ISSN , Volume 07, No. 04

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1 Indexed in Scopus Compendex and Geobase Elsevier, Chemical Abstract Services-USA, Geo-Ref Information Services-USA, List B of Scientific Journals, Poland, Directory of Research Journals ISSN , Volume 07, No. 04 August 2014, P.P Identification of Total Suspended Sediment (TSS) Distribution at Surabaya East Coast Area in East Java Indonesia Using TSS Algorithm Implementation on Multi Temporal Satellite Images HARIYANTO T, CAHYONO A B, KRISNA T C AND HEPI HAPSARI H H Department of Geomatic Engineering, Sepuluh Nopember Institute of Technology, Sukolilo-60111, Surabaya, Indonesia tgh_hary@yahoo.com, agungbc@geodesy.its.ac.id, trismonocandrakrisna@yahoo.com, hapsari@geodesy.its.ac.id Abstract: Based on the previous research, it was obtained that a total area of Surabaya increased caused by changes in the coastline Surabaya east coast, where the acceleration of the addition from 2000 to 2009 amounted to 37.1 acres per year. The one of reasons is caused by the change of sedimentation from the East Coast of Surabaya (Hariyanto et al., 2011). In order to determine a source of shoreline change on the East Coast of Surabaya, it is necessary to conduct a TSS research using various types of multitemporal images by applying a variety of TSS algorithm. Types of satellite data used are the ASTER in 2005, SPOT-4 in 2009 and Landsat 8 in 2013, and then apply the existing TSS algorithm according to the used images. The results indicate that distribution of TSS in the East Coast of Surabaya is on range 50 mg/l to 200 mg/l. Region with TSS more than 200 mg/l gain rise located on near of the East Coast of Surabaya. This condition gives negative impact for environmental quality (sedimentation and degradation) on the East Coast of Surabaya. Keywords: Surabaya East Coast, Multi Temporal Satellite Image Data, TSS Algorithm 1. Introduction 1.1. Background Surabaya City as the second largest city in Indonesia has been developing rapidly, both in terms of physical and non-physical aspect. Total area of Surabaya is ha, where 60.17% of the total area is building region. With population is approximately 3 million people, Surabaya becomes one of the city having a relatively high economic growth (Surabaya in figures, 2011). Surabaya is located on side of the northern coast at East Java Province mainly consisting of alluvial soil since sediment from river and beach, moreover the existence of several large and small rivers that it all boils down to the East Coast Surabaya. Like other large cities lying on coast region, Surabaya is also not separated from the increasing coastline due to the sedimentation. Indication of sedimentation in coastal region of Surabaya was detected in the previous years (Hariyanto, 2011). Therefore, it requires a system for monitoring and surveying about the causes of sedimentation regularly to gain well knowledge of a horizontal area. The method used in this study is using of indirect methods to process TSS (Total Suspended Sediment) based on multi temporal satellite imagery. The images used in this study are ASTER in 2005, SPOT-4 in 2009 and Landsat 8 in Study Goal The study goals are: 1. Applying TSS algorithm for ASTER satellite image (2005), SPOT-4 satellite image (2009) and Landsat-8 satellite image (2013). 2. Monitoring and evaluating value of TSS distribution by multitemporal data mentioned above. 2. Method 2.1. Research Site The study area covers Surabaya to Bangkalan (North side), Sidoarjo regency (South side), Gresik (west side) and Madura Strait (East side). As for the land use of coastal and marine areas in Surabaya, especially in Madura Strait is Tanjung Perak Harbor. In northern part, there is Suramadu Bridge connecting Surabaya and Madura having a length about 5,438 km with a system of bridge suspension in the center and operated since Research about TSS and its distribution is performed on the study area in the East Coast of Surabaya which is partly a tourism area, settlement, and # Copyright 2014 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.

2 Identification of Total Suspended Sediment (TSS) Distribution at Surabaya East Coast Area in East Java Indonesia Using TSS Algorithm Implementation on Multi Temporal Satellite Images mangrove ecosystem. The previous research was carried out on the same area, and then the results indicated coastline changes because of sedimentation process 1342 Table 2. SPOT-4 spectral Band (source : Spot Images@Cnes, 2003 ) Mode Band Wavelength (µm) Resolution (m) B1 (Green) Multispectral B2 (Red) B3 (NIR) B4 (SWIR) Monospectral PAN Figure 1. Study area is located on the east coast of Surabaya. East Java, Indonesia. (Source: Google Earth 2013 and ASTER) Appearing on Figure 1, Kali Wonokromo flows eastward and ends at the Madura Strait. Function of Kali Wonokromo for the Surabaya today, among other things: 1. As city drainage for flood control during a large debit, it is by throwing Surabaya river water into the Madura Strait. This mechanism is more effective than the one of through the river Kali Mas. 2. Supply freshwater for fish farms. Up to now, there are many existing fish farms in East Surabaya. Wonokromo River is affected by tides of the Madura Strait water, it is used as guide by fishermen for sailing, besides used to build fish farms. Sediment transport occurring in rivers is much influenced by concentration of sediment contained in water flow and tidal water. Rate of sedimentation in the Wonokromo River is quite high, appeared by the previous evaluation for Landsat images. The evaluation showed development of estuary from year to year, it grows jutting into the sea Spectral Characteristics of Each Image In this research we used ASTER, SPOT-4, and LANDSAT-8 image. Each image have spectral characteristics as following : Table 1. ASTER spectral Band (source : The Yale Center for Earth Observation, 2014 ) Band Label Wavelength Resolution (VNIR) (µm) (m) B1 Band Nadir B2 Band Nadir B3 Band 3N Nadir B4 Band 3B Backward Table 3. Landsat-8 (OLI) spectral Band (source : Edgardh, 2013) Band Wavelength (µm) Resolution (m) B1-Visible (Aerosol) B2-Visible B3-Visible B4-NIR B5-NIR B6-SWIR1 B7-SWIR2 B8-PAN B9-Cirrus B10-TIRS1 B11-TIRS Image to Image Geometric Correction Image-to-image registration is the matching of one image to another so the same geographic area is positioned coincident with respect to the other. This type of geometric correction is used when it is not necessary to have each pixel assigned a unique x, y coordinate in a map projection (Baboo, 2011). Figure 2. Image to image Geo-Correction. (source : /html/sect44.htm) In order to get ( x y ) = T (u v ) (1) Every step involved in the imaging process has to be known, i.e., we need to know the inverse process of geometric transformation. ( u v ) = T 1 ( x y ) (2)

3 1343 HARIYANTO T, CAHYONO A B, KRISNA T C AND HAPSARI H H This is a complex and time consuming process. However, there is a simpler and widely-used alternative: polynomial approximation. n n q=0 u = p=0 a pq x p y q (3) n n q=0 v = p=0 b pq x p y q (4) Coefficients a's and b's are determined by using Ground Control Points (GCPs). For example, we can use very low order polynomials such as the affine transformation. u = ax + b y + c (5) v = dx + e y + f (6) A minimum of 3 GCPs will enable us to determine the coefficients in the above equations. In this way, we don't need to use the transformation matrix T. However, in order to make our coefficients representative of the whole image that is transformed, we have to make sure that our GCPs are well distributed all over the image (Camper, 2011). The distance between the input location of a GCP and the retransformed coordinates of the same GCP is called the Root Mean Square (RMS) error. This is calculated in the rectification formula as follows: R x = 1 n XR n i=1 i 2 (7) R y = 1 n YR n i=1 i 2 (8) T = R x 2 + R y 2 or R x = 1 n XR n i 2 2 i=1 + YR i (9) Where, Table 4 Parameter of geometric correction R x = X RMS Error R y = Y RMS Error T = Total RMS Error n = the number of GCPs i = GCP number XR i = the X residual for GCP 1 YR i = the Y residual for GCP i SATELITE IMAGE GCP x y u y x y RMS ASTER 2005 SPOT LANDSAT RMS ERROR Algorithm used to determine TSS Algorithm of TSS from remote sensing satellite image consists of ASTER satellite imagery on March 11, Jing Li formula is a formula applied for multi band in the calculation. This formula uses band reflectance values of band 1 and band 2 for ASTER image. Value of Suspended Sediment Concentration (SSC) or TSS (Li et al., 2008) based on the following models:

4 Identification of Total Suspended Sediment (TSS) Distribution at Surabaya East Coast Area in East Java Indonesia Using TSS Algorithm Implementation on Multi Temporal Satellite Images X=[R w (λ 550 )+R w (λ 670 )]*[R w (λ 670 )/R w (λ 550 )] (10) Where, R w (λ i ) is the reflectance value of the sediment with the wavelength units is nm. So reflectance can be calculated by R w (λ 550 ) and R w (λ 670 ) when they are taken to produce high TSS concentration. At the same time, R w (λ 550 ) is suitable for detection of high reflectance for chlorophyll, whereas R w (λ 670 ) is applied to detect the high absorption from chlorophyll. So [R w (λ 670 )/R w (λ 550 )] can reduce chlorophyll interference. Therefore, Jing Li proposed the following formula: Log 10 TSS (mg/l)= *X (11) Where TSS is sediment value, and X = [R w (tm1)+r w (tm2)]*[r w (tm2)/r w (tm1)] is determined by ASTER imagery. Then that formula can be changed into: TSS (mg/l) = 10.(0, *X) (12) SPOT-4 satellite imagery on July 21, 2009 The algorithm is based on Budhiman (2004) for the image of SPOT-4. This algorithm equation utilizes reflectance of band 2. Equation algorithm used to obtain the value of TSS (mg/l) is as follows: TSS (mg/l) = 7,9038*exp(23,942*red band) (13) Where: Red band = band 2 reflectance Landsat 8 satellite imagery on July17, 2013 Parameter TSS content of Landsat data is derived by algorithms Budhiman (2004) which was the result of research in the waters of the Mahakam Delta. The algorithm applied irradian reflectance values (R (0 - )) of red band as the input. However, this study used a red band reflectance values with atmospheric corrected. The algorithm used is as following: TSS (mg/l) = A * exp (S * R(0-) red band) (14) Where, TSS = total suspended sediment R(0-) = irradian reflectance from band 3 of Landsat 8 imagery A,S = variabel equation 3. Results and Discussion The algorithm described above is employed to calculate the amount of TSS in each type of images; they are ASTER image (2005), SPOT-4 image (2009) and Landsat 8 image (2013) where value of TSS interval is 50 mg / L. Therefore, be obtained distribution of TSS magnitude as following: 3.1. ASTER Satellite Image in 2005 Based on figure 3, it can be conclude that dominant TSS values are obtained at interval of 50 mg/l to 100 mg/l The greater TSS values that is over than 150 mg/l located near the coaslline. Based on TSS distributon map for 2005, area classification based on interval value of TSS can be derived as following in table 2 as follows. Figure 3. Distribution of TSS values around East Coast of Surabaya in 2005 Table 5. Area based on TSS interval value of ASTER satellite imagery in Nr TSS Interval Value (mg/l) Area (km 2 ) > SPOT-4 Satellite Image in 2009 Figure 4. Distribution of TSS values around East Coast of Surabaya in 2009 Based on the distribution of TSS values on figure 4, it is found that the value TSS less than 50 mg / l does not exist. Dominant TSS value stands for interval 50 mg/l to 100 mg/l. While, region is alongside with coastline, it has TSS more than 100 mg/l. This condition is similar to the one of The results of the TSS distribution

5 1345 HARIYANTO T, CAHYONO A B, KRISNA T C AND HAPSARI H H value based on area classification are presented in table 3 as follows. Table 6. TSS value of SPOT-4 satellite image in No. TSS Interval Value (mg/l) Area (km2) > Landsat 8 Satellite Image in 2013 Predicated from the map 2013 above, it is conclude that distribution and condition of sediment in East Coast of Surabaya is different from those of 2005 and Dominant value of TSS stays on interval 100 mg/l to 150 mg/l. Area with TSS value more than 150 mg/l on 2013 map is wider than those of 2009 map. Region is adjacent with coastline, mainly having TSS value more than 200 mg/l. Kali Wonorejo boiled down to East Coast of Surabaya having TSS value more than 200 mg/l. Its condition is completely different to the previous one. Transport sediment on Kali Wonorejo is on warning level, if it is not responded seriously, it will be affected to surrounding environments.. The results of the TSS distribution value based on area classification are presented in table 4 as follows. Figure 5. Distribution of TSS values around East Coast of Surabaya in 2013 Table 7. TSS value of Landsat 8 satellite image in 2013 No. TSS Interval Value (mg/l) Area (Km2) > Regulation of TSS Value Based on an Indonesian Law According to Decree of Indonesian Environment Minister Number : KEP-51/MENLH/10/1995 about wastewater quality standard, threshold value allowed for the maximum BOD (Biochemical Oxygen Demand) is mg/l, whereas maximum TSS is 200 mg/l. Waste water has BOD and TSS more than standard value, it will involve adverse effects for environmental quality. Based on the rule, TSS value in East Coast of Surabaya indicated based on TSS map in 2013 have passed the limit set. If this problem is not noticed and not solved, it would be impact to the environment around the coast. 4. Conclusion and Suggestion 4.1. Conclusion Application of the TSS algorithm for ASTER, SPOT-4 and Landsat 8 satellite image by taking the existing data can produce interpretations about model of the TSS distribution which have suitability between the 3 types of images. Based on monitoring conducted with different images and different years (2005, 2009 and 2013), it is conclude that TSS values on study area is increase that is more than 200 mg/l, and the area with that value is wider. Likewise, the distribution of high TSS value is close to coastline of the eastern coast of Surabaya. This is initial cause of sedimentation on the coastline, besides type of land use in the region which effects negative impact for environmental quality in East Coast of Surabaya Suggestion This research study is recommended for further activity such as direct retrieval of water samples needed to get the actual value of TSS as a field data to validate the TSS data from satellite image results. Monitoring the condition of the east coast of Surabaya can be performed continuously to monitor the existence of bad environmental quality changes and to investigate an extended coastline caused by sedimentation process. References [1] Baboo. S.S, Geometric Correction in Recent High Resolution Satellite Imagery : A Case Study in Coimbatore, International Journal of Computer Applications, Volume 14 No.1, , 2011 [2] Budhiman, S, Mapping TSM Concentrations From Multi Sensor Satellite Images in Turbid Tropical Coastal Waters of Mahakam Delta Indonesia, Enschede : MSc Thesis ITC Enschede, The Netherlands, ( syarif_thesis.pdf) [3] Camper, Georeferencing (Geometric Correction), Remote Sensing and Image Analysis, University of

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