Example Applications

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Example Applications

• visible / NIR / MIR - day only, no cloud cover
• vegetation presence
• geological mapping (structure, mineral /
  petroleum exploration)
• urban and land use
• phytoplankton blooms
• meteorology (clouds, atmospheric scattering)
• DEM generation (stereo imagery)

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Example Applications

• Thermal infrared - day / night, rate of heating /
  cooling
• heat loss (urban)
• thermal plumes (pollution)
• mapping temperature
• geology
• forest fires
• meteorology (cloud temp, height)

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Example Applications

• Active microwave - little affected by atmospheric
  conditions, day / night
• surface roughness (erosion)
• water content (hydrology) - top few cms
• vegetation - structure (leaf, branch, trunk
  properties)
• DEM production (SAR interferometry)

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Optical Mechanisms
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Completed Researches at CRSUTM
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App 1 Pemetaan Kedalaman
Objective To extract depth information from
satellite data, and to devise a fast and
cost-effective alternative for acquiring
depth information Study Area Pulau Tioman
Satellite remote sensing data Landsat Thematic
Mapper - band 1
Determination of depth information Elimination of
atmospheric geometric errors Computation of
depth
Depth information in digital file Production of
Hydrographic Chart
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Depth information in digital file Production of
Hydrographic Chart
Digital File of Depth Information
Automatic Generation of Hydrographic Chart
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App 2 Pemetaan Dasar Laut
Objective To extract sea bottom information
from satellite data and to devise a fast
and cost-effective alternative for
acquiring sea bottom information Study Area
Langkawi
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Product from Sea bottom feature mapping
Production of Sea bottom features Plan

• Sea bottom features information is vital for
• navigational hazards
• monitoring
• dredging operation
• exploration
• offshore engineering
• fisheries application

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App 3 Water Quality
Objective To map water quality and determine
suspended sediment from satellite
data Study Area Straits of Klang
Satellite remote sensing data Landsat Thematic
Mapper - band 1
Automatic Production of SSC Maps
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Radar Remote Sensing for Land and Coastal
Applications
Objectives To develop a suitable methodology
for mapping coastal features and land
cover using multi-temporal ERS-1 SAR
satellite data Study Area Kuala Terengganu
Baram, Sarawak
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Research 7 Vegetation Index Mapping
Objective Identifying analysing biomass
for vegetation mapping Study Area Raub,
Pahang
Data from Red and Infrared Bands of Landsat-5 TM
and NOAA AVHRR Satellites
Computation of Vegetation Indices Correlation of
index to ground biomass
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Research 8 Sea Surface Temperature Mapping
Objective To determine sea surface
temperature (SST) from satellite data at
regional and sub-regional levels Study Area
Straits of Malacca South China Sea surrounding
Peninsular Malaysia
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Research 8 output
Automatic Generation of Sea Surface
Temperature off coastal waters surrounding
Peninsular Malaysia
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Global Rainforest Mapping Activities in Malaysia
Radar Remote Sensing For Forest Survey and
Biomass Indicator
Retrieval of tree parameters for model generation
Mangrove forest segmented from SAR data.
Biomass Estimation Map over study area
JERS-1 SAR data over Sg. Pulai, Johore
Figure 1
Figure 4
Figure 3
Figure 2
Figure 1 Measurement of in-situ data for
biomass obsevation. Figure 2
Determination of mangrove patches using
specific segmentation algorithm. Figure 3
Corrected image of JERS-1 SAR
(Synthetic Aperture Radar ) of study area.
Figure 4 Biomass estimation map over study
area. Figure 5 Survey of the study area carried
out jointly with Johore Forestry
Department.
Figure 5
Field verification of calculated biomass
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Phytoplankton sampling at the time of satellite
pass in the study area.
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Ocean colour and seagrass mapping from satellite
remotely sensed data for fisheries application
Phytoplankton distribution of Kedah waters
(Landsat image )
Ocean colour mapping (NOAA satellite)
Seagrass distribution in Kedah waters
( Landsat image )
Derived sea-grass (a)and ocean colour (b)
covering Langkawi island
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Spectral Signature Surface

• Surfaces dont reflect all wavelengths equally.
  They tend to absorb certain wavelengths, while
  reflecting others.The percentage of reflectance
  across the Electromagnetic Spectrum that a
  surface reflects is called its spectral
  signature.Spectral signatures can be affected
  by the time of year, weather, and environmental
  factors.

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Spectral Signature Vegetation

• For example, vegetation tends to reflect green
  light at a higher reflectivity than blue or red
  light, thus plants appear green to our eyes.
  Vegetation also has a high spectral response in
  the Near Infrared (NIR) and if our eyes could see
  this wavelength, then plants would appear very
  bright to us.
• Blue 0.4um-0.5um
• Green 0.5um-0.6um
• Red 0.6um-0.7um
• Near Infrared (NIR) 0.7um-1.2um

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Spectral Signature Water

• The spectral signature for water exhibits
  moderate reflectance in the visible portion of
  the Electromagnetic Spectrum, but plunges to
  almost nothing in the NIR.In images displaying
  NIR, water appears black because of its low
  reflectivity in the Near Infrared.
• Blue 0.4um-0.5um
• Green 0.5um-0.6um
• Red 0.6um-0.7um
• Near Infrared (NIR) 0.7um-1.2um

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TERIMA KASIH