Mangroves are known as salt-tolerant evergreen forests, whereas its create land-ocean interface ecosystems. Besides,
mangroves bring direct and indirect benefits to human activities and play a major role as significant habitat for
sustaining biodiversity. However, mangrove ecosystem study based on the mangrove species are very crucial to get a
better understanding of their characteristics and ways to separate among them. In this paper, discriminant functions
obtained using statistical approach were used to generate the score range for six mangrove species (Rhizophora
apiculata, Acrostichum aurem, Acrostichum speciosum, Acanthus ilicifolius, Ceriops tagal and Sonneratia ovata) in
Matang Mangrove Forest Reserve (MMFR), Perak. With the computation of score range for each species, the fraction of
the species can be determined using the proposed algorithm. The results indicate that by using 11 discriminant functions
out of 16 are more effective to separate the mangrove species as the higher accuracy was obtained. Overall, the
determination of leaf sample’s species is chosen base on the highest fraction measured among the six mangrove species.
The obtained accuracy for mangrove species using statistical approach is low since it is impossible to successfully
separate all the mangrove species in leaf level using their inherent reflectance properties. However, the obtained
accuracy results are satisfactory and able to discriminate the examined mangrove species at species scale.
Aceh had been the focus of an unprecedented international rehabilitation effort in response to the extreme SumatraAndaman earthquake and tsunami disaster on December 24, 2004. During this period, most researchers have contributed to better understanding what happened in the past, and what going to happen in the future. This paper is related to the environmental impact assessment of post-disaster recovery and reconstructions in Banda Aceh city of Indonesia. The indicators are based on the use of the moderate spatial resolution optical satellite sensor by assessing the impacts of land use and land cover change (LULC) on land surface temperature (LST). LULC classification and LST were derived and estimated utilizing visible and thermal infrared data of the Landsat-5 TM + Landsat 8 OLI within the period 2000 and 2015. The surface temperature-vegetation index space of LULC was established to investigate the impacts of land changes over LST sensitivity. The result demonstrated that the post-disaster recovery and reconstruction has had a significant impact to the LULC in Banda Aceh and its fringes. Dramatic LULC in Banda Aceh significantly increases the LST, the temporal trend of pixels space migrated from the dense vegetation-low temperature condition to the less dense vegetation-high temperature condition.
Ozone (O3) is unique among pollutants because it is not emitted directly into the air, and its results from complex chemical reactions in the atmosphere. O3 can bring different effects for all the living on earth (either harm or protect), depending on where O3 resides. This is the main reason why O3 is such a serious environmental problem that is difficult to control and predict. The objective of this paper is to analyze the variations of total column O3 measured by Brewer O3 spectrophotometer over Global Atmosphere Watch Station (GAW) regional station, which is located at southwest of Peninsular Malaysia, Petaling Jaya. Total column O3 observations in Petaling Jaya are studied for the period January 2008 to December 2008. Ozone shows seasonal variation with maximum (276.8 DU in May 2008) during pre-monsoon season and minimum (246.8 DU in January 2008) during northeast monsoon season. In addition, the monthly O3 maps for the year of 2008 were obtained from the NASA-operated Giovanni portal to overview the distribution of total column O3 in Peninsular Malaysia. For the upcoming studies, validation of ground measurements with satellite O3 data and study of tropospheric O3 over the study area is recommended.
Lidar ratio (LR) is an important parameter to invert the lidar equation to subsequently get information from the lidar
signals. Therefore, it is the objective of this study to assess the LR values for each day to implement into the inversion
method. An algorithm has been generated to estimate the lidar ratios in Penang for the Raymetrics ground-based lidar.
Daily average humidity and visibility parameters was obtained and the lidar ratios for each day in year 2014 and year
2015 were assessed. It is found that the LR values in the year 2014 and 2015 generally lie in the range from 55 sr to 85
sr. Maximum LR values in the year 2014 and 2015 is 141 sr and 177 sr respectively. Both years has the same minimum
LR value of 46 sr. Extreme values are found in both years during the haze events that occurred in Penang. The LR
values estimated are valuable as they represent the atmospheric conditions in Penang and plays an utmost important role
in the lidar inversion method.
In this paper, we study the self-phase modulation on a sub-micro graphene waveguide to show the nonlinear optical properties of graphene. Self-modulation is one of the most popular nonlinear effects that has been observed due to selfinfluence of a mode propagation in third-order materials. This effects is capable to demonstrate the nonlinearity based the structure. Our study is aimed to show the appearance of SPM in considered waveguide as a common effect of nonlinear refraction to proof the capability of graphen on apply the based waveguide in nonlinear regime to access the desired parameters such as dimension and insertion power. An interest aspect is placed in this simulation may be conversion step-index to grade-index due to change from linear to the nonlinear that causes high confinement of light in waveguide.
The atmosphere over Penang Island is monitored for one year using a ground based Lidar. The Lidar signals are processed to obtain the AOD, extinction coefficients and the PBL heights to provide an overview of the atmospheric conditions in Penang. The data are averaged daily and plotted for the year of 2014. The AOD and extinction coefficients display seasonal trends that increase during the monsoon seasons (Southwest monsoon and Northeast monsoon) and decrease during the inter-monsoon seasons. During the monsoon seasons, a mixture of clear and hazy atmospheric conditions is found due to the presence of rain which removes the particulates or aerosols from the atmosphere. If no rain occurs, aerosols transported over Penang will stay in the atmosphere and be removed after a certain period. The average AOD is 0.4034 for year 2014 with a maximum of 1.0787 on a hazy day and a minimum of 0.0354 on a clear day. The extinction coefficient range is quite wide especially during the monsoonal months owing to the intervention of aerosol layers in the atmosphere of Penang. A clear day will have a smaller range of extinction coefficients. The planetary boundary layer has an average height of 0.878 km. Thicker PBLs are found after monsoon seasons as the aerosols has sunk to the earth surface from higher altitudes. The PBL has an opposing trend to the AOD and extinction coefficients. The atmosphere over Penang Island consists of a mixture of marine particles and fine particles that are mainly transported to Penang by the monsoon winds from the surrounding sea and biomass burnings in the neighboring SEA countries. An overview of the atmospheric conditions in Penang for a whole year is meaningful for further research.
This paper presents the prediction models which analyze and compute the CO2 emission in Malaysia. Each prediction model for CO2 emission will be analyzed based on three main groups which is transportation, electricity and heat production as well as residential buildings and commercial and public services. The prediction models were generated using data obtained from World Bank Open Data. Best subset method will be used to remove irrelevant data and followed by multi linear regression to produce the prediction models. From the results, high R-square (prediction) value was obtained and this implies that the models are reliable to predict the CO2 emission by using specific data. In addition, the CO2 emissions from these three groups are forecasted using trend analysis plots for observation purpose.
Mangrove vegetation is widely employed and studied as it is a unique ecosystem which is able to provide plenty of goods and applications to our country. In this paper, high resolution airborne image data obtained the flight mission on Kuala Sepetang Mangrove Forest Reserve, Perak, Malaysia will be used for mangrove species mapping. Supervised classification using the retrieved surface reflectance will be performed to classify the airborne data using Geomatica 2013 software package. The ground truth data will be used to validate the classification accuracy. High correlation of R2=0.873 was achieved in this study indicate that high resolution airborne data is reliable and suitable used for mangrove species mapping.
Carbon dioxide (CO2) is an inodorous and transparent gas, and naturally originates in our atmosphere. Due to its optical characteristics, CO2 is the most important greenhouse gas and play a key role in climate change due to an effective thermal infrared (IR) radiation absorber. Satellite observations of atmospheric carbon dioxide (CO2) can significantly improve our knowledge about the sources and sinks of CO2. The remote sensing satellite, namely Greenhouse Gases Observing Satellite (GOSAT) was employed to investigate the spatial and variations of CO2 column-averaged dry airmole fractions, denoted XCO2 over Peninsular Malaysia from January 2013 to December 2013. The analysis of CO2 in the study area shows the significant differences between northeast monsoon (NEM) and the southwest monsoon (SWM). During NEM season, cold air outbreaks from Siberia spreads to equatorial region in the form of north-easterly cold surge winds and associated with a low-level anticyclone over Southeast Asia. Inversely, air masses from the southwest contribute to long–range air pollution due to transportation of atmospheric CO2 by wind is associated with biomass burning in Sumatra, Indonesia. The GOSAT data and the Satellite measurements are able to measure the increase of the atmosphere CO2 values over different regions.
Lead (Pb2+) and copper (Cu2+) ions are very common pollutants in water which have dangerous potential causing serious disease and health problems to human. The aim of this paper is to determine lead and copper ions in aqueous solution using direct UV detection without chemical reagent waste. This technique allow the determination of lead and copper ions from range 0.2 mg/L to 10 mg/L using UV wavelength from 205 nm to 225 nm. The method was successfully applied to synthetic sample with high performance.
The Kelantan estuary, located in the northeastern part of Peninsular Malaysia, is characterized by high levels of
suspended sediments. Kuala Besar is the estuary of the river directly opposite South China Sea. Spectral reflectance (Rr)
and transparency measurements were carried out in the Kelantan estuary. The objective in this study is to establish
empirical relationships between spectral remote sensing reflectance in ALOS satellite imagery and water column
transparency, i.e. nephelometric turbidity unit (NTU) and Secchi disc depth (SDD) through these numerous in situ
measurements. We detected that remote sensing reflectance are linear and power regression functions against NTU and SDD. The results of this sampling show that the wavelengths range from 500-620 nm is the most suitable band for
measuring water column transparency. The calibrated reflectance of ALOS AVNIR-2 bands was also regressed against
NTU and SDD field data to derive two empirical equations for water transparency estimation. These equations were
calculated using ALOS images data on June 12, 2010. The result obtained indicated that reliable estimates of turbidity
and transparency values for the Kelantan Estuary, Malaysia, could be retrieved using this method.
This paper presents the utilities of remote sensing technique for water quality assessment in Kelantan Delta, Malaysia. Remote sensing is one of the effective methods for water quality monitoring through image analysis of study area. Spectral reflectance signatures of Kelantan Delta were measured from 20 stations using ASD Handheld
spectroradiometer from regions with different turbidity level. Water samples collected from these stations were taken to the laboratory for measure turbidity in Nephelometric Turbidity Unit (NTU). The objective of this study is to examine the potential of ALOS on Japanese Earth Observing Satellite (JEOS) for assessing water quality in Kelantan Delta. There is a large correlation between NTU and the in-situ reflectance at 500 - 620 nm (maximum spectra band between 300 and 1100 nm) is shown by multiple linier regression model, resulting from increasing of turbidity levels, was developed and applied to ALOS band 2 and band 3 (0.42-069 nm). A simple atmospheric correction, based on darkest pixel technique was performed in this study. The ALOS data provides accurate estimates of the mean water quality (R2 = 0.95 and RMSE = 2.26 NTU). The result acquired is reliable to estimate of water quality values for the Kelantan Delta and its implication for future operation.
The problem of difficulty in obtaining cloud-free scene at the Equatorial region from satellite platforms can be overcome by using airborne imagery. Airborne digital imagery has proved to be an effective tool for land cover studies. Airborne digital camera imageries were selected in this present study because of the airborne digital image provides higher spatial resolution data for mapping a small study area. The main objective of this study is to classify the RGB bands imageries taken from a low-altitude Cropcam UAV for land cover/use mapping over USM campus, penang Island, Malaysia. A conventional digital camera was used to capture images from an elevation of 320 meter on board on an UAV autopilot. This technique was cheaper and economical compared with other airborne studies. The artificial neural network (NN) and maximum likelihood classifier (MLC) were used to classify the digital imageries captured by using Cropcam UAV over USM campus, Penang Islands, Malaysia. The supervised classifier was chosen based on the highest overall accuracy (<80%) and Kappa statistic (<0.8). The classified land cover map was geometrically corrected to provide a geocoded map. The results produced by this study indicated that land cover features could be clearly identified and classified into a land cover map. This study indicates the use of a conventional digital camera as a sensor on board on an UAV autopilot can provide useful information for planning and development of a small area of coverage.
Among all the greenhouse gases, methane is the most dynamic and abundant greenhouse gas in the atmosphere. The global concentrations of atmospheric methane has increased more than doubled since pre-industrial times, with a current globally-averaged mixing ratio of ~ 1750 ppbv. Due to its high growth rate, methane brings significant effects on climate and atmospheric chemistry. There has a significant gap for variables between anthropogenic and natural sources and sinks of methane. Satellite observation of methane has been identified that it can provide the precise and accurate data globally, which sensitive to the small regional biases. We present measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) included on the European environmental satellite ENVISAT, launched on 1st of March 2002. Main objective of this study is to examine the methane distribution over Peninsular Malaysia using SCIAMACHY level-3 data. They are derived from the near-infrared nadir observations of the SCIAMACHY at the University of Bremen through scientific WFM-DOAS retrieval algorithm version 2.0.2.Maps of time averaged (yearly, tri-monthly) methane was generated and analyzed over Peninsular Malaysia for the year 2003 using PCI Geomatica 10.3 image processing software. The maps show dry-air column averaged mixing ratios of methane (denoted XCH4). It was retrieved using the interpolation technique. The concentration changes within boundary layer at all altitude levels are equally sensitive through the SCIAMACHY near-infrared nadir observations. Hence, we can make observation of methane at surface source region. The results successfully identify the area with highest and lowest concentration of methane at Peninsular Malaysia using SCIAMACHY data. Therefore, the study is suitable to examine the distribution of methane at tropical region.
We investigate the effect of orography on the relationship of wind-cloud systems with tropical cyclones (TC). The impact of orography is important for the cloud distribution and wind flow patterns of TC activity. Furthermore, the influence of orography on TCs remains unclear and is an active area of scientific research because of the complexity of orography effects caused by the presence of mountains. We focus on typhoons in the open sea, near coastal regions, and in the mountains. The atmospheric circulation of the level between the troposphere and the stratosphere (TS level) varies when disturbances, such as a high mountain range, occur in the surrounding edges or outflow of a typhoon. Orographic effects can influence the types of clouds (e.g., nimbus and cirrus) that form in different altitudes (high, middle, and low levels of the troposphere). Our results imply that the interaction between TCs and high-altitude topography (e.g., mountains) leads to changes in the features of TCs.
Increasing of atmospheric ozone concentrations have received great attention around the whole because of its characteristic, in order to degrade air quality and brings hazard to human health and ecosystems. Ozone, one of the most pollutants source and brings a variety of adverse effects on plant life and human being. Continuous monitoring on ozone concentrations at atmosphere provide information and precautions for the high ozone level, which we need to be established. Satellite observation of ozone has been identified that it can provide the precise and accurate data globally, which sensitive to the small regional biases. We present measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) included on the European environmental satellite ENVISAT, launched on 1st of March 2002. Main objective of this study is to examine the ozone distribution over Peninsular Malaysia using SCIAMACHY level-2 of total ozone column WFMD version 1.0 with spatial resolution 1° x 1.25°. Maps of time averaged (yearly, tri-monthly) ozone was generated and analyzed over Peninsular Malaysia for the year 2003 using PCI Geomatica 10.3 image processing software. It was retrieved using the interpolation technique. The concentration changes within boundary layer at all altitude levels are equally sensitive through the SCIAMACHY nearinfrared nadir observations. Hence, we can make observation of ozone at surface source region. The results successfully identify the area with highest and lowest concentration of ozone at Peninsular
Malaysia using SCIAMACHY data. Therefore, the study is suitable to examine the distribution of ozone at tropical region.
The objective of this study is to investigate the effects of orography on the rainfall, wind, and cloud systems of the TCs in Malaysia and Indochina. To determine the relationship of the typhoon with the orographic effect, remote sensing techniques such as the Global Digital Elevation Model (GDEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite, rainfall data from the Fengyun 2D (FY-2D), and radiosonde data were applied in this study. From this study, the following conclusions can be drawn: 1) rainfall tends to be distributed over high mountain regions; 2) wind flow will change its direction upon encountering any restrictions, especially those of high terrain regions; and 3) cloud patterns are deformed by high mountains and tend to flow with the mountains' structure because of the orographic effects. The regions most affected by Typhoon Ketsana in the study area were Vietnam in Indochina, Sabah in East Malaysia (EM), Kelantan and Terengganu in Peninsular Malaysia (PM). From the comparison among the study areas, it was found that Indochina had the most significant results for the orographic effects on typhoon activity, followed by the tail effects in EM. This phenomenon was found in PM, although it was not as significant as the other study areas. This remote sensing technique allows tropical cyclones to be forecasted and their impacts to be defined, and it allows disaster zones to be determined.
This study encompasses air surface temperature (AST) modeling in the lower atmosphere. Data of four atmosphere pollutant gases (CO, O3, CH4, and H2Ovapor) dataset, retrieved from the National Aeronautics and Space Administration Atmospheric Infrared Sounder (AIRS), from 2003 to 2008 was employed to develop a model to predict AST value in the Malaysian peninsula using the multiple regression method. For the entire period, the pollutants were highly correlated (R = 0.821) with predicted AST. Comparisons among five stations in 2009 showed close agreement between the predicted AST and the observed AST from AIRS, especially in the southwest monsoon (SWM) season, within 1.3 K, and for in situ data, within 1 to 2 K. The validation results of AST with AST from AIRS showed high correlation coefficient (R = 0.845 ?startto end?0.918), indicating the model's efficiency and accuracy. Statistical analysis in terms of β showed that H2Ovapor (0.565 to 1.746) tended to contribute significantly to high AST values during the northeast monsoon season. Generally, these results clearly indicate the advantage of using the satellite AIRS data and a correlation analysis study to investigate the impact of atmospheric greenhouse gases on AST over the Malaysian peninsula. A model was developed that is capable of retrieving the Malaysian peninsulan AST in all weather conditions, with total uncertainties ranging between 1 and 2 K.
Atmospheric components (aerosol and molecules) scatter and absorb solar radiation. This study investigated the used
of a handheld spectroradiometer for the retrieval of atmospheric optical thickness (AOT) values over Penang Island
derives this period. The objective of this study is to introduce a new technique for retrieval of aerosol optical
thickness (AOT) for air quality determination. Measured spectroradiometer data was used to calculate the aerosol
optical thickness (AOT) values at the earth surface. The transmittance values were measured using a handheld
spectroradiometer over Penang Island. Particulate matters of size less than 2.5 micron (PM2.5) were collected
simultaneously with the acquisition of the transmittance measurements. The results of the calculated AOT were used
to retrieve the air quality at Penang, Malaysia. The retrieved AOT data were linearly correlated with the particulate
matter of less than 2.5 micro meter (PM2.5). An AOT map and PM10 map were generated using interpolation
technique. The relationship between AOT and PM10 was investigated and we obtained a linear relationship between
these two parameters. Finally, an interpolating technique was used to generate a PM2.5 map over Penang Island.
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 presents a technique for land use/cover
mapping using THEOS data of the Penang Island, Malaysia. The objective is to assess the capability of a THEOS
image to provide useful remotely sensed images for land cover mapping. The land cover information was extracted
from the visible digital spectral bands using PCI Geomatica 10.3 software package. A frequency based contextual
classifier was applied to the imagery to extract the spectral information from the acquired scene. Contextual
classification is employed when neighbouring pixels are taken into account. The accuracy of each classification map
was assessed using the reference data set consisted of a large number of samples collected per category. The study
revealed that the frequency based contextual classifier produced superior result and achieved a high degree of
accuracy. The preliminary result indicates that THEOS image can be provided useful data for remote sensing to
retrieve land cover information at local scale.
The purpose of this study was to evaluate the original PALSAR radar, and radar texture, for land cover classification.
The primary methodology was standard image processing, including spectral signature extraction and the application
of a statistical decision rule to classify the surface features .Seven land covers were identified and the probability of
correct classification of classes was assessed by using the Transformed Divergence (TD) separability measures. TD
values were obtained for all original and texture derived bands along with various multiple band combinations. The
radar texture bands greatly improved upon the TD values in comparison to the original radar values. Combination of
original radar and radar texture bands consistently showed excellent Transformed Divergence (TD) separability. The
use of texture was able to improve separability between different land cover/use classes.
Mangrove vegetations are normally present in river estuaries and along the coast where the land meets the sea.
Remote sensing can be used to obtain mangrove distribution information. The objective of this study was to study
the current condition of mangrove forest using remote sensing over Penang Island, Malaysia. An attempt has been
made based on supervised Maximum Likelihood Classification (MLC), various land use and land cover classes have
been mapped and classified. A red-green-blue (RGB) colour was used to display and quantify mangrove forest
distribution using Thailand Earth Observation System (THEOS) satellite imagery. Reference data was based on
ground truth. High accuracy of 91.7% was obtained in mapping of mangrove cover.
Remote sensing sensors are now able to deliver greatly increased amount of information with the used of high resolution
sensor. But high or very high resolution sensors lead to noise in generally homogeneous classes as the data contains
increased information with more internal variability. Conventional classification methods commonly cannot handle the
complex landscape environment in the image. The result of each method has often "a salt and pepper appearances"
which is a main characteristic of misclassification. It seems clear that information from neighboring pixels should
increase the discrimination capabilities of the pixel based measured, and thus, improve the classification accuracy and
the interpretation efficiency. This information is referred to as the spatial contextual information. In this paper, we shall
present a contextual classification method based on a frequency-based approach for the purpose of land cover mapping.
Additionally, classification maps are produced which have significantly less speckle error. In order to evaluate the
performances of the classifier, 9 different window sizes ranging from 3x3 to 19x19 with an increment of 2 is tested.
We had explored the relationship between particulate matters of size less than 10 micron (PM10) derived from the
THEOS using regression technique over Penang Island, Malaysia. The objective of this study was to evaluate the
high spatial resolution satellite data for air quality mapping. The development of the algorithm was based on the
optical aerosol characteristic in the atmosphere. PM10 measurements were collected simultaneously with the image
acquisition using a DustTrak Aerosol Monitor 8520. The station locations of the PM10 measurements were
determined using a handheld GPS. 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, ACTOR3 was used to
retrieve the surface reflectance values from remotely sensed data. The surface reflectance values for the visible
wavelengths of THEOS data was determined based on the Landsat TM data that acquired same date, nearly same
time with the THEOS data. The relationship between the image reflectance values and the corresponding air quality
data was determined using regression analysis. Various forms of algorithms were tested and their accuracies were
noted. The algorithm that produced the highest correlation coefficient (R) and lowest root-mean-square error (RMS)
was used for further analysis. Results show that the digital camera imageries can be used for estimating PM10
accurately. This study shows the potential of using the THEOS data for air quality mapping.
Traditional aerial images provided by satellite, manned aircraft or stock photography are often expensive, difficult to
obtain or outdated. The CropCam provides GPS based digital images on demand and real time data with high temporal
resolution throughout the equatorial region where the sky is often covered by clouds. The images obtained by the
CropCam will allow producers to detect, locate, and have better assessment of the actions required to overcome the
problem of unclear images obtained by the satellite and manned aircraft in this area. A Pentax digital camera, model
Optio A40, was used to capture images from the height of 320 meters on board the CropCam UAV autopilot. The
objective of this study is to evaluate the land use /land cover (LULC) features over Penang Island using the images
obtained during the CropCam flying mission. The study also test the effectiveness of neural network approach instead
of conventional methods in classification process in order to overcome or minimize the difficulty in classification of the
mixed pixel areas using high resolution images with spatial ground 8 cm. The technique was applied to the digital
camera spectral bands (red, green and blue) to extract thematic information from the acquired scene by using PCI
Geomatica 10.3 image processing software. Training sites were selected within each scene and four LULC classes were
assigned to each classifier. The accuracy assessment of each classification map produced was validated using the
reference data sets consisting of a large number of samples collected per category. The results showed that the neural
network classifier produced superior results and achieved a high degree of accuracy. The study revealed that the neural
network approach is effective and could be used for LULC classification using high resolution images of a small area of
coverage acquired by the CropCam UAV.
Traditional sampling method for marine environment monitoring is time consuming and needs a high cost to carry
out the survey. Remote sensing data have been widely used for monitoring marine environment and remote sensing
is an efficient method to overcome the problem. This paper assesses the use of multispectral satellite imagery from
THEOS for mapping spatial distribution of TSS in a coastal zone. The aim of this study is to evaluate the feasibility
of using THEOS satellite image for the water quality studies. Simultaneous in situ measurements of total suspended
solids (TSS) concentration and acquisition of satellite imageries were carried out over Penang Island, Malaysia. The
locations of in situ sample were determined using a handheld Global Positioning System (GPS). The algorithm used
is based on the reflectance model which is a function of the inherent optical properties of water and this in turn can
be related to the concentration of its constituents. Multiple regression algorithm was employed using the multi-band
data for retrieval of the water constituent. Digital numbers corresponding to the water sample locations were
determined for algorithm calibration. Various types of algorithms were tested; R and RMS value were noted. The
proposed algorithm is considered superior based on the values of the correlation coefficient and root-mean-square
The algorithm was used to generate the TSS map for the Penang Island, Malaysia. Geometric correction was
performed to the TSS map and colour-coded for visual interpretation. This study shows the potential application of
THEOS satellite images for TSS mapping using the proposed multispectral algorithm.
Aerosols play an important role in the global climate balance, and therefore they could be important in climate change. Natural variations of aerosols, especially due to dust storm are recognized as a significant climate forcing, that is, a factor that alters the Earth's radiation balance and thus tends to cause a global temperature change. Aerosol optical depth, τ(λ) is the most comprehensive variable to characterize aerosol due to atmospheric pollution. The aerosol optical properties in Makkah observed during dust period (March-May) from 2006 to 2009 had been presented in this study. Aerosol optical depths at all wavelengths showed a sharp increase during major dust outbreak in spring when compared with the average for the season. For example at Makkah, aerosol optical depths increase from the spring average value of 0.43±0.02 at 550 nm to values >0.70 during major dust event days in 2006. These tend to increasingly of temperature during this period as results of absorbing aerosol effect. In this paper, we used AOD data from Terra MODIS to evaluate the trend of dust aerosol events in Makkah throughout 4 years dataset with supported data of subtype of aerosol and air temperature from CALIPSO and MERRA respectively. The higher values of AOD are corresponding to the low visibility due to presents of high concentration of dust.
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.
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.
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 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.
Total suspended solid (TSS) is a major factor affecting water quality in aquatic
ecosystem. An investigation has been conducted to test the feasibility of using SPOT
5 data for estimating TSS in the coastal waters of Penang Island, Malaysia.
Atmospheric correction of the satellite measurements is critical for aquatic remote
sensing. Atmospheric correction of the remotely sensed image was performed using
the ENVI FLAASH. Water samples were collected simultaneously with the satellite
image acquisition and later analyzed in the laboratory. The digital numbers for each
band corresponding to the sea-truth locations were extracted and then converted into
reflectance values. The variables of the reflectance were used for calibration of the
water quality algorithm. Regression technique was employed to calibrate the
algorithm using the SPOT multispectral signals. An algorithm was developed based
on the reflectance model, which is a function of the inherent optical properties of
water that can be related to the concentration of its constituents. Spatial distribution
map of the water quality parameter was produced using the calibrated algorithm. The
efficiency of the present algorithm, in comparison to other forms of algorithm, was
also investigated. Finally, the TSS map was generated using the proposed algorithm.
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.
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.
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.
The sea surface temperature (SST) mapping could be performed with a wide spatial and temporal extent in a reasonable
time limit. The space-borne sensor of AVHRR was widely used for the purpose. However, the current SST retrieval
techniques for infrared channels were limited only for the cloud-free area, because the electromagnetic waves in the
infrared wavelengths could not penetrate the cloud. Therefore, the SST availability was low for the single image. To
overcome this problem, we studied to produce the composite of three day's SST map. The diurnal changes of SST data
are quite stable through a short period of time if no abrupt natural disaster occurrence. Therefore, the SST data of three
consecutive days with nearly coincident daily time were merged in order to create a three day's composite SST data. The
composite image could increase the SST availability. In this study, we acquired the level 1b AVHRR (Advanced Very
High Resolution Radiometer) images from Malaysia Center of Remote Sensing (MACRES). The images were first
preprocessed and the cloud and land areas were masked. We made some modifications on the technique of obtaining the
threshold value for cloud masking. The SST was estimated by using the day split MCSST algorithm. The cloud free
water pixels availability were computed and compared. The mean of SST for three day's composite data were calculated
and a SST map was generated. The cloud free water pixels availability were computed and compared. The SST data
availability was increased by merging the SST data.
This study employs the developed algorithm for retrieving land surface temperature (LST)
from Landsat TM over Saudi Arabia. The algorithm is a mono window algorithm because the
Landsat TM has only one thermal band between wavelengths of 10.44-12.42 μm. The
proposed algorithm included three parameters, brightness temperature, surface emissivity and
incoming solar radiation in the algorithm regression analysis. The LST estimated by the
proposed developed algorithm and the LST values produced using ATCORT2_T in the PCI
Geomatica 9.1 image processing software were compared. The mono window algorithm
produced high accuracy LST values using Landsat TM 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.
Shoreline mapping and shoreline change detection are critical in many coastal zone applications.
This study focuses on applying remote sensing technology to identify and assess coastal changes in
the Banda Aceh, Indonesia. Major changes to land cover were found along the coastal line. Using
remote sensing data to detect coastal line change requires high spatial resolution data. In this study,
two high spatial data with 30 meter resolution of Landsat TM images captured before and after the
Tsunami event were acquired for this purpose. The two satellite images was overlain and compared
with pre-Tsunami imagery and with after Tsunami. The two Landsat TM images also were used to
generate land cover classification maps for the 24 December 2004 and 27 March 2005, before and
after the Tsunami event respectively. The standard supervised classifier was performed to the
satellite images such as the Maximum Likelihood, Minimum Distance-to-mean and Parallelepiped.
High overall accuracy (>80%) and Kappa coefficient (>0.80) was achieved by the Maximum
Likelihood classifier in this study. Estimation of the damage areas between the two dated was
estimated from the different between the two classified land cover maps. Visible damage could be
seen in either before and after image pair. The visible damage land areas were determined and draw
out using the polygon tool included in the PCI Geomatica image processing software. The final set
of polygons containing the major changes in the coastal line. An overview of the coastal line changes
using Landsat TM images is also presented in this study. This study provided useful information that
helps local decision makers make better plan and land management choices.
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.
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.
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.
The design of optical sensor systems is based on the interaction between the photons of the electromagnetic radiation and suspended
particles in water. The objectives of this study area are to design and develop a dwi-detector optical sensor for measuring the
concentration of total suspended solids, TSS, in polluted water samples. An algorithm which requires calibration analysis has been
derived for estimating TSS concentrations. The proposed optical system uses a single light emitting diode, LED, as an emitter and two
phototransistors are used as detectors. Detected radiations were measured at scattering angles of 90° and 180° between the source and the detectors. The algorithm produced a high correlation coefficient and low root mean square error value.
In this study, we used the Landsat TM data captured on 9 March 2006 for the retrieval of PM10 over the water surface of Penang Straits, Malaysia. PM10 measurements were collected using a handheld DustTrakTM meter simultaneously with the remotely sensed data acquisition. The PCI Geomatica version 9.1 digital image processing software was used in all image-processing analysis. An algorithm was developed based on the atmospheric optical characteristic. The digital numbers were extracted corresponding to the ground-truth locations for each band and then converted into radiance and reflectance values. The reflectance measured from the satellite [reflectance at the top of atmospheric, &rgr;(TOA)] was subtracted by the amount given by the surface reflectance to obtain the atmospheric reflectance. Then the atmospheric reflectance was related to the PM10 using regression analysis. These atmospheric reflectance values were used for calibration of the PM10 algorithm. The developed algorithm was used to correlate the digital signal and the PM10 concentration. The proposed algorithm produced a high correlation coefficient (R) and low root-mean-square error (RMS). The PM10 concentration was generated using this algorithm over the water surface of Penang straits.
This study determined the relationship between in situ and remote sensing observation to derive an algorithm for PM10
mapping. The main objective of this study was to test the feasibility of using Landsat TM imagery captured on 17
January 2002 for PM10 mapping over Penang Island, Malaysia. A new algorithm was developed based on the aerosol
characteristic for air quality estimation. The corresponding PM10 data were measured simultaneously with the
acquisition of satellite scene for algorithm regression analysis. Accuracy of the retrieved surface reflectance values is
very importance to determine the atmospheric component from the remotely sensed data. In this study, we computed the
surface component properties by using ACTOR2 in the PCI Geomatica 9.1 image processing software. The proposed
algorithm produced high correlation coefficient (R) and low root-mean-square error (RMS) between the measured and
estimated PM10 values. A PM10 map was generated using the proposed algorithm. Finally, the created PM10 map was
geometrically corrected and colour-coded for visual interpretation. This study indicated the usefulness of remotely
sensed data for air quality studies using the proposed algorithm.
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.
A method to retrieve the land surface temperature (LST) over Mecca, Saudi Arabia are developed
using band 6 of the Landsat TM thermal channel. The objective of this study was to focus on the
estimation of the LST from Landsat TM 5 imageries. The data used was captured by Thematic
Mapper (TM) sensor onboard the Landsat 5 satellite. Landsat TM has only one thermal band, and
therefore the spilt-window algorithm cannot be used for the retrieval of LST. In this study, we are
proposed a single channel algorithm for retrieving LST. The land surface emissivity and solar angle
values are needed in order to apply these in the proposed algorithm. The surface emissivity values
were computed based on the NDVI values. The correlation between the LST and the brightness
temperature had increased significantly after the surface emissivity and solar zenith angle were
included in the algorithm. The reference values LST were determined using ATCOR2_T in the PCI Geomatica image 9.1 processing software for algorithm calibration. The results indicate that the
single channel algorithm was suitable for retrieving LST values from remotely sensed data.
This paper presents and describes an approach to retrieve concentration of particulate matter of size
less than 10- micron (PM10) from Landsat TM data over Penang Island. The objective of this study is
test the feasibility of using Landsat TM for PM10 mapping using our proposed developed algorithm.
The development of the algorithm was developed base on the aerosol characteristics in the atmosphere.
PM10 measurements were collected using a DustTrak Aerosol Monitor 8520 simultaneously with the
image acquisition. The station locations of the PM10 measurements were detemined using a hand held
GPS. The digital numbers were extracted corresponding to the ground-truth locations for each band
and then converted into radiance and reflectance values. The reflectance measured from the satellite
[reflectance at the top of atmospheric, ρ(TOA)] was subtracted by the amount given by the surface
reflectance to obtain the atmospheric reflectance. Then the atmospheric reflectance was related to the
PM10 using regression analysis. The surface reflectance values were created using ACTOR2 image
correction software in the PCI Geomatica 9.1.8 image processing software. The proposed developed
algorithm produced high accuracy and also showed a good agreement (R =0.8406) between the
measured and estimated PM10. This study indicates that it is feasible to use Landsat TM data for
mapping PM10 using the proposed algorithm.
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.
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.
The conventional method used to measure the total suspended solids (TSS) in seawater is by collecting samples and analysing them in the lab. This method is not efficient and cannot provide a real-time result, whereas, digital image processing and remote sensing techniques have been widely used in estimating and mapping the concentration of the TSS in vast areas. Various algorithms have been developed and used for the measurement of TSS concentration. In this work, an algorithm developed for remote sensing techniques is used. The technique employed involves radiating the samples with a laser and then correlating the back-scattering with the concentration of the total suspended solids in the water sample. A laser emitting multiple wavelengths are radiated into the water sample and back-scattering radiations for every wavelength are analysed and correlated with the total suspended solids concentration. The back-scattering radiation is then analysed and the proposed water quality algorithm is calibrated for measuring the total suspended solids level in the water. In this work the total suspended solids concentration is correlated with the ratio of back scattering radiation of wavelengths 780nm and 633nm. The linear correlation coefficient square (R2) between the spectrometer reading and the laboratory analysis produced in this work is 0.98.
An algorithm was developed based on reflectance model of inherent properties of seawater. A digital camera was used to capture digital images of river estuaries of Prai, Muda, and Merbok from a low altitude flying light aircraft. Water samples were collected simultaneously with the airborne image acquisition and later analyzed in the laboratory. Vertical images were captured through a special hole at the floor of the aircraft. Atmospheric correction for multidate images was performed by selecting average digital number of grass as a reference. The digital colour images of the study areas were separated into three bands (red, green and blue) for multi-spectral analysis. The digital numbers for each band corresponding to the sea-truth locations were extracted and used to calibrate the algorithm. The calibrated total suspended solids (TSS) algorithm was then used to generate the water quality maps of the study areas. This study indicates that a digital camera can be a useful tool for airborne remote sensing. The newly developed algorithm can estimate TSS concentration with linear correlation coefficient square (R2) of 0.94.
The feasibility of using digital camera imagery for estimating the concentration of total suspended sediments (TSS) in the Timah Tasoh reservoir was investigated. Digital images were captured from a low-altitude light aircraft. Three selected images were mosaiced to produce bigger image of the study area. Atmospheric corrections and mosaic sincronourization were performed by using the average digital number of grass in the images as a reference value. Water samples were collected simultaneously with the airborne image acquisition and later analyzed in the laboratory. The color mosaic image was separated into three bands assigned as red, green and blue bands and analysis separately. The digital numbers for each bands corresponding to the ground-truth locations were extracted and used for calibration of the water quality algorithm. A single band second order polynomial algorithm was used in this study due to limited ground-truth data. The calibrated algorithm was used to generate the water quality maps. The proposed algorithm can estimate TSS concentration with linear correlation coefficient square (R2) of 0.98.
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