Environmental monitoring through the method of traditional ship sampling is time consuming and requires a high survey
cost. The objective of this study is to evaluate the feasibility of Landsat TM imagery for total suspended solids (TSS)
mapping using a newly developed algorithm over Penang Island. The study area is the seawater region around Penang
Island, Malaysia. Water samples were collected during a 3-hour period simultaneously with the satellite image
acquisition and later analyzed in the laboratory above the study area. The samples locations were determined using a
handheld GPS. The satellite image was geometrically corrected using the second order polynomial transformation. The
satellite image also was atmospheric corrected by using ATCOR2 image processing software. The digital numbers for
each band corresponding to the sea-truth locations were extracted and then converted into reflectance values for
calibration of the water quality algorithm. The proposed algorithm is based on the reflectance model that is a function of
the inherent optical properties of water, which can be related to its constituent's concentrations. The generated algorithm
was developed for three visible wavelenghts, red, green and blue for this study. Results indicate that the proposed
developed algorithm was superior based on the correlation coefficient (R) and root-mean-square deviation (RMS)
values. Finally the proposed algorithm was used for TSS mapping at Penang Island, Malaysia. The generated TSS map
was colour-coded for visual interpretation and image smoothing was performed on the map to remove random noise.
This preliminary study has produced a promising result. This study indicates that the empirical algorithm is suitable for
TSS mapping around Penang Island by using satellite Landsat TM data.
The height of cloud and aerosol layers in the atmosphere is believed to affect climate change and air pollution because
both of them have important direct effects on the radiation balance of the earth. In this paper, we study the ability of
Cloud Aerosol LIDAR and Infrared Pathfinder Satellite Observation (CALIPSO) data to detect, locate and distinguish
between cloud and aerosol layers in the atmosphere over Peninsula Malaysia. We also used image processing technique
to differentiate between cloud and aerosol layers from the CALIPSO images. The cloud and aerosol layers mostly are
seen at troposphere (>10 km) and lower stratosphere (>15km). The results shows that CALIPSO can be used to
determine cloud and aerosol layers and image processing technique has successfully distinguished them in the
atmosphere.
The application of remote sensing to assess water quality for coastal and open ocean has escalated
recently due to its capability of scanning wide water bodies within a short time period. In this paper,
we examined the spatial variability of chlorophyll within Penang straits, Malaysia. Coastal and
estuarine ecosystems typically exhibit high temporal and spatial variability in phytoplankton biomass
that is often too difficult to characterize with a limited set of in situ shipboard measurements. In this
study, we used ALOS satellite imagery acquired on 24 April 2007. An algorithm for retrieval of
chlorophyll level was developed for ALOS data. Chlorophyll samples were collected using a small
boat simultaneously with the acquisition of the satellite image. The water locations were determined
using a handheld Global Positioning System (GPS). And then the digital numbers for each band
corresponding to the sea-truth locations were extracted and then converted into radiance values and
reflectance values. The reflectance values were used for calibration of the chlorophyll algorithm. For
the regression model, the correlation coefficient (R) and the root-mean-square deviation (RMS) were
noted. The proposed algorithm is considered superior based on the values of the correlation
coefficient and root-mean-square The water quality image was generated using the multispectral data
set and the proposed calibrated TSS algorithm. This study demonstrates that remote sensing can play
an important role in water quality assessment by using high resolution satellite image of ALOS data.
Remote sensing using the satellite borne LIDAR systems are currently providing new features for global atmospheric sensing from space. The LIDAR on board the Cloud Aerosol LIDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is currently obtaining global aerosol and cloud measurements from space since launched on April 28, 2006. The CALIPSO satellite carries a polarization-sensitive LIDAR system that records backscatter measurements at 532 nm and 1064 nm. In this study, we investigated the stratospheric aerosol backscatter coefficients over Peninsular Malaysia. An initial result of actual data supports that the CALIPSO LIDAR data exhibits sensitivity to the presence of stratospheric aerosol in this study area.
LIDAR backscatter signal analysis can establish surface elevation at the LIDAR footprint, in kilometers above local
mean sea level. Since aberrations in the signal caused by a non-ideal transient response in the 532 nm detectors, the
geometric thickness associated with the LIDAR surface elevation can be utmost misleading. In this place, the provisional
LIDAR surface elevation should treated all signal beneath the reported LIDAR surface elevation top as being pure
instrument artifact introduced by the non-ideal transient response of the detectors. Apparently, no geophysical
significance should be ascribed to the subsurface portion of the LIDAR return. This study will present the comparison
between the LIDAR Surface Elevation and Digital Elevation Map (DEM) using CALIPSO LIDAR data over Peninsular
Malaysia.
Remote sensing offers an important means of detecting and analyzing temporal
changes occurring in our landscape. This research used remote sensing to quantify land use/land
cover changes at the Nanggroe Aceh Darussalam (Nad) province, Indonesia on a regional scale.
The objective of this paper is to assess the changed produced from the analysis of Landsat TM
data. A Landsat TM image was used to develop land cover classification map for the 27 March
2005. Four supervised classifications techniques (Maximum Likelihood, Minimum Distance-to-
Mean, Parallelepiped and Parallelepiped with Maximum Likelihood Classifier Tiebreaker
classifier) were performed to the satellite image. Training sites and accuracy assessment were
needed for supervised classification techniques. The training sites were established using
polygons based on the colour image. High detection accuracy (>80%) and overall Kappa (>0.80)
were achieved by the Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier
in this study. This preliminary study has produced a promising result. This indicates that land
cover mapping can be carried out using remote sensing classification method of the satellite
digital imagery.
We attempted to investigate the potential of using satellite image for
acquiring data for remote sensing application. This study investigated the potential of
using digital satellite image for land cover mapping over AlQasim, Saudi Arabia.
Satellite digital imagery has proved to be an effective tool for land cover studies.
Supervised classification technique (Maximum Likelihood, ML, Minimum Distance-to-
Mean, MDM, Parallelepiped, P) techniques were used in the classification analysis to
extract the thematic information from the acquired scenes. Besides that, neutral network
also performed in this study. The accuracy of each classification map produced was
validated using the reference data sets consisting of a large number of samples collected
per category. The study revealed that the ML classifier produced better result. The best
supervised classifier was chosen based on the highest overall accuracy and Kappa
statistic. The results produced by this study indicated that land cover features could be
clearly identified and classified into a land cover map. This study suggested that the land
cover types of AlQasim, Saudi Arabia can be accurately mapped.
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