Geographical Information System data of physiographic divisions, geologic divisions, land cover classification, elevation height, drainage network, administrative districts and population density. Flood-affected frequency and flood depth categories were estimated using NOAA AVHRR images. Special attention was paid to population density for the construction of the land development priority map, because highly dense populated areas represent the highly important urban and industrial areas of Bangladesh. The land development priority map offers a new opportunity for flood risk management, planning, design and operation of flood control measure in Bangladesh, and should be useful in assigning priorities for the development of at-risk-areas.
INTRODUCTION
There have been many destructive floods in Bangladesh, including very severe floods of 1987, 1988 and 1998. The 1988 flood set a new record for flooded area, while 1998 flood was unprecedented with its long duration. Different Governmental organizations such as Bangladesh University of Engineering and Technology, Space Research and Remote Sensing Organization, and the Bangladesh Water Development Board normally investigate the flood conditions in Bangladesh. After the big flood of 1987, a Japanese inquiry commission investigated the disaster from the aspect of meteorology, geomorphology, hydrology, river engineering and sociology in order to be able to plan structural and non-structural countermeasures (Muramoto 1988; Oya 1993). In 1988, Bangladesh experienced one of the worst floods in living memory, which resulted in a total cost to the national economy of approximately $2 billion, and it was estimated that 45 million people were directly affected (Brammer 1990). The official death toll was put at 2379; the number of houses affected was 12.8 million, including 3.8 million totally destroyed; and crop damage was 7.54 million ha (Sado & Islam 1997). In addition, the damage to the infrastructure was enormous. Therefore, flood management is necessary not only for saving lives, but also for safety of crops and infrastructure. Then a study team comprising a core group of international consultants provided by the United Nations Development Program (UNDP), the Asian Development Bank, the European Economic Community, and the World Bank (Bangladesh government and UNDP 1989; World Bank 1989) and local experts investigated the flood damages and its remedy. In the course of its work, the team consulted various donor country missions to Bangladesh. Different donor countries have made extensive investments in the development of flood control works.
Although various flood control and management measures have been adopted and some flood control works have been done by extensive investment of the donor countries, the 1998 and 2003 severe floods proved that floods and damages are not decreasing. The flood damage potential is increasing due to the possible causes of climate change, urban concentration in the three river basins, encroaching of settlements into flood prone areas, and overreliance on the safety provided by flood control works such as levees, reservoirs, and so on (Kundzewich & Takeuchi, 1999). Bangladesh lies in the downstream area of the three river basins of the Ganges, Brahmaputra and Meghna as shown in Fig. 1. The three mighty rivers enter Bangladesh from India through northwest, north, and northeast of the country, respectively. High magnitude floods strike on a regular basis in these river basins in Bangladesh, India and the peninsular area (Bhattacharyya 1997; Kale & Pramod 1997; Kundzewich & Takeuchi 1999; Muramoto 1988; Rahman 1996; Islam & Sado 2000c) because of the passage of tropical depressions and cyclone storms during the monsoon season. Bangladesh has limited control over the Ganges, Brahmaputra and Meghna Rivers. For adequate and timely flood forecasting, Bangladesh depends on information from surrounding countries. The frequently occurring floods are very costly in terms of human life and economic loss. Therefore, the ability to estimate damages associated with the flood events is very important and is necessary for the evaluation of future alternate flood control policies. In a roundtable discussion following the 1998 flood event, experts from different fields recommended the need for flood hazard maps for proper planning and management for future flood disasters (Center for Alternatives 1998; Nishat 1998). In our previous studies (Islam and Sado 2000a,b,c), we have developed flood hazard maps using only the 1988 event. Therefore, in this study we focus on the 1988 and 1998 severe floods and 1995 medium flood.
Fig. 1. Whole are of Bangladesh selected for study
The capacity of the European Remote Sensing Satellite (ERS) and Landsat series of satellites to assess flooding has been well documented (Hallberg et al. 1973; Rango & Salomonson 1973) as well as some works already done using NOAA AVHRR data (Wiesnet et al. 1974; Hue et al. 1985a,b,c; Ali & Quadir 1987; Islam & Sado 2000a,b,c). NOAA AVHRR data are found to be very useful for monitoring large surface phenomena, such as floods, in the fields throughout the world on local, regional, and international scales. The results of analysis of NOAA AVHRR data, from the series of NOAA satellite 10 and 12, are used for the flood hazard assessment in Bangladesh in this paper. There are no available land development priority maps in Bangladesh incorporating flood hazard assessment and population density.
The objective of this study is to utilize Remote Sensing (RS) technique with the available geographic information system (GIS) data to construct a set of GIS data, a flood hazard map, and land development priority map to help the responsible authorities develop, design, and operate flood control infrastructure and prepared aid and relief operations for high-risk areas during future floods. The GIS plays a major role in flood control technique, and the integration of this data in a spatial database is crucial --especially for a development country. The role of GIS as a tool to enable the visualization and analysis of inundation with RS for flood hazard assessment, and the development of a map for land development on priority basis, is obviously important. Consequently, the paper presents a unique use of GIS and RS to delineate flood prone areas, and shows how to determine the relative sensitivity of the individual pixel of flood prone areas within Bangladesh. This enhances the capability in Bangladesh to utilize GIS for water resources planning and management, and may help implement or act as the basis for a hydrological decision support system to ascertain critical locations. This technique can be used from a local or regional scale to a global scale.
BASIC DATA
The three NOAA AVHRR images covering the whole area of Bangladesh and parts of neighboring countries were collected from NOAA. The three images of September 18, 1988; October 31, 1995; and September 18, 1998 were used for the flood of 1988, 1995 and 1998, respectively. These three images from three flood events of 1988, 1995 and 1998 have been found to be of suitable quality, with less cloud amount, and fairly close to the combined peak flow of the three mighty rivers among the available less-cloud-covered images.
Correction is needed to avoid geometric distortion from a distorted image. To establish the relationship between the image coordinate system and the geographic coordinate system positions of several ground control points (GCPs) were chosen on the satellite images. GCPs were selected from points easily identified on the satellite images and on the topographic map of Bangladesh. Geometric corrections were carried out until the root-man square errors were less than one pixel. Both the lengths of one pixel and of one line for NOAA AVHRR images represent 1.1 km on the ground surface. This is the ground resolution of NOAA AVHRR data. After the geometric correction the whole area of Bangladesh was extracted by using a vector layer which was prepared from the geographic map of Bangladesh.
Digital elevation data, physiographic divisions, geological divisions, administrative districts, drainage network and population density data were prepared in a common coordinate system for Bangladesh. Each of GIS and RS image yields 118736 pixels on a display monitor, and each pixel covers 1.1km ´ 1.1km on ground surface after geo-coding. Finally, all digital GIS data were incorporated with geometrically corrected NOAA AVHRR data within a GIS approach.
ESTIMATION OF FLOODED AREA
Estimation of Flooded Area
Although each image for a particular date during the flood permitted mapping of all water bodies present at the time of data acquisition, only three NOAA AVHRR image data of September 18, 1988; October 31, 1995; and September 18, 1998 were used to estimate the flood effects of 1988, 1995 and 1998, respectively.
In our previous studies, we used three NOAA AVHRR image data for the event of 1988 (Islam & Sado 2000a,b,c). To differentiate between water and non-water, Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering of unsupervised classification was performed. ISODATA clustering is an iterative non-hierarchical clustering that use minimum spectral distance to assign a cluster for each candidate pixel. Initially all pixels were classified into several categories, and then these categories were divided into three classes of water, non-water and cloud. After the interpretation of cloud cover pixels, the three categories were divided into two categories of water and non-water. Cloudy skies are expected during the flood, and the NOAA AVHRR cannot receive the radiance from a cloud-covered ground surface. The presence of cloud over the damaged areas after an event limits the usefulness of this data, and difficulty arises with the interpretation of whether a given area beneath the clouds is dry or covered by water.
Therefore, low cloud covered images (cloud coverage for the images taken September 18, 1988; October 31, 1995; and September 18, 1998 were 15.00, 9.08 and 13.07%, respectively) were employed to estimate flooded area, flood affected frequency and floodwater depth category. The cloud-covered pixels were interpreted by using the algorithm that was developed for the recovery of cloud-covered pixels as water or non-water. The details of the algorithm have been described by Islam & Sado (2000b).
Estimated flooded areas for September 18, 1988; October 31, 1995; and September 18, 1998 are 34.74, 27.71 and 36.08%, respectively, when the drainage network map was superimposed onto the flood season images. The flooded area was estimated after subtracting the normal water area (river, lake, pond, etc.) from total inundated area; it was then converted to percentage of land area (non-water area in dry season) of the whole country. Our previous study showed that the flooded area for September 18, 1988 was 40.00% when the dry season image of January 20, 1988 was superimposed onto the September 18, 1988 flood season image. Moreover, the total flooded area estimated by different methods ranged from 47% to 50% where three different images of September 18 and 24 and October 8, 1988 were considered within one event of 1988 (Islam & Sado 2000b,c). The flooded area decreased due to the consideration of drainage network map instead of dry season’s classification image (two classes of water and non-water). Because the dry season image of 1988 indicated the normal water area was only 5.32% whereas drainage map showed the normal water area as 15.15%. Our previous study included error pixels (water area in dry season and non-water area in flood season, 4.57%). Those did not appear in this study, due to superimposing of drainage network map instead of dry season’s classified image. Some studies indicated that flood extent surpassed the previous record and the flood of 1988 covered nearly 60% of the country (Bangladesh Government & UNDP 1989; Rasid & Pramanik 1990). According to the local organization, the flood of 1998 also affected more than 50% of the country. The flood effect of 1998 is still under investigation.
Fig. 2. Concept for flood-affected frequency
Fig. 3. Map of flood-affected frequency using the three events of 1988, 1995 and 1998 floods
CONSIDERED HYDRALIC FACTORS
Flood-affected Frequency
The flood-affected frequency was estimated by using the images of September 18, 1988; October 31, 1995; and September 18, 1998. The flood-affected frequency is determined for each pixel as the ratio of the number of NOAA images within the three flood events of 1988, 1995 and 1998, showing inundation to the total number of cloud free NOAA images available near the peak flood time. The concept of the different degree of flood-affected frequency is shown in Fig. 2. The inundated area that did not appear in any of the above-mentioned three images was considered to be a non-hazard area, and that which appeared in a single image was considered to be a low hazard area. The common inundated area that appeared in two images (MH in Fig. 2) and that which appeared in all three images (HH in Fig. 2) were considered to be medium and high hazard areas, respectively. The flood-affected frequency for the area flooded for the three events was categorized as non-hazard, low, medium and high, according to their hit number of the floods, as shown in Fig. 3. Fig. 3 shows that high flood affected areas are Sylhet trough, flood plain areas of the Meghna and Brahmaputra Rivers and Faridpur trough.
Fig. 4. Map of flood depth for 18 September 1988
Flood Depth
Although flood depth determining from remote sensing imagery is very difficult, maximum likelihood method of supervised classification was used to determine flood depth categories for the individual pixel. Flood depths were classified as shallow, medium and deep by using the images of September 18, 1988; October 31, 1995; and September 18, 1998. Training areas of shallow, medium and deep floods were selected on each image, according to the visual interpretation of difference in color and gray scales for different categories of depth for supervised classification. These were interpreted after superimposing the NOAA images onto a digital elevation image of Bangladesh. The results of different categories of flood depth were examined by the estimated average albedo of the pixels with the same flood depth category.
Generally, deeper waters show lower albedo; the results of albedo estimation show good agreement with these results (Islam & Sado 2000a,c). The ranking for flood depth was categorized as no-flooding, shallow, medium and deep flooding. Three flood depth maps were constructed for three flood events of 1988, 1995 and 1998 by using the above-mentioned images, respectively. Different categories of flood depth for 1988 image are shown in Fig. 4. Fig. 4 shows that Sylhet trough is affected by very deep flooding, with some scattered areas across the Brahmaputra and Meghna Rivers. Medium flooding appear in Sylhet trough; floodplain areas of the Meghna, Ganges and Brahmaputra Rivers; and the lower part of the Ganges River, including Faridpur trough. The flood-affected frequency map (Fig. 3) shows the maximum areas affected by high flooding.
FLOOD HAZARD ASSESSMENT
Best Combination of Thematic Data for a Flood Hazard Map
The concept of two and three dimensional ranking matrix in multiplication mode for different combination of thematic maps is shown in Fig. 5. The best combination of thematic data for flood hazard map was examined among the ten combinations (4C2=6 and 4C3=4) of the thematic data; physiography, geology and land cover classification, for flood-affected frequency and flood depth using a ranking matrix for three dimensional multiplication mode. The selection technique for the best combination of GIS data for flood hazard maps created for flood depth and flood-affected frequency was described in detail in authors previous paper (Islam and Sado, 2000a). Summation of diagonal elements for the possible combinations, 4C2 and 4C3, are shown in Table 1. Possible combinations show that the summations of diagonal elements ranges from 37% to 66%. The combination which shows the maximum total pixels for diagonal elements is considered as the best –in this case, the combination of physiography, geology and land cover. The combination of physiography, and geology is the second best, whilst that of land cover and elevation is the worst. Therefore, a flood hazard map may be derived from land cover classification, physiographic and geological divisions using either flood-affected frequency or flood depth.
In this study, flood hazard assessments were undertaken using land cover, physiographic and geologic features and drainage network data. Land cover classification was carried out using dry season’s NOAA AVHRR image of January 20, 1988. Flood depth and floodaffected frequency were used as hydraulic parameters of the floods.
Hazard Rank Assessment through Land Cover, Physiograpy and Geology Flood hazard ranks were estimated based on a weighted score for land cover, physiographical and geological data for each pixel of the land area of Bangladesh. A weighted score was estimated by
where A, B, C and D represent the occupied area percentage by nonhazardous area, and low, medium, respectively, when flood-affected frequency was considered to be a hydraulic factor, for each category of GIS component. Included are land cover categories (9 categories), physiographic divisions (31 divisions), and geologic divisions (28 divisions) (Islam & Sado 2000a,b). Similarly, A, B, C and D represent the occupied area percentage by nonflooded area, shallow, medium and deep flooding, respectively, for each category of above-mentioned GIS components, when flood depth was considered to be a hydraulic factor.
The coefficients of 0.0, 1.0, 3.0 and 5.0 for A-D in Eq. (1) were used to describe the weight for the flood damage. The top upper half of Table 2 shows the acquired area percentage by nonhazardous, low, medium and high damage areas only for the land cover categories for flood affected-frequency while the lower half of the Table 2 shows the acquired area percentage of nonflooded, shallow, medium and deep flooding only for land cover classification for flood depth of 18 September 1988, with calculated weighted score and hazard rank (HR). Points for the categories of land cover were estimated on the basis of linear interpolation between 0 and 100, where 0 corresponds to the lowest (0), and 100 to the highest (230.21 in the top half of Table 2) weighted score. To quantify the flood hazard, the three rankings for flood damage (HR 1~3) were obtained from the allocated point. Hazard ranks were fixed according to the corresponding value of the points. Points 0-33 corresponded to hazard rank 1, 33-66 correspond to rank 2, and 66-100 correspond to rank 3, as shown in Table 2. Hazard ranks were determined using the same algorithm for geologic divisions and physiographic divisions, using flood-affected frequency.
Fig. 5. Concept of the ranking matrix
Four different categories of flood depth (no-flooding, shallow, medium and deep depth) were estimated independently for the images of September 18, 1988; October 31, 1995; and September 18, 1998. Hazard ranks were determined for each event using the above-mentioned algorithm for physiographic divisions, geologic divisions, and land cover categories by using the flood depth.
Hazard Map
Flood hazard maps were constructed by considering the interactive effect of flood-affected frequency and flood depth on the land cover categories, physiographic and geological divisions.
Flood Hazard Map using Flood-affected Frequency as Hydraulic Factor
The flood-affected frequency map (Fig. 3) was constructed by using the images of September 18, 1988; October 31, 1995; and 18 September 1998, consisted of four classes --non-hazardous, low, medium and high damaged areas. Before considering the interactive effect of land cover categories, physiographic divisions and geologic divisions on the flood-affected frequency, each hazard map consisted of three ranks (HR 1-3), which were only developed by land cover categories or physiographic divisions or geologic divisions. Hazard ranks were considered from 1 to 27 after combining the hazard rank of land cover categories (HR 1- 3), physiographic divisions (HR 1-3) and geologic divisions (HR 1-3) simultaneously, using the ranking matrix of three-dimensional multiplication mode (Fig. 5). A model was considered for the assessment of the flood hazard. The schematic concept of the model is shown in Fig 6.
Fig. 6. Schematic concept of model
Flood Hazard Map using Flood Depth as Hydraulic Factor
Three flood depth maps were constructed by using three images of September 18, 1988; October 31, 1995; and September 18, 1998, respectively, which consisted of four classes --non-flooded area, shallow, medium and deep flood. Therefore, initially three different hazard maps were developed for three different flood depth maps by considering the interactive effect of land cover categories, physiographic divisions and geologic divisions on the flood depth, using the ranking matrix of three-dimensional multiplication mode (Fig. 6). Each hazard map consisted of hazard ranks ranging from 1 to 27. Table 3(a) and (b) show the comparison among the hazard maps of 1988 to those of 1995 and 1998, when only flood depths were considered independently. The rows in these tables present the area percentage occupied by the hazard ranks of the hazard map for the 1988 flood, while columns represent the area percentage occupied by the hazard ranks of the hazard maps developed by 1995 and 1998 flood, respectively. The tables show that flood hazard map developed for the flood depth of 1988 exhibits the deviation of the marginal distribution toward higher ranks among these three hazard maps. Therefore, the hazard map of 1988 event for the flood depth was selected as a hazard map for flood depth among the three hazard maps, because the histogram of the hazard map of 1988 shows many frequencies over higher hazard ranks among the three hazard maps. Design and development purposes for flood countermeasures considering higher hazard ranks provide a higher factor of safety.
Considering the Interactive Effect of Flood-affected Frequency and Flood Depth
Two flood hazard maps were developed by flood-affected frequency and flood depth, respectively, considering the flood hazard rank ranging from 1 to 27. Table 4 shows comparison between the area occupied by the same hazard rank, when the flood hazard maps were developed by flood depth and floodaffected frequency independently. The columns of the table show the area percentage occupied by the hazard rank of the hazard map developed by using flood depth, while rows of the table present the area percentage occupied by the hazard rank of the hazard map developed by using flood-affected frequency. In these two hazard maps, 56.31% areas exhibited the same hazard ranks and 43.69% were different, as shown in Table 4. Therefore, the authors need to integrate the flood hazard maps developed by considering flood-affected frequency and floodwater depth. Comparing between the hazard maps of flood-affected frequency and flood depth, the higher rank for a pixel was assigned for that pixel for the new developed hazard map. As a result, the newly developed hazard map represents the higher rank between the two ranks of two hazard maps for each pixel. Finally, more pixels were occupied by higher ranks of this hazard map. The flood hazard map developed by flood-affected frequency shows the deviation of the marginal distribution toward higher ranks between these two hazard maps. Consequently, it is to be said that the flood-affected frequency comparatively dominates higher hazard ranks for flood hazard map and land development priority map. Watercourses were not included to the developed flood hazard map. Therefore, the drainage map was overlaid onto the hazard map, and final hazard map with watercourses is shown in Fig. 7. Ten hazard ranks, 1, 2, 3, 4, 6, 8, 9, 12, 18, 27, ranging from 1 to 27, are shown, because the ranking matrix of threedimensional multiplication mode in Fig. 6 was used. Flood plain area of the Meghna River including Sylhet trough and floodplain area of the Brahmaputra River fall into highest flood hazard areas; these have a hazard rank of 27, and these areas also capture high flood-affected frequency (Fig 3) as well as deep or medium flood depth (Fig. 4). Flood plain area of the Ganges River, Faridpur trough and lower southwest part (tidal area) except for mangrove areas fall into higher hazard areas; hazard rank 12 and 18. Southeast lower part (eastern hill), northwest upper part and west part of lower flood plain of Ganges River show low hazard rank; hazard rank 1, 2, 3 and 4.
LAND DEVELOPMENT PRIORITY MAP FOR FLOOD COUNTERMEASURE
The major cities of Bangladesh are extremely highly populated. The planning of river works for flood countermeasure should be undertaken by considering the economical effects of the infrastructure and the importance of the concerned areas. Therefore, using flood hazard map and population density map, a land development priority map was developed. Digital population data was prepared using the population map of Bangladesh. Urban and industrial areas show highly dense population, while agricultural low lands and agricultural flat plains show low-density population. According to the population density, the digital population data was categorized into five zones. Areas that show a population density 1 to 500 per square kilometer were considered as zone 1, similarly, areas that show a population density 501 to 1000, 1001 to2500, 2501 to 4000 and over 4000 were considered as zone 2, zone 3, zone 4 and zone 5, respectively. Hazard ranks of flood hazard map were categorized into five groups. Hazard ranks 1, 2 and 3 are grouped as 1. Similarly, 4 and 6 are group 2; 8 and 9 are group 3; 12 and 18 are group 4, and 27 is group 5.
Fig. 7. Flood hazard map
This new hazard-grouped map was incorporated with digital population categories map and finally, using ranking matrix of two-dimensional multiplication mode, a land development priority map was developed. Fig. 8 shows the land development priority map for flood countermeasures. The land development priority score (PS) range from 1 to 25. Higher score indicate that higher priority must be given for the development for flood countermeasure. Therefore, the highest score 25 shows the first priority (priority rank, PR=1) for the development, and score 1 shows the last priority for the land development. Therefore, land development priority rank (HR) ranges from 1 to 14 for the PS 25 to 1. A priority rank 1 indicates first priority for the development and then 2, 3, 4 and so on. This development priority map shows the priority score on the basis of pixel. Comparing between the hazard map (Fig. 7) and the development priority map (Fig. 8), it is understood that some high hazardous areas do not show the high score for the development. The northeast part of the Meghna River and southwest lower parts of Bangladesh show high hazard ranks, whereas the development map shows the low score for the development of the same areas. Some parts of Dhaka and Narayanganj districts show the higher development score due to the high-density population with high hazard area. Study has shown that Dhaka, the capital city of Bangladesh, was highly affected during the 1988 flood (Sado & Islam 1997).
Fig. 8. Land development priority map
The hazard ranks for the administrative districts were estimated by using the mean value of the pixels belonging to the particular administrative district using the following equation
where ni =number of pixels occupied by ith rank of hazard map for each administrative district; and hi= value of ith rank. Similarly, the development score rank for the administrative districts were estimated by using the mean value of the pixels belonging to the particular administrative district using the following equation
where ni =number of pixels occupied by ith rank of development priority map for each administrative district; pi =value of ith rank. A comparison between the flood hazard ranks and development priority score is shown in Table 5. Comparing the hazard ranks and development priority score, it is found that some higher hazardous districts, Jamalpur, Netrokona and Sunamganj, do not show the higher score for development, because those areas are comparatively low-density populated areas. On the other hand, some lower hazard districts, Munshiganj, Dhaka and Chandpur, show a higher development score due to the highdense population, compared to the above-mentioned districts.
CONCLUSIONS
Flood hazard assessment can be performed using NOAA AVHRR data with physiographic, geological, elevation, administrative district and drainage network data. Flood-affected frequency and flood depth are essential components for the evaluation of flood hazard. In this study, categories of flood affected frequency and flood depth were estimated using NOAA satellite data. Flood hazard rank assessment was undertaken on the basis of land cover classification, physiographic divisions, geological divisions and administrative districts. The summarized results can be concluded as follows:
Flood hazard assessments were undertaken and a new flood hazard map for Bangladesh was developed by using the flood events of 1988, 1995 and 1998; considering the interactive effect of flood-affected frequency and flood depth, those were estimated from NOAA AVHRR images of September 18, 1988; October 31, 1995; and September 18, 1998.
The floods hazard maps based on each pixel and each administrative district represent the magnitude of flood damage for each pixel and each administrative district, respectively. This type of map helps the responsible authorities to better comprehend the inundation characteristics of the floodplains.
The land development priority map for flood countermeasure was based on each pixel and each administrative district. Although flood hazard rank for some urban areas are comparatively less than the hazard for some rural areas, development should be undertaken for those urban areas (higher dense populated area) on a reasonable priority basis.
The results described in this study should provide helpful information about flood risk management and should be useful in assigning priority for the development of very high risk areas for flood control planning, and the construction and development of flood countermeasures. In addition, this study may have considerable management implications for emergency preparedness, including aid and relief operation in high risk areas in the future. Finally, these types of flood hazard and land development priority map in digital form can be used as a database to be shared among the various government and non-government agencies responsible for the construction and development of flood defence.
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