The atmosphere surrounding earth surface plays an important role in the daily life of human beings. Total suspended
particulates (TSP) found in the atmosphere are made up of many compounds including soil, nitrate, sulphate, soot and
organic carbon. In this study, an optical sensor has been designed and developed for measuring TSP concentrations in
air. Our aim is to measure TSP concentrations in polluted air by using a newly developed optical sensor. The developed
spectral sensor was based on the measured radiation transmitted through the air samples. Three pairs of visible LEDs and
photodiodes are used as sensing emitters and detectors respectively. The transmitted radiation was measured in terms of
the output voltage of the photodetector of the sensor system. A new multispectral algorithm has been developed to
correlate TSP concentrations and the transmitted radiation. The newly developed system produced a high degree of
accuracy with the correlation coefficient of 0.999 and the root-mean-square error of 5.535mg/l. The TSP concentration
can be measured and monitored accurately using this sensor.
Atmospheric fine particles with diameter less than 10 micron (PM10) have already become a major public health concern
in any part of the world. These particles can be breathed more deeply into the lungs to cause adverse health effects. In
order to prevent long exposure to these harmful atmospheric particles, this motivates a growing interest in developing
efficient methods of fine particles monitoring. In this study, we proposed that an internet protocol camera and
LANDSAT5 satellite image be used to monitor the temporal and spatial air quality. We developed an algorithm to
converts multispectral image pixel values acquired from digital images into quantitative values of the concentrations of
PM10. This algorithm was developed based on the regression analysis of relationship between the measured reflectance
and the reflected components from a surface material and the ambient air. The computed PM10 values were compared to
other standard values measured by a DustTrak meter. The correlation results showed that the newly develop algorithm
produced a high degree of accuracy as indicated by high correlation coefficient (R2) and low root-mean-square-error
(RMS). This study indicates that the temporal and spatial development of air quality can be monitored by using internet
protocol camera and LANDSAT5 images.
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.
Nowadays application of webcam becomes more and more popular. Thus webcams are being developed to have better
resolution but lower cost. This has motivated us to evaluate the suitability of using webcam for indoor air quality
monitoring. This monitoring involved determining the concentration of particulate matter with diameter less than 10
micron (PM10). An algorithm was developed to convert multispectral image pixel values acquired from this camera into
quantitative values of the concentrations of PM10. This algorithm was developed based on the regression analysis of
relationship between the measured reflectance and the reflected components from a surface material and the ambient air.
The computed PM10 values were compared to other standard values measured by a DustTrakTM meter. The correlation
results showed that the newly develop algorithm produced a high degree of accuracy as indicated by high correlation
coefficient (R2) and low root-mean-square-error (RMS). This has showed that Webcam can be used for indoor air quality
monitoring.
Many researches showed that pollution particles are associated with adverse health effects. These particles are 10
micrometers in diameter or smaller (PM10). Now the challenge is to develop a better technique for monitoring the
concentration of these particles. In this study, we intend to use a Single Lens Reflex (SLR) camera to determine particle
concentration in the air using our own developed algorithm. This algorithm was developed based on the regression
analysis of relationship between the measured reflectance and the reflected components from a surface material and the
atmosphere. This algorithm is used to convert multispectral image pixel values acquired by this camera into quantitative
values of the concentrations of PM10. These computed
PM10 concentrations were compared to other standard values
measured by a DustTrakTM meter. The validated result of the newly develop algorithm produced a high degree of
accuracy as indicated by high correlation coefficient (R2) and low root-mean-square-error (RMS).
The current sea surface temperature (SST) algorithms were derived empirically using a large amount of in-situ
observation data. The algorithms derived had no guarantee to be used for the different regions and time. Large amount
of in-situ data was required for the algorithm regression analysis. The new algorithm did not require a large amount of
in-situ data. The algorithm requires additional input of transmittance and emissivity values. The transmittance depends
on atmospheric profiles. The standard profile was used. The input sensor zenith angle and water vapor contents were
changed within a certain range. The data were then simulated by MODTRAN to obtain the transmittance. The derived
equation of sea surface emissivity as a function of sensor zenith was used. Brightness temperature, sea surface emissivity
and transmittance values were use to calculate the sea surface temperature of each cloud free water pixel using an image
processing software. The results show that the new algorithm produce a comparable the R2=0.6569 and RMSE= 1.24 K.
The new algorithm did not require the large amount of in-situ SST data, but still can give the SST data in moderately
high accuracy.
The optical properties of aerosols such as smoke from burning vary due to aging
processes and these particles reach larger sizes at high concentrations. The objectives
of this study are to develop and evaluate an algorithm for estimating atmospheric
optical thickness from Landsat TM image. This study measured the sky transmittance
at the ground using a handheld spectroradiometer in a wide wavelength spectrum to
retrieve atmospheric optical thickness. The in situ measurement of atmospheric
transmittance data were collected simultaneously with the acquisition of remotely
sensed satellite data. The digital numbers for the three visible bands corresponding to
the in situ locations were extracted and then converted into reflectance values. The
reflectance measured from the satellite was subtracted by the amount given by the
surface reflectance to obtain the atmospheric reflectance. These atmospheric
reflectance values were used for calibration of the AOT algorithm. This study
developed an empirical method to estimate the AOT values from the sky
transmittance values. Finally, a AOT map was generated using the proposed
algorithm and colour-coded for visual interpretation.
Traditional air pollution monitoring by using ground based instruments cannot provides air
pollution over a large spatial scale. Aerosol over Penang Island was mapped with SPOT
satellite image in this study. The objective of this study is to evaluate the relationship
between SPOT satellite observation and particulate matter of size less than 2.5 micron
(PM2.5) parameter. It is based on detection of dark surface targets in the blue band. Only one
visible wavelength band were used in this study. The surface reflectance values for the only
one visible wavelength were determined based on the information given by using ATCOR2
image processing technique. The atmospheric components were then estimated from the
image. A total of 25 ground truth PM2.5 data were collected simultaneously with the
acquired satellite image by using a hand held DustTrak Meter and their locations were
determined using a hand held Global Positioning System (GPS). The digital numbers of the
corresponding in situ data were converted into irradiance and then reflectance. The
atmospheric reflectance values was extracted from the satellite observation reflectance values
subtrated by the amount given by the surface reflectance. The atmospheric reflectance values
were later used for PM2.5 mapping using the calibrated algorithm. An algorithm was
developed based on the atmospheric optical characteristic. The developed algorithm was used
to correlate the digital signal and the PM2.5 concentration. A good linear correlation between
the satellite signal and PM2.5 parameter was found in this study (R > 0.8). Finally, a PM2.5
map was generated using the proposed algorithm. This study indicates that the feasibility of
using the visible band from SPOT for PM2.5 mapping.
Nowadays, air quality is a major concern in many countries whether in the developed
or the developing countries. Due to the high cost and limited number of air pollutant
stations in each area, they cannot provide a good spatial distribution of the air
pollutant readings over a city. Satellite observations can give a high spatial
distribution of air pollution. The objective of this study is to test the feasibility of
using Landsat TM for retrieving the concentration of the particulate matter of size less
than 10- micron (PM10). The retrieval of surface reflectance is important to obtain the
atmospheric reflectance in remotely sensed data and later used for algorithm
calibration. In this study, we retrieve the surface reflectance using the relationship
between the two visible bands (blue and red) and the mid infrared data at 2.1 μm. We
use the assumption that the mid infrared band data is not significantly affected by
atmospheric haze. An algorithm was developed based on the aerosol properties to
correlate the atmospheric reflectance and PM10. We also evaluate the used of the
thermal band in the air quality study which is added into the proposed regression
algorithm. The in situ PM10 data were collected simultaneously with the acquired
satellite image. A high correlation coefficient (R) was obtained in this study between
the measured and predicted PM10 values. Finally, a PM10 map was generated using
the proposed algorithm and geometrically corrected. The generated PM10 was also
colour coded for visual interpretation and smoothed using an average filter to
minimize the random noise. This study indicated that the Landsat TM can be a very
good tool for air quality study.
An algorithm for haze determination was developed based on the atmospheric optical properties
to determine the concentration of particulate matter with diameter less than 10 micrometers
(PM10). The purpose of this study was to use digital camera images to determine the PM10
concentration. This algorithm was developed based on the relationship between the measured
PM10 concentration and the reflected components from a surface material and the atmosphere.
A digital camera was used to capture images of dark and bright targets at near and far distances
from the position of the targets. Ground-based PM10 measurements were carried out at selected
locations simultaneously with the digital camera images acquisition using a DustTrakTM meter.
The PCI Geomatica version 9.1 digital image processing software was used in all imageprocessing
analyses. The digital colour images were separated into three bands namely red,
green and blue for multi-spectral analysis. The digital numbers (DN) for each band corresponding
to the ground-truth locations were extracted and converted to radiance and reflectance values.
Then the atmospheric reflectance was related to the PM10 using the regression algorithm
analysis. The proposed algorithm produced a high correlation coefficient (R) and low root-meansquare
error (RMS) between the measured and estimated PM10. This indicates that the
technique using the digital camera images can provide a useful tool for air quality studies.
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.
A new nondestructive methods based on optical properties at multiple wavelengths is being applied to measure the
freshness of some vegetables. The principle of this method is to determine the absorbance and the reflectance of a sample
in visible and near infrared region. When a light beam is illuminated upon a piece of vegetable sample, the majority of
the lights penetrate into the sample tissue. Upon entering the tissue, photons scatter in different directions. Some are
absorbed, some pass-through to the whole sample and emerge from the opposite side, and some scatter back and
reemerge from the region adjacent to the incident center. While the absorption is related to certain chemical constituent
of the sample, scattering is influenced by the density, compositions, cells and intercellular structures of samples and
therefore can be useful for measuring samples freshness. Our objectives are to investigate the spectral behavior of some
vegetables and to develop an algorithm for a non-destructive freshness sensor system using visible and near infrared light
sources. The preliminary results of the study showed that the freshness of green mustard leaf and onion using a red (λ =
633 nm) and green (λ = 808 nm) light sources were closely related.
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.
Nowadays internet video surveillance cameras are widely use in security monitoring. The quantities of
installations of these cameras also become more and more. This paper reports that the internet video
surveillance cameras can be applied as a remote sensor for monitoring the concentrations of particulate
matter less than 10 micron (PM10), so that real time air quality can be monitored at multi location
simultaneously. An algorithm was developed based on the regression analysis of relationship between the
measured reflectance components from a surface material and the atmosphere. This algorithm converts
multispectral image pixel values acquired from these cameras into quantitative values of the
concentrations of PM10. These computed PM10 values were compared to other standard values measured
by a DustTrakTM meter. The correlation results showed that the newly develop algorithm produced a high
degree of accuracy as indicated by high correlation coefficient (R2) and low root-mean-square-error
(RMS) values. The preliminary results showed that the accuracy produced by this internet video
surveillance camera is slightly better than that from the internet protocol (IP) camera. Basically the spatial
resolution of images acquired by the IP camera was poorer compared to the internet video surveillance
camera. This is because the images acquired by IP camera had been compressed and there was no
compression for the images from the internet video surveillance camera.
For this study, an algorithm was developed to determine concentration of particles less than 10&mgr;m (PM10) from still images captured
by a CCTV camera on the Penang Bridge. The objective of this study is to remotely monitor the PM10 concentrations on the Penang
Bridge through the internet. So, an algorithm was developed based on the relationship between the atmospheric reflectance and the
corresponding air quality. By doing this, the still images were separated into three bands namely red, green and blue and their digital
number values were determined. A special transformation was then performed to the data. Ground PM10 measurements were taken by
using DustTrakTM meter. The algorithm was calibrated using a regression analysis. The proposed algorithm produced a high
correlation coefficient (R) and low root-mean-square error (RMS) between the measured and produced PM10. Later, a program was
written by using Microsoft Visual Basic 6.0 to download still images from the camera over the internet and implement the newly
developed algorithm. Meanwhile, the program is running in real time and the public will know the air pollution index from time to
time. This indicates that the technique using the CCTV camera images can provide a useful tool for air quality studies.
Tropospheric aerosols play an important role in climate change. Aerosols are typically studied over deep clear water, due to the relatively constant reflectance of water and the ability to easily separate surface and atmospheric contributions on the satellite signal. A methodology based on multi-spectral approach was employed to map tropospheric aerosols concentrations over the water areas surrounding Penang Island. The aim of this study was to estimate the pollutants concentrations using remote sensing techniques. In this study, we attempted to derive AOT (Aerosol Optical Thickness) values from the sky transmittance measurements in the visible spectrum. The transmittance values were measured at the sea surface using a handheld spectroradiometer. The correspond PM10 readings were taken simultaneously during the transmittance measurements acquisition of the imageries using a Dust Trak meter. The PCI Geomatica version 9.1 digital image processing software was used in all image-processing analyses. The results produced a linear relationship between PM10 and AOT values over the water surface of Penang Island. Finally, The PM10 concentration map over the water surface of Penang Island was generated using Kriging interpolation technique. This study has indicated the potential use of a handheld spectroradiometer for air quality study.
An optical sensor has been designed and developed to measure transmitted light through water samples for the retrieval of total suspended solids TSS concentrations. The proposed optical system uses light emitting diodes LEDs to transmit light through the total suspended solids in water samples. The transmitted radiation values were obtained from output voltage readings of the multimeter of the optical detector. The voltage readings are correlated with the
TSS concentrations of the samples. A developed algorithm was used to
determine the relationship between these two parameters. The results showed a good correlation between the radiation values and the total suspended solids TSS concentrations. The accuracy is high and low value of the root mean square.
A novel optical sensor equipment has been developed for the measurement of total suspended solids TSS concentrations. The sensing equipment makes use of light-emitting diodes, LED's, as sources, and a silicon photodiode as a detector. The photodetector detects light scattered at 90°. The signal was correlated with
the measured TSS concentrations. Scattered light was measured in terms of the output voltage of the photodetector of the proposed optical system. The output voltage was read using a conventional voltmeter. The correlation coefficient produced was high and the root mean square error was low.
Modern digital technology allows image data transfer over the internet protocol, which
provides real time observation and more frequent air quality studies can be carried at
multi locational simultaneously. The objective of this study is to evaluate the suitability
of using internet protocol camera to transfer image data, and then these data were
analysed using a developed algorithm to determine air quality information. The
concentrations of particulate matter of size less than 10 micron (PM10) were collected
simultaneously with the image data acquisitions. The atmospheric reflectance
components were subtracted from their corresponding recorded radiance values for
algorithm regression analysis. The proposed algorithm produced high correlation
coefficient (R) and low root-mean square error (RMS) values. The efficiency of the
present algorithm, in comparison to other forms of algorithm, was also investigated.
Based on the values of the correlation coefficient and root-mean-square deviation, the
proposed algorithm is considered superior. The accuracy of using IP camera data was
compared with a normal digital camera, Kodak DC290 data in this study. This
preliminary study gave promising results of air quality studies over USM campus by
using internet protocol data.
Air quality is a major concern in many large cities of the world. This paper studies the
relationship between particulate matter of size less than 10-micron meter (PM10) and satellite
observation from OCTS. The objective of this study is to map the PM10 distribution in
Peninsular Malaysia using visible and thermal bands data. The in-situ PM10 measurements were
collected simultaneously with the acquisition of the satellite image. The reflectance values in the
visible and digital numbers in the thermal bands were extracted corresponding to the ground-truth
locations and later used for algorithm regression analysis of the air quality. A developed
algorithm was used to predict the PM10 values from the satellite image. The novelty of this study
is the algorithm uses a combination of reflectance measurements from the visible bands and the
corresponding apparent temperature values of the thermal band. This study investigates the
relationship between the extracted OCTS signals and the PM10 values. The reflectance at 3.55-3.88 micro meter is computed after correction for the emission by the atmosphere. The surface
emissivity values were computed based on the NDVI values. The developed algorithm produced
a high correlation between the measured and estimated PM10 values of 0.97. Finally, a PM10
map was generated over Peninsular Malaysia using the proposed algorithm. This study has
indicated the potential of OCTS satellite data for air quality study.
Remote sensing data have been widely used for land cover mapping using supervised and unsupervised methods. The produced land cover maps are useful for various applications. This paper examines the use of remote sensing data for land cover mapping over Saudi Arabia. Three supervised classification techniques Maximum Likelihood, ML, Minimum Distance-to-Mean, MDM, and Parallelepiped, P were applied to the imageries to extract the thematic information from the acquired scene by using PCI Geomatica software. Training sites were selected within each scene. This study shows that the ML classifier was the best classifier and produced superior results and achieved a high degree of accuracy. The preliminary analysis gave promising results of land cover mapping over Saudi Arabia by using Landsat TM imageries.
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