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Precipitable water vapor (PWV) is a highly variable, but important greenhouse gas that regulates the radiation budget of the earth. Its variability in time and space makes it difficult to quantify. Knowledge of its vertical distribution, in particular, is crucial for many reasons. In this study, empirical relationships between isobaric layers of PWV over Peninsular Malaysia are examined. Analysis of variance (ANOVA) technique on Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) observations, from 2005 to 2011, has been used to propose a relationship of the form, W=α(WL)β for the middle (MW) and upper (UW) layers PWV. W is either MW or UW with α and β as regression coefficients, which are functions of latitude. Coefficients of determination (R2) and root mean square error (RMSE) of respective values between 0.75–0.86 and 1.65–2.38 mm, across the zones, were obtained for both the MW and UW predictions, with a mean bias (MB) below ±1 mm.The predicted and observed PWV presented a better agreement northerly. Initial predictability test for each model was done on two independent data sets: ATOVS (2012–2015), and radiosonde (2010–2011) at Penang, Kuantan and Sepang stations, with very good outcomes. The results of the tests revealed remarkable performances, when compared with two previously reported models. The inclusion of variable regression coefficients, and the utilization of satellite-derived data, which provide soundings of data-void regions between radiosonde networks, proved to have optimized the results.

An airborne survey was conducted to produce land cover/use maps. The feasibility of using a conventional digital camcorder to acquire remotely sensed data was investigated, and the imagery for land cover mapping using remote sensing technique was evaluated. The study area was the Universiti Sains Malaysia campus, Penang, located in Peninsular Malaysia. Digital images were taken from a low-attitude light aircraft, Cessna 172Q, at an average altitude of 2.4384 km above sea level. The use of a digital camcorder as a sensor to capture digital images is more economical compared with other airborne sensors. This technique is designed to overcome the problem of obtaining cloud-free photographs from a satellite platform in equatorial regions. Digital video imageries were taken in the red, green, and blue bands. A comparison between frequency-based contextual and neural network classification techniques for analyzing digital camcorder imagery is presented. Frequency-based contextual and neural network classification techniques were applied to the digital camera spectral bands (red, green, and blue) to extract the thematic information from the acquired scenes. The classified map was compared with the ground truth data, and accuracy was evaluated by an error matrix. Results indicate that a conventional digital camcorder can be used to acquire digital imageries for land cover/use mapping of a small area of coverage.

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

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 (

The study of atmospheric aerosols is an important element to understand the earth's solar radiation budget, water cycle balance, and climate change dynamics. Estimating aerosol properties is also one of the first steps in generating high-level land and ocean surface products from satellite observations. In this paper, a simple method to estimate the aerosol optical depth (AOD) over ocean is proposed. The method is based on the intercept of the line connecting the 0.47, 1.24, 1.64 and 2.14 µm. The lines that intercept the apparent reflectance axis are higher over the high aerosol contribution area and lower over low aerosol contribution area. The comparison of the intercept map line and AOD map distributed by MODIS ocean color team are in high correlation with R>0.90. The AOD map from the applications of the algorithm was constructed. The sample result of AOD map from several MODIS datasets acquired over the Gulf of Martaban is presented.

The presence of aerosol in the atmospheric plays a significant role in the earth's radiation budget through radiative forcing and chemical perturbations. They scatter solar radiation back to space, absorb solar and terrestrial radiation, and act as cloud condensation nuclei, influencing cloud/precipitation processes and changing cloud reflectivity. Estimating aerosol properties is also one of the first steps in generating high-level land and ocean surface products from satellite observations. The study of atmospheric aerosols is an important element to understand the earth's solar radiation budget, water cycle balance, and climate change dynamics. In this paper, a simple method to estimate the aerosol optical depth (AOD) over ocean is proposed. The method is based on the gradient of the line connecting the 0.47, 1.24, 1.64 and 2.14 µm. The line gradients are decreased over the high aerosol contribution area and increased over low aerosol contribution area. The comparison of the gradient line and AOD map distributed by MODIS ocean color team are in high correlation (R>0.9). The AOD map from the applications of the algorithm was constructed. The sample result of AOD map from several MODIS datasets acquired over the Gulf of Martaban is presented.

The relationship between the outgoing longwave radiation (OLR) and rainfall over the South East Asia was examined by using daily OLR data from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites and rainfall data from Tropical Rainfall Measuring Mission (TRMM) 3B42 daily V006. The study was carried out using data collected from January to March in year 2007 which covered a region from longitudes 92.5°E-122.5°E; latitudes 20°N-20°S. Bilinear interpolation technique was used to generate new TRMM rainfall grid data from resolution of 0.25° × 0.25° grid to 2.5° x 2.5° grid to superimpose with the OLR data. The results show high negative correlation between OLR and rainfall at lower latitude regions compare to higher latitude regions.

Satellite remote sensing technique has provided new datasets for monitoring air quality at regional and urban area. Air pollution in Asian cities has grown with the progressing industrialization and urbanization. Air quality standards often refer to respirable suspended particulate matter (PM), being aerosols with a diameter smaller than 2.5 μm (PM2.5). The generated algorithm was based on the aerosol optical depth. Remote sensed data provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as particulate matters of size less than 2.5 micron (PM2.5). Remote sensing has been widely used for environmental application such as for air quality and water quality studies. But the atmosphere affects satellite images of the Earth’s surface in the solar spectrum. So, the signal observed by the satellite sensor is the sum of the effects from the ground and atmosphere. Surface reflectance is a key to the retrieval of atmospheric components from remotely sensed data. The accuracy of aerosol remote sensing techniques heavily depends on the information on the ground reflectance (Kokhanovskya, et al., 2005). Environmental pollution is coeval with the appearance of humans. When Homo sapiens first lighted fire, its smoke provided the first medium of environmental pollution. The burning of fuels for heating and cooking has contributed to the air pollution of inner spaces (Makra and Brimblecombe, 2004). Air pollution is one of the most important environmental problems, which concentrates mostly in cities. Generally, human activities induce monotonous accumulation of pollutants [Makra, et al., (2001a) and Makra, et al., (2001b)]. Sources of exposure to particulate air pollution are many, Indoor sources, including cooking, unvented heating appliances and pets often make important contributions to exposures. Road traffic, however, generally provides the major source of ambient particulate pollution (Gulliver and Briggs, 2004). We used a DustTrak Aerosol Monitor 8520 to collect in situ data. The particulate matters of size less than 2.5 micron data were collected simultaneously during the satellite Landsat overpass the study area. An empirical relationship between PM2.5 derived from the SPOT using regression technique is explored. A new algorithm was developed base on the aerosol characteristics in the atmosphere. We were obtained the atmospheric reflectance values by subtracting surface reflectance from the amount of reflectance measured from the satellite. The satellite recorded reflectance is the sum of the surface reflectance and atmospheric reflectance. The surface refleatance values were retrieved using ATCOR3 in the PCI Geomatica 10.3 image

Rapid development of countries can cause many environmental problems. One of the problem is water pollution which is the result of urban runoff from industrial and resident area. It has become a major global problem as it can lead to many deaths and diseases among human being. One of the main sources of water pollution is the suspended or colloidal particles which commonly referred as total suspended solids (TSS). Before the launch of satellite imagery technology, the biological parameters of ocean is obtained by using traditional sampling method which is time consuming and expensive. With remote sensing technique, water quality monitoring can be carried out by using a large coverage of satellite images analysis for water quality mapping. It is proven to be effective as it has been widely used in many environmental studies such as environmental pollution monitoring and oceanography. The purpose of this study is to obtain the water quality over Penang Straits using remote sensing imagery data. By using four scenes of Landsat TM images and in-situ data sets of particular dates, the new algorithm for water quality mapping which is based on optical model of water is developed. First, the images were atmospherically corrected and filtered using PCI Geomatica software as it gave better results compared to raw data especially when performing multi-date analysis. The digital numbers of each images were extracted and regressed using Excel. The efficiency of the algorithm was determined based on the observations of correlation coefficient (R) and root-mean-square (RMS) deviations with in-situ data. The accuracy of the algorithm, in comparison of other algorithm was also investigated. Finally, the calibrated TSS algorithm was used to generate the water quality table of difference and this study shows that the research of water quality can be carried out using remote sensing technique.

Ozone (O 3 ) is a radiatively active trace gas that plies important role in atmosphere heating rates because of its good ability to absorb the infrared radiation and occur both at the ground level and naturally in the earth's upper atmosphere. Results from the analysis of the retrieved monthly (AIRX3STM) 1°×1° spatial resolution Atmosphere infrared sounder (AIRS) data were utilized to analyze the distribution of O 3 column burden in Peninsular Malaysia for the period 2003–2009. AIRS is one of the several instruments onboard the Earth Observing System (EOS), onboard NASA's Aqua Satellite, launched on May 4, 2002. The analysis of O 3 above five dispersed stations in study area shows the seasonal variation in the O 3 values fluctuated considerably between wet and dry periods, and O 3 values strongly correlated with weather conditions. The lowest O 3 observed during rainy months, low temperature and low sunshine hours, vice versa. The highest O 3 values occurred over Industrial and congested urban zones. The monthly O 3 maps were generated, to study O 3 distribution over peninsular Malaysia for 2009, using Kriging Interpolation technique. The AIRS data and the Satellite measurements are able to measure the increase of the atmosphere O 3 concentrations over different regions.

Environmental monitoring through the method of traditional ship sampling is time consuming and requires a high survey cost. This study uses an empirical model, based on actual water quality of total suspended solids (TSS) measurements from the Penang Island, Malaysia to predict the TSS values based on optical properties of Oceansat satellite digital imagery. 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. Water samples were collected simultaneously with the airborne image acquisition and later analyzed in the laboratory. Water sample's locations were determined by using a handheld GPS. 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 water quality algorithm. The efficiency of the proposed algorithm was investigated based on the observations of correlation coefficient (R) of 0.98 and root-mean-square deviations (RMS) of 8.5121 mg/l with the sea-truth data. This algorithm was then used to map the TSS concentration over Penang Island, Malaysia. The TSS map was color-coded and geometrically corrected for visual interpretation. This study indicates that TSS mapping can be carried out using remote sensing technique of the satellite digital photography system over Penang Island, Malaysia.

Image acquired from the satellite Landsat TM was successfully used for PM10 mapping over Penang Island, Malaysia. The developed algorithm produced a high correlation between the measured and estimated PM10 concentration. Further study will be carried out to verify the results. A multi regression algorithm will be developed and used in the

Microwave Remote sensing data have been widely used for detecting and analyzing land cover/use feature in our environment. The objective of this project is to investigate the use of multi-polarized data of ALOS-PALSAR data for land cover/use mapping. ASF MapReady programs from Alaska satellite Facility Geographical Institute at the University of Alaska Fairbanks was used for the preprocessing of ALOS-PALSAR data. Standard supervised classification techniques such as the maximum likelihood, minimum distance-to-mean, and parallelepiped were chosen for the ALOS-PALSAR images in land cover mapping. Some filtering and enhancement methods were applied to reduce speckle noise and to contrast the images. The ALOS-PALSAR data was classified into four categories, such as forest, urban, water and open land. The ALOS-PALSAR data training areas were choose based on optical satellite imagery. The land cover information was extracted from the digital data using PCI Geomatica 10.1 software package. The classification accuracy was used to evaluate the best performing data combination. This study indicates that the land cover/use of Penang Island, Malaysia can be mapped accurately using ALOS PALSAR data.

Land surface temperature (LST) have been widely usedfor environmental studies and urban heat island (UHI),using satellite derived images. LST retrieval fromsatellite images has many advantages for research ascompared with in-situ measurement of air temperatures.For example, it will help to reduce the time consuming,more coverage of the study area with the high spatialresolution, etc. The objective of our study is to retrievethe LST over Penang Island and compare the trend andaverage value of LST within different types of (land useor land cover) LULC changes in single satellite image.LST was retrieved by ATCOR3_T using PCI Geomatica10.1 image processing software. The supervisedclassification was chosen to classified LULC changesover Penang Island. From the result obtained fromdifferent classification method, maximum likelihoodclassification showed the most accurate result comparedwith minimum distance-to-mean and parallelepipedmethods. The urban areas with high density resultedshown the highest LST between different LULC changes.These changes in LULC yield the significant differencein LST between urban and rural area. Therefore, LSTproved to be a good and effective tool to predict theimpact of LST to different LULC. Besides, therelationship between LST and NDVI also beeninvestigated for different LULC changes in this study.The strong negative and positive correlation wasobserved between LST and NDVI for different LULCchanges.

The sea surface temperature (SST) data availability by infrared measurement was low compared to microwave measurement. The infrared radiation cannot penetrate the cloud, so their availabilities are severely limited by clouds. The microwave can penetrate the clouds and give accurate SST measurements under clouds. The TRMM Microwave Imager (TMI) derived SST data has an RMS error of 0.6-0.7 K. TMI is a microwave radiometer onboard the Tropical Rainfall Measurement Mission (TRMM) Satellite. The infrared derived SST data has low data availability but has higher spatial resolution compared to microwave derived data. The spatial resolution of infrared radiometer, Moderate-resolution Imaging Spectroradiometer (MODIS) is about 1km, but the spatial resolution of TMI is 25km. The objective of our study is to increase the spatial resolution of microwave derived SST data and increase the availability of infrared derived SST data. In this study, we used MODIS SST data with grid size of about 0.01? and TMI SST data with grid size of 0.25?. We re-sampled these data into the new map with grid size of 0.1?. We find the new merged SST data from the MODIS SST and TMI SST. If any of the MODIS or TMI SST data is available, then the merged SST was assigned the value of the existing data. If both of the SST data are available, then the new merged SST was given the value of the average of these data. Otherwise, the new SST data was determined by interpolation method. We used the neighbourhood pixels for interpolation. All of the processing steps were programmed in MATLAB code. . However we checked our results by comparing the SST data availability of TMI and MODIS images before processing and data availability of new image after processing.

Water quality is an important factor for human health and quality of life. This has been recognized many years ago. Remote sensing can be used for various purposes. Environmental monitoring through the method of traditional ship sampling is time consuming and requires a high survey cost. This study uses an empirical model, based on actual water quality of chlorophyll measurements from the Penang Strait, Malaysia to predict chlorophyll based on optical properties of satellite digital imagery. The feasibility of using remote sensing technique for estimating the concentration of chlorophyll using Landsat satellite imagery in Penang Island, Malaysia was investigated in this study. The objective of this study is to evaluate the feasibility of using Landsat TM image to provide useful data for the chlorophyll mapping studies. The chlorophyll measurements were collected simultaneously with the satellite image acquisition through a field work. The in-situ locations were determined using a handheld Global Positioning Systems (GPS). The surface reflectance values were retrieved using ATCOR2 in the PCI Geomatica 10.1.3 image processing software. 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 water quality algorithm. The efficiency of the proposed algorithm was investigated based on the observations of correlation coefficient (R) and root-mean-square deviations (RMS) with the sea-truth data. Finally the chlorophyll map was color-coded and geometrically corrected for visual in terpretation. This study shows that the Landsat satellite imagery has the potential to supply useful data for chlorophyll studies by using the developed algorithm. This study indicates that the chlorophyll mapping can be carried out using remote sensing technique by using Landsat imagery and the previously developed algorithm over Penang, Malaysia.

Air pollution of fine particles with diameters less than 10 micrometers (PM 10 ) is a major concern in many countries due to their ability to penetrate further into our lungs to cause adverse health effects. PM 10 also have a significant influence on climate change and visibility. Due to the high cost to set-up air pollution monitoring stations, there are limited numbers of these stations in use. As a result, researchers do not have good temporal development and spatial distribution of the air pollutant readings over a city. This paper is to report upon the usage of an internet surveillance camera to record the temporal development and to map the spatial distribution of air quality concentration. An internet surveillance camera was used to quantify air quality with our own developed algorithm, which is based on the regression analysis of the relationship between measured reflectance components from a surface material and the atmosphere. A newly developed algorithm was applied to compute the temporal development of PM 10 values. Advanced Land Observing Satellite (ALOS) images were used to map air quality concentration of the study area. The algorithm was developed based on the aerosol characteristics in the atmosphere to measure PM 10 spatial distribution. These PM 10 values were compared to other standard values measured by a DustTrak™ meter. The correlation results of both techniques showed that these newly developed algorithms produced a high degree of accuracy as indicated by high correlation coefficient (R2) and low root-mean-square-error (RMS) values.

Air pollution is an important issue being monitored and regulated in industrial and developing cities. In this study, we explored the relationship between particulate matters of size less than 2.5 micron (PM 2.5) derived from the SPOT using regression technique. The aim of this study was to evaluate the high spatial resolution satellite data for air quality mapping by using FLAASH software. The corresponding PM 2.5 data were measured simultaneously with the acquired satellite scene and their locations were determined using a handheld Global Positioning System (GPS). Due to the fact that the current commercial aerosol retrieval method (FLAASH) was designed to work reasonably well over land, but is not accurate for water applications, adjustments had to be made to the software to optimize it for reflectance retrieval over water. In-situ reflectance spectra will be plot against spectra derived from the atmospherically corrected images. In this study, we ensure that all the atmospherically corrected spectra match reasonably with the in-situ spectra. We have developed a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from remotely sensed imagery under general atmospheric and surface conditions. The algorithm was developed base on the aerosol characteristics in the atmosphere. The efficiency of the developed algorithm, in comparison to other forms of algorithm, will be investigated in this study. Results indicate that there is a good correlation between the satellites derived PM 2.5 and the measured PM 2.5. This study shows the potential of using the thermal infrared data for air quality mapping. The finding obtained by this study indicates that the FLAASH can be used to retrieve air quality information for remotely sensed data.

Remotely sensed can be the only data source in rural and remote areas where no ground-based measurements are taken. This image is used to detect air pollution at regional level, in coarser resolution. Remotely sensed data can be used qualitatively to provide a regional view of pollutants and to help assess the impact of events such as biomass burning or dust transport from remote sources. In this study, we explored the relationship between particulate matters of size less than 10 micron (PM10) derived from the Landsat TM using regression technique. We was developed a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from remotely sensed imagery. The algorithm was developed base on the aerosol characteristics in the atmosphere and the algorithm is modified to accommodate the visible and thermal bands of Landsat TM5 image. We were obtained the atmospheric reflectance values by subtracting surface reflectance from the amount of reflectance measured from the satellite. The satellite recorded reflectance is the sum of the surface reflectance and atmospheric reflectance. The efficiency of the developed algorithm, in comparison to other forms of algorithm, will be investigated in this study. Based on the values of the correlation coefficient and root-mean-square deviation, the proposed algorithm is considered superior. The calibrated algorithm will be used to generate the air quality maps over Penang Island, Malaysia. The finding obtained by this study indicates that the Landsat TM data can be used to retrieved air quality information for remotely sensed data.

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.

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.

Aerosol optical thickness, AOT is one of the factors on determining air quality. The concentration of atmospheric aerosol is determined from radiances of image taken. On this study, we use AVHRR image which is appropriate to retrieve AOT from a wide coverage area. The methods of retrieving aerosol from visible and near-infrared radiance data were applied. The algorithm for mapping was based on dark target method using path radiance and contrast reduction method using atmospheric transmission. Besides, discussion on the level-1B of AVHRR image was also done on preprocessing for calibration and geometrical correction. Satellite and solar geometrical effects would be studied. Besides the environmental factors and distribution of aerosol particles would also be considered in the algorithm. Comparison between aerosol optical derived from each method would be compared with data from MODIS Aerosol Product available online. Regression analysis was performed to find the correlation between these two set of data graphs of radiance measured at visible channel and AOT was generated by the algorithm to show the relationship between these variables. The look-up table could be used to retrieved the aerosol optical thickness with the sufficient input.

This paper outlines recent developments in optical remote sensing of Landsat TM data for air quality monitoring for atmospheric particulate matter having a diameter less than 10-micro meter (PM10). The objective of this study is to evaluate the performance of the developed algorithm and suitability of remote sensing data for PM10 mapping. We used a DustTrak Aerosol Monitor 8520 to collect in situ data. The PM10 data were collected simultaneously during the satellite Landsat overpass the study area. An algorithm has been developed based on the optical aerosol characteristic in the atmosphere to estimate PM10 over Penang Island, Malaysia. The digital numbers were determined corresponding to the ground-truth locations for each band and then converted to radiance and reflectance values. The atmospheric reflectance values was extracted from the satellite observed reflectance values subtracters by the amount given by the surface reflectance. The surface refleatance values were retrieved using ATCOR2 in the PCI Geomatica 9.1 image processing software. The atmospheric reflectance values were later used for PM10 mapping using the calibrated algorithm. Results from this research have indicated that PM10 data have positive correlation with atmospheric reflectance in two visible bands (Red and Blue band). Finally, the map of pollution concentration generated from the satellite image using the proposed algorithm to illustrate spatial distribution pattern of air pollution for the study area. The proposed algorithm produced high correlation coefficient, R, and low root-mean-square, RMS, values. The concentrations of PM10 are high in the industrial zones and urban areas of Penang, Malaysia.

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 was to map air quality concentration in Penang Island, Malaysia using our proposed developed algorithm from Landsat TM data. The algorithm was developed base on the aerosol characteristics 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. For each visible band, the dark target surface reflectance was estimated from that of the mid-infrared band. The reflectance measured from the satellite [reflectance at the top of atmospheric, p(TOA)] was subtracted by the amount given by the surface reflectance to obtain the atmospheric reflectance. Then the atmospheric reflectance was related to the AOT using the regression algorithm. Similarly, the atmospheric reflectance was related to the measured PM10 values. In this study, the atmospheric reflectance derived from Landsat TM signals were used as independent variables in our calibration regression analyses. The newly developed algorithm produced a high degree of accuracy. The generated PM10 map was also colour coded for visual interpretation and smoothed using an average filter to minimize random noise. This study indicated that the Landsat TM can be a useful tool for air quality study.

The selected study area is the Strait of Penang, located in the north western part of peninsula Malaysia. Satellite imagery is employed as an aid to bathymetric charting. The depth sounding points retrieved from published bathymetric chart are used for calibration. Cloud free scene of ADEOS AVNIR data acquired on 14 January 1997 was available for use in the present study. This scene was rectified to the corresponding bathymetric chart. Image locations were related to the chart coordinates through the second degree polynomial transformation equations. Multiband water depth algorithm was used in the calibration analysis. Regression technique was used for calibration of the satellite signals for water depth recorded in the bathymetric map of the selected locations. The correlation coefficient and root- mean-square deviations were examined. The accuracy of calibrated algorithm was further verified using other known sounding points. The calibrated algorithm was then applied to the corresponding image to generate a water depth map. The color coded bathymetric map was used for visual interpretation and assessment of the present water depth pattern.

Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products have been widely used to address environment and climate change subjects with daily global coverage. Aerosol optical depth (AOD) is retrieved by different algorithms based on the pixel surface, determining between land and ocean. MODIS-Terra and Global Aerosol Robotic Network (AERONET) products can be obtained from the Multi-sensor Aerosol Products Sampling System (MAPSS) for coastal regions during 2000-2010. Using data collected from 83 coastal stations worldwide from AERONET from 2000-2010, accuracy assessments are made for coastal aerosol optical depth (AOD) retrieved from MODIS aboard the Terra satellite. AOD retrieved from MODIS at 0.55μm wavelength has been compared With the AERONET derived AOD, because it is reliable with the major wavelength used by many chemistry transport and climate models as well as previous MODIS validation studies. After removing retrievals with quality flags below1 for Ocean algorithm and below 3 for Land algorithm, The accuracy of AOD retrieved from MODIS Dark Target Ocean algorithms (correlation coefficient R2 is 0.844 and a regression equation of τM = 0.91τA + 0.02 (where subscripts M and A represent MODIS and AERONET respectively), is the greater than the MODIS Dark Target Land algorithms (correlation coefficient R2 is 0.764 and τM = 0.95τA + 0.03) and the Deep Blue algorithm (correlation coefficient R2 is 0.652 and τM = 0.81τA + 0.04). The reasons of the retrieval error in AOD are found to be the various underlying surface reflectance. Therefore, the aerosol models and underlying surface reflectance are the dominant factors which influence the accuracy of MODIS retrieval performance. Generally the MODIS Land algorithm implements better than the Ocean algorithm for coastal sites.