department of remote sensing faculty of geoinformation science and engineering universiti teknologi...

Department of Remote SensingFaculty of Geoinformation Science and EngineeringUniversiti Teknologi Malaysia, 81310 UTM Skudaihttp://www.fksg.utm.my

JSPS National Coordinators’ Meeting, Coastal Marine Science JSPS National Coordinators’ Meeting, Coastal Marine Science 19 – 20 May 2008 Melaka19 – 20 May 2008 Melaka

SEA GRASS MAPPING FROM SEA GRASS MAPPING FROM SATELLITE DATASATELLITE DATA

Department of Remote SensingDepartment of Remote SensingFaculty of Geoinformation Science and EngineeringFaculty of Geoinformation Science and EngineeringUniversiti Teknologi Malaysia, 81310 UTM Skudai Universiti Teknologi Malaysia, 81310 UTM Skudai

Mohd Ibrahim Seeni MohdMohd Ibrahim Seeni Mohd, , Nurul Hazrina Idris, Samsudin AhmadNurul Hazrina Idris, Samsudin Ahmad


PRESENTATION OUTLINE PRESENTATION OUTLINE

1.1. IntroductionIntroduction

2.2. Objectives of StudyObjectives of Study

3.3. Study of Sea Grass Features from Study of Sea Grass Features from Satellite DataSatellite Data

4.4. ResultsResults

5.5. Concluding Remarks Concluding Remarks


INTRODUCTIONINTRODUCTION

• Mapping of sea grass is important to fishing Mapping of sea grass is important to fishing industry and ocean science studies.industry and ocean science studies.

• Remote sensing satellites provide large area Remote sensing satellites provide large area coverage and a range of temporal scale coverage and a range of temporal scale which allow the parameters to be studied which allow the parameters to be studied continuously. Previous study used the continuously. Previous study used the AVNIR-2 AVNIR-2 (Advanced Visible and Near Infrared (Advanced Visible and Near Infrared Radiometer type 2)Radiometer type 2) data from ALOS Satellite data from ALOS Satellite for sea grass mapping. for sea grass mapping.


OBJECTIVESOBJECTIVES

• To extract the To extract the sea grass featuressea grass features from from LANDSAT TM satellite data. LANDSAT TM satellite data.

• To map the To map the sea bottom featuressea bottom features in the in the coastal waters of Sibu Island, Malaysia.coastal waters of Sibu Island, Malaysia.


LANDSAT TM SATELLITE CHARACTERISTICSLANDSAT TM SATELLITE CHARACTERISTICS

• The data used was acquired on November 25, 2002.The data used was acquired on November 25, 2002.

Altitude Approximately 705 km

Orbit Polar, sun-synchronous

Inclination 98.20

Repeat coverage 16 days


Swath width 185 km (at nadir)

Spatial resolution 30 m / 120 m

Wavelength band 1: 0.45 - 0.52 µm (visible blue)band 2: 0.52 - 0.60 µm (visible green)band 3: 0.63 - 0.69 µm (visible red)band 4: 0.76 - 0.90 µm (near infrared) band 5: 1.55 – 1.75 µm (near infrared)band 6:10.40 – 12.50 µm (thermal)band 7: 2.08 – 2.35 µm (infrared)


STUDY AREASTUDY AREA


• The technique for extracting bottom-The technique for extracting bottom-type information depends upon the fact type information depends upon the fact that bottom-reflected radiance is that bottom-reflected radiance is approximately a linear function of the approximately a linear function of the bottom reflectance and an exponential bottom reflectance and an exponential function of the water depth. function of the water depth.

• Thus, the measured radiance are Thus, the measured radiance are transformed according to the following transformed according to the following equation (Lyzenga, 1981),equation (Lyzenga, 1981),

LANDSAT TM DATA PROCESSINGLANDSAT TM DATA PROCESSING


XXii = Ln (L = Ln (Lii – L – Lsisi))

XXjj = Ln (L = Ln (Ljj – L – Lsjsj))

where,where,Li = measured radiances in band i Lsi= deep-water radiances in band iLj = measured radiances in band jLsj= deep-water radiances in band j


• If XIf Xii is plotted versus X is plotted versus Xjj and water depth and water depth varied, the data points will fall along a varied, the data points will fall along a straight line whose slope is Kstraight line whose slope is Kii / K / Kjj where K where Kii and Kand Kjj is the attenuation coefficient of water is the attenuation coefficient of water in band i and band j, respectively .in band i and band j, respectively .

• If the bottom reflectance is changed, the data If the bottom reflectance is changed, the data points will fall along a parallel line which is points will fall along a parallel line which is displaced from the first.displaced from the first.

• By measuring the amount of displacement, a By measuring the amount of displacement, a change in bottom reflectance can be change in bottom reflectance can be detected even if the water depth is unknown. detected even if the water depth is unknown.


• The amount of displacement is given The amount of displacement is given by,by,

YYii = = [ K[ Kjj ln (L ln (Lii – L – Lsisi) – K) – Kii ln (L ln (Ljj – L – Lsjsj)])]

( K( Kii22 + K + Kjj

22 ) )1/21/2


• The technique used for extracting bottom-The technique used for extracting bottom-type features combines the information in type features combines the information in band 1 and band 3 of the satellite data.band 1 and band 3 of the satellite data.

• This procedure was implemented on the This procedure was implemented on the LANDSAT TM data by calculating the variable LANDSAT TM data by calculating the variable YYii at each point in the scene and using this at each point in the scene and using this variables as a depth-invariant index of the variables as a depth-invariant index of the bottom type.bottom type.

• The depth invariant index was density sliced The depth invariant index was density sliced into three sea bottom types, namely sea into three sea bottom types, namely sea grass, coarse sand and fine sand. grass, coarse sand and fine sand.


y = 0.8766x + 0.7682

0

0.5

1

1.5

2

2.5

3

3.5

0 0.5 1 1.5 2 2.5 3

X1

X3

Graf Xi vs Xj


RAW LANDSAT TM IMAGERAW LANDSAT TM IMAGE

Band combination

(RGB): 3, 2, 1 respectively.


Band combination

(RGB): 3, 2, 1 respectively.


DEPTH INVARIANT INDEX IMAGEDEPTH INVARIANT INDEX IMAGE


Depth invariant index

Features Depth invariant index

Seagrass 1.6 – 1.7

Fine sand 1.7 – 1.8

Course sand 1.8 – 1.9


LEGEND

Sea Grass

Fine Sand

Course Sand

SEA GRASS DISTRIBUTIONSEA GRASS DISTRIBUTION


CONCLUDING REMARKSCONCLUDING REMARKS

• In this study, three bottom-type In this study, three bottom-type features have been found surrounding features have been found surrounding Sibu Island i.e. seagrass, fine sand and Sibu Island i.e. seagrass, fine sand and course sand. This result needs to be course sand. This result needs to be verified by ground truth observation verified by ground truth observation and multitemporal LANDSAT TM data and multitemporal LANDSAT TM data need to be used to analyze the need to be used to analyze the capability of LANDSAT data for sea capability of LANDSAT data for sea grass studies.grass studies.


ACKNOWLEDGEMENTSACKNOWLEDGEMENTS

We would like to thank Prof. T. Yanagi of We would like to thank Prof. T. Yanagi of Kyushu University, Japan and the Kyushu University, Japan and the

Japan Society for Promotion of Science Japan Society for Promotion of Science (JSPS) for making this study possible.(JSPS) for making this study possible.

department of remote sensing faculty of geoinformation science and engineering universiti teknologi...

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