seasonal and spatial variability of sea surface temperature (sst)

8/7/2019 Seasonal and Spatial Variability of Sea Surface Temperature (SST)

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Seasonal and spatial variability of Sea Surface Temperature (SST)and Chlorophyll- a concentration using MODIS data

in East Kalimantan waters, Indonesia

IDHAM BIN KHALILMarch, 2007


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Course Title: Geo-Information Science and Earth Observation

for Environmental Modelling and Management

Level: Master of Science (MSc)

Course Duration: September 2005 - March 2007

Consortium partners: University of Southampton (UK)Lund University (Sweden)University of Warsaw (Poland)International Institute for Geo-Information Scienceand Earth Observation (ITC) (The Netherlands)

GEM thesis number : 2005-22


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Seasonal and spatial variability of Sea Surface Temperature (SST)and Chlorophyll- a concentration using MODIS data

in East Kalimantan waters, Indonesia

by

Idham bin Khalil

Thesis submitted to the International Institute for Geo-information Science andEarth Observation in partial fulfilment of the requirements for the degree of MScGeo-information Science and Earth Observation for Environmental Modelling andManagement, Specialisation: Environmental Modelling and Management.

Thesis Assessment Board

Chairman : Dr. Ir. Kees de Bie (ITC, The Netherlands)External Examiner : Prof. Peter Atkinson (University of Southampton, UK)First Supervisor : Dr. Ir. Chris Mannaerts (ITC, The Netherlands)Second Supervisor : Mr. Valentijn Venus (ITC, The Netherlands)

International Institute for Geo-Information Science andEarth Observation, Enschede, The Netherlands


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Disclaimer

This document describes work undertaken as part of a programme of study atthe International Institute for Geo-information Science and Earth Observation.All views and opinions expressed therein remain the sole responsibility of theauthor, and do not necessarily represent those of the institute.


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Abstract

Regular monitoring of near shore and open water parameters for marinemanagement in East Kalimantan waters, Indonesia is still limited. The objective of this research is to determine and interpret the seasonal and spatial variability of seasurface temperature (SST) and chlorophyll- a concentration (Chl- a ) in EastKalimantan waters. A standard MODIS SST split window algorithm and empiricalchlorophyll- a OC-3M algorithm were used to generate the Level 2 MODIS SST andChl- a images. From March 2005 to August 2006, the SST and Chl- a were retrievedfrom the sensor data in East Kalimantan coastal and open sea waters. In situ measurements from near shore waters were used to validate the MODIS Level 2data. A comparison of MODIS with in situ values for SST and Chl- a shows

promising result (RMSE=1.21 0C, Bias=-3.42, n=121 and RMSE=1.01mg.m -3,Bias=+2.45, n=75), although some anomalies were observed in the retrievals in bothdatasets. The analysis of seasonal variations indicates that there was low SSTvariability between wet and dry season. There was also low variability between SSTvalues in near shore and open sea waters. However, for both seasons, open sea SSTwas paradoxically found to be warmer than the near shore waters. The Chl- a maps

revealed low Chl- a variability between wet and dry season. Different value rangesin Chl- a were found between near shore waters (1.00-56.00 mg.m -3) and openwaters (1.00-4.00 mg.m -3). The Chl- a values retrieved from MODIS for bothseasons were higher in near shore water. The SST and Chl- a in near shore watershave a low positive interrelationship in wet season. During dry season, therelationship between these two variables varies from positive to negative. Thisstudy demonstrated that MODIS Level 2 data from Malaysia Ground ReceivingStation (MGRS) can successfully be used to obtain SST and Chl- a in SoutheastAsian coastal and open waters.


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Acknowledgements

I would like to express my deep and sincere gratitude to a number of people andorganisations who became involved with this thesis, one way or another.

GEM Erasmus Mundus consortium particularly Prof. Andrew Skidmore (ITC, The Netherlands), Prof. Petter Pilesjo (Lund University, Sweden), Prof. KatarzynaDabrowska (University of Warsaw, Poland) and Prof. Peter Atkinson (University of Southampton, United Kingdom) who have always been very supportive throughoutthese 18 months.

My first supervisor, Dr. Chris Mannaerts from Department of Water Resource, ITCfor his understanding, encouraging and personal guidance has been a great value for me. Mr. Valentijn Venus, my second supervisor from Department of NaturalResources, ITC who has supported me throughout my thesis with his valuablecomments and suggestions. I would have been lost without both of you.

Malaysian Government particularly Malaysian Centre for Remote Sensing

(MACRES) for providing the satellite imageries and giving me the opportunity tostudy in Europe. It was truly an eye-opening and rewarding experience of my life.

I also wish to thank GEM students 2005, the most reliable friends one could have! Not forgotten, Malaysians in Enschede; Edna, Zaki, Fidah, Fauzi and Prof.Kamaruzaman Jusoff whose presence helped make the completion of my study

possible.

Ayahanda Khalil bin Yusof, Bonda Siti Rohani bt. Sh. Abdullah serta keluarga diKuantan dan Pontian; your love, support, and patience have been a blessingthroughout my study.

I owe my loving thanks to my wife Nor Haslinda, my kids Muhammad Amnan,Muhrizah Humaira and Muhrizah Huda. Pengorbanan mereka tidak dapat digambarkan. Thesis ini didedikasikan istimewa buat mereka. Alhamdulillah.

Idham Khalil

Enschede


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Table of contents

1. Introduction .. ................................................................................................... 1 1.1. Research background .. ............................................................................ 1 1.2. Research problem.. .................................................................................. 2 1.3. Research objectives .. ............................................................................... 4 1.4. Research questions .. ................................................................................ 4 1.5. Thesis Outline.. ........................................................................................ 5

2. Literature Review.. .......................................................................................... 7 2.1. Sea Surface Temperature (SST) .. ............................................................ 7

2.1.1. SST from space.. ................................................................................. 8 2.1.2. In Situ SST Measurement ... .............................................................. 10

2.2. Chlorophyll- a concentration (Chl- a)... .................................................. 11 2.2.1. Chlorophyll- a from space ... .............................................................. 11 2.2.2. In situ Chl- a measurement... ............................................................. 13

3. Study Area... ................................................................................................... 15 3.1. Introduction ... ........................................................................................ 15 3.2. Climate ... ............................................................................................... 16 3.3. Oceanography... ..................................................................................... 16

3.3.1. Salinity... ........................................................................................... 17 3.3.2. Dissolved Oxygen (DO) ... ................................................................ 17 3.3.3. Nitrate ... ............................................................................................ 17 3.3.4. Phospate... ......................................................................................... 17

4. Materials and Method ... ................................................................................ 19 4.1. MODIS Datasets... ................................................................................. 19

4.1.1. Atmospheric correction ... ................................................................. 20 4.1.2. Cloud mask... .................................................................................... 20 4.1.3. Geometric correction ... ..................................................................... 20 4.1.4. Sea Surface Temperature... ............................................................... 21 4.1.5. Chlorophyll- a concentration... .......................................................... 21 4.1.6. Correlation analysis between SST and Chl- a ................................... 22

4.2. Accuracy Assesment ... .......................................................................... 22 4.3. In situ measurement... ............................................................................ 23 4.4. Research approach... .............................................................................. 24

5. Results... .......................................................................................................... 25 5.1. Sea surface temperature... ...................................................................... 25

5.2. Chlorophyll- a concentration... ............................................................... 29 5.3. Correlation between SST and Chl- a ...................................................... 33


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6. Discussion ... .................................................................................................... 35 6.1. Cloud cover in the study area ... ............................................................. 35

6.2. Sea Surface Temperature... .................................................................... 37 6.2.1. In situ SST versus MODIS Level 2 SST ... ....................................... 37 6.2.2. SST in the near shore waters... .......................................................... 38 6.2.3. SST in the open sea... ........................................................................ 39

6.3. Chlorophyll- a concentrations ... ............................................................. 40 6.3.1. In situ Chl- a vs MODIS Level 2 Chl- a ............................................. 40 6.3.2. Chl- a in the near shore waters ... ....................................................... 42 6.3.3. Chl- a in the open sea ... ..................................................................... 43 6.3.4. Striping lines in Chl- a images ... ....................................................... 43

6.4. Correlation between SST and Chl- a ...................................................... 44 7. Conclusions and Recommendations... .......................................................... 47

7.1. Conclussions... ....................................................................................... 47 7.2. Recommendations ... .............................................................................. 47 References... ......................................................................................................... 49 Appendices... ........................................................................................................ 57 Appendix-1 : In situ stations, SST and Chl- a concentrations ... ........................... 57 Appendix-1 : Cont ... ........................................................................................... 58

Appendix-1 : Cont ... ........................................................................................... 59 Appendix-1 : Cont ... ........................................................................................... 60 Appendix-2 : Bivarite Fit of IDWA SST by in situ SST... ................................... 61 Appendix-3 : Bivarite Fit of Kriging SST by in situ SST... ................................. 62


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List of figures

Figure 1-1: Berau Marine Protected Area (WWF, 2006)... ........................................ 3 Figure 2-1: NOAA AVHRR SST Global Map 6 8 May 2006 (NOAA, 2006) .. ..... 7 Figure 2-2: Plot of radiance from a blackbody against wavelength, with temperatureas a variable (Lillesand, T. M., and R. W. Kiefer, 2004). .. ........................................ 8 Figure 2-3: Absorption coefficient and penetration depths of infrared waves(Wieliczka et al. , 1989). .. ........................................................................................... 9 Figure 2-4: Idealized temperature profiles of the near-surface layer (10-m depth).. 10 Figure 3-1: Study area, East Kalimantan waters, Indonesia ..................................... 15 Figure 3-2: Map of Indonesian Throughflow (ITF) (Susanto et. al , 2006)..............16 Figure 4-1: SeaBass data points 2006 (SeaBass, 2006)............................................ 22 Figure 4-2: In situ sample stations............................................................................23 Figure 4-3: Simplified schema of the research approach .........................................24 Figure 5-1: Daily SST derived from MODIS; Wet Season...................................... 26 Figure 5-2: Daily SST derived from MODIS; Dry Season April August 2005 .... 27 Figure 5-3: Daily SST derived from MODIS; Dry Season August 05August 06 .. 28 Figure 5-4: Daily Chlorophyll- a concentration derived from MODIS; Wet Season30 Figure 5-5: Daily Chl- a derived from MODIS; Dry Season, April August 2005 . 31

Figure 5-6: Daily Chl- a derived from MODIS; Dry Season August 05August 06 32 Figure 6-1: Percentage of SST pixels compared to cloud and land pixels ............... 35 Figure 6-2: Percentage of Chl- a pixels compared to cloud and land pixels............36 Figure 6-3: Atmospheric Transmission Window (CRISP, 2006)............................. 36 Figure 6-4: In situ SST vs MODIS IDWA vs MODIS Kriging ...............................37 Figure 6-5: Bivarate Fit of IDWA and Kriging SST................................................37 Figure 6-6: Minimum and maximum SST in near shore area .................................. 38 Figure 6-7: Minimum and maximum SST in open waters ....................................... 39 Figure 6-8: In situ Chl- a vs. MODIS IDWA vs. MODIS Kriging...........................40 Figure 6-9: Bivarate Fit of IDWA and Kriging Chl- a ..............................................41 Figure 6-10: Minimum and maximum Chl- a in near shore waters .......................... 42 Figure 6-11: Minimum and maximum Chl- a in open waters ................................... 43 Figure 6-12: SST and Chl- a correlation in East Kalimantan near shore waters...... 44 Figure 6-13: MODIS cloud mask for SST and Chl- a Level 2.................................. 45


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List of tables

Table 2-1: MODIS specifications (NASA, 2006) ....................................................11 Table 2-2: Detailed MODIS band specification (NASA, 2006)...............................12 Table 4-1: MODIS Level 2 data structure ................................................................19 Table 4-2: WGS_1984_UTM_Zone_50N parameters .............................................20 Table 4-3: Coefficients for the MODIS Band 31 and 32 SST retrieval ...................21 Table 5-1: SST of East Kalimantan Water from March 2005 to August 2006.........25 Table 5-2: Chl- a of East Kalimantan Water from March 2005 to August 2006 ...... 29 Table 5-3: SST and Chl- a relationship ....................................................................33 Table 6-1: Cold pixels percentage in near shore waters ...........................................39 Table 6-2: Cold pixels percentage in open waters....................................................40


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List of Abbreviations

ASEAN The Association of Southeast Asian NationsATBD Algorithm Theoretical Basis DocumentsChl- a Chlorophyll- a concentrationCO 2 Carbon dioxideCH 4 MethaneGeoTIFF Geographic Tagged Image File FormatHDF Hierarchical Data FormatICMM Integrated coastal and marine managementIDWA Inverse distance weighted averageITF Indonesian ThroughflowIUCN International Union for the Conservation of Nature & Natural ResourcesK KelvinLWIR long-wave Infraredmg.m -3 milligram per cubic meter mg/l milligram per liter MODIS Moderate Resolution Imaging Spectroradiometer MPA Marine protected area

NO 2 Nitrogen dioxide ppt part per thousandRMSE Root Mean Square Error SeaBass SeaWiFS Bio Optical Archive and Storage SystemSeaWIFS Sea-viewing Wide Field-of-view Sensor SST Sea Surface TemperatureSWIR short-wave InfraredUNESCO United Nations Educational, Scientific and Cultural OrganizationUTM Universal Transverse Mercator SystemWGS84 World Geodetic System 1984WWF World Wildlife Federation0C degree Celciusm micrometer


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SEASONAL AND SPATIAL VARIABILITY OF SEA SURFACE TEMPERATURE (SST)AND CHLOROPHYLL- a CONCENTRATION USING MODIS DATA

IN EAST KALIMANTAN WATERS, INDONESIA

1

1. Introduction

1.1. Research background

The Association of Southeast Asian Nations (ASEAN) consist of 10 countries,Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, thePhilippines, Singapore, Thailand and Vietnam. For the past few decades, ASEAN

has been successfully been a leader in economic development among developingcountries. However in a development and environmental study, McDowell (1989)described that apart from the most promising economic growth in this region, it issuffering with the depletion of natural resources. Deforestation, erosion and over fishing mainly because of the rapid population growth have been exhibited as anindicator to an unbalanced development in ASEAN. As there is a close relationship

between people and the environment, economic development and environmentalissues should not be approached separately in this region. McDowell (1989) further described that in order for ASEAN to maintain its position as a new growing

economic power, management of natural resource in this region is very crucial.

In reaction to the above suggestion and parallel to The 1992 UN Rio deJaneiro the Earth Summit, the ASEAN Summit 1997 formed a sustainabledevelopment framework for ASEAN countries. Known as ASEAN Vision 2020, themain aim was described as : a clean and green ASEAN with fully establishedmechanisms for sustainable development to ensure the protection of the regionsenvironment, the sustainability of its natural resources and the high quality of life of its peoples (UP-MSI et al ., 2002).

In terms of the marine environment, ASEAN contributed to about 14% of the worlds marine fish production and has 30% of the worlds coral reefs: the mostspecies diversity in the world (UP-MSI et al ., 2002). In order to achieve sustainablefisheries, The ASEAN Vision 2002 has translated into 2001 Plan of Action onSustainable Fisheries for Food Security for the ASEAN Region. One major step inthis plan is to establish marine protected areas (MPAs). The International Union for the Conservation of Nature and Natural Resources (IUCN) defines marine protected

areas (MPA) as an area of sea especially dedicated to the protection andmaintenance of biological diversity, and of natural and associated cultural resources,


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and managed through legal or other effective means (IUCN, 1994). In lay manterms, marine protected areas could be described as an alternative way to protect

marine fauna and flora.

As more marine protected areas have been established in the ASEAN region, thechallenge to continuously provide decision makers with sufficient and reliable datais consider as a major constraint in MPAs management. With limited budget, a costestimated at USD 1,584.00 per km 2 per year for collecting data and maintainingMPAs (Balmford et al. 2004) is consider very high for developing countries. Therapid development in remote sensing technology and its capability to provide two-dimensional synoptic view offers an alternative to help in these issues. While thereare always arguments on the surface monitoring of ocean, there are positivetendencies in ecosystembased precautionary management to rely on surrogatesdata (Vanderklift and Ward, 2000) e.g geophysical features (Ray, 1997) because of the complexity of ecosystems and lack of data. The reliability of the ocean surfaceskin information has also been proven by Macintyre (1977) who stated that thefirst millimetres below the surface represents the top half of the ocean.

In this study, the capability of geophysical product from Moderate Resolution

Imaging Spectroradiometer (MODIS) which is Level 2 sea surface temperature(SST) and chlorophyll- a concentration (Chl- a) were assessed in providing ocean

parameters for Indonesian waters from May 2005 August 2006 for a better marine protected areas management.

1.2. Research problem

This study focuses on marine resources depletion in Indonesian marineenvironment. The study area, East Kalimantan waters which consist of BerauMarine Protected Area as shown in Figure 1-1 is well known as one of the most

beautiful area for coral reef in the world. With 507 species of hard coral, Beraucoral biodiversity is considered as the second highest in Indonesia and the third inthe world. Among 120 potential outstanding universal values, Derawan islandswhich are located in Berau waters have been classified as one of the top seven(UNESCO, 2002).

Several studies had shown that the total fish catch from Berau waters have been

declining for the past ten years (Ismuranty, 2003) due to the depletion of the coralreefs. Less coral reef areas means less fish stock as coral reefs area are breeding


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Chapter 1 - Introduction

3

grounds for fish. While the caused of coral depletion is well known coming frominland activities e.g deforestration and sea temperature changes e.g El-Nino 1997-

1998, continuous monitoring and studies for better marine management is verylimited (Burke et al. 2002) .

Figure 1-1: Berau Marine Protected Area (WWF, 2006).

In order to maintain the sustainable marine resources in the area, the Indonesiangovernment under Berau Regent Regulation No. 31, 2005 has declared BerauDistricts coastal and marine area as a marine conservation area (WWF, 2006).Integrated coastal and marine management (ICMM) of this area requires mapping of marine resources and monitor basic changes (e.g SST, chlorophyll- a concentration

etc.) as revealed by Dahuri (2000); Erdmann and Mossa (1990).

Considering the huge area of 1.2 million hectares of the Berau Marine ProtectedAreas (MPA), this study used satellite remote sensing imageries as to provide suchoceanographic datasets. Previous research has proved that continuous monitoring of SST from space can act as early warning to detect any SST anomalies that couldharm the coral reef such as coral bleaching events (Liu et al. , 2003). In day to dayoperations, spatial information on SST and Chl- a distribution has been used widely,e.g. in coral reef management (Udy et al. , 2005), fishery forecast (Solanki et al. ,2003; Solanki et al. , 2005) or as a fundamental data source for coastal management

planning (Mumby et al. , 1999).


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1.3. Research objectives

The main objective of this research is to determine and interpret the seasonal andspatial variability of SST and Chl- a in the study area.

The specific objectives of this study are:i. To map the spatial distribution of SST in East Kalimantan waters

using MODIS data during wet (November March) and dry season(April October).ii. To map the spatial distribution of Chl- a in East Kalimantan waters

using MODIS data during wet and dry season.iii. To determine the relationship between SST and Chl- a during wet

and dry season in open and near shore waters.

1.4. Research questions

The research will try to answer the following questions:

i. Are there any major differences in SST distribution between wet anddry season?

ii. Are there any major differences in SST distribution in open sea andnear shore?

iii. Are there any major differences in Chl- a distribution between wet

and dry season?iv. Are there any major differences in Chl- a distribution in open sea and

near shore?v. What kind of relationship exists between SST and Chl- a distribution

in Kalimantan during wet and dry season in open waters, andvi. What kind of relationship exists between SST and Chl- a distribution

in Kalimantan during wet and dry season in near shore waters?


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Chapter 1 - Introduction

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1.5. Thesis Outline

This thesis contains seven chapters.

The first chapter contains the general overview of the research. Research problemswere clearly defined in section two. Research objectives and research questions willthen explained in section three and four.

Chapter two describes the overall description of the study area.

Chapter three reports the previous studies in the research field. Major concepts anddefinition has been reported in 2 sections. The first section covers the description of detection of sea surface temperature followed by the literature of chlorophyll- a insecond section.

Chapter 4 presents a comprehensive methodology used in this study including therelevant algorithms and coefficients.

Chapter 5 presents results for the SST and Chl- a distribution in East Kalimantanwaters during dry and wet season. The result also included the variation between thenear shore water and open sea.

Chapter 6 presents the discussion for the SST and Chl-a distribution in EastKalimantan waters during dry and wet season. The variation of minimum andmaximum values between near shore and open sea also will be discussed.Furthermore, the correlation between SST and Chl- a in this region was alsoexplained.

Finally, Chapter 7 presents the conclusions derived from the analysis andsuggestions for future works.


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2. Literature Review

2.1. Sea Surface Temperature (SST)

Historically, measurements of SST have been made in situ , from ships and buoys.MetOffice (2006) described that such in situ observations has two disadvantages.Firstly, in situ measurement offer very limited spatial coverage. Secondly, itenlarges the margin of error between one measurement to the next. These two issuescould be resolved by satellite observation throughout the globe. McClain et al.(1985; 1989) stated that the retrieval of SST from satellite measurements offers theadvantage of global coverage (Figure 2-1) and has been performed routinely since1981 to a point accuracy of ~ 0.5 K.

Figure 2-1: NOAA AVHRR SST Global Map 6 8 May 2006 (NOAA, 2006)

Marine researchers found that the measurement of SST is essential to understandhow our ocean behaves; understand its relationship with ocean flora and fauna andnot least to understand the global climate. Operationally, SST information has beenused as a boundary layer input and an assimilation data set for atmosphericcirculation/forecast models (Emery et al. , 2001). Alexander and Scott (2002) statedthat measurements of SST are extremely useful for applications such as monitoring


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large-scale anomalies e.g El Nino and trends possibly associated with global climatechange (e.g. Lawrence et al. , 2004).

2.1.1. SST from space

According to the Planck Law, each warm object on Earth emitted certain amount of thermal radiation at a particular wavelength depending on its temperature (Rybickiet al., 1979). For a much clearer picture, the relationship between Earths surface, its

brightness temperature and the spectral radiance can be understood by referring toFigure 2-2. From the graph, it was clearly shown that for Earth, the spectral radiance

peaks at a wavelength around 10 m.

Figure 2-2: Plot of radiance from a blackbody against wavelength, with temperatureas a variable (Lillesand, T. M., and R. W. Kiefer, 2004).

This is the basic concept why most of the infrared satellite sensors used band 10 mto detect infrared radiation for SST detection. As there will be never 100% or clear sky condition (without atmospheric disturbance), one channel measurement willnever be a success. Water vapor, CO

2, CH

4, NO

2and aerosols are the major

constituents that determine the atmospheric disturbance (Minnett, 1990). McMillin


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Chapter 2 Literature Review

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(1975) and Barton (1995) described that most satellite SST algorithms are derivedfrom the measurement using two channels. Two commonly use wavelengths range

are the long-wave Infrared (LWIR; 10.00-12.5 m) and the short-wave IR (SWIR;3.7-4.2 m) (Brown et al. , 2005). The absorption for each wavelength is different.These differences were then used to correct the atmospheric disturbance before thesea surface temperature can be obtained.

Apart from thermal infrared remote sensing, SST can also be detected usingmicrowave sensors. However due to lower signal strength of the Earth's Planck radiation curve in the microwave region (approximately in the range of 30 cm to 1mm), accuracy and resolution is poorer for SST derived from passive microwavemeasurements.

Bear in mind that, although it is possible to obtain SST reading from thermalinfrared sensors, the maximum sea depth that infrared wavelength can penetrate isaround 500 m (Wieliczka et al. , 1989). In other words, if our interest is in gettingthe sea surface temperature more than that depth, we might end up with such a biguncertainty. In his research, Wieliczka et al ., (1989) has plot a detailed relationship

between infrared optical constants and clear water as shown in Figure 2-3.

Figure 2-3: Absorption coefficient and penetration depths of infrared waves(Wieliczka et al. , 1989).


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2.1.2. In Situ SST Measurement

While the understanding of SST measurement will become a dominant knowledge,we could not admit the importance of the in situ SST measurement. There areseveral important terms that have been used to describe in situ SST. Donlon et al.(2002) categorized the vertical structure of SST into four major categories/definitions as shown in Figure 2-4. The first category is interface SST, SSTint.Interface SST is the temperature at the very top of the ocean. To be more specific, itis the temperature at the air-sea thin layers. The second category is the skin SST,SSTskin. By definition, the SSTskin is the temperature that can be measure up to

500 m. Just below SSTskin, we have the layer of ocean where heat transfer processis actively happened. SST from this layer is called subskin SST, SSTsubskin. Thefinal in situ SST categories is the subsurface SST, SSTdepth. In layman term,SSTdepth is also called bulk SST.

Figure 2-4: Idealized temperature profiles of the near-surface layer (10-m depth)of the ocean (Donlon e. al., 2002).


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2.2. Chlorophyll- a concentration (Chl- a )

Similar to SST, the capability of satellite remote sensing in providing globalcoverage snapshot has been an advantage to the measurement of Chl- a in the marineenvironment. In marine remote sensing, Chl- a has been used a proxy to theexistence of phytoplankton. Butler et al. (1972) reported that the Chl- a above 0.2mg/l indicate the presence of sufficient fish food to sustain a viable commercialfishery. In general term, chlorophyll- a can be described as a vital pigment for

photosynthesis in phytoplankton. Phytoplankton are marine microscopic plantswhich well known as the base of marine food chain. A detailed definition has beendescribed by Water Resource Disciplines USGS (2005) as Phytoplankton is the

plant part of the plankton. They usually are microscopic, and their movement issubject to the water currents. Phytoplankton growth is dependent upon solar radiation and nutrient substances. Because they are able to incorporate as well asrelease materials to the surrounding water, the phytoplankton have a profound effectupon the quality of the water. They are the primary food producers in the aquaticenvironment and commonly are known as algae.

2.2.1. Chlorophyll- a from space

The earliest sensor that has been used for mapping and measuring chlorophyll- a concentration was Coastal Zone Color Scanner (CZCS) (Mitchell, 1994), Feldmanet al . (1989). Realising the importance of synoptic global observation of marine

parameters, several ocean color sensors than were launched,; Sea-viewing WideField-of-view Sensor (SeaWIFS) July 1997; Moderate Resolution ImagingSpectroradiometer AM (MODIS AM) also known as MODIS Terra in December 1999 and MODIS PM or MODIS Aqua - May 2002 (NASA, 2006) . The detailspecifications of the MODIS sensors are shown in Tables 2-1 and 2-2.

Table 2-1: MODIS specifications (NASA, 2006)

Parameters Details

1. Orbit 705 km, 10:30 a.m. descending node (Terra)or 1:30 p.m ascending node (Aqua), sun-synchronous, near-polar, circular

2. Swath Dimensions 2330 km (cross track) by 10 km(along track at nadir)

3. Spatial Resolution 250 m (bands 1-2)

500 m (bands 3-7)1000 m (bands 8-36)


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Table 2-2: Detailed MODIS band specification (NASA, 2006)

Primary Use Band Bandwidth Primary Use Band Bandwidth 1 620 - 670 20 3.660 -3.840

Land/Cloud/ AerosolsBoundaries

2 841 - 876 21 3.929 -3.989

3 459 - 479 22 3.929 -3.989

4 545 - 565

Surface/ CloudTemperature

23 4.020 -4.080

5 1230 -

125024 4.433 -

4.4986 1628 -1652

Atmospheric

Temperature 25 4.482 -4.549

Land/Cloud/ AerosolsProperties

7 2105 -2155 261.360 -1.390

8 405 - 420 27 6.535 -6.895

9 438 - 448

CirrusCloudsWater Vapor

28 7.175 -7.475

10 483 - 493 Cloud

Properties 29 8.400 -

8.70011 526 - 536 Ozone 30 9.580 -9.880

12 546 - 556 31 10.780 -11.280

13 662 - 672

Surface/ CloudTemperature 32 11.770 -12.270

14 673 - 683 33 13.185 -13.485

15 743 - 753 34 13.485 -

13.785

OceanColor/ Phytoplankton/ Biogeochemi

stry

16 862 - 877 35 13.785 -14.085

17 890 - 920 36 14.085 -14.38518 931 - 941

AtmosphericWaterVapor

19 915 - 965

Cloud TopAltitude

Notes : Bands 1 to 19 are in nm; Bands 20 to 36 are in m


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The main reason Chl- a being used as an indicator for phytoplankton existence is because it is the most common pigment in most of marine phytoplankton (Li et al. ,

2002). The spectral characteristic of this pigment which located between bluegreenwavelengths of light spectrum has made it detectable from optical remote sensing.Other pigments that can be also found in plants are Carotene - an orange pigment,Xanthophyll- a yellow pigment, Chlorophyll b - a yellow-green pigment andPhaeophytin - a gray pigment. In other literature, chlorophyll pigment in

phytoplankton can also be categorized as chlorophyll a , b, c and carotenoids(Doerffer et al. , 1999).

To date, most Chl- a detection methods from satellite sensors used band-ratioalgorithms (blue-green band reflectance ratio). In general, phytoplankton absorbs

blue light more strongly than green light. Thus, the estimation of the relativeamounts of phytoplankton can be achieved by measuring the amounts of lightleaving the ocean waters at those two bands (blue and green).

2.2.2. In situ Chl- a measurement

The normal practice for in situ Chl- a measurement is by direct water sampling

(NRMA, 2006). Filter papers with the size of 0.45 normally were used during thefiltering process. The concentration of chlorophyll- a measured in g/l thendetermined using spectrophotometer. With the current technology, it is now possibleto directly measure the chlorophyll from water column using fluorescence.


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15

3. Study Area

3.1. Introduction

The study area located within the rectangular region 0 0 45 2.93N to 4 0 3943.86N and 117 0 11 22.38E to 120 0 34 24.21E encompass an area of about228,254 km 2 (Figure 3-1).

Figure 6 : Study area : East Kalimantan waters

Figure 3-1: Study area, East Kalimantan waters, Indonesia


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16

In order achieve the objective of understanding the variability of SST andchlorophyll- a between near shore and open waters, Kalimantan waters has been

subdivided into Open sea area: 17,771 km2

and near shore area: 13,046.6 km2

whichis actually the Berau Marine Protected Area. The open sea was defined as the areamore than 24 nautical miles (44 km) from the shoreline and near shore area is thearea within the 24 nautical miles.

3.2. Climate

Like other parts of Indonesian waters, Kalimantan waters is affected by the Asia-Australia (AA) monsoon; southeast monsoon (April-October) and northwestmonsoon (November- Mac), (Susanto et. al , 2006) . The southeast monsoon bringsthe warm and dry air from Australia into Kalimantan waters whereas the northwestmonsoon is associated with warm and moist air. Nurlidasari (2004) stated that thereare not many differences in SST values between coral reef area and the area close tothe Berau river. SST in the coral reef area is reported to have values ranges from29.5 0C to 30 0C while 29.5 0C to 30.5 0C for the second one.

The highest wind speeds in this area were recorded during July and August whilethe lowest wind speed is in October and November.

3.3. Oceanography

Kalimantan waters play an important role in the transfer of heat and fresh water inthe region because it is located in the Indonesian Throughflow (ITF) as shown inFigure 3-2.

Figure 3-2: Map of Indonesian Throughflow (ITF) (Susanto et. al , 2006)


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Chapter 3 Study Area

17

Notes :

i. The solid black arrows - North Pacific thermocline water;ii. The black dashed arrows - South Pacific lower thermocline water.iii. The dashed arrows (green and blue) - seasonally reversed surface water

flow due to the Asia-Australia monsoon.

Wiryaman et al. (2004) reported a detailed summary of physical oceanographic inEast Kalimantan waters as follows:

3.3.1. SalinityThe average surface salinity in East Kalimantan for open sea is 33.5 ppt. However,as approaching the river mouth, the salinity decreases (32.5 33.0 ppt). For salinityat 100 meter depth, the value range from 34.0-34.5 ppt for open sea and 33.5 ppt for river mouth area.

3.3.2. Dissolved Oxygen (DO)

Dissolved oxygen (DO) could be explained as the oxygen gas that is dissolved inwater. Most of DO exist in marine environment produced by phytoplankton during

photosynthesis process. In East Kalimantan waters, the average value for coastaland open waters is 2.5 to 4.5 mg/l.

3.3.3. Nitrate

The open sea and coastal water surface nitrate not vary much (0.4 to 1.8 mg/l). The100 meter depth nitrate however show slightly different pattern. For near shore the

value is 0 to 1.2 mg/l while > 1.2 mg/l for open sea.

3.3.4. Phospate

The average 100 meter depth phosphate in East Kalimantan for open sea is 1.2 to2.4 mg/l and 0 to 1.2 mg/l for river mouth area.


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4. Materials and Method

4.1. MODIS Datasets

The primary remotely sensed data used in this study were obtained from MalaysiaGround Receiving Station (MGRS) which has been acquiring MODIS data since 2 nd February 2005. TeraScan software was used to process MODIS raw data into Level2 MODIS with a spatial resolution of 1 km. The initial MODIS Hierarchical Data

Format (HDF) was exported into Geographic Tagged Image File Format (GeoTIFF)for further analysis in ArcGis. For the purpose of this study, only Channel 1 of SSTMODIS Level 2 was used while Channels 5 and Channel 10 for Chl- a analysis.Channel 10 was used for the cloud masking. The detail specification of the 32 byteGeoTIFF files are shown in Table 4-1 below:

Table 4-1: MODIS Level 2 data structure

SST MODIS Level 2 GeoTIFFChannels ParametersChannel 1 SSTChannel 2 SST4

Chl- a MODIS Level 2 GeoTIFFChannels ParametersChannel 1 CZCS_pigmentChannel 2 : K_490Channel 3: L2_flagsChannel 4 : calcite_concChannel 5 chlor_MODIS mgm -3 Channel 6 chlor_MODIS_log mg m-3

Channel 7 chlor_fluor_baseChannel 8 chlor_fluor_efficChannel 9 chlor_fluor_htChannel 10 cldmsk_flagsChannel 11 cocco_conc_detachChannel 12 cocco_pigmnt_concChannel 13 common_flagsChannel 14 phycoeryth_concChannel 15 phycou_conc

Channel 16 pigment_c1_total

Channel 17 qualityChannel 18 susp_solids_conc


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4.1.1. Atmospheric correction

The atmospheric corrections applied to MODIS raw data can be categorised intotwo components. The first component uses the atmospheric correction algorithmdescribed by Gordon and Voss (2004) in Algorithm Theoretical Basis Documents(ATBD) 18 version 5. The output of the algorithm is the normalized water-leavingreflectance which then be used for determinations of Chl- a as shown below:

[ ( )] N= ( )/t *(0, )where;

= water leaving reflectance

t * = diffuse transmittance0 = solar zenith angle = MODIS band wavelength

The second component of atmospheric correction which is for SST data has beentaken into account in the SST split window algorithm (Brown and Minnet, 1999).See SST algorithm in section 4.1.4 for further details. All atmospheric correction

procedures were readily built into the TeraScan software processing module.

4.1.2. Cloud mask

A cloud mask with a spatial resolution of 1 km used in this study was generated byMODIS algorithm. A total of 17 to 19 spectral bands ranging from 0.55-13.93 mwere considered in the process of developing the mask as described by Baum andPlatnick (2006) and Kathleen (2006).

4.1.3. Geometric correction

Shoreline vector and MODIS dataset were geo-referenced to the same WorldGeodetic System 1984 (WGS84) Universal Transverse Mercator (UTM) Projection

System in ArcGIS software. The detail parameters are listed in Table 4-2 below.

Table 4-2: WGS_1984_UTM_Zone_50N parameters

Projection : Transverse_Mercator(WGS84_UTM_Zone_50N)

False_Easting: 500000.000000False_Northing: 10000000.000000Central_Meridian: 117.000000Scale_Factor: 0.999600Latitude_Of_Origin: 0.000000


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Chapter 4 Materials and Method

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4.1.4. Sea Surface Temperature

A total of 21 scenes of SST were processed using split window technique based onATBD-MOD25 (Brown and Minnet, 1999). The equation is given as follows:

SST = C 1 + C 2 * T 31 + C 3*T 3132 +C 4(sec( )-1)* T 3132

where;T31 is the band 31 brightness temperature (BT)T3132 is (Band32 - Band31) BT difference is the satellite zenith angle

C i coefficients listed in Table 4-3

Table 4-3: Coefficients for the MODIS Band 31 and 32 SST retrievalalgorithm ( Brown and Minnet, 1999)

By using the above coefficients, Brown and Minnet found that the SST result has a predicted RMS error of 0.337K with a bias near 0.

4.1.5. Chlorophyll- a concentration

Chlorophyll- a data were processed based on ATBD MOD19, Carder et al. (2003) using empirical chlorophyll- a OC3M algorithm as follows:

Ca = 100.283 2.753R + 1.457 R 2 + 0.659R 4 ;

where R = log10

Ca : Chlorophyll- a concentration (milligram per cubic metre, mg.m-3)

R rs : remote sensing reflectanceR : blue-green band ration [dimensionless]

Coefficients

T30 T 31 0.7C1 1.228552 1.692521

C2 0.9576555 0.9558419C3 0.1182196 0.0873754C4 1.774631 1.199584

>

rs551

rs488rs443

R R R


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4.1.6. Correlation analysis between SST and Chl- a

The SST and chlorophyll- a images which has the same spatial resolution (1 km) and projection system were brought into ArcGis. Due to ArcGis limitation in performingraster to raster correlation analysis, the raster layers were converted into pointfeatures. Then, an extraction of x-y coordinates were performed to the point featuresin order to get the x-y coordinates. This step was repeated for every SST andchlorophyll- a layers. Having the SST values, chlorophyll- a values together withtheir x-y coordinates for every pixel, a correlation analysis were carried out for each

pixel.

4.2. Accuracy Assesment

This study does not call for the creation of a new local empirical MODIS algorithm.Research approach was conducted using the existing standard MODIS algorithmand compared with limited in situ data . The current accuracy of standard SSTMODIS algorithm based on ATBD 25 is 0.337K (Brown and Minnet, 1999). Inorder to maintain the accuracy of their Chl- a algorithm, MODIS has a continuousvalidation programme using the in situ data from SeaWiFS Bio Optical Archive andStorage System (SeaBass) as shown in Figure 4-1. The MODIS Terra chlorophyll- a

product gives root mean square error (RMSE) of 0.554 mg.m -3.

Figure 4-1: SeaBass data points 2006 (SeaBass, 2006)


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Chapter 4 Materials and Method

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Near shore waters

Berau District

4.3. In situ measurement

In situ measurements were conducted from 11 to 29 th August 2006. Measurementsof SST were made using the Horiba U-10 water quality checker. The accuracy of the instrument is 0.3 0C as stated by Cole-Parmer (2006). SST was measured

between 0-20 cm from the ocean surface (bulk temperature). Chlorophyll- a concentrations measurement were made using Aqua fluor Handheld Fluorometer andTurbidimeter with a detection limit of 0.30 mg.m -3 (Turnerdesigns, 2006).

The station locations for the sea truth are shown in Figure 4-2. Detailed location of in situ stations appear in Appendix-1

Figure 4-2: In situ sample stations


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4.4. Research approach

A simplified flow chart of research approach is indicated in the following diagram.

Figure 4-3: Simplified schema of the research approach

IMAGE PROCESSING(MODIS)

DATA PREPARATION

SATELLITEIMAGES

LITERATUREREVIEW

Atmospheric Correction

Geometric Correction

Cloud covers % statistic determination

SST dryseason

SST wetseason

Chl-a dryseason

Chl-a wetseason

Correlation between SST& Chl-a in near shore

Correlation betweenSST & Chl-a in open sea

ACCURACY ASSESSMENT

REPORT WRITING

Land and cloud masking


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5. Results

5.1. Sea surface temperature

A total of 21 scenes of SST were processed and the detailed results are shown inTable 5-1. The SST maps are shown in Figure 5-1, 5-2 and 5-3.

Table 5-1: SST of East Kalimantan Water from March 2005 to August 2006

Date Near Shore SST ( 0C) Open Sea SST ( 0C)

Min Max Min Max

3rd March 2005 18.02 27.66 22.99 27.664th March 2005 16.49 27.48 24.43 27.665th March 2005 19.64 27.66 21.93 27.6630 th April 2005a 8.23 27.66 22.85 27.6530 th April 2005b 9.57 27.66 14.17 27.677th June 2005 10.34 27.66 12.90 27.5127 th July 2005 23.02 27.77 27.55 27.5530 th July 2005 14.55 27.66 24.34 27.666th August 2005 20.81 27.66 26.06 27.6611 th August 2005 10.67 27.54 - -12 th August 2005 21.62 27.66 26.29 27.6620 th August 2005 19.34 27.66 - -29 th August 2005 14.11 27.66 - -4th September 2005 11.31 27.66 - -9th October 2005 20.84 27.63 18.42 27.645th May 2006 10.41 27.62 20.29 27.659th May 2006 20.16 27.64 21.96 27.6618 th May 2006 22.42 27.66 - -10 th July 2006 8.31 27.65 23.77 27.6121 th July 2006 18.61 27.67 25.13 27.657th August 2006 19.67 27.67 - -


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Figure 5-1: Daily SST derived from MODIS; Wet Season

3rd March 2005 4 th March 2005

5th March 2005

0

28

SST 0C25

Cloud and land mask


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Chapter 5 - Results

27

Figure 5-2: Daily SST derived from MODIS; Dry Season April August 2005

30 th Apr 05a 30 th Apr 05b 7th June 05

27 th July 05 30 th July 05 6 th Aug 05

11 th Aug 05 12 th Aug 05 20 th Aug 05

0

28

SST 0C25

Cloud and land mask


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Figure 5-3: Daily SST derived from MODIS; Dry Season August 05August 06

9th Oct 054th Sept 0529 th Aug 05

5th Ma 06 9 th Ma 06 18 th May 06

10 th July 06 21 st July 06 7 th Aug 06

0

28

SST 0C25

Cloud and land mask


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Chapter 5 - Results

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5.2. Chlorophyll- a concentration

A total of 21 scenes of chlorophyll- a were processed and the detailed result appearsin Table 5-2 below. The Chl- a maps are shown in Figure 5-4, 5-6 and 5-7.

Table 5-2: Chl- a of East Kalimantan Water from March 2005 to August 2006

Date Near Shore Chl- a

mg.m -3

Open Sea Chl- a

mg.m -3

Min Max Min Max

3rd March 2005 1.00 56.00 1.00 2.00

4th March 2005 1.00 4.00 1.00 1.00

5th March 2005 1.00 20.00 1.00 2.00

30 th April 2005a 1.00 56.00 1.00 2.00

30 th April 2005b 1.00 52.00 1.00 1.00

7th June 2005 1.00 52.00 1.00 2.00

27 th July 2005 1.00 35.00 1.00 3.00

30th

July 2005 1.00 38.00 1.00 4.006th August 2005 1.00 57.00 1.00 2.00

11 th August 2005 1.00 16.00 - -

12 th August 2005 1.00 22.00 1.00 2.00

20 th August 2005 1.00 55.00 1.00 2.00

29 th August 2005 1.00 54.00 1.00 1.00

4th September 2005 1.00 29.00 1.00 1.00

9th October 2005 1.00 54.00 1.00 2.00

5th May 2006 1.00 41.00 1.00 3.00

9th May 2006 1.00 38.00 1.00 2.00

18 th May 2006 1.00 38.00 1.00 3.00

10 th July 2006 1.00 60.00 1.00 13.00

21 st July 2006 1.00 59.00 1.00 2.00

7th August 2006 1.00 18.00 1.00 3.00


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3rd March 05 4th March 05

5th March

Figure 5-4: Daily Chlorophyll- a concentration derived from MODIS; Wet Season

0 - 1

10 - 60

3 - 4 Chl-a mg.m -3

Cloud and land mask


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Chapter 5 - Results

31

Figure 5-5: Daily Chl- a derived from MODIS; Dry Season, April August 2005

30 th Apr 05a 30 th Apr 05b 7 th June 05

27th

July 05 30th

July 05 6th

Aug 05

11 th Aug 05 12 th Aug 05 20 th Aug 05

0 - 1

10 - 60

3 - 4 Chl-a mg.m -3

Cloud and land mask


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Figure 5-6: Daily Chl- a derived from MODIS; Dry Season August 05August 06

9th Oct 054 th Sept 0529 th Aug 05

5th Ma 069

th

May 06 18th

May 06

10 th July 06 21 th July 06 7 th Aug 06

0 - 1

10 - 60

3 - 4 Chl-a mg.m -3

Cloud and land mask


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Chapter 5 - Results

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5.3. Correlation between SST and Chl- a

A correlation analysis between SST and Chl- a was performed for near shore andopen sea. The detail result for near shore appears in Table 5-3. The correlation for open sea has not been reported due to no match up points between SST and Chl- a .

Table 5-3: SST and Chl- a relationship

* Rs : Spearman correlation coefficients

Date Rs

P value n3rd March 2005 0.1569 0.0130 68

4 th March 2005 0.1403 0.0390 217

5 th March 2005 0.1192 0.0155 412

30 th April 2005a -0.0848 0.6558 30

30 th April 2005b - - -

7 th June 2005 -0.0026 0.9938 11

27th

July 2005 - - -30 th July 2005 0.0876 0.7135 20

6 th August 2005 -0.2189 0.0416 87

11 th August 2005 - - -

12 th August 2005 -0.5161 0.0042 29

20 th August 2005 0.1472 0.054 172

29 th August 2005 0.222 0.0176 114

4th

September 2005 -0.0063 0.9808 179 th October 2005 - - -

5 th May 2006 -0.7364 0.0591 7

9 th May 2006 - - -

18 th May 2006 - - -

10 th July 2006 - - -

21 st July 2006 0.0671 0.1149 554

7 th August 2006 -0.5536 0.0001 45


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6. Discussion

This study demonstrated that MODIS Level 2 data from Malaysia GroundReceiving Station (MGRS) can successfully be used to obtain SST and Chl- a inEast Kalimantan coastal waters and the Sulawesi open sea waters. This is the firstattempt to validate in situ SST and Chl- a data with MODIS Level 2 data in EastKalimantan waters exclusively in Berau Marine Protected Area since it was gazettedin December 2005. This chapter will start with a section that considered as animportant step for satellite SST and Chl- a measurement which is cloud cover

percentage.

6.1. Cloud cover in the study area

Efforts to obtain SST and Chl- a information in Kalimantan waters during wet anddry season encountered major obstacle of cloud cover especially during wet season.Queries from MACRES Data Quicklook System for wet season result in only threescenes that were cloud free and suitable for further analysis. These three scenesobtained in Mac 2005 were used to represent the whole wet season period. Query

result for dry season however ended up with better results with 18 suitable scenes.Analysis in ArcGis was then conducted to determine the detail percentage of cloudfree pixels. As this study focuses in the ocean region, the land area has been addedinto the cloud statistic. The percentage of cloud covers and land area for SST andChl- a MODIS Level 2 data are shown in Fig 6-1 and Fig 6-2.

SST pixels andcloud mask pixels

80

90

100

3 r d M

a c 0 5

5 t h M

a c 0 5

3 0 A

pr i l 0 5 b

2 7 t h J ul y 0 5

6 t h A

u g 0 5

1 2 t h A

u g 0 5

2 9 t h A

u g 0 5

9 t h O c t 0 5

9 t h M

a y 0 6

1 0 t h J ul y 0 6

7 t h A

u g 0 6

Dates % SST pixels

% Cloud & Land pixels

Figure 6-1: Percentage of SST pixels compared to cloud and land pixels


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Chlorophyll-a pixels and

cloud mask pixels

80

90

100

3 r d M

a c 0 5

5 t h M

a c

0 5

3 0 A

pr i l 0 5 b

2 7 t h J ul y 0 5

6 t h A

u g

0 5

1 2 t h A

u g 0 5

2 9 t h A

u g 0 5

9 t h O c t 0 5

9 t h M

a y

0 6

1 0 t h J ul y 0 6

7 t h A

u g

0 6

Dates % Chl pixels% Cloud & land pixels

Figure 6-2: Percentage of Chl- a pixels compared to cloud and land pixels

Figure 6-1 and 6-2 clearly indicated that although it is possible to map the SST andChl- a in the region using MODIS datasets, the chances of getting 100% clear skythroughout the year is very slim. In general, Chl- a data had higher cloud free pixelscompare to SST datasets. The average value for cloud free pixels in Chl- a datasetswas 6.09 % and 2.17 % for SST datasets. One possible explanation is that thevisible blue wavelengths (0.438 0.493 m) used for generating Chl- a map islocated in the bigger region of atmospheric transmission window (Figure 6-3)compared to the thermal infared wavelengths (10.780 - 12.270 m) to generate SSTvalues. CRISP (2006) defined the atmospheric transmission window as thewavelength region where there are less absorption and scattering by the atmosphericgases.

Figure 6-3: Atmospheric Transmission Window (CRISP, 2006)


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Chapter 6 - Discussion

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6.2. Sea Surface Temperature

6.2.1. In situ SST versus MODIS Level 2 SSTThe August 2006 in situ SST values from 121 stations had been compared to the 7 th August 2006 MODIS data as shown in Figure 6-4 below. We acknowledge there isa time difference between the satellite SST retrieval and the ground measurements,and are fully aware that validation should be carried out at time of satellite overpass,

but as an indication, we performed the comparison and validation exercise.

SST : In situ vs MODIS IDWA vs MODIS Kriging

20.00

22.00

24.00

26.00

28.00

30.00

32.00

34.00

D 1

T A 2

T A 9

T B 4

T B 1 2

T C 6

T E 1

T E 8

T E 1 5

T E 2 2

T F 5

T F 1 2

T G 7

T H 2

T H 9

T I 7

T I 1 4

T I 2 1

In situ Stations

SST 0 C'

Insitu

IDWA

Kriging

Figure 6-4: In situ SST vs MODIS IDWA vs MODIS Kriging

Figure 6-4 clearly indicated that MODIS data tends to underestimate the SST valuesin East Kalimantan waters. Further analysis then found that root mean square error (RMSE) for in situ SST vs MODIS inverse distance weighted average (IDWA) was1.35 0C and for in situ SST vs MODIS Kriging was 1.21 0C. The details are shownin Figure 6-5 and Appendix 2.

Kriging SST = 8.9002256 + 0.5842152 Insitu SST (n=121, r =0.48)

IDWA SST = 10.163081 + 0.5374372 Insitu SST (n=121, r = 0.42)Figure 6-5: Bivarate Fit of IDWA and Kriging SST


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At this stage, one assumption that best explained why MODIS underestimated theSST in this region is because of cold cloud pixels, which affect neighbouring SST

pixels. Further analysis than were conducted to the remaining 20 SST datasets.There categories of SST values; SST Maximum=Tmax, SST Minimum=Tmin andSST Average=Tavg were discussed. The discussion also focused on the differences

between the SST values for open sea and near shore; during wet and dry season.

6.2.2. SST in the near shore waters

Result shows that Tmax for near shore waters during wet and dry season does notvary much. Tmax for the near shore during wet season is 27.66 0C and 27.77 0Cduring dry season. The difference is only 0.11 0C. Throughout the year, Tmax for this region can be concluded as relatively constant as shown in Figure 6-6 below.

Min and Max SST values:Near Shore (3 rd March 2005 - 7 th August 2006)

0.00

5.00

10.00

15.00

20.00

25.00

30.00

3 r d M

a c 0 5

4 t h M

a c 0 5

5 t h M

a c 0 5

3 0 t h A

pr 0 5 a

3 0 t h A

pr i l 0 5 b

7 t h J un e 0 5

2 7 t h

J ul y 0 5

3 0 t h J ul y 0 5

6 t h A

u g 0 5

1 1 t h A

u g 0 5

1 2 t h A

u g 0 5

2 0 t h A

u g 0 5

2 9 t h A

u g 0 5

4 t h S e p t 0 5

9 t h O c t 0 5

5 t h M

a y 0 6

9 t h M

a y 0 6

1 8 t h M

a y 0 6

1 0 t h J ul y 0 6

2 1 t h

J ul y 0 6

7 t h A

u g 0 6

Dates

SST 0C

SST Min

SST Max

Figure 6-6: Minimum and maximum SST in near shore area

Tmin however shows a fluctuating pattern all year round. The minimum SST valuerecorded during wet season is 16.49 0C and 8.23 0C during dry season. The low SSTvalues (pixels < 15 0C) could be unrealistic for SST in tropical sea SST such as EastKalimantan waters. Detail inspections to all datasets then revealed that the

percentage of the low SST values pixels is very low. It ranged from 0.34% to 5.07%of the total SST pixels in the respective datasets. This confirmed that the low SSTvalues are cold cloud pixels or edge cloud pixels and not the true SST. The detailcounts of the cold pixels represented in Table 6-1.


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Table 6-1: Cold pixels percentage in near shore waters

Date Number of pixels

< 15 0C Total %

30 th Apr 05a 7 138 5.0730 th April 05b 5 216 2.317th June 05 1 197 0.5130 th July 05 1 158 0.6311 th August 05 7 161 4.3529 th August 05 4 567 0.714th September 05 2 588 0.345th May 06 7 176 3.9810 th July 06 1 26 3.85

By setting the cut off point of valid SST to 15 0C, the average temperature, Tavgwere recalculated for all the SST imageries. The new Tavg in near shore for wetseason were 26.23 0C and 26.47 0C during dry season.

6.2.3. SST in the open sea

Similar to the near shore waters, Tmax for open sea during wet and dry season does

not vary much. Tmax for the open sea during wet season is 27.66 0C and 27.67 0Cduring dry season. The average Tmax throughout the year is 27.64 0C. Tmin in theopen sea however shows a unique pattern compared to the near shore waters. Figure6-7 clearly indicated that Tmin for open sea is much higher than near shore waters.

Min and Max SST values:Open Sea (3 rd March 2005 - 7 th August 2006)

0.00

5.00

10.00

15.00

20.00

25.00

30.00

3 r d M

a c 0 5

4 t h M

a c 0 5

5 t h M

a c2 0 0 5

3 0 t h A pr 0 5 a

3 0 t h A pr i l 0 5 b

7 t h J un e 0 5

2 7 t h J ul y 0 5

3 0 t h J ul y 0 5

6 t h A u g

0 5

1 2 t h A u g

0 5

9 t h O c t 0 5

5 t h M

a y 0 6

9 t h M

a y 0 6

1 0 t h J ul y 0 6

2 1 t h J ul y 0 6

Dates

SST 0C

SST Min

SST Max

Figure 6-7: Minimum and maximum SST in open waters


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Analyses were also carried out to calculate the cold cloud pixels in the open seaarea. Similar to near shore areas, result shows that the low SST values in open

waters does not depict the overall pictures. The low SST values only exist in twoSST images and the percentage is low as shown in Table 6-2 below.

Table 6-2: Cold pixels percentage in open waters

Number of pixels

Date < 15 0C Total %30 th April 05b 7 424 1.657th June 05 2 20 10.00

By setting the cut off point of valid SST to 15 0C, the average temperature wererecalculated from the SST imageries and it was found that Tavg for open sea duringwet season were 26.69 0C and 26.49 0C for dry season.

6.3. Chlorophyll- a concentrations

6.3.1. In situ Chl- a vs MODIS Level 2 Chl- a

The August 2006 in situ Chl- a values from 121 stations were compared to the 7th

August 2006 MODIS data as shown in Figure 6-8 below. As stated earlier, we agreethat the time of satellite Chl- a retrieval does not coincide fully with the groundmeasurements ( 2 week difference). As an indicative exercise, we however conducted a comparison.

Log 10 Chl-a : In situ vs MODIS IDWA vs MODIS Kriging

-2.5

-0.5

1.5

D 1

T A 2

T A 9

T B 4

T B 1 2

T C 6

T E 1

T E 8

T E 1 5

T E 2 2

T F 5

T F 1 2

T G 7

T H 2

T H 9

T I 7

T I 1 4

T I 2 1

In situ Stations

L o g 1 0

C h l - a m g . m - 3

Log_Insitu

Log_Idwa

Log_Kriging

Figure 6-8: In situ Chl- a vs. MODIS IDWA vs. MODIS Kriging


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Figure 6-8 clearly indicated that Level 2 MODIS chlorophyll- a product hasoverestimated the Chl- a values in East Kalimantan waters. Similar to SST, further

analysis has been carried out to find the RMSE values for this result. It was foundthat the RMSE for in situ Chl- a vs MODIS IDWA was 1.01 mg.m -3 and for in situChl- a vs MODIS Kriging was 1.22 mg.m -3. The cut off point for in situ Chl- a valueis 0.1 mg.m -3 due to the accuracy of the instrument used. From 121 values, 46 Chl-a values were excluded. The details are shown in Figure 6-9 and Appendix 3.

IDWA CHL = 2.2111408 + 0.1802974 Insitu (n = 75, r = 0.05)Kriging CHL = 2.5006469 + 0.1483307 Insitu (n=75, r = 0.04)

Figure 6-9: Bivarate Fit of IDWA and Kriging Chl- a

The overestimation of low Chl- a values by MODIS also reported by Barbini et al. (2004). Barbini et al. (2004) further describe MODIS sensors tend to underestimatehigh Chl- a . This statement also supported by Blondeau-Patissier et al. (2004). Inhis finding, Blondeau-Patissier et al. (2004) stated that in order to gain moreaccurate result in detecting Chl- a using MODIS, effort should be focused onimproving the atmospheric correction rather than more complicated Chl- a algorithms.

This might explained very well why there were reasonable differences betweenMODIS Chl- a and in situ values in East Kalimantan waters. With none of thevalidation stations of SeaWiFS Bio Optical Archive and Storage System (SeaBass)located in the study area, this result was expected. However, the RMSE of 1.01mg.m -3 gained can be a good starting point in understanding the variation of actual

in situ values and MODIS sensors for East Kalimantan waters. A detailed analysis


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was then carried out in order to understand the variability of Chl- a in near shore andopen sea area during the wet and dry season.

6.3.2. Chl- a in the near shore waters

Result shows that there is a large variability of Chl- a in the near shore waters. Nounique pattern exists between wet and dry season. The average Chl- a in near shorecalculated from the imageries were 2.12 mg.m -3 during wet season and 3.46 mg.m -3 during dry season. The Chl- a range for wet season is between 1.00 to 56.00 mg.m -3 while it varies from 1.00 to 59.00 mg.m -3 during dry season. Our result shows hugedifferences when compared to previous Chl- a mapping in this area. Abu Daya(2004) in his study found that the range of Chl- a in near shore East Kalimantanwaters ranges from 0 9 mg.m -3. Although it is true to say that it might be due tothe different sensor used (SeaWiFS) and different acquisition dates but further research need to be carried out to compare these two sensors. Blondeau-Patissier et al. (2004) stated that SeaWiFs was more accurate (RMS = 0.24; n = 26) comparedto MODIS Level 2 (RMS = 0.40; n = 26). However, as his study was in NorthernEuropean waters, the result might be different in tropical waters.

One important advantage of MODIS Chl- a dataset due to its complex turbid water

algorithm, is the ability to measure Chl- a in near shore waters. Barbini et al. (2004)stated that, SeaWifs atmospheric correction failed in some near shore areas. Thisstudy proved the capability of MODIS in measuring near shore Chl- a . Theminimum and maximum Chl- a in near shore waters shown in Figure 6-10.

Min and Max Chl-a Values :Near Shore (3 rd March 2005 - 7 th August 2006)

0.00

10.00

20.00

30.00

40.0050.00

60.00

70.00

3 r d M

a c 0 5

5 t h M

a c 0 5

3 0 t h A

pr i l 0 5 b

2 7 t h J ul y 0 5

6 t h A

u g 0 5

1 2 t h A

u g 0 5

2 9 t h A

u g 0 5

9 t h O c t 0 5

9 t h M

a y 0 6

1 0 t h J ul y 0 6

7 t h A

u g 0 6

Dates

mgm -3 '

Chl Min

Chl Max

Figure 6-10: Minimum and maximum Chl- a in near shore waters


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6.3.3. Chl- a in the open sea

Result in Figure 6-11 shows that the value of Chl- a ranges from 1.00 to 13 mg.m -3 in the East Kalimantan open waters. Further inspection found that the highestconcentration value (13.00 mg.m -3) which happened on 10 th July 2005 was onlyfrom one pixel. Assuming this as noise, the new Chl- a range for open waters is 1.00to 4.00 mg.m -3 which is considerably low. The average Chl- a calculated from theimageries were 1.00 mg.m -3 during wet season and 1.02 mg.m -3 during dry season.

Min and Max Chl-a Values :Open Sea (3 rd March 2005 - 7 th August 2006)

0.00

2.00

4.00

6.00

8.00

10.00

12.0014.00

3 r d M

a c 0 5

5 t h M

a c 0 5

3 0 t h A

pr i l 0 5 b

2 7 t h J ul y 0 5

6 t h A

u g 0 5

2 0 t h A

u g 0 5

4 t h S e p t 0 5

5 t h M

a y 0 6

1 8 t h M

a y 0 6

2 1 t h J ul y 0 6

Dates

mgm-3 '

Chl Min

Chl Max

Figure 6-11: Minimum and maximum Chl- a in open waters

The possible explanation for this small range might be best described by Arnoneand Parsons (2005) which stated that in open seas, chlorophyll maximum layer normally located below the surface, out from satellite sensors view.

6.3.4. Striping lines in Chl- a images

In few Chl- a images as shown in Figure 5-5, 5-6 and 5-7, striping lines were veryobvious. These lines were generated by the residual differences between MODISTerra detectors during solar diffuser (SD) calibrations. Bryan (2007) reported that,

NASA Ocean Biology Production Group (OBPG) is still trying to reduce theresidual detector striping lines. Bryan (2007) further described that the responsivityof MODIS/Terra sensors were degraded up to 40% since launched (compared to15% for Aqua). This issue needs to be taken into account seriously if MODIS Level

2 Chl- a data will be use for future study in this area.


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6.4. Correlation between SST and Chl- a

Having the opportunity to obtain the SST and Chl- a at the same time and date isalways a dream of marine researchers as it is valuable information for further application research e.g fish forecasting and modelling. Mansor et al . (2001) foundthat SST and Chl- a relationship are the most important variables to model the fishdistribution in tropical regions. Solanki et al. (1998) and Solanki et al. (2003)revealed that there was negative/inverse correlation between SST and Chl- a .Solanki et al . (2003) then used the correlation of SST and Chl- a information tomodel the fish distribution in Arabian Sea. In contrast to his finding, this researchhowever found that that there was no specific pattern for correlation between SST

and Chl- a in East Kalimantan waters except for wet season. Figure 6-12 shows that,the SST and Chl- a correlation in wet season has positive relationship. Despite therelatively low Spearman correlation cooefficient (~ Rs = 0.10), the relationship wassignificant (p


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Figure 6-13: MODIS cloud mask for SST and Chl- a Level 2

Bryan Franz (2006) reported that for MODIS SST Level 2 product, thedetermination of invalid pixel values is based on how different the pixels value isfrom the reference SST (Reynolds). If the difference is huge then the pixels will belabelled as SSTWARN. If the SST algorithm could not calculate any values, the

pixels will then be labelled as SSTFAIL. As the final SST pixels also took into

account the night and day value, sun glint did not effect SST pixels.

The Chl- a determination however is very sensitive to sun glint. This is the mainreason why both datasets cannot have similar cloud mask and analysis need to becarefully done to select only overlapping pixels for the correlation analysis. Thelimited overlapping pixels available in open sea of East Kalimantan need to be takenas an important consideration if fish forecasting model need to be run using MODISLevel 2 data in this waters.

SST 30th

Apr 05 Chl-a 30th

Apr 05


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7. Conclusions and Recommendations

7.1. Conclussions

The seasonal and spatial variability of SST and Chl- a in East Kalimantan near shoreand open waters, Indonesia were determined. The minimum, maximum and average

values calculated from temporal datasets were examined and reported. In situ datawere used to validate the accuracy of MODIS Level 2 SST and MODIS Level 2Chl- a data.

The major findings of the research revealed:

SST maps of East Kalimantan waters from March 2005 to August 2006 show thatthere was low SST variability between wet and dry season. There was also lowvariability between SST values in near shore and open sea waters. However, for

both seasons, open sea SST was found to be paradoxically slightly warmer than thenear shore waters.

Chl- a maps of East Kalimantan waters from March 2005 to August 2006 watersrevealed that there was low Chl- a variability between wet and dry season. Therewas also low variability between Chl- a values in near shore and open sea waters.The Chl- a values for both seasons is higher in near shore waters.

SST and Chl- a in near shore waters has low positive relationship in wet season.During dry season, the relationship between these two variables varies from positiveto negative.

7.2. Recommendations

The work presented in this study has left some important questions still unanswered.Effort should be focused in several issues as discussed below if further researchneed to be conducted.


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In section 6.2.1 and 6.3.1, this study had successfully compared the August 2006MODIS Level 2 data with August 2006 in situ data although we admit the time

difference between satellites overpass and ground sampling. There were 4 daysdifference between the MODIS acquisition date and the in situ campaign. In idealcase, it is necessary to validate the satellite imageries with in situ that coincide withthe satellite acquisition time. The RMS error between MODIS and in situ valuecould be refined if this can be achieved.

In section 6.2.2, this study had made the assumption that the cut off value for minimum SST value in East Kalimantan waters was 15 0C. This has raised twointeresting questions. What is the best cut off SST value for East Kalimantanregion? How would the changes in the cut off value affect the SST values for thisregion?.

The unique positive relationship between SST and Chl- a in wet season was veryinteresting. This was somehow questionable because only three images were used torepresenting wet season due to the unavailability of satellite data (cloud cover). For future works, more datasets for the wet season should be obtained and analysed to

proof the relationship.


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Abu Daya, M.I. (2004), Coastal water quality monitoring with remote sensing in East Kalimantan, Makassar strait, Indonesia. MSc. Thesis. InternationalInstitute for Geo-Information Science and Earth Observation (ITC).

Arnone R.A and Parsons A.R. (2005), Real-time Use of Ocean Color RemoteSensing for Coastal Monitoring, chapter 14 in Remote Sensing of AquaticCoastal Environments, ed. R.L. Miller, C.E. Del Castillo, and B.A. McKee,

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Baum B.A. and Platnick S. (2006), Introduction to MODIS Cloud Products.Retrieved 12 th October 2006 from http://climate.gsfc.nasa.gov/publications/

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