Applied Mechanics and Materials ISSN: 1662-7482, Vol. 862, pp 90-95 doi:10.4028/www.scientific.net/AMM.862.90 © 2017 Trans Tech Publications, Switzerland
Submitted: 2016-06-24 Revised: 2016-08-10 Accepted: 2016-11-01 Online: 2017-01-18
Estimation of Sea Surface Temperature (SST) Using Split Window Methods for Monitoring Industrial Activity in Coastal Area AGUNG Budi Cahyono1, a*, DIAN Saptarini2, b, CHERIE Bhekti Pribadi1, c, HARYO D. Armono3, d 1
Geomatics Engineering, Faculty of Civil Engineering and Planning, Institut Teknologi Sepuluh Nopember, Indonesia
2
Biology, Faculty of Mathematics and Science, Institut Teknologi Sepuluh Nopember, Indonesia 3
Ocean Engineering, Faculty of Marine Technology, Institut Teknologi Sepuluh Nopember, Indonesia
a
[email protected],
[email protected],
[email protected], d
[email protected]
Keywords: Sea Surface Temperature; Split Windows Algorithm
Abstract. The three drivers of environmental change are climate change, population growth and economic growth. This Changes result in a range of pressures on our coastal environment. Coastal development for industry and farming are a major pressure on terrestrial and environmental quality. In their process, most of industry using sea water as cooling water. When water used as a coolant is returned to the natural environment at a higher temperature, the change in temperature decreases oxygen supply and affects marine ecosystem. This research is presents results from ongoing study on application of Landsat 8 for monitoring the intensity and distribution area of sea surface temperature changed by the heated effluent discharge from the power plant on Paiton coast, Probolinggo, East Java province. Remote sensing technology using a thermal band in Operational Land Imager (OLI) sensor of Landsat 8 sattelite imagery (band 10 and band 11) are used to determine the intensity and distribution of temperature changes. Estimation of sea surface temperature (SST) using remote sensing technology is applied to provide ease of marine temperature monitoring with a large area coverage. This research use the Split Window Algorithm (SWA) methods which is an algorithm with ability to perform extraction of sea surface temperature (SST) with brigthness temperature (BT) value calculation on the band 10 and band 11 of Landsat 8. Formula which was used in this area is Ts = BT10 + (2.946*(BT10 - BT11)) - 0.038 (Ts is the surface temperature value (°C), BT10 is the brightness temperature value (°C) Band 10, BT11 is the brightness temperature value (°C) Band 11. The result of this algorithm shows the good performance with Root Mean Square Error (RMSE) amount 0.406. Introduction Coastal development for industry and farming are a major pressure on terrestrial and environmental quality. In their process, most of industry use sea water as cooling water. When water used as a coolant is returned to the natural environment at a higher temperature, the change in temperature decreases oxygen supply and affects marine ecosystem. The coastal sea surface temperature (SST) is one of the important oceanic environmental factors in determining the change of marine environments and ecological activities [2]. Data with spatial resolution finer than 1 km have been used to interpret circulation and front movement [3]. Water heat (‘air bahang’) generated by the activities of the electricity industry in the region Paiton, Situbondo Regency – East Java, Indonesia potentially affecting the availability of marine organisms in coastal waters Paiton. The Paiton power generation is operated by PT. Jawa Power owns a 1,220 MW coal fired power station. Jawa Power is one of the largest IPPs in Indonesia with a 30-year power purchase agreement with PT. PLN (Persero), the state-owned electric utility company. The power station supplies electricity into the Java-Bali 500 kV grid, which is owned and operated by PLN. All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, www.ttp.net. (#73420991, Iowa State University, Ames, USA-31/01/17,00:04:33)
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The spatial distribution of thermal power plant exhaust heat water studied by processing the image data of Landsat 8 thermal band. So that, we can know the direction and broad distribution and the impact of water heat from the electricity industry activities in Paiton. Methods To develop a new split window algorithm for sea surface temperature (SST), we collected in situ and Landsat 8 data from Paiton power plants area, Probolionggo district, East Java with geographical location being on 7°43'30" south latitude and 113°32'32" east longitude, on March 06, 2015.
Figure 1. Paiton Power Plants Area (RGB 432) The in situ data were measured and collected at 10 stations (were measured using termometer in a depth of 15 cm) as shown in Fig. 2 and Table 1.
Figure 2. Field Measurements Location at Paiton Power Plant Table 1. Field Measurements Data Time Series (WIB)
Station
Depth in 15 cm
10:10
9-1
33
10:17
9-2
32
10:30
9-3
32
10:24
9-4
30,5
10:54
9-5
30
10:40
9-6
31
10:49
9-7
29
11:02
9-8
29
11:36
9-9
31
11:13
9-10
28
The estimated data was obtained by the Landsat 8 Thermal Infrared Sensor (TIRS). Landsat 8 provides metadata of the bands such as thermal constant, rescaling factor value, etc. shown at table 1, 2, and 3.
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Table 2. K1 and K2 value Thermal Constant K1 K2
Band 10 774.89 1321.08
Band 11 480.89 1201.14
Path/Row
Acquisition Date
118/65
March, 28 2015
Table 3. Rescaling Factor Thermal Constant ML AL
Band 10 3.3420E-04 0.10000
Band 11 3.3420E-04 0.10000
Path/Row
Acquisition Date
118/65
March, 28 2015
First, pre-proccessing sattelite imagery step is a process to improve the visual quality of the image, in terms of improving the pixel values that do not correspond to the value of emission spectral reflectance or the actual object. Digital Number (DN) to the value of the spectral radiant on satellite images Landsat-8 using dedicated calibration parameters in satellite imagery processing Level 1 : Lλ
= ML*Qcal + AL
(1)
Where, Lλ = Spectral Radian Value (W/(m2 * sr * μm)) ML = Radiance multiplicative scaling factor for the band (RADIANCE_MULT_BAND_n from the metadata), AL = Radiance additive scaling factor for the band (RADIANCE_ADD_BAND_n from the metadata), Qcal = Level 1 pixel value in D Since, spectral radian value of Landsat 8 data has to be converted to the effective temperature value (°C) as the brightness temperature value (Bt). In the process of conversion of the value of the spectral radiant effective temperature value (°K), the equation is used as follows : (2)
T=
Where, T = Brightness Temperature (°Kelvin) L = Spectral Radian Value (Watts/(m2 * sr * μm)) K2 = Temperature Constant (°Kelvin) K1 = Temperature Constant (°Kelvin) The process of conversion of the effective temperature value (°K) to effective temperature value (°C) using the following equation : T (°C) = T (°K) -273
(3)
Where, T (°C) = Brightness Temperature Value (°C) T (°K) = Brightness Temperature Value (°K) The measured data was obtained by using the termometer on Friday, March 06, 2015. For this study, we just used 10 points in developing and validating the algoritm, Table 4 shows the statistical attribute of those point. Table 4. Statistical Information of in situ data Thermal Constant Total Points Mean Maximum Minimum
For Development 10 30.55 33 28
For Validation 10 32.2 33 27
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Split window methods has been develop to retrieve cloud-free, sea and land surface temperature (SST and LST) automatically from sattelite derived radiances. In this study, we use split window algorithm for paiton area, the equation is used as follows : (4)
Ts = BT10 + (2.946*(BT10 - BT11)) - 0.038
Where, Ts = Surface Temperature (°C) BT10 = Brightness Temperature Value (°C) Band 10 BT11 = Brightness Temperature Value (°C) Band 11 To assess the accuarcy of Landsat 8 thermal sensor brightness temperature, we calculate coefficient of determination (R2) and Root Mean Square Errorr (RMSE). the equation is used as follows : ∑ √
∑
∑
∑
∑ ∑
(5)
∑
Where, x= The estimated sea surface temperature y = The measured sea surface temperature n = Total point √
∑
(6)
Where, = The estimated value of sea surface temperature using the algorithm = The measured value of sea surface temperature using termometer N = Total point
Result And Discussion To assess the accurate of algorithm by validating it using 10 data points had a value of RMSE amount 0.406, it shows a good performance which below 1.000. Table 5. Value of Sea Surface Temperature Using Split Window Algorithm and Single Channel Algorirthm Station 9-1 9-2 9-3 9-4 9-5 9-6 9-7 9-8 9-9 9-10 Average Temperature (°C) Maximum Temperature (°C) Minimum Temperature (°C)
In Situ Sea Surface Temperature (SST)
Estimated Sea Surface Temperature (SST) using Split Window Algorithm (SWA)
33 32 32 30,5 30 31 29 29 31 28
27 33 33 33 33 33 33 32 32 33
Estimated Sea Surface Temperature (SST) using Single Channel Algorithm (SCA) 29.8 29.6 29.9 29.8 29.8 29.7 29.7 29.8 29.6 29.9
30,55
32,2
29.76
33
33
29.9
28
27
29.6
Relationship between Landsat 8 thermal sensor brightness temperature and sea surface temperature using termometer have a value of R2 amount 0.2634 that indicate a relationship but still weak because it was below 0.500 however there is still potential to be developed as alternative method to obtain sea surface temperature value, and it has a value of R amount 0.5132. Figure 3 shows the graphic of relationship between both of them.
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Estimate Sea Surface Temperature Using SWA (Celcius)
Relationship Between In Situ and Estimate SST Using SWA 35 34 33
y = -0,6119x + 50,894 R² = 0,2634
32
In Situ SST and SWA
31 30 29 28 27 26 25 26
28
30
32
34
In Situ Sea Surface Temperature (Celcius)
Figure 3. Graphic of Relationship Between Landsat 8 Thermal Sensor Brightness Temperature And Sea Surface Temperature Using Termometer Table 5 shows value of sea surface temperature by in situ, split window algorithm, and single channel algorithm. In average temperature (°C), value of estimated sea surface temperature (SST) using SWA has a higher value than measured sea surface temperature (SST) and estimated sea surface temperature (SST) using SCA.
Figure 4. Sea Surface Temperature by using split window algorithm Figure at the appendix 1 tell us about value of sea asurface temperature by using split window algorithm that minimum temperature amount 28°C - 29°C, and maximumn temperature more than 32°C .
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Figure 5. Sea Surface Temperature by using single channel algorithm Figure at the appendix 2 tell us about value of sea asurface temperature by using single channel algorithm that minimum temperature less than 25°C, and maximumn temperature more than 32°C. Conclusions The result for this research indicate that there is relationship between Landsat 8 thermal sensor brightness temperature and sea surface temperature at the 10 station which was below 0.500 with the value of R2 amount 0.2634. References [1] Sobrino, J., Jiménez, J. C., Laporta, S., & Nerry, F. 2001. Split-Window Methods for Surface Temperature Estimation from DAIS Data, The Digital Airborne Spectrometer Experiment (DAISEX), Proceedings of the Workshop held July, 2001. [2] Chen D, Huazhong R, Qiming Q, Jinjie M, Jing L. 2014. Split-window Algorithm For Estimating Land Surface Temperature From Landsat 8 Tirs Data, Geoscience and Remote Sensing Symposium (IGARSS). IEEE International. [3] M.A. Syariza, L.M. Jaelani, L. Subehi, A. Pamungkas, E.S. Koenhardono, A. Sulisetyono, 2015. Retrieval Of Sea Surface Temperature Over Poteran Island Water Of Indonesia With Landsat 8 Tirs Image: A Preliminary Algorithm, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-2/W4, 2015 Joint International Geoinformation Conference 2015, 28–30 October 2015, Kuala Lumpur, Malaysia [4] Meijun J, Li J, Wang C. and Shang, R. 2015. A Practical Split-Window Algorithm for Retrieving Land Surface Temperature from [5] Landsat-8 Data and a Case Study of an Urban Area in China. Open access Journal of Remote Sensing, Vol 7 Issue 4, p.4371 – 4390 ; doi : 10.3390/rs70404371 http://www.mdpi.com/ 20724292 /7/4/4371
