Transcript Bab 2 : Application Of Remote Sensing (Slide)

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)
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)
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)
Optical Mechanisms
Faculty of Geoinformation Science and Engineering
Universiti Teknologi Malaysia
81310 UTM Skudai. Johor Bahru
http://www.fksg.utm.my
Completed Researches at
CRSUTM
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
Depth information in digital file Production of Hydrographic
Chart
Digital File of Depth Information
Automatic Generation of Hydrographic Chart
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
Determination of sea bottom features
Elimination of atmospheric & geometric errors
Formation of “depth invariant index” for sea bottom features
Classification of sea bottom features
based on depth invariant
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
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
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
Wave Spectra Analysis
Detection of Oil Slicks
Mapping of Natural & Artificial Features
Modelling for Vegetation Backscattering
Modelling Shallow Water Bathymetry
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
Research 8 : Sea Surface Temperature Mapping
Objective :
Study Area :
To determine sea surface temperature (SST) from
satellite data at regional and sub-regional levels
Straits of Malacca & South China Sea surrounding
Peninsular Malaysia
AVHRR Data of NOAA Satellite were used to
derive regional SST coverage of 1000 km2.
Landsat-5 TM band 6 was used for sub-regional
SST coverage of 185 km2.
Determination of SST
Elimination of “noises” and “errors”
Customizing thermal algorithms
Classification of SST
Automatic generation of SST Map
Research 8 output :
Automatic Generation of Sea Surface Temperature
off coastal waters surrounding Peninsular Malaysia
SST is one of the prime
input into analysis of
fisheries / marine
research.
SST can be associated
with pelagic fish species,
hence, offers a “powerful”
forecasting tool in deep
sea fishing industries of
Japan and Nordic
countries.
Global Rainforest Mapping Activities in Malaysia: Radar
Remote Sensing For Forest Survey and Biomass Indicator
Retrieval of tree
parameters for
model
generation
Field verification of
calculated biomass
Mangrove forest
segmented from
SAR data.
JERS-1 SAR
data over Sg.
Pulai, Johore
Biomass
Estimation Map
over study area
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.
Phytoplankton sampling at
the time of satellite pass in the study area.
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
Spectral Signature : Surface
Surfaces don’t 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.
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) – 0.7um1.2um
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) – 0.7um1.2um