A case study on Bathymetry and SST Mapping using Landsat-TM Data over Coastal Area of Bangladesh

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1 A case study on Bathymetry and SST Mapping using Landsat-TM Data over Coastal Area of Bangladesh Abstract M H Sarker, S M M Rahman & M R Akhand Bangladesh Space Research and Remote Sensing Organization (SPARRSO), Agargaon, Sher-e-Bangla Nagar, Dhaka-1207, Bangladesh Coastal bathymetry is important for monitoring the emergence of new land, navigational channel maintenance as well as for fish resources tracking purposes. But coastal bathymetry undergoes frequent changes due to coastal processes such as erosion and deposition of soil. As a result, hydrographic charts in these areas have to be updated frequently. But Hydrographic surveying by conventional ship borne sounding technique is slow and expensive. Remote sensing techniques can be used with limited ground observation to study and monitoring these changes. The unique character of the shorter wave length visible channel, such as blue ( µm) has the ability to penetrate water to a significant depth and generates radiance that reflects submarine albedo. Sea Surface Temperature (SST) is important for climate modeling, study of the earth's heat balance, atmospheric and oceanic circulation patterns and anomalies (such as El Niño/La Niña) in global scale. It is also important for tropical cyclogenesis. In local scale it is used as an indicator of the environment required for the living of some marine biota. Conventional techniques of obtaining SST is time consuming and expensive. Again, the satellite remote sensing techniques found very useful. A number of studies have been performed on bathymetry and SST mapping using TM, AVHRR and MODIS data but most of the studies have been conducted outside of Bangladesh. In this paper, an attempt has been made to prepare a digital map showing the distribution of bathymetry and SST using Landsat-TM data over coastal area of Bangladesh. The average water depth depicted this image in between 1 to 12.5 meter. The distribution of SST varies of location. Higher the surface temperature at the location near the shoreline compared to location farther. Distribution of higher temperature contributed by suspended sediment and residential area. Keywords: Bathymetry, SST, Hydrographic, Environment, Remote sensing 1. Introduction Bangladesh has three distinct coastal regions, namely the western, central and eastern regions. The western zone is very flat and low and is crises-crossed by numerous rivers and channels. It houses the famous mangrove area called the Sundarbans. The central region is the most active one and continuous process of accretion and erosion is going on there. The eastern region is covered by hilly areas and it is more stable and has a long beach there. The coastal region is characterized by: (i) A vast network of rivers, (ii) An enormous discharge of river waters heavily laden with sediments, (iii) A large number of islands in between the channels and rivers, (iv) Northward converging Bay of Bengal towards Bangladesh, (vi) A very shallow area all along the coast, particularly in the central region, and (vii) A submarine canyon, named Swatch of No Ground, in the western region (M Hossain, 2008). Coastal areas are dynamic and undergo frequent changes over a period of time. As a result, hydrographic charts in these areas have to be updated frequently for safe navigation. Hydrographic surveying by conventional ship borne sounding technique is slow, risky and expensive. Satellite remote sensing technique is able to map the water depth at the critical shallow water areas which are frequently used by the ships forthcoming or leave-taking the ports. Satellite can provide an extremely effective means of carrying out preliminary surveys over wide areas especially in remote regions. A number of studies have been performed on bathymetry using TM and MODIS data but most of the studies have been conducted outside of Bangladesh. Bay of Bengal is a breeding ground of fish and other aquatic animal. Hunting of fish and other resources continues throughout the year. With its resources, Bangladesh meets its own demand and exports marine fish and other resources. Bay is also a favorable breeding ground of tropical cyclones > RJSITM: Volume: 02, Number: 02, December-2012 Page 1

2 and Bangladesh is the worst suffer of all cyclonic casualties in the world. About 5.5% cyclonic storms form in the Bay of Bengal and about 1% cyclonic storm of the global total hits in Bangladesh. Information concerning Sea Surface Temperature (SST) is needed in the assessment of potential fishing zone and site selection for marine culture (grouper, snapper, seaweed, and pearls) (Nontji, 1987), which are normally these areas are rich of nutrient. SST is also used as indicator of the environment required for the living of some marine biota. It is also important for early warning for cyclone formation. In conventional way, the temperature is observed by using the standard mercury thermometer from the water sample collected from the ship. This conventional method applies only for surface water. It is also time consuming and expensive for a large area. With the use of remote sensing techniques, the skin temperatures at the sea air interface are measured with limited ground observations. The study of SST distribution model mostly has been used using low to moderate spatial resolution satellites data, such as NOAA, and MODIS and the result always used for global scale applications. A lot of papers and reports have been published about algorithm/model for SST mapping using NOAA data, most common algorithm known are algorithm model by McMillin and Crosby (Pellegrini and Penrose, 1986; Goda, 1993; McClain, 1981 cited in Hasyim et. al, 1996). Hasyimet. al. (1996) reported that algorithm model by McMillin and Crosby can represent the condition of SST distribution in global scale. In this paper, attempt has been made to prepare a digital map showing the distribution of bathymetry and SST using Landsat-TM data over selected coastal area of Bangladesh. This will be helpful for decision makers of coastal zone management and fishing industries development. 2. Objectives To introduce the new technology in coastal management by using remote sensingtechnique as a replacement of costly and time consuming conventional way. To produce a bathymetry map over Bangladeshcoastal areas using satellite image for safe navigation. To produce a sea surface temperature map over Bangladesh coastal areas using satellite imagesfor fishing industry development. 3. Study area, data and software used 3.1 Study area The study report in this paper is carried out on the coastal area of Bangladesh extending from 89º to 91º E and 20º to 22º N (TM frame 137/45). Figure 1 shows the image of study area. Study Area Figure 1: Coastal area of Bangladesh extending from 89º to 91º E and 20º to 22º N. > RJSITM: Volume: 02, Number: 02, December-2012 Page 2

3 3.2 Data used There are many satellites such as Landsat series, IRS series, SPOT series, NOAA series etc are potential usable for bathymetry and SST mapping. But most important is the spectral coverage of the satellites as well as the temporal resolution. As the area coverage is significantly wide. The lower spatial resolution (even up to 1 km) impacts little to view the features. Whereas, higher spectral resolution may be better to separate different features more correctly. It is important to note that blue spectrum region of Landsat -TM occupies most upper part of the visible area in compared to other satellite sensors. TM channel blue having spectrum width of 0.45 μm to 0.52 μm was found to be the most suitable. Among other visible spectrums the blue has the maximum water penetration capacity of up to 20m (Lillesand and Kiefer, 2002) due to its shorter wave length but susceptible to back scattering (Rayleigh s effect) due to the presence of smaller suspended particles. Also, availability of Landsat data is easier and cheaper than all others. Band 10 of MODIS satellite having a bandwidth between and μm can provide much better bathymetric maps. Major problem incorporating MODIS in present research was its radiometric resolution of 12 bit, which was unable to be processed due to software limitation. NOAA-AVHRR data are relatively low resolution compare to TM. So for the present research the data of Landsat-Thematic Mapper (TM) captured on 31 January 2010 have been used for Bathymetry and SST mapping. TM bands 6( µm)was used for SST mapping and band 4 is used for land-water delineation captured on same date. 3.3 Software used ERDAS Imagine and ArcGIS were used for data pre-processing, generation, and analysis. 4. Procedure of data generation Before doing the main process in mapping the water depth and sea surface temperature at study area, the image must undergo some pre-processing. The images must be geometric corrected and also radiometric corrected. The image must be geometrically rectified to enable the further quantitative comparison between the remotely sensed data and the existing chart and maps. Land and cloud masking process to be used to mask the cloud and land area. The land and the cloud cover area are unwanted information in bathymetry and SST mapping. The cloud areas must be masked in order to get the correct result of water depth and sea surface temperature mapping. 4.1 Geometric correction The Landsat TM image of the study area was analyzed to identify the geographical features. The image was first geo-referenced to Bangladesh Transverse Marcator (BTM) projection system by selecting10 Ground Control Point (GCPs). Second order polynomial and then re-sampled with bilinear algorithm have been used during the re-sampling method. All the 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. 4.2 Land and cloud separation A simple algorithm was used to mask the land and the cloud cover areas. If the land and cloud are not masked in the images, it will give the wrong water depth and SST values and will disturb the all processing. For land and water separation near infra-red (NIR) band 4 (0.76 to 0.90 μm) 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. Luckily I have found the cloud free image so no need to mask out the cloud. Either (Landsat-5 TM) IF (Band 4 < 41) or 0 otherwise (1) > RJSITM: Volume: 02, Number: 02, December-2012 Page 3

4 4.3 Bathymetry mapping The method of bathymetry mapping is based on Marghanyet. al(2007). Bathymetry mapping involves data acquisition, pre-processing, data processing and output. Download of TM data and collection of bathymetry chart under data acquisition. Pre-processing involves geometric& radiometric correction and masking of land. Data processing involves the estimation of pixel intensity and calculation of water depth.accuracy assessment needs to improve the accuracy of output product. For pixel intensity and water depth calculation Band 1 ( µm) of Landsat TM has been used. For accuracy assessment bathymetry chart is very much useful but due to unavailability of data accuracy assessment was not incorporated in this study. Figure 2 shows the overall methodology of bathymetry mapping. 4.4 Calculation of pixel intensity andwater depth Pixel intensities are determined in order to calculate the water depth. The unique character of the shorter wave length visible channel, such as blue ( µm) has the ability to penetrate water to a significant depth and generates radiance that reflects submarine albedo. The simple algorithm based on Marghanyet. al (2007) was used to estimate the pixel intensities. Algorithm (2) shows the calculation of pixel intensity. Xi = (log (Li Lmean))/ 2Ki-----(2) Where Xi is the intensity of the pixel, Li is the image Landsat-5 TM band 1, Lmean is the mean of value of digital number in band 1 and Ki is the coefficient. After getting the pixel intensities, the water depth can be measured by using an algorithm based Marghanyet. al (2007). The following algorithm shows the calculation of water depth. Z = (Ai-Xi)/2Ki (3) Where Z is the water depth, Ai and Ki be the coefficient. The value of Ai and Ki are and respectively. The value of Z shows the depth of water along selected coastal areas. Figure 5 shows the bathymetry map of study area. 4.5 Sea surface temperature (SST) mapping The method of estimating the temperature of the sea surface is based on the Bambang et al. (2002). The Landsat-5 TM band 6 digital data ( µm) which is located in thermal infrared region of electromagnetic spectrum was used. Because of the direct solar irradiance at this band location is negligible, thermal temperatures of surface features can be estimated by conversion of radiance to > RJSITM: Volume: 02, Number: 02, December-2012 Page 4

5 temperature value. Figure 3 shows the overall methodology of SST mapping. The TM band 6 radiances represent average values of 60*60 m and temperature derived are thus average temperature. The method of obtaining the surface temperature of the water was implementing on Landsat-5 band 6 digital data over coastal areas ofbangladesh. The data used in this mapping are same as describe in section 3.2 with the same area of bathymetry study. Constant relating the digital numbers to the effective at satellite spectral radiance were used in the determination of the temperature. The radiance and effective temperature are determined by using equation (4) and (5). R = α (DN) + β (4) Where R is radiance and α and β be the coefficient. After having the radiance image from Landsat- TM band 6 images, the effective temperature was compute by using the following algorithm. T e = K 2 / ln((k 1 /R)+1) (5) Where, T e is the effective temperature in degrees Kelvin, K 1 and K 2 be the coefficient. The final step in determining the temperature of sea surfaces is to compute the actual temperature. T = T e T e T e (6) Where T is actual temperature in K and T e is the effective temperature. Figure 4 shows the SST map of study area. 5. Results and discussions The final output of figures 4 and 5 of the study were depicted the sea surface temperature and bathymetry maps over the selected coastal region of Bangladesh by using Landsat-TM captured on 31 January The SST and bathymetry maps are a symbolic in further analysis towards the study of coastal management.the SST map produced by the proposed methodology utilized in remote sensing shows the distribution of surface s temperature over the study area of Bangladesh coast. Based on the output map (figure 4), the distribution of SST varies with location. The location near to the shoreline suffered a higher surface temperature compared to the location farther. The distributions of higher surface temperature near the shore because, the contribution of suspended sediment that gives a higher reflectance of the surface area. The theoretical concepts explained that more sediment suspended at the > RJSITM: Volume: 02, Number: 02, December-2012 Page 5

6 ocean, the more the reflectance will be, thus the higher surface temperature was recorded. In addition, the higher surface temperature recorded nearer the shore line was contributed by the effect of residential area from the location. It is well-known that the residential area will emit pollutant sources that will also supply the addition suspended sediment, thus the recorded surface temperature was increased near the shore. The average range of sea surface temperature was recorded in between 25-27degree Celsius. The result of bathymetry mapping (Figure 5) depicted that the average water depth recorded in this image was in between 1 to 12.5 meter depth. The depth of sea-bed varies with locations. The mouth of Haringhatariver is shallower than the Shahbazpur channel because of the contribution of soil. In addition, the natural shape of the selected area supported the opportunity of deposition process and erosion. Thus, the lower water depth measurement was estimated from the satellite image. Both the results was compared with theoretically idea of the typical SST and water depth of the location since the absent of ground measurement data. However in various studies suggested that methodology used and algorithms proposed have been scientifically proven and could give a high accuracy in certain published literatures. Further utilization of this methods are encourage to be validate with ground data in order to determine the accuracy of estimated values that derived from this remote sensing technique. > RJSITM: Volume: 02, Number: 02, December-2012 Page 6

7 6. Conclusions The SST and bathymetry maps are a symbolic in further analysis towards the study of coastal management and fish industries development. The water depth values give an idea to the coastal authority in order to detect location with high deposition of sedimentation that reduced the water depth. The result was compared with theoretically idea of the typical SST and water depth of the location since the absent of ground measurement data. The accuracy assessment couldn t be done because of non-availability of field data. Validation with ground data are encourage to determine the accuracy of estimated values derived from remote sensing technique 7. References BambangTrisakti, SayidahSulma and SyarifBudhiman Study of Sea Surface Temperature (SST) using Landsat-7 ETM(In Comparison with Sea Surface Temperature of NOAA-12 AVHRR) Dr. Mohd Ibrahim, SeeniMohd Water depth determination from satellite data Etsuji I SHIGURO et al. studies on the evaluation of water depth around seashore and the land classification in Yap Island using satellite data. Goda, H.H Remote Sensing for Fisheries in India.Asian-Pacific Remote Sensing Journal Vol. 5 No. 2. Hasyim, B.; KhairulAmri and MaryaniHartuti Pemanfaatan Data PenginderaanJauh NOAA- AVHRR untukpengamatanpolaaruslautdandaerahpotensipenangkapanikan.kumpulan Makalah Seminar Maritim Indonesia Jakarta. (In Indonesian) H. J. CHO Depth-variant spectral characteristics of submersed aquatic vegetation detected by Landsat 7 ETM+. Lecture notes on remote sensing in oceanography application. > RJSITM: Volume: 02, Number: 02, December-2012 Page 7

8 Lillesand, T.M. and Kiefer, R. W., 2002, Remote Sensing and Image Interpretation, John Willy & Sons, Inc., New York, p, 318, 396 and 415. M Hossain, 1 Sept 2008, The New Nation, Bangladesh MazlanHashim, Adeli Abdullah and Abd. Wahid Rasib Integration of remote sensing-gis Techniques for mapping Seagrass and Ocean Colour off Malaysian Coasts MohdIbrahim;MazlanHashim; Adeli Abdullah; R&D In Remote Sensing Application For Coastal Studies in UniversitiTeknologi Malaysia Marghanyet. al (2007 and Mainozalawatiet. al (2011). Lecture note during the training at GeomatikaInternationa at Kuala Lumpur, Malaysis Nontji, A Laut Nusantara. Penerbit Djambatan. Jakarta. (In Indonesian) Pellegrini, J.J. dan I.D. Penrose Comparison on Ship Based Satellite AVHRR Estimates of Sea Surface Temperature.Proceeding 1st Australian AVHRR Conference. Perth, Australia > RJSITM: Volume: 02, Number: 02, December-2012 Page 8