Monitoring Lower Meghna River of Bangladesh using Remote Sensing and GIS Technology M Rahman Akhand, S M Mizanur Rahman & M H Sarker Bangladesh Space Research and Remote Sensing Organization (SPARRSO) Agargaon, Sher-e-Bangla Nagar, Dhaka-1207, Bangladesh ABSTRACT The Lower Meghna River region of Bangladesh is a unique environment where the constant process of land formation and erosion takes place due to the complex interactions between large river discharge, enormous sediment load and strong tidal forces. These hydrodynamic factors and their interactions shape the morphology of the lower Meghna River. During dry season upland fresh water flow into the Bay through the river is very much lower than that of monsoon season. Tidal action becomes stronger and dominates water flow pattern in the study area of the lower Meghna River. The distribution of flow and water level in the channel of the Meghna River are governed by river discharge, the tide and the wind speed. Velocity is higher during ebb tide than that of flood tide due to combined effect of upstream flow and downward tidal current. Velocity in monsoon is much higher compare to dry period because upstream discharge is higher in monsoon. Most of the accretion and erosion mainly occurs during monsoon and post monsoon period. In pre-monsoon and post-monsoon, wave height is less compared to monsoon as wind speed is less during these periods. Along the Bank of this study area, significant wave is considerable. As a result, tremendous whirl action due to tidal effects and whirl action due to enormous drainage flow during monsoon, wave action, tidal surge of the lower Meghna causes breaching of the major parts of the embankments of and adjacent area. The present paper focused on monitoring the lower Meghna River using remote sensing and GIS technology. The study reveals that both sides of the river bank of the study area have been eroded during 1973-2010 and number of Islands and also area of Island have been increased in 2010 compare to 1973. Keywords: Hydrodynamic, Monsoon, Remote sensing, GIS 1. INTRODUCTION The Bay of Bengal (BoB) drains the combined discharges of the Ganges, Brahmaputra, Meghna (GBM) rivers amounting on the average to 35 000 m 3 /s (Magnus K, 1999). These three rivers drain about 85 % of the total volume of water brought into Bangladesh. These are distinct seasonal in fluctuations inflow with extreme discharge in the monsoon (Siddiqi. p168). The average annual sediment load carried by the GBM Rivers to the Bay of Bengal is around 2 billion tons annually (Viles and Spencer 1995, p 294). Heavy sediment load coming from GBM and high tidal flow resist the sediment to go directly to the Bay of Bengal may a factor for accretion of inner rivers of the island. The area is dominated by semi-diurnal tidal currents, the maximum tidal range of 5 meters occurs in the The lower Meghna River which gradually decreases southeastwards along the Chittagong coast (Viles et. al, 1995) www.theinternationaljournal.org > RJSITM: Volume: 04, Number: 04, February- 2015 Page 75
Figure 1: River system of Bangladesh including Ganges, Braumaputra and Meghna Basin Landsat MSS (80 m) data of 1973, Landsat TM (30 m) data of 1989 and 2010 were used to generate digital data base of different time period, analysis of erosion and accretion and finally produce digital map of Meghna River of Bangladesh. The combination of Remote Sensing and GIS analysis can produces a vital input for better planning, policy formulation for management of Island (Hossain et al. 2003). The objectives of this study is (i) to extraction of digital data sets for the year 1973, 1989 and 2010 (ii) to generate change detection maps of Meghna River of the study area over 37 years and construction of statistics under different time period 2. STUDY AREA The study area is bounded by Chandpur district on the north-northeast, Mouth of Meghna estuary on the south, Laksmipur & Noakhali on the east and Bhola district on the west. Its coordinates are lies between 22 19'48" to 22 51'36" N and 90 38'22" to 91 16'29" E. Figure 2 show the study area and figure 3 show the location of tide gauge station & TM frames of study area. Meghna River 137/44 137/45 Figure 2: Study area shown on the Meghna estuary 136/44 136/45 Figure 3: Location of tide gauge stations and TM frames of study area www.theinternationaljournal.org > RJSITM: Volume: 04, Number: 04, February- 2015 Page 76
3. DATA AND SOFTWARE USED 3.1 Remote sensing data and Tide data Selection of data is very important for delineation of river bed due to tidal effect. For avoiding low tide data a large number of data sets have been collected for selection of high tide data. Because our study have been conducted on high tide data. 3.1.1. Landsat data Landsat MSS data of 80 meter resolution of 1973, Landsat TM data of 30 meter resolution of 1989 and 2010 have been used in the study. Data sets for one season has four frames. Table 1 show the Landsat MSS/TM frame numbers and dates of study data and figure 2 shows the location of four frames of 1973, 1989 and 2010 of study area. All the data are IMG format. 3.1.2. Tide data Most of the Landsat data during study period were found low and medium tide condition. Only two data received during study period almost in high tide condition in one station out of four stations. 3.1.3 Software ERDAS Imagine and Raster based GIS have been used for data pre-processing, generation and analysis. 4. APPROACH To monitor the lower Meghna River, extraction of digital data sets of river bed and finally generation of change detection maps as well as statistics is very difficult due to tidal effect as well as frequent erosion and accretion process of river bank. To overcome the tidal effect, high tide water line have been selected to delineate the coastline in this study and spectral signature on remotely sensed data also provides information on different parts of the tidal flat i.e. foreshore intersection (Sarker M H et. al 2013). 5. PROCEDURE OF DATA GENERATION Monitoring of the lower Meghna River involves remote sensing data collection /reception, preprocessing, data generation, analysis, finally extraction of change detection maps i.e. output of study. Procedures are shown in figure 4. 5.1 Generation of Digital data sets of the lower Meghna River The lower Meghna River bed changes have been generated from Landsat MSS/TM data of 1973, 1989 and 2010. To generate digital data sets from Landsat MSS/TM images during 1973-2010, some preprocessing steps are involved those are describe bellow: www.theinternationaljournal.org > RJSITM: Volume: 04, Number: 04, February- 2015 Page 77
5.1.1 Reception Landsat-MSS/TM datain Figure 4: Generation of digital data sets including change detection and statistic Bangladesh, there is no ground station to receive LANDSAT data. For research work we have downloaded Landsat-MSS/TM data from the website http://glovis.usgs.gov during the dry period of 1973, 1989 and 2010. Total numbers of downloaded data sets are shown in table-1. The data we have downloaded are frame wise. Figure-5 shows the sample of down loaded frames of one season including atmospheric corrected and geo-referenced. 5.1.2 Pre-processing of Landsat-MSS/TM data a) Atmospheric correction Many atmospheric correction methods have been proposed for use with multi-spectral satellite imagery. The dark-object atmospheric correction method, (Hadjimitsis et al., 2010), was probably the most widely used approach for atmospherically correcting different remotely sensed imagery. The scheme is based on identification of dark cluster of pixels of water having near-zero percent reflectance which is considered to be free from intervention of the parameters like turbidity and chlorophyll concentration. To fulfill the above mentioned requirement, the clusters of pixels were selected in the deeper part of the Bay of Bengal, where the intervention of the above mentioned parameters was minima. A seed cluster of water having the lowest Digital Number (DN) values was selected from a 12 frames of MSS/TM images. Based on the mean value of the seed cluster, the images used in the study were corrected. Figure 5 shows the atmospheric corrected images of 2010. It is mentioned that atmospheric, geometric as well as geo-referencing was done during my central coast study (Sarker M H et. al 2013). www.theinternationaljournal.org > RJSITM: Volume: 04, Number: 04, February- 2015 Page 78
Figure 5: Landsat TM geo-referenced images of one season. b) Geo-referencing Many geo-referencing methods have been used to geo-reference multi-spectral satellite imagery. One is the method is collection of Ground Control Point (GCP) from study area using Global Positioning System (GPS)/Differential GPS. In this method GCP collected from study area and transformed this coordinate to input image (same area images) by using geometric model of ERDAS imagine software (AutoSync). At first our frames/images of 2010 were geometrically corrected and were projected to Bangladesh Transverse Mercator (BTM) system by selecting 10 GCPs per image. Second order polynomial and then re-sampled with bilinear algorithm have been used during the re-sampling method. All the ten selected GCP s were easily identifiable and permanent in nature for measuring accurate results. A Root Mean Square (RMS) error of 0.30 (less than one pixel, 30m) was accepted for the correction process. The images/frames of other two seasons (1989 and 1973) were corrected from the geo-referenced images of 2010 as referenced images. Figure 5 shows the geo- referenced images of one season. 5.1.2 Land-water Classification based on Algorithm A simple algorithm was used to land-water classification (Sarker M H et. al 2013). For land and water separation band 4 (0.76 to 0.90 µm) NIR have been used because band 4 of Landat-TM is suitable for land and water separation. In this case DN values of water have been collected carefully from the histogram of the selected image and found DN value 40. This value applied in the equation 1. An algorithm also needs to use for mask the cloud cover areas. If the clouds are not masked in the images, it will reflect the wrong value in processing. Luckily we have found the cloud free images, so no need to mask out the cloud. Figure 6 shows the land-water classification of frame 137/45 of 2010. Similarly all the images have been classified in similar way. Either (Landsat-5 TM) IF (Band 4<41) or 0 otherwise ---- (1) www.theinternationaljournal.org > RJSITM: Volume: 04, Number: 04, February- 2015 Page 79
INPUT Figure 6: The land-water classification of frame 137/45 of 2010 5.1.3 Generation of lower Meghna River data sets Digital data sets of lower Meghna River of 1973, 1989 and 2010 have been generated from Landsat MSS/TM images using land-water classification of equation-1. Tidal flat areas have been masked carefully by using spectral signature of TM band4. Figure 7 shows the digital data sets of study area of the Meghna River during 1973, 1989 and 2010 respectively at high tide condition. Land Water Figure 7: Digital data sets of study area of the lower Meghna River during 1973, 1989 and 2010 5.1.4 Generation of change detection map For generation of and change detection map during 1973 2010, the base layers of 1973, 1989 and 2010 generated by algorithm (equation 1) have been used as an input of raster based GIS environments. Figure 8 shows the erosion and accretion map of study area of the lower Meghna River of Bangladesh 5.1.5 Field data collection, verification and incorporation Field visit was conducted in three times during study period. Firstly ground control point (GCP) was collected using GPS for geo-referencing of satellite images. Secondly, generated geo-referenced data was verified by field using GPS at laboratory. Lastly, field survey has been conducted to verify the land-water classified images of study period 1973, 1989 and 2010. During field visit information aimed to collect the accuracy of land classified images and also migration of islands. Classified images as well as raw images along with GPS and digital camera have been carried during field visit. Collection of tidal information was also very important. After the fieldwork, field data have been incorporated into land water classified time series images and for final analysis these were brought into raster based GIS environments. www.theinternationaljournal.org > RJSITM: Volume: 04, Number: 04, February- 2015 Page 80
Figure 8: Change detection map of the lower Meghna River during 1972 to 2010 6. RESULT AND DISCUSSIONS GIS analysis shows the change detection study of the lower Meghna River during past 37 years (1973-2010) in figure 8. It is clearly seen from the figure that the lower Meghna has gone to intensive morphological changes through migration of channels and formation of new islands. Figure 7 shows Table 2: Width of Meghna River in different point in meter Figure 9: Width of the Meghna River in different point during the study period 1973, 1989& 2010 the land-water classified images of 1973, 1989 and 2010. It is clearly seen from the figure that the image of 1973 has few islands having small size compare to images of 1989 and 2010. Due to erosion of river bank the river shows wider in image 2010 compare to images 1973 and 1989. Table 2 and GIS analysis of figure 8 & bar diagram of figure 9 shows the width (meter) of the lower Meghna River in different points and different study period. It is clearly seen from the table 2 and figures 8 & 9 that except point B and F the river gradually wider during 1973 to 2010. At point B slightly accreted in 1989 but in percentage it is nominal. At point F the channel found drastically thinner in 2010. It is between mainland of Noakhali district and Hatia Island. This is due to accretion of mainland and less erosion in north of Hatia Island. Main land reclaimed higher percentage due to changes of water flow direction of Meghna River to the south-west may one of the main reasons. 7. CONCLUSIONS AND RECOMENDATIONS Lower Meghna has gone to intensive morphological changes through migration of channels and formation of new islands. Image of 1973 found few islands having small size compare to images of 1989 and 2010. Due to erosion of river bank the river shows wider in image 2010 compare to images 1973 and 1989. Further research on hydro-dynamic factors like large river discharge, enormous sediment load, strong tidal forces, up-stream speed of water flow, depth of water, etc will be added to improve the accuracy of study. www.theinternationaljournal.org > RJSITM: Volume: 04, Number: 04, February- 2015 Page 81
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