K. Abdullah

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery

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%.

Temporal air quality monitoring using surveillance camera

Various studies showed that inhaled fine particles with diameter less than 10 micrometers (PM10) in the air can cause adverse health effects on human, such as heart disease, asthma, stroke, bronchitis and the like. This is due to their ability to penetrate further into the lung and alveoli. The aim of this study is to develop a state-of-art reliable technique to use surveillance camera for monitoring the temporal patterns of PM10 concentration in the air. Once the air quality reaches the alert thresholds, it will provide warning alarm to alert human to prevent from long exposure to these fine particles. This is important for human to avoid the above mentioned adverse health effects. In this study, an internet protocol (IP) network camera was used as an air quality monitoring sensor. It is a 0.3 mega pixel charge-couple-device (CCD) camera integrates with the associate electronics for digitization and compression of images. This network camera was installed on the rooftop of the school of physics. The camera observed a nearby hill, which was used as a reference target. At the same time, this network camera was connected to network via a cat 5 cable or wireless to the router and modem, which allowed image data transfer over the standard computer networks (Ethernet networks), internet, or even wireless technology. Then images were stored in a server, which could be accessed locally or remotely for computing the air quality information with a newly developed algorithm. The results were compared with the alert thresholds. If the air quality reaches the alert threshold, alarm will be triggered to inform us this situation. The newly developed algorithm was based on the relationship between the atmospheric reflectance and the corresponding measured air quality of PM10 concentration. In situ PM10 air quality values were measured with DustTrak meter and the sun radiation was measured simultaneously with a spectroradiometer. Regression method was use to calibrate this algorithm. Still images captured by this camera were separated into three bands namely red, green and blue (RGB), and then digital numbers (DN) were determined. These DN were used to determine the atmospherics reflectance values of difference bands, and then used these values in the newly developed algorithm to determine PM10 concentration. The results of this study showed that the proposed algorithm produced a high correlation coefficient (R2) of 0.7567 and low root-mean-square error (RMS) of plusmn 5 mu g/m3 between the measured and estimated PM10 concentration. A program was written by using microsoft visual basic 6.0 to download the still images automatically from the camera via the internet and utilize the newly developed algorithm to determine PM10 concentration automatically and continuously. This concluded that surveillance camera can be used for temporal PM10 concentration monitoring. It is more than an air pollution monitoring device; it provides continuous, on-line, real-time monitoring for air pollution at multi location and air pollution warning or alert system. This system also offers low implementation, operation and maintenance cost of ownership because the surveillance cameras become cheaper and cheaper now.

Modeling groundwater vulnerability prediction using geographic information system (GIS)-based ordered weighted average (OWA) method and DRASTIC model theory hybrid approach

A groundwater vulnerability prediction modeling, based on geographic information system-based ordered weighted average (OWA)-DRASTIC approach, is investigated in southern part of Perak, Malaysia. The proposed approach is a mix of curiosity that allows the uses of different decision strategies for the purpose of quantifying level of risk in vulnerability prediction. Seven pollution potential factors based on DRASTIC model theory were individual evaluated. Their results were model using OWA generic model. The OWA model integrates a pair-wise comparison method and quantifier-guided OWA aggregation operators to form a groundwater pollution potential mapping method that incorporates different decision strategies. With OWA operators, ANDness, ORness, and Trade-off parameters were calculated as a function of fuzzy (linguistic) quantifiers. The calculated parameters lies between the aggregations that uses “AND” operator (which requires all the criteria to be satisfied) and OR operator (which requires at least one criterion to be satisfied). The model results in multiple groundwater vulnerability prediction scenarios, which apply different decision strategies and provide users with the flexibility to select one of them based on the level of risk controls in decision-making process. The risk adverse model associated with OWA AND operator was selected for groundwater vulnerability prediction map in the area. The results showed that predominant portions of the area belonged to the no vulnerable zones. The model was validated with groundwater quality data, and results show a strong relationship between the groundwater vulnerability model and pH, NO3, Ca, Fe, and Zn concentrations whose correlation coefficients are 0.50, 0.55, 0.60, 0.69, and 0.91, respectively. The results obtained confirmed that the methodology hold significant potential to support the complexity of decision making in evaluating groundwater pollution potential mapping in the area.

AERONET data-based determination of aerosol types

Aerosols are among the most interesting topics investigated by researchers because of their complicated characteristics and poor quantification. Moreover, significant uncertainties are associated with changes in the Earths radiation budget. Previous studies have shown numerous difficulties and challenges in quantifying aerosol influences. In addition, the heterogeneity from aerosol loading and properties, including spatial, temporal, size, and composition features, presents a challenge. In this study, we investigated aerosol characteristics over two regions with different environmental conditions and aerosol sources. The study sites are Penang and Kuching in Malaysia, where a ground-based AErosol RObotic NETwork (AERONET) sun-photometer was deployed. The types of aerosol, such as biomass burning, urban/industrial, marine, and dust aerosols, for both study sites were identified by analyzing aerosol optical depth and angstrom exponent. Seasonal monsoon variation results in different aerosol optical properties, characteristics, and types of aerosols that are dominant in Penang and Kuching. Seasonal monsoon flow trend patterns from a seven-day back-trajectory frequency plotted by the Hybrid Single-Particle Lagrangian Integrated Trajectory model illustrated the distinct origins of trans-boundary aerosol sources. Finally, we improved our findings in Malaysian sites using the AERONET data from Singapore and Indonesia. Similarities in the optical properties of aerosols and the distribution types (referred to as homogeneous aerosol) were observed in the Penang-Singapore and the Kuching-Pontianak sites. The dominant aerosol distribution types were completely different for locations in the western (Penang-Singapore) and eastern (Kuching-Pontianak) parts of the South China Sea. This is a result of spatial and temporal heterogeneity. The spatial and temporal heterogeneities for the western and eastern portions of South China Sea provide information on the natural or anthropogenic processes that take place.

Multispectral absorption algorithm for retrieving TSS concentrations in water

The environmental problems caused by the increase of pollutant. It is the result of industrial waste and environmental accidents. One of the main sources of water pollution is suspended solids. When these suspended particles settle to the bottom of a water body, they become sediments. Optical methods offer two possibilities: scattering and transmission. The design of optical sensor systems is based on the interaction between the photons of the electromagnetic radiation and suspended particles in water. Pollution usually results in higher total suspended solids (TSS) concentrations or turbidity. Water pollution is caused by any chemical, physical or biological change in the quality of water. Suspended solids are small particles of solid pollutants that float on the surface of, or are suspended in waters. Water pollution has become increasingly critical in this present-day, whether in the developed or the developing countries. Total suspended solids (TSS) in water can be detected by a number of optical sensing techniques, which involve light interaction with suspended particles in water. The measurements of the transmittance give a method for determining the concentration of the particles. The objectives of this study are to derive multispectral absorption algorithm and to develop an optical system, which is based on absorption measurements for measuring the concentration of total suspended solids,TSS in water. The multispectral absorption algorithm has been developed and a new multispectral optical sensor system designed for measuring total suspended solids TSS concentrations in polluted water. The development of the optical algorithm was based on the Beer-Lambert law. Firstly, the water samples used for calibration were filtered and dried to obtain the TSS values. In this study, three light emitting diodes were used as sensing emitters (sources) and a silicon phototransistor as radiations detector. The radiation values were determined from the output voltage readings of the sensor system. An electronic circuit was designed and the readings were measured by using a digital multimeter. The major advantages in using an LED as the light sources are its relatively low power consumption and ability to be modulated electronically at rapid rates. Standard polluted samples were prepared for sensors calibration. As the concentration of total suspended solids P was increased, the intensity of the transmitted light decreased. The level of the photocurrent was linearly proportional to the pollutants concentration. The proposed multispectral algorithm was calibrated using the measured parameters. Its accuracy was determined based on correlation coefficient,R and the value of the root-mean square errors,RMSE. The results showed a good correlation between the radiation values and the total suspended solids concentrations. This optical system provides various advantages over the current portable turbidity meter, which uses a single infrared source. The main advantage is its capability provide water pollution levels accurately (i.e. total suspended solids concentration). It only requires inexpensive components and can be assembled easily. The proposed algorithm produced a high correlation coefficient and low root mean square error value. This new methodology is very important for measuring and monitoring TSS in polluted water areas.

Accuracy assessment of Terra-MODIS aerosol optical depth retrievals

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.

Regression analysis in modeling of air surface temperature and factors affecting its value in Peninsular Malaysia

Abstract. 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 to 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.

Assessment of ALOS PALSAR Data For Land Cover/Land Use Mapping In Malaysia

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.

Water quality mapping using digital camera images

The objective of this study was to implement a low-cost airborne remote sensing system to provide an alternative solution for remote sensing given the difficulty in obtaining cloud-free satellite scenes of the equatorial region. An inexpensive sensor, a Kodak DC 290 digital camera, was used, onboard a light aircraft, Cessna 172Q. The feasibility of using camera images for remote sensing applications was tested for quantifying total suspended solids (TSS) from three estuaries located in the northern region of Peninsular Malaysia. The aircraft was flown at altitudes of 914.4 to 2438.4 m for digital image acquisition of the study areas. Water samples were collected simultaneously with the aircraft overpasses and their locations were determined by using a hand-held Global Positioning System (GPS). Oblique images were corrected for brightness variation. A simple relative atmospheric correction was performed on the multidate images. The captured colour images were then separated into three bands (red, green and blue) for multispectral analysis. The digital numbers (DNs) were extracted corresponding to the sea data locations for each band. A multiband regression algorithm was developed based on a reflectance model, which is a function of the inherent optical properties of water, and this in turn can be related to the concentration of the TSS. Data from different scenes were combined and then divided into two sets, one for calibration of the algorithm and the other for a validation analysis. The calibrated algorithm had a correlation coefficient (R) of 0.96 and a root mean square error (RMSE) of approximately 17 mg l−1. The validation analysis showed that the algorithm could estimate the TSS concentration within an RMSE of about 23 mg l−1 and an R value of 0.95. The calibrated algorithm was used to generate water quality maps for all images. The maps were geometrically corrected and colour coded for visual interpretation.

Temporal and spatial air quality monitoring using internet surveillance camera and ALOS satellite image

Air pollution of fine particles with diameters less than 10 micrometers (PM10) is a major concern in many countries due to their ability to penetrate further into our lungs to cause adverse health effects. PM10 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 PM10 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 PM10 spatial distribution. These PM10 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.