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JSPS National Coordinators’ Meeting, Coastal Marine Science 19 – 20 May 2008 Melaka

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, 81310 UTM Skudai. Mohd Ibrahim Seeni Mohd ,

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JSPS National Coordinators’ Meeting, Coastal Marine Science 19 – 20 May 2008 Melaka

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  1. 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, 81310 UTM Skudai Mohd Ibrahim Seeni Mohd, Nurul Hazrina Idris, Samsudin Ahmad

  2. PRESENTATION OUTLINE • Introduction • Objectives of Study • Study of Sea Grass Features from Satellite Data • Results • Concluding Remarks

  3. INTRODUCTION • 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.

  4. OBJECTIVES • 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.

  5. LANDSAT TM SATELLITE CHARACTERISTICS • The data used was acquired on November 25, 2002.

  6. STUDY AREA

  7. LANDSAT TM DATA PROCESSING • 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),

  8. Xi = Ln (Li – Lsi) Xj = Ln (Lj – Lsj) where, Li = measured radiances in band i Lsi= deep-water radiances in band i Lj = measured radiances in band j Lsj= deep-water radiances in band j

  9. If Xi is plotted versus Xj and water depth varied, the data points will fall along a straight line whose slope is Ki / Kj where Ki and Kj 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. • By measuring the amount of displacement, a change in bottom reflectance can be detected even if the water depth is unknown.

  10. The amount of displacement is given by, Yi = [ Kj ln (Li – Lsi) – Ki ln (Lj – Lsj)] ( Ki2 + Kj2 )1/2

  11. 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 Yi 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.

  12. Graf Xi vs Xj

  13. RAW LANDSAT TM IMAGE Band combination (RGB): 3, 2, 1 respectively.

  14. Band combination (RGB): 3, 2, 1 respectively.

  15. DEPTH INVARIANT INDEX IMAGE

  16. Depth invariant index

  17. SEA GRASS DISTRIBUTION LEGEND Sea Grass Fine Sand Course Sand

  18. 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.

  19. 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.

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