assessing the potential of landsat-7 thermal band for ... el battay1, alaa el-sadek1 and mona...

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______________________________________________________________________________ Page 85 International Journal of Geosciences and Geomatics, Vol. 2, Issue 2, 2014,ISSN:2052-5591

Assessing the Potential of Landsat-7 Thermal Band for Monitoring

Essential Water Quality Parameters; Case Study: Lake Nasser, Egypt.

Ali El battay1, Alaa El-Sadek

1 and Mona Radwan

2

1Arabina Gulf University, College of Postgraduate Studies, P.O.Box: 26671, Kingdom of Bahrain,

2National Water Research Center, Nile Research Institute, Delta Barrage, Cairo, Egypt

KEYWORDS: Landsat 7, thermal band, water surface temperature, dissolved oxygen, Lake Nasser, regression model

ABSTRACT

Most applications using remote sensing tend to assess fresh water quality via regression models between in situ data and spectral bands. Suspended Sediment (Turbidity), Dissolved Oxygen and Temperature are common parameters derived from RS and recurrently used in WQI and/or TDML indicators. In this study a series of ETM+ Landsat images, thermal band, in combination with in situ measurement over 7 years, 2001 to 2007, were used over the northern part of Lake Nasser (Egypt) to develop a regression model linking thermal band to water surface temperature. Relationship between Water Surface Temperature and Dissolved Oxygen was then extracted statistically at surface and at 80% depth of water column. A second series of eighteen Landsat ETM+ thermal band images was tested then to produce temporal and spatial pattern changes in the above mentioned parameters over various months of 2001 to 2003 and proposed to be implemented as shown in four dates of 2012/2013. The results showed a good response of the developed semi-empirical model to describe qualitatively and quantitatively the spatio-temporal variation of water temperature and dissolved oxygen levels. Amid the fact that Landsat 7 images are SLC-OFF since late 2003, however, they are the only available in their category between 2003 and 2013 and their potential is very good to cover a large area such as Lake Nacer and to produce reliable and frequent measurements of two essential water quality parameters; temperature and DO. Overall, using RS water quality generated models offers a huge potential to monitor effectively a large water body such as lake Nasser and retrieve significant water quality/pollution related parameters.

INTRODUCTION

Water quality is a term used to describe the chemical, physical, and biological characteristics of water, usually in respect to its suitability for a particular purpose (Radwan, 2002; Radwan et al, 2005). Water quality monitoring plays an important role in water management where the environment and human health are vital dimensions (Willems, 2000; Willems et al, 2005). Monitoring involves collecting routine and ongoing information on water characteristics in order to ensure the laws and regulations are properly enforced and to detect trends necessary for modeling and predicting the future state of water quality (Radwan et al, 2001). Lake Nasser is the main fresh water reservoir for Egypt. Water quality monitoring on Lake Nasser describes the quality of water entering Egypt and of that released from Aswan High Dam (NRI, 2004a; NRI, 2004b; NRI, 2005). Monitoring the status of inland lakes is critical because they provide an important recreational, commercial and aesthetic resource to the public (El-Sadek, 2007; El-Sadek, 2010). The water quality monitoring includes the variables related to key management indicators, such as the trophic state such as total phosphorus (TP), chlorophyll-a (Chl), and Secchi disk (SD) transparency depth (Radwan, 2002). The objective of this study is to develop a new model of water quality to help in the future to predict systematically the Lake trophic status using satellite imagery and for monitoring and mapping water properties in Lake Nasser. The model used is using ETM+ thermal band 62. This band was compared with in situ temperature measurements. Correlation and regression models were developed directly between water surface temperature from RS imagery and field temperature and indirectly with Dissolved Oxygen near surface and at 80% depth of the water column. Maps of water surface temperature and

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NL03

NL02

NL01

NL00

DO were produced based on the derived regression models. LAKE NASSER AS A CASE STUDY

Lake Nasser is the main fresh water reservoir for Egypt. Water quality monitoring on Lake Nasser describes the quality of water entering Egypt and of that released from HAD. Four locations are included in the national program, which are monitored twice per year in February and August. Location NL00, 281 km upstream HAD and about 50 km below the border with Sudan, describes the quality of water entering Egypt. Location NL01, 256 km upstream HAD, describes the quality of water entering the Sheikh Zayed Canal through the Mubarak Pump station. Locations NL02 and NL03 are located 10 km and 2 km upstream HAD respectively (Table 1), and describe the quality of the water being released to the Nile River (Figure 1).

Figure 1: Monitoring Locations at Lake Nasser

Table 1: Monitoring Sites along Lake Nasser

MATERIAL AND METHOD

The study area is the northern part of Lake Nasser (Figure 2) just upstream of Sad-elAali (Aswan). Table 2 presents the three Landsat 7 ETM+ Images covering the site. LANDSAT 7 ETM+ Path: 174 Row: 044 Product type: L1T

Figure 2: Study area location

To achieve objective one of this study all Landsat images were converted to Surface Reflectance. All cloud-free Landsat 7 ETM+ scenes (path: 174/row: 44) were acquired from USGS (http://earthexplorer.usgs.gov) temporally match the ground data collection and the satellite overpass. Landsat 7 ETM+ images consist of six spectral bands 1 to 5 and 7 (B1: blue-0.45-0.52 μm; B2: green-0.52-0.60 μm; B3: red-0.63-0.69 μm; B4: near infrared-0.77-0.90 μm; B5: middle infrared-1.55-1.75 μm; and B7: middle infrared-2.09-2.35 μm) at a 30-m spatial resolution in addition to bands 6 (thermal infrared: 10.4-12.5 μm) and 8 (panchromatic: 0.52-0.90 μm) at 60-m and 15-m resolutions, respectively. Band 6_2 with a high gain was used essentially in this study to retrieve Water Surface Temperature while Band 5 was used to create a mask over the Lake area hence discarding land area from all processing. The models used to inverse the reflectance into water parameters are as following: The results of these models are then compared to the National water quality survey data (covering from 2000 to 2007) to monitor the spatio-temporal variability. ETM+ Band 6 imagery can also be converted from spectral radiance (as described above) to a more physically useful variable. This is the effective at-satellite temperatures of the viewed Earth-atmosphere system under an assumption of unity emissivity and using pre-launch calibration constants listed in Table 11.5. The conversion formula is:

Catchments Location

Code Location from

AHD (Km) Site Name

Lake Nasser

NL00 281.0 Abu-Simbel

NL01 247.0 Tushka

NL02 10.0 Aswan

NL03 2.0 Aswan

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Where:

T = Effective at-satellite temperature in Kelvin

K2 = Calibration constant 2 from Table 11.5

K1 = Calibration constant 1 from Table 11.5

L = Spectral radiance in watts/(meter squared * ster * µm)

Lλ = Grescale * QCAL + Brescale

Table 11.5 ETM+ and TM Thermal Band

Calibration Constants

Constant 1- K1

watts/(meter

squared * ster *

μm)

Constant 2 - K2

Kelvin

Landsat 7 666.09 1282.71

RESULTS AND DISCUSSION

Water surface temperature extracted from

Landsat 7 Thermal band B62 (first set of images in

table 2) and the one measured in the field show

very similar behavior through time ( Figure 4). The

variation in values are due to many factors

including (i) the location of sampling points is not

exactly the same (a possible shift of up to 60

meter), (ii) the date of images is not exactly the

one of field work (few days ahead or later) and (iii)

landsat images measure skin surface temperature

while in the field temperature is measured at 50

cm from surface.

Figure 4: Water Surface obtained from Landsat Images (dates)

vs collected during field work (February and August)

Despite all above mentioned factors, a strong

regression was observed with R2 of 0.87. Hence, to

obtain subsequently near surface temperature

from images the following linear model was

adopted:

Where:

NST is the near surface water temperature (ºC)

WST is the Water surface temperature (ºC)

obtained from Landsat 7 B62

Once this correction model was established to

encompass the difference between near surface

water temperature, collected in field, and the

water surface (skin) temperature extracted from

thermal bands, the second phase of this study was

to find significant relationship between near

surface temperature and dissolved oxygen in both

surface and in depth. Figure 5 depicts the various

obtained regressions. from there it is noticed (i)

significant regression between the temperature

and DO near surface; (R2 = 0.79), (ii) good

regression between near surface DO regression to

DO at 80% of depth; (R2 = 0.79) and (iii) and strong

relation between DO at 50% of depth vs 80% of

depth; (R2 = 0. 9).

Figure 4: Various regression models obtained from field data

For subsequent processing, the following models

were retained:

Where:

NSDO is the near surface Dissolved Oxygen (mg/l)

NST is the near surface water temperature (ºC)

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DO80% is the near surface Dissolved Oxygen at

80% of depth (mg/l)

Once the three explained models were obtained, a

Geomatica Model was built to automatically apply

them to thermal band of 17 Landsat 7 images

(from the second and third sets of images in table

2). The goal was to retrieve NST, NSDO and DO80%

for dates and locations where there was no field

measurements. Figures 5,6 and 7 show the

obtained results. The available images covered 8

months of the year, namely; January, February,

March, April, June, August, October and

November. The results show a good response of

the used model to describe both qualitatively and

quantitatively the spatio-temporal variation of

temperature and dissolved oxygen. In fact, figure 5

shows that available winter months January and

February being have a NST that ranges from 15 to

19 °C. Spring months; March and April show a

warming with NST ranging from 19 to 25°C.

Summer Months; June and August have the

highest temperature in the range of 29 to 35°C.

Finally, the Autumn months of October and

November show a cooling off phase with

temperature going from 27-29°C early October to

a range of 23-25°C by end of November.

The fluctuation of NST is translated to an inverse

proportional response in DO both near surface and

at 80% of depth, which is a classical case in

limnology literature. Figure 6 shows that from

January to mid March the NSDO ranges from 8 to

10 mg/l for most of the study area, while from

June to late October it goes to the range of 6-8

mg/l. The critical value of 4 mg/l necessary for

fauna is not observed in any part of the study area

both in space and time.

As for DO80%, Figure 7 shows that from January to

April the DO80% ranges from 6 to 8 mg/l for most

of the study area. The critical value of 4 mg/l is

observed in June, Agust and Early October, while

from Mid October to April it goes back to the

values above 4 mg/l.

CONCLUSION

The in situ data colledted over the study area

consisted of two points both in time and in space.

In fact, the National Program for Lake Nacer's

water quality monitoring, covers only point NL02

and NL03 in the study area. In addition,

measurments are done one day in February and

August each year. The developed model, within

the scope of this study, has allowed the extraction

of three essential water parameters;Near Surface

Temperature, Near Surface Dissolved Oxygene and

Disolved Oxygen at 80% of Depth, over the whole

study area, spatially a sample point for every 60 x

60 meter, and at least twice monthly over the

year. Using such technique which is (1) cost

reasonable as Landsat Images are freely available

and (2) quantitaively accurate using the historical

in situ date to callibrate the developed models.

With the venue of Landsat 8 OLI, such technique

should be pushed towards opperational

implementation.

ACKNOWLEDGMENT

The authors would like to acknowledge the contribution of the (i) Arabian Gulf University for promoting scientific research and providing research facilities, (ii) PCI Geomatica for providing processing capabilities via its Educational Alliance with AGU.

AUTHORS BIOGRAPHY

Ali ELBATTAY: Assistant Professor-Remote Dr. Ali Elbattay got his PhD (Environmental SAR specialization) from INRS-ete (Canada) in 2006. He joined Faculty of Geoinformation Science and Engineering at Universiti Teknologi Malaysia in 2007 as researcher then lecturer and subsequently senior lecturer. In 2011, Dr. Ali joined Arabian Gulf University as Assistant Professor (Remote Sensing and Geoinformatics) at College of Graduate Studies. His research interests are in Theoretical aspects of Proactive Remote Sensing, Remote Sensing applications for solar energy harnessing, and Development of Geoinformatics Applications . Alaa El-Sadek: Dr. Alaa El-Sadek holds the academic position of Professor of Water Resources Management at the National Water Research Center, Egypt. He obtained his PhD degree in 2001 in the field of surface water modeling from Catholic University of Leuven, Belgium. He held a postdoctoral position at Catholic University of Leuven, where he worked on applying his model to water management and validating it using the most recent data from three catchments in

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Belgium. He has published more than 70 papers in peer-reviewed journals and international conferences. His research interests include but are not limited to Water Resources Management; Virtual Water; Project Management and Environmental Impact Assessment. He serves as a consultant for many international and regional organizations. Mona Radwan: Dr. Mona is a civil engineer holds a PhD from K.U.Leuven, Belgium, 2002. She has been working at National Water Research Center, Nile Research Institute, Egypt, Arabian Gulf University, Bahrain and University of Bahrain. Since Nov. 2010, has been working as a Consultant at United Nation Environmental Program, Regional office of West Asia (UNEP/ROWA). Her Scope of the experiences is integrated water resources management, water quality modelling, vulnerability and climate change. She has 50 publications in international scientific journals international conferences. She got several awards and recognition: His Royal Highness PRINCE SULTAN BIN ABUDULAZIZ International Prize for Water, 2007; Encouragement State Prize in Engineering Science from Academy of Scientific Research and Technology, Cairo, 2005; integrated water resources management (Abdel-Azeem Abu El-Atta) award from the Ministry of water resources and irrigation, Egypt 2004. REFERENCES:

Brezonik, P.; Menken, K. D.; Bauer, M., (2005). Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM).Lake Reserv. Manage., 21 (4), pp 373–382 ).

Brivio, P. A.; Giardino, C.; Zilioli, E., (2001). Validation of satellite data for quality assurance in lake monitoring application. Science. Total Environment., 268 (1-3), pp3–18 .

Chen, C., P. Shi, and Q. Mao. 2003. Application of remote sensing techniques for monitoring the thermal pollution of cooling-water discharge from nuclear power plant. J.Environ. Sci. Heal. A, A38(8), 1659-1668.

El-Sadek, A., 2007. Upscaling field scale hydrology and water quality Modeling to catchment scale. Water Resources Management, 21: 149-169.

El-Sadek, A., 2010. Monte Carlo approach to developing a water quality process-factor. International Journal of Water Resources and

Environmental Management, 1: 97-104. Hereher, M. E.; Salem, M. I.; and Darwish, D. H.,

(2010). Mapping water quality of Burullus Lagoon using remote sensing and geographic information system. Journal of American Science, 2010; 7(1). PP138-143

Landsat Project Science Office. 2006. Landsat 7 Science Data User’s Handbook. Available from WWW: <http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_toc.html>[cited: 2013-02-26].

NRI (2004a) Water quality data report for NAWQAM project in February 2004. Nile Research Institute, National Water Research Center, Delta barrage, Egypt.

NRI (2004b) Water quality data report for NAWQAM project in August 2004. Nile Research Institute, National Water Research Center, Delta barrage, Egypt.

NRI (2005) Water quality data report for NAWQAM project in February 2005. Nile Research Institute, National Water Research Center, Delta barrage, Egypt.

Qin, Z, A. Karnieli, and P. Berliner. 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel–Egypt border region. Int. J. Remote Sens., 22(18), 3719-3746.

Radwan, M. (2002) River quality modelling as water resources management tool at catchment scale. Ph.D thesis, Catholic University of Leuven, Belgium.

Radwan, M., El-Sadek, A., Willems, P., Feyen, J., and Berlamont, J., 2001. Modelling of nitrogen in river water using a detailed and a simplified model. TheScientificWorld, 1 (S2): 200-206.

Radwan, M., Willems, P, El-Sadek, A, and Abdel-Gawad, S (2005) Physico-chemical water quality modelling of the Rosetta branch in the Nile Delta. Towards a better cooperation. International conference of Unesco Flanders fit Friend/Nile project.

Ritchie, J.C. and C.M. Cooper. 2001. Remote sensing techniques for determining water quality: Application to TMDLs. p.367-374. In: TMDL Science Issues Conference, Water

Willems, P (2000). Probabilistic immission modelling of receiving surface waters. Ph.D thesis, Catholic University of Leuven, Belgium.

Willems, P, Radwan, M, El-Sadek, A, and Abdel-Gawad, S (2005) Hydrodynamic modelling of the Rosetta branch in the Nile Delta. International Conference of UNESCO Flanders FIT FRIEND/Nile Project – “Towards a better cooperation”, Sharm El-Sheikh, Egypt, 20-23 November 2005.

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Table 2: Landsat 7 ETM+ Data used

Acquisition date Scene_ID Methodology Usage

2003-02-07 2001-08-12 2002-02-20 2003-08-18 2004-02-26 2004-08-04 2004-08-20 2005-02-12 2005-02-28 2005-08-07 2005-08-23 2006-02-15 2006-08-10 2006-08-26 2007-02-02 2007-02-18 2007-08-13

LE71740442003038SGS00 LE71740442001224SGS00 LE71740442002051SGS00 LE71740442003230ASN01 LE71740442004057ASN01 LE71740442004217ASN01 LE71740442004233ASN01 LE71740442005043ASN00 LE71740442005059ASN00 LE71740442005219ASN00 LE71740442005235ASN00 LE71740442006046ASN00 LE71740442006222ASN00 LE71740442006238ASN00 LE71740442007033ASN00 LE71740442007049ASN00 LE71740442007225ASN00

This first set of 17 images acquired during February and August of years from 2001 to 2007 was used with In Situ Data to develop the empirical model of Water Surface Temperature. The first three were acquired with the SLC-ON while all others the SLC was OFF.

2000-01-14 2000-05-21 2000-09-10 2000-10-28 2000-11-29 2001-02-17 2001-03-05 2001-05-08 2001-10-31 2002-03-08 2002-03-24 2002-04-09 2002-06-12 2002-09-16 2002-10-02 2002-10-18 2002-12-21 2003-01-22 2003-03-11

LE71740442000014SGS00 LE71740442000142SGS00 LE71740442000254SGS00 LE71740442000302SGS00 LE71740442000334EDC00 LE71740442001048SGS00 LE71740442001064SGS00 LE71740442001128SGS00 LE71740442001304SGS00 LE71740442002067SGS00 LE71740442002083SGS00 LE71740442002099SGS01 LE71740442002163SGS00 LE71740442002259SGS00 LE71740442002275SGS01 LE71740442002291SGS01 LE71740442002355SGS00 LE71740442003022SGS00 LE71740442003022SGS00

This second set of 19 images was used to apply the developed model in the phase above to different months of the years. Hence, images were selected covering all months except February and August. All these images are SLC-ON and thus allow a better visualization and water temperature and Dissolved Oxygen maps generation.

2012-08-10 2012-08-26 2013-02-02 2013-02-18 2013-03-22

LE71740442012223ASN00 LE71740442012239ASN00 LE71740442013033EDC00 LE71740442013049PFS00 LE71740442013081ASN00

This third set of 5 images, all acquired with the SLC-OFF, ,were used to demonstrate the potential of monitoring WST and DO over time. They might be also used to compare the level of these two parameters within a decade (2003-2013)

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Figure 3: Methodology Flowchart

Figure 5: Surface temperature (ºC) obtained from Landsat images

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Figure 6: Near Surface Dissolved Oxygen (mg/l) obtained from regression model and Landsat’s surface temperature

Figure 7: Dissolved Oxygen (mg/l) at 80% of depth obtained from regression model based on near surface DO

assessing the potential of landsat-7 thermal band for ... el battay1, alaa el-sadek1 and mona...

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