Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai JSPS National Coordinators’ Meeting, Coastal Marine Science 19 – 20 May 2008 Melaka SEA GRASS MAPPING FROM SATELLITE DATA Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai Mohd Ibrahim Seeni Mohd, Nurul Hazrina Idris, Samsudin Ahmad

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai PRESENTATION OUTLINE 1.Introduction 2.Objectives of Study 3.Study of Sea Grass Features from Satellite Data 4.Results 5.Concluding Remarks

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai INTRODUCTION Mapping of sea grass is important to fishing industry and ocean science studies.Mapping of sea grass is important to fishing industry and ocean science studies. Remote sensing satellites provide large area coverage and a range of temporal scale which allow the parameters to be studied continuously. Previous study used the AVNIR-2 (Advanced Visible and Near Infrared Radiometer type 2) data from ALOS Satellite for sea grass mapping.Remote sensing satellites provide large area coverage and a range of temporal scale which allow the parameters to be studied continuously. Previous study used the AVNIR-2 (Advanced Visible and Near Infrared Radiometer type 2) data from ALOS Satellite for sea grass mapping.

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai OBJECTIVES To extract the sea grass features from LANDSAT TM satellite data.To extract the sea grass features from LANDSAT TM satellite data. To map the sea bottom features in the coastal waters of Sibu Island, Malaysia.To map the sea bottom features in the coastal waters of Sibu Island, Malaysia.

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai LANDSAT TM SATELLITE CHARACTERISTICS The data used was acquired on November 25, 2002.The data used was acquired on November 25, AltitudeApproximately 705 km OrbitPolar, sun-synchronous Inclination Repeat coverage16 days

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai Swath width185 km (at nadir) Spatial resolution30 m / 120 m Wavelengthband 1: µm (visible blue) band 2: µm (visible green) band 3: µm (visible red) band 4: µm (near infrared) band 5: 1.55 – 1.75 µm (near infrared) band 6:10.40 – µm (thermal) band 7: 2.08 – 2.35 µm (infrared)

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai STUDY AREA

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai The technique for extracting bottom- type information depends upon the fact that bottom-reflected radiance is approximately a linear function of the bottom reflectance and an exponential function of the water depth.The technique for extracting bottom- type information depends upon the fact that bottom-reflected radiance is approximately a linear function of the bottom reflectance and an exponential function of the water depth. Thus, the measured radiance are transformed according to the following equation (Lyzenga, 1981),Thus, the measured radiance are transformed according to the following equation (Lyzenga, 1981), LANDSAT TM DATA PROCESSING

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai X i = Ln (L i – L si ) X i = Ln (L i – L si ) X j = Ln (L j – L sj ) X j = Ln (L j – L sj )where, L i = measured radiances in band i L si = deep-water radiances in band i L j = measured radiances in band j L sj = deep-water radiances in band j

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai If X i is plotted versus X j and water depth varied, the data points will fall along a straight line whose slope is K i / K j where K i and K j is the attenuation coefficient of water in band i and band j, respectively.If X i is plotted versus X j and water depth varied, the data points will fall along a straight line whose slope is K i / K j where K i and K j is the attenuation coefficient of water in band i and band j, respectively. If the bottom reflectance is changed, the data points will fall along a parallel line which is displaced from the first.If the bottom reflectance is changed, the data points will fall along a parallel line which is displaced from the first. By measuring the amount of displacement, a change in bottom reflectance can be detected even if the water depth is unknown.By measuring the amount of displacement, a change in bottom reflectance can be detected even if the water depth is unknown.

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai The amount of displacement is given by,The amount of displacement is given by, Y i = [ K j ln (L i – L si ) – K i ln (L j – L sj )] ( K i 2 + K j 2 ) 1/2 ( K i 2 + K j 2 ) 1/2

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai The technique used for extracting bottom- type features combines the information in band 1 and band 3 of the satellite data.The technique used for extracting bottom- type features combines the information in band 1 and band 3 of the satellite data. This procedure was implemented on the LANDSAT TM data by calculating the variable Y i at each point in the scene and using this variables as a depth-invariant index of the bottom type. This procedure was implemented on the LANDSAT TM data by calculating the variable Y i at each point in the scene and using this variables as a depth-invariant index of the bottom type. The depth invariant index was density sliced into three sea bottom types, namely sea grass, coarse sand and fine sand.The depth invariant index was density sliced into three sea bottom types, namely sea grass, coarse sand and fine sand.

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai Graf X i vs X j

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai RAW LANDSAT TM IMAGE Band combination (RGB): 3, 2, 1 respectively.

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai Band combination (RGB): 3, 2, 1 respectively.

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai DEPTH INVARIANT INDEX IMAGE

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai Depth invariant index FeaturesDepth invariant index Seagrass1.6 – 1.7 Fine sand1.7 – 1.8 Course sand1.8 – 1.9

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai LEGEND Sea Grass Fine Sand Course Sand SEA GRASS DISTRIBUTION

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai CONCLUDING REMARKS In this study, three bottom-type features have been found surrounding Sibu Island i.e. seagrass, fine sand and course sand. This result needs to be verified by ground truth observation and multitemporal LANDSAT TM data need to be used to analyze the capability of LANDSAT data for sea grass studies.In this study, three bottom-type features have been found surrounding Sibu Island i.e. seagrass, fine sand and course sand. This result needs to be verified by ground truth observation and multitemporal LANDSAT TM data need to be used to analyze the capability of LANDSAT data for sea grass studies.

Department of Remote Sensing Faculty of Geoinformation Science and Engineering Universiti Teknologi Malaysia, UTM Skudai ACKNOWLEDGEMENTS We would like to thank Prof. T. Yanagi of Kyushu University, Japan and the Japan Society for Promotion of Science (JSPS) for making this study possible.