Journal of Coastal Research
29
3
624–630
Coconut Creek, Florida
May 2013
A Two-Band Algorithm for Total Suspended Solid Concentration Mapping Using THEOS Data H.S. Lim, M.Z. MatJafri, K. Abdullah, and R. Asadpour www.cerf-jcr.org
School of Physics Universiti Sains Malaysia 11800 Penang, Malaysia
[email protected] [email protected] [email protected]
ABSTRACT Lim, H.S.; MatJafri, M.Z.; Abdullah, K., and Asadpour, R., 2013. A two-band algorithm for total suspended solid concentration mapping using THEOS data. Journal of Coastal Research, 29(3), 624–630. Coconut Creek (Florida), ISSN 0749-0208. Environmental monitoring through the method of traditional ship sampling is time consuming and requires a high survey cost. This study was carried out to investigate the relationship between total suspended solids (TSS) and satellite THEOS data by using a calibrated algorithm. The study area is the seawater region around Penang Island, Malaysia. Water-quality TSS values were collected simultaneously during the acquisition of the satellite imagery and later analyzed in the laboratory. The digital numbers of the corresponding TSS measurements were extracted and converted into reflectance values for algorithm regression. The two-band algorithm used here is based on the reflectance model, which is a function of the inherent optical properties of water, and this in turn can be related to the concentration of its constituents. The developed algorithm was used to correlate the digital signal and the TSS values. The generated algorithm was developed for two visible wavelengths, red and blue for this study. The digital numbers corresponding to the sea truth locations were extracted for algorithm calibration. Based on the values of the correlation coefficient squares (R2) and root-mean-square deviation (RMS), the proposed algorithm is considered superior. A water-quality image was generated using the multispectral data set and the proposed calibrated TSS algorithm. Finally, the generated TSS map was geometrically corrected to produce a geocoded map. Filtering was performed to the generated map to remove random noise, and it was color-coded for visual interpretation. This study indicates that the TSS algorithm developed from optical properties is a promising TSS model for high-accuracy TSS mapping using satellite data.
ADDITIONAL INDEX WORDS:
TSS, THEOS, algorithm.
INTRODUCTION The most common pollutants, both in terms of volume and weight, are suspended sediments, which have undesirable effects in surface-water systems and on water quality in lakes and reservoirs. These can be used as an indicator of erosion problems in a watershed. For detecting suspended sediment, the remote-sensing technique is one of the best methods; many investigations in the world have shown this phenomenon. Suspended sediments change the radiance emergent from surface waters, which can increase these phenomena in the visible and near-infrared portion of the electromagnetic spectrum (Schultz and Engman, 2000). There is a curvilinear or linear relationship between suspended sediments and radiance or reflectance, which depends on the saturated suspended sediment (Ritchie, Cooper, and Schiebe, 1990). Studies have found that the amounts of reflected radiance tend to saturate when suspended sediment concentration increases. Reflectance from almost any wavelength will be linear when
DOI: 10.2112/JCOASTRES-D-11-00152.1 received 18 August 2011; accepted in revision 5 February 2012. ’ Coastal Education & Research Foundation 2013
the variety of suspended sediments is between 0 and 50 mg/l, which is related to suspended sediment concentrations. When this range is from 50 mg/l to 150 mg/l or higher, curvilinear relationships will be expected (Reza, 2008). The traditional sampling method for marine environment monitoring is time consuming and needs a high cost to carry out the survey. Traditionally, monitoring of water quality is carried out through shipboard water sampling and laboratory analysis. Such methods are not only labor-intensive, but sampling is also discrete in time and space. Remote sensing offers potentially a significant source of information, and methods are being developed for operational large-scale monitoring of water quality (Koponen et al., 2002). Water quality is an important factor for human health and quality of life. This has been recognized for many years (Sivertun and Prange, 2003). Satellite data have been used successfully to map coastal and inland water areas (Lohrenz et al., 2008; Odermatt et al., 2008); Randolph et al., 2008). Many researchers have shown the relationship between the concentration of suspended sediments and radiance or reflectance by developed algorithms and have used these algorithms to estimate suspended sediments for different times or locations (Reza, 2008; Ritchie and Cooper, 1988). Various models have been used to correlate water quality with remote-sensing
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measurements. An empirical calibration approach that uses regression analysis has been widely quoted in the literature (Forster, Xingwei, and Baide, 1993). The regression models rely on simultaneous measurements of ground data and the remote-sensing observations. Such a calibrated algorithm is site specific because it depends on the type of water constituent involved in the calibration analysis. Remotesensing techniques have been widely used for water-quality studies in coastal regions and in inland lakes (Allee and Johnson, 1999; Baban, 1993; Dekker and Peters, 1993; Dekker, Vos, and Peters, 2001; Ekstrand, 1992; Kabbara et al., 2008; Koponen, 2006; Koponen et al., 2002). This paper present preliminary findings vis-a`-vis the potential application of satellite THEOS data in local coastal studies. The main emphasis was placed on the development of an algorithm to retrieve total suspended solids (TSS) values from remote-sensing data, because in the field, monitoring programs are often insufficient to resolve the temporal and spatial characteristics of the phenomenon under study. Coastal water is optically complex because of the varying concentrations of its constituents, such as inorganic suspended solids, phytoplankton pigments, and dissolved organic matter. In this study, the water-quality parameter that we used was TSS measurement. An algorithm was generated based on the reflectance model for total suspended solids. The main objective of the present study is to test the performance of our proposed algorithm for mapping total suspended solids in marine environments using THEOS satellite image. An empirical relationship was established between the reflectance values of the digital satellite imagery bands and TSS readings from numerous in-situ measurements. The algorithm was used to estimate TSS at the Penang Islands, Malaysia.
Water samples were collected between 0900 h and 1100 h local time from a small boat in order to retrieve the TSS concentration around Penang Islands, Malaysia. The sampling locations were determined using a handheld global positioning system (GPS). Water samples were collected from 13 sampling stations including clear and turbid waters simultaneously with the satellite images on 8 December 2009 and 29 January 2010. Polyethylene bottles were used to store the collected samples. The water sampling points are shown in Figure 2. A handheld GPS was used to establish the position of each sampling location. The sampling locations were carefully selected to cover a wide range of TSS concentrations and to be representative of the study areas. The water samples were filtered through 0.45 mm Nuclepore ultracellulose membrane filters to determine the concentration of TSS (Lim et al., 2010). Laboratory analysis for the determination of TSS followed the procedure as suggested by Strickland and Parsons (1972).
STUDY AREA AND DATA COLLECTION
where l 5 the spectral wavelength; b 5 the backscattering coefficient; and a 5 the absorption coefficient. Then, the remote sensing reflectance can be simplified (Lim et al., 2010) and is calculated as:
Penang Island is located in the northern part of Malaysia, within latitudes 5u129 N to 5u309N and longitudes 100u099 E to 100u269 E (Figure 1). George Town is the capital city of the state of Penang and is also the second-largest city in Malaysia. It is located in the eastern region of Penang Island. Additionally, Penang Island is the most populated island in the country, with an estimated population of 720,000 and an area of approximately 295 km2. Normally, Penang Island experiences a warm and sunny equatorial climate throughout the entire year (Tan et al., 2009). The average annual temperature varies between 27uC and 30uC, and the mean daily temperature is about 27uC. The average annual relative humidity ranges between 70% and 90%. The average annual rainfall is about 267 cm, though the annual total can be as high as 624 cm (Ahmad, Yahaya, and Farooqi, 2006). During the period of monsoon winds, weather changes drastically. In particular, there is sunshine during the day but rainfall in the evenings. Penang Island consists primarily of hilly terrain, with the highest point being Western Hill (part of Penang Hill) at about 830 m above sea level. The terrain is mostly composed of coastal plains, hills, and mountains. The coastal plains are narrow; the most extensive ones are located in the northeast and form a triangular promontory where George Town is located.
OPTICAL MODEL OF WATER A physical model relating radiance from the water column and the concentrations of the water-quality constituents provides the most effective way of analyzing remotely sensed data for water-quality studies. Reflectance is particularly dependent on inherent optical properties: the absorption coefficient and the backscattering coefficient. Remote-sensing reflectance, R (unitless), is related to the irradiance reflectance just beneath the water surface, Rird (Kirk, 1984; Kratzer, Brockmann, and Moore, 2008; Zhang, 2005) and is calculated as: Rird (l) ~ 0:33b(l)=a(l)
R~c
b Qa
ð1aÞ
ð1bÞ
where c 5 constant. The inherent optical properties are determined by the contents of the water. The contributions of the individual components to the overall properties are strictly additive (Gallegos and Correld, 1990). The total absorption coefficient at wavelength l, a(l), can be considered to be the sum of absorption due to water, aw(l), phytoplankton, ac(l), non chlorophyll particles of biological and terrestrial origin, ap(l), and dissolved organic matter or yellow substance, ay(l) (Gallegos and Correld, 1990; Koponen, 2006). Thus, a(l) ~ aw (l)zac (l)zap (l)zay (l)
ð2Þ
The absorption of pure seawater is practically the same as pure water in the visible region (400–700 nm). Absorption by dissolved salts is known to be negligible in this region (Gallegos and Correld, 1990). The absorption related to each substance is expressed as the product of its concentration of C (phytoplankton), P (nonchlorophyll particles), or Y (yellow substance) and
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Figure 1.
Lim et al.
The geographical features of the study area.
its corresponding specific absorption coefficients, ac (l), ap (l), and ay (l), respectively. Therefore, the total absorption is described by a(l) ~ aw (l)zac (l)Czap (l)Pzay (l)Y
ð3Þ
Similarly, the equation for the backscattering coefficients
(Koponen, 2006; Prieur and Sathyendranath, 1981) is bb (l) ~ bbw (l)zbbc (l)zbbp (l)
ð4Þ
where bbw(l), bbc(l), and bbp(l) are the backscattering coefficients of water, chlorophyll, and suspended matter, respectively. It is reasonable to assume that the effects of the
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Algorithm for THEOS Total Suspended Solid Concentration
Figure 2.
627
Raw satellite image and the sampling locations (a) 8 December 2009 and (b) 29 January 2010, respectively.
backscattering due to yellow substance are negligible (Eleveld et al., 2008; Koponen, 2006; Vahtmae et al., 2006). Then bb (l) ~ bbw (l)zbbc (l)Czbbp (l)P
ð5Þ
The symbol * denotes specific coefficients. The magnitude of bbw(l) is 0.5bw(l) because the molecular volume-scattering function of pure seawater, bw(l), is symmetrical (Gallie and Murtha, 1992; Vahtmae et al., 2006). In this study, the analysis of reflectance is focused just on the effects of suspended sediment and chlorophyll. Thus, in this analysis, we assume that the optical coefficients in Equations (2) and (4) are produced only by the water itself, the suspended sediment, and the chlorophyll. For a case involving only suspended sediment, P, and chlorophyll, C, the simultaneous equations using Equations (1) to (5) for the two channels (l1 5 band 1 of THEOS data and l2 5 band 3 of THEOS data) can be expressed as: ½0:5bbw (l1 )zbc (l1 )Czbp (l1 )P R(l1 ) ~ R1 ~ 0:33 ½aw (l1 )zac (l1 )Czap (l1 )P
ð6Þ
R(l2 ) ~ R2 ~ 0:33
½0:5bbw (l2 )zbc (l2 )Czbp (l2 )P ½aw (l2 )zac (l2 )Czap (l2 )P
ð7Þ
where bbw (l) 5 water backscattering coefficient, bc (l)5 chlorophyll specific backscattering coefficient,bp (l)5 sediment specific backscattering coefficient, aw (l)5 pure water absorption coefficient, ac (l)5 chlorophyll specific absorption coefficient and ap (l)5sediment specific absorption coefficient.
REGRESSION ALGORITHM TSS concentrations can be obtained by solving the simultaneous Equations (6) and (7) to yield the series consisting of the terms in R1 and R2 (ignoring higher-order terms): P ~ e0 ze1 R1 ze2 R2 ze3 R1 R2 ze4 R21 ze5 R22
ð8Þ
where the coefficients ej, j 5 0, 1, 2, …, are the functions related to the coefficients used in Equations (6) and (7), which are to be determined empirically from multiple regression analysis. This
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Table 1.
Lim et al.
where: Ll 5 spectral radiance at the sensor’s aperture [W/ (m2 mm str)]; and DN 5 the cell value digital number. The gain and bias values are unique for each spectral band acquired in header file.
Spectral bands of THEOS.
Band
Range (mm)
1 2 3 4
0.45–0.52 0.53–0.60 0.62–0.69 0.77–0.90
R(l) ~
Source: GISTDA–THEOS Characteristics (available online: http://www. gistda.or.th/gistda_n/en/index.php?option5com_content&view5article&id5 21&catid535&Itemid534).
equation is used to relate reflectance values from the image bands to the observed TSS concentrations.
DATA ANALYSIS AND RESULTS Two THEOS satellite imageries that covered Penang, Malaysia, were acquired for this study on 8 December 2009 (Figure 2a) and 29 January 2010 (Figure 2b). THEOS (THailand Earth Observation Satellite), the first Earth observation satellite of Thailand, was successfully launched by Dnepr launcher from Yasny, Russian Federation, on Wednesday, 1 October 2008 at 06:37:16 UTC (Universal Time Coordinated) or 13:37:16 h, Bangkok Time. Table 1 shows the sensor characteristics for THEOS scene. The spatial resolution of the image pixel is 15 m. Figure 2 show the raw THEOS satellite scene. The THEOS satellite data set was selected corresponding to the ground-truth measurements of the water-pollution levels. The PCI Geomatica version 10.3 image processing software was used in all the analyses. The preprocessing used in this study included geometric correction, radiometric correction, and atmospheric correction. Using PCI Geomatica 10.3 image processing software, both satellite images were resampled to 15 m pixel size. Then, they were georectified using a secondorder polynomial equation and the nearest neighbor method. The Polynomial Math Model is a simple math model that produces the best mathematical fit to a set of two-dimensional ground control points (GCPs) and then resamples by using the nearest neighbor method. The GCPs were determined according to the image-to-image method. Overall, both satellite images achieved RMS errors less than 0.5 pixels in this study (Kabbara et al., 2008). The digital number (DN) for each water-pollution measurement location was determined for each of the three visible bands and converted into reflectance values. The locations were determined with reference to the GCPs used in the imageto-map rectification method using the PCI Geomatica 10.3 image processing software. Digital numbers were determined for each band using different window sizes, such as 1 by 1, 3 by 3, 5 by 5, 7 by 7, 9 by 9, and 11 by 11. The DN values extracted using the window size 3 by 3 were used because the data produced a higher correlation coefficient. In the radiometric correction step, the DN values were converted into reflectance values, which involves two basic steps. These steps are: (1) Convert the raw DNs to radiance, and then (2) convert the obtained value of radiance to reflectance by using Equations 9 and 10. Ll ~ gain|DNzbias
ð9Þ
pd2 Ll eðlÞ cosh
ð10Þ
where R(l) 5 unitless planetary reflectance; Ll 5 radiance; d 5 Earth-Sun distance; e(l) 5 mean solar atmospheric irradiance; and h 5 solar zenith angle in degrees. A simple atmospheric correction, namely, darkest pixel technique, was performed in this study. This is a very simple correction, based on two assumptions: (1) It is assumed that in the darkest water pixel of the image, there is total light absorption, and the radiation light recorded by this pixel comes from the atmospheric path radiance. (2) It is assumed that the atmospheric path radiance is uniform all over the image. The radiation of the darkest water pixel (assumed to represent the atmosphere) is subtracted from the whole image. The darkest pixel is found by searching for the lowest values over water for all wavelengths. The pixel with the lowest value for each band was selected as the darkest pixel. The raw DN values were converted into radiance and reflectance values and later used as independent variables in the calibration regression analysis. All of the data points were collected, and the values of TSS were plotted against reflectance. As the concentration of TSSs increases, the response from each band also increases. In this study, a regression equation was calculated by using SPSS 18.0 statistical analysis. For precision of the regression model, two statistical parameters were used, correlation coefficient (R2) and root-mean-square error (RMSE).The multiple regression analysis would recognize the relationship between the dependent variable (in-situ data) and the independent variables (reflectance). In this study, among the THEOS bands 1, 2, 3, and 4, only bands 1 and 3 were used in the regression model, and the proposed algorithm (Equation 8) was found to be the best model, which had the highest correlation. For the proposed developed algorithm, the regression coefficients produced from the data set available in this study, the square of the correlation coefficient, R2, and RMS values, were 0.945 and 4.088mg/l, respectively, for 8 December 2009. For 29 January 2010, the square of the correlation coefficient, R2, and RMS values were 0.945 and 5.366 mg/l, respectively. TSS maps of the water-quality parameter were generated using the calibrated proposed algorithm. The generated maps were filtered using 3 by 3 pixels average to remove random noise, and then they were color-coded for visual interpretation. Figure 3 illustrates the TSS patterns distribution within the coverage of the study area. The observed TSS ranged from moderate to high concentrations. The map depicts higher TSS concentration in the vicinity of the river mouth. The TSS concentration in other parts of the region is less than 80 mg/l. This is caused by sedimentation near the river mouth as the river velocity drops. The water also becomes clearer farther
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Figure 3. TSS map around Penang Island, Malaysia (a) 8 December 2009 and (b) 29 January 2010. (Color for this figure is only available in the online version of the paper).
away from the coastal site as the water get deeper. The values for the regression coefficients in this calibrated algorithm were calculated by Equations 11 and 12 for 8 December 2009 and 29 January 2010, respectively. TSS ~ {1676:167(R1 )z10:256(R2 )z4542:319(R1 )2 z295:256(R2 )2 z0:211(R1 )(R2 )z73:852 TSS ~ 3077:668(R1 ){8433:642(R2 ){4181:335(R1 )2 z18156:153(R2 )2 z76:685(R1 )(R2 )z629:838
ð11Þ
ð12Þ
(Color code: blue 5 TSS , 50 mg/l, green 5 51–100 mg/l, yellow 5 101–150 mg/l, brown 5 151–200 mg/l, red . 200 mg/l, and black 5 cloud and area outside the image.) For the verification analysis, the in-truth data points were divided into two groups; half of the water samples were randomly selected for algorithm calibration and the other half for verification analysis. The verification analysis produced high accuracy, with R value of 0.912 and RMS value of 5.0581 mg/l for 8 December 2009. For 29 January 2010, the verification analysis produced high accuracy, with R value of 0.905 and RMS value of 7.0259 mg/l. This indicates that the algorithm is stable in calculating TSS concentrations within the observed range in these areas. Therefore, it is clear that the TSS map can be generated using satellite imagery with the proposed developed two-band algorithm.
CONCLUSION This study indicates that THEOS satellite imagery can be used in remote-sensing studies and can provide very useful information for estimating and mapping water pollution. With the present constraints of having only two scenes, the proposed model was found to provide a useful water-quality calibration model for the Penang Islands, Malaysia. The developed twoband algorithm was a reliable model for estimating TSS. The
proposed algorithm produced high correlation coefficients and low root-mean-square error values. Traditionally, the waterquality monitoring method has been based on water sample collection, which is time consuming and requires a high operating cost. The technique presented herein has been proven to be reliable and cost effective for such environmental study. Therefore, satellite imagery data corresponding to the in-situ data will be required in the future for this verification analysis.
ACKNOWLEDGMENTS The authors gratefully acknowledge financial support from the Simulation and Modeling of the Atmospheric Radiative Transfer of Aerosols in Penang, account number 203/PFZIK/ 671166, and a Universiti Sains Malaysia (USM) short-term grant. We would like to thank the technical staff who participated in this project. Thanks are also extended to USM for support and encouragement. This research was conducted under the agreement of the Geo-Informatics and Space Techology Development Agency (GISTDA) Research Announcement. We also would like to thank the GISTDA for providing the free satellite data used in this project.
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