H. S. Lim

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.

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.

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.

Water quality assessment in Kelantan delta using remote sensing technique

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.

Aerosol Optical Thickness data retrieval over Penang Island, Malaysia

In this study, we propose a new cost effective approach to retrieve Aerosol Optical Thickness (AOT) data in the visible spectrum by using sky transmittance measurements measured by a handheld spectroradiometer. The transmittance values were measured in spectral region from 350 nm to 1050 nm at the earths surface. The well known Beer-Lambert law was used in this study to retrieve AOT values from the measured transmittance value. The concentrations of particulate matter of less than 2.5 micron (PM2.5) were measured simultaneously with the measurements of the transmittance data. The station locations of the PM2.5 measurements were detemined using a handheld GPS. Three interpolation techniques, namely Kriging Interpolation, Inverse Distance Interpolation and Natural Neighbour Interpolation, were used for mapping the PM2.5 concentration in this study. The accuracies of the three interpolation techniques were determined in this study in order to select the most suitable technique for mapping the air pollution concentration over Penang Island, Malaysia. The results of the analysis indicated that the AOT values were linearly correlated with the PM2.5 readings. AOT and PM10 maps were generated using an interpolation technique (Kriging) based on the measured data. Basically, both PM2.5 and AOT maps agree reasonably well over Penang Island, Malaysia. The highest PM2.5 concentrations were found in densely populated and industrialized areas.

Chlorophyll measurement from Landsat TM imagery

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.

Development of Air Quality Monitoring Remote Sensor Using a Digital SLR Camera

This paper reports that a digital single-lens reflex (SLR) camera can be applied as a remote sensor for monitoring the concentrations of particulate matter less than 10 micron (PM10). An 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 converts multispectral image pixel values acquired from this camera 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) of 0.75 and low root-mean- square-error (RMS) of plusmn5 mug/m3.

Algorithm for total suspended solids mapping using digital camera images

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.

PM10, PM2.5 and PM1 distribution in Penang Island, Malaysia

Particulate Matter (PM) consist of tiny solid or liquid particles that floating freely in the air. PM10 refers to the particles which have size up to 10 microns (μm). The smaller the particle size (such as PM1), the more severe it will affect our human health if we inhaled too much into our lungs. In this paper, we used the DustTrack{trade mark, serif} Aerosol Monitor with Model 8520 to obtain the PM distribution (PM10, PM2.5 and PM1) in Penang Island. The in-situ measurement was taken on 10 August 2012 at Georgetown, Batu Ferringhi and Permatang Damar Laut open area. The data obtained was analyzed and interpreted. The results show that the dust level at Permatang Damar Laut was low as compare to the other two areas which are located in town area. The highest PM10, PM2.5 and PM1 dust level with an averaging time of 6 hours was observed at Batu Ferringhi with a reading of 200, 194 and 185μg/m3. This study can provide a guideline to detect and monitor the dust level at Penang Island. Further study can be done to monitor the temporal changes of air quality over Penang Island, Malaysia.

Contextual classification of Cropcam UAV high resolution images using frequency-based approach for land use/land cover mapping case study: Penang Island

Cropcam UAV 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 UAV system in this research were used to overcome the problem of unclear images obtained by the satellite and manned aircraft in our study area. Conventional classification methods commonly cannot handle the complex landscape environment in the image. The result of each image has often a salt and pepper appearances which are the main characteristic of misclassification. The objective of this study is to evaluate the land use/land cover features over Penang Island using contextual classification method based on the frequency-based approach. The technique was applied to the high resolution images in three bands collected from a digital camera equipped with the platform system to extract thematic maps. Contextual classifier that utilized both spectral and spatial information could be reduce the speckle error and improve the classification performance significantly. Four classes could be classified clearly within the study area, and a high accuracy was achieved in the classification process. In order to evaluate the performance of the classifier, nine different window sizes ranging from 3 by 3 to 19 by 19 with an increment are tested. The study revealed that the frequency based-contextual classifier is effective with the images used in this research compare with the satellite images and images collected from conventional manned platforms and could be used for land use/cover mapping for the small area of coverage.