Hwee San Lim

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%.

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.

Investigation on the Carbon Monoxide Pollution over Peninsular Malaysia Caused by Indonesia Forest Fires from AIRS Daily Measurement

Carbon monoxide (CO) is an important pervasive atmospheric trace gas affecting climate and more than 50% of air pollution nationwide and worldwide, which also plays as a significant indirect greenhouse gases due to its influences on the budgets of hydroxyl radicals (OH) and ozone (O3). We present a study on Atmosphere Infrared Sounder (AIRS), onboard NASAs Aqua Satellite, detection of CO emission from large forest fire in the year 2005 in the Sumatra, Indonesia. AIRS daily coverage of 70% of the planet symbolizes an important evolutionary advance in satellite trace gas remote sensing. AIRS is one of several instruments onboard the Earth Observing System (EOS) launched on May 4, 2002, with its two companions microwave instruments the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB) form the integrated atmospheric sounding system. AIRS providing new insights into weather and climate for the 21st century, as well as AIRS’ channels include spectral features of the key carbon trace gases CO2, methane(CH4), and CO. AIRS is an infrared spectrometer/radiometer that covers the 3.7– 15.4 m spectral range with 2378 spectral channels. Troposphere CO abundances are retrieved from AIRS 4.55 mm spectral region, and measure CO total column by 52 channels with the uncertainty, which is estimated approximately 15-20 % at 500mb. Results from the analysis of the retrieved CO daily Level 3 standard (AIR×3STD) and Monthly product (AIRX3STM) were utilized in order to study the impact of Indonesia forest fire on CO distribution, and the monthly CO distributions in Peninsular Malaysia. AIRS daily CO maps from 12 – 25 August 2005 for study area show large-scale, long-range transport of CO from anthropogenic and natural sources, most notably from forest fire biomass burning. The sequence of daily maps shows the CO advection from central Sumatra to Malaysia. AIRS can also capture the temporal variation in CO emission from forest fires through 6-day composites so it may offer a chance to enhance our knowledge of temporal fire emission over large areas. The result was compared with daily CO emission (13-24) August 2007. The daily measurements of CO concentration on August 2005 are higher than August 2007. The northern region (uppers the latitude 4) was more affected by forest fires than the rest area. Substantial seasonal variations demonstrate season-to-season changes in rainfall and drought patterns in different seasons. We see such seasonal variations in the biomass burning emissions in the late dry season, while industrial contributions are evident at

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.

Comparison of Neural Network and Maximum Likelihood classifiers for land cover classification using landsat multispectral data

Land cover classification is one of the remote sensing applications, in order to identify features such as land use by utilizing typically multispectral satellite data. Numerous algorithms have been developed for classification purpose and different classifiers have their own characteristics. Different data and study area, especially the landscape complexity bring different impact on the different classifiers. Therefore, the aim of this study is to compare Neural Network and Maximum Likelihood approaches and find a suitable classifier in land cover classification using medium spatial resolution satellite images in equatorial tropical region. These two classifiers were tested using Landsat Thematic Mapper (TM) data in Penang Island, Malaysia using the same training sample data sets. Five land cover classes — forest, grassland, urban, water, and cloud — were classified. In addition, the study also been carried out in order to obtain the performances of both classifiers for the purpose of land cover mapping. Overall classification accuracy and Kappa Coefficient were calculated. The results indicated that Neural Network algorithm provided better classification accuracy than Maximum Likelihood algorithm. The overall accuracy of Neural Network approach reaches 93.5 % associated with 0.909 Kappa coefficient, which is more reliable than Maximum Likelihood, with 80.5 % overall accuracy and 0.722 Kappa coefficient.

Development of groundwater favourability map using GIS-based driven data mining models: an approach for effective groundwater resource management

Abstract The development of groundwater favourability map is an effective tool for the sustainability management of groundwater resources in typical agricultural regions, such as southern Perak Province, Malaysia. Assessing the potentiality and pollution vulnerability of groundwater is a fundamental phase of favourability mapping. A geographic information system (GIS)-based Boolean operator of a spatial analyst module was applied to combine a groundwater potentiality map (GPM) model and a groundwater vulnerability to pollution index (GVPI) map, thereby establishing the favourable zones for drinking water exploration in the investigated area. The area GPM model was evaluated by applying a GIS-based Dempster–Shafer–evidential belief function model. In the evaluation, six geoelectrically determined groundwater potential conditioning factors (i.e. overburden resistivity, overburden thickness, aquifer resistivity, aquifer thickness, aquifer transmissivity and hydraulic conductivity) were synthesized by employing the probability-based algorithms of the model. The generated thematic maps of the seven hydrogeological parameters of the DRASTIC model were considered as pollution potential conditioning factors and were analysed with the developed ordered weighted average–DRASTIC index model algorithms to construct the GVPI map. Approximately 88.8 and 85.71% prediction accuracies for the Groundwater Potentiality and GVPI maps were established using the reacting operating characteristic curve method and water quality status–vulnerability zone relationship scheme, respectively. Finally, the area groundwater favourability map (GFM) model was produced by applying a GIS-based Boolean operator on the Groundwater Potentiality and GVPI maps. The GFM model reveals three distinct zones: ‘not suitable’, ‘less suitable’ and ‘very suitable’ zones. The area analysis of the GFM model indicates that more than 50% of the study area is covered by the ‘very suitable’ zones. Results produce a suitability map that can be used by local authorities for the exploitation and management of drinking water in the area. The study findings can also be applied as a tool to help increase public awareness of groundwater issues in developing countries.

Modeling groundwater vulnerability to pollution using Optimized DRASTIC model

The prediction accuracy of the conventional DRASTIC model (CDM) algorithm for groundwater vulnerability assessment is severely limited by the inherent subjectivity and uncertainty in the integration of data obtained from various sources. This study attempts to overcome these problems by exploring the potential of the analytic hierarchy process (AHP) technique as a decision support model to optimize the CDM algorithm. The AHP technique was utilized to compute the normalized weights for the seven parameters of the CDM to generate an optimized DRASTIC model (ODM) algorithm. The DRASTIC parameters integrated with the ODM algorithm predicted which among the study areas is more likely to become contaminated as a result of activities at or near the land surface potential. Five vulnerability zones, namely: no vulnerable(NV), very low vulnerable (VLV), low vulnerable (LV), moderate vulnerable (MV) and high vulnerable (HV) were identified based on the vulnerability index values estimated with the ODM algorithm. Results show that more than 50% of the area belongs to both moderate and high vulnerable zones on the account of the spatial analysis of the produced ODM-based groundwater vulnerability prediction map (GVPM).The prediction accuracy of the ODM-based – GVPM with the groundwater pH and manganese (Mn) concentrations established correlation factors (CRs) result of 90 % and 86 % compared to the CRs result of 62 % and 50 % obtained for the validation accuracy of the CDM – based GVPM. The comparative results, indicated that the ODM-based produced GVPM is more reliable than the CDM – based produced GVPM in the study area. The study established the efficacy of AHP as a spatial decision support technique in enhancing environmental decision making with particular reference to future groundwater vulnerability assessment.

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.

Multiple regression analysis in modeling of columnar ozone in Peninsular Malaysia

This study aimed to predict monthly columnar ozone (O3) in Peninsular Malaysia by using data on the concentration of environmental pollutants. Data (2003–2008) on five atmospheric pollutant gases (CO2, O3, CH4, NO2, and H2O vapor) retrieved from the satellite Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) were employed to develop a model that predicts columnar ozone through multiple linear regression. In the entire period, the pollutants were highly correlated (R = 0.811 for the southwest monsoon, R = 0.803 for the northeast monsoon) with predicted columnar ozone. The results of the validation of columnar ozone with column ozone from SCIAMACHY showed a high correlation coefficient (R = 0.752–0.802), indicating the model’s accuracy and efficiency. Statistical analysis was utilized to determine the effects of each atmospheric pollutant on columnar ozone. A model that can retrieve columnar ozone in Peninsular Malaysia was developed to provide air quality information. These results are encouraging and accurate and can be used in early warning of the population to comply with air quality standards.

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.