Lim Hwee San

Mangrove mapping in Penang Island by using Artificial Neural Network technique

Environmental study is crucial in order to understand deeply about the flora and fauna living in the Earth. Mangrove forest is a unique and natural ecosystem that can be used to produce forestry product such as charcoal, timber, supply food to their surrounding marine life, and protect the inland from disturbance like erosion, flood, and tsunami. Due to the uncontrolled planning of human activity, many mangrove forests had been deforested for development of industry area, urban land and agriculture. In this study, Multi-layer Feed Forward/Multilayer Perceptrons (MLP) network system in Artificial Neural Network technique was used to map out the current state of mangrove trees. This network system require user to have a ground truth data such as in supervised classification in order to generate the training area for classification. Generally, this network comprise of a simple structure layer which consist of three layers namely input layer, hidden layer and output layer. Multi-layer Feed Forward algorithm has at least one hidden layer of neuron between the input and output layer. Each successive layer of neurons is fully interconnected with connection weight determine the strength of the connection. 2010 Thailand Earth Observing System (THEOS) satellite imagery was used as the source for the data processing with the aid of PCI Geomatica version 10.3.2 software packaging. The classification result of Multi-layer Feed Forward yield 5 category of classes. Post-classification analysis was further carried out to validate the classified data with reference data. High overall accuracy of 93.5% and kappa coefficient of 0.900 was obtained for the mangrove cover mapping. Final thematic map was produce to quantify and display the current distribution of mangrove land. The result indicates that neural network approach is suitable and reliable used for mangrove mapping.

Multiple regression analysis in modelling of carbon dioxide emissions by energy consumption use in Malaysia

Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model’s accuracy and efficiency. These result...

Aerial Photogrammetry Method for Water Quality Monitoring Using Digital Camera

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) meas- urements from the Prai River estuary Penang, Malaysia, to predict TSS based on optical properties of digital camera im- agery. The proposed algorithm is based on the reflectance model that is a function of the inherent optical properties of wa- ter, which can be related to its constituents concentrations. Water samples were simultaneously collected with the air- borne image acquisition and analyzed later in the laboratory. These locations were determined by using a handheld GPS. The digital numbers for each band were extracted corresponding to the sea-truth locations and were later used for calibra- tion 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. This algorithm was then used to map the TSS concentration in Prai River estuary, Penang, Malaysia. The TSS map was color-coded and geometri- cally corrected for visual interpretation. This study indicates that TSS mapping can be carried out using digital camera im- ageries. In this study, we proposed a new application of a conven- tional digital camera for remote sensing data acquisition. Digital images were captured from a low altitude flying aircraft of the suspected polluted areas. A new algorithm was also developed to detect and map the water pollution. Water pollution levels were detected with an acceptable accuracy by this new technique. The digital images were taken from altitude 4400 feet. Satellites and airborne imaging are widely used for mapping water quality. Due to high cost and cloud cover problem with the present airborne and satellite tech- niques, respectively, we introduced this new technique. This new technique used a normal digital camera as a sensor to provide remote sensing data mapping the water quality of coastal region. The traditional method that uses ship for wa- ter pollution monitoring is time consuming and requires a high operating cost (4, 5). Conventionally, water pollution can be measured from ground instruments such as turbidity meters, however, this method is impractical if measurement are to be made over a relatively large area or for continuous monitoring. Field

Investigation of aerosol distribution patterns and its optical properties at different time scale by using LIDAR system and AERONET

Atmospheric aerosol is a major health-impairment issue in Malaysia especially during southeast monsoon period (June-September) due to the active open burning activities. However, hazy days were an issue in Penang, Malaysia during March, 2014. Haze intruded Penang during March and lasted for a month except for the few days after rain. Rain water had washed out the aerosols from the atmosphere. Therefore, this study intends to analyse the aerosol profile and the optical properties of aerosol during this haze event and after rain. Meanwhile, several days after the haze event (during April, 2014) were also analyzed for comparison purposes. Additionally, the dominant aerosol type (i.e., dust, biomass burning, industrial and urban, marine, and mixed aerosol) during the study period was identified according to the scattering plots of the aerosol optical depth (AOD) against the Angstrom exponent.

Observed atmospheric total column ozone distribution from SCIAMACHY over Peninsular Malaysia

The increase in atmospheric ozone has received great attention because it degrades air quality and brings hazard to human health and ecosystems. The aim of this study was to assess the seasonal variations of ozone concentrations in Peninsular Malaysia from January 2003 to December 2009 using Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Level-2 data of total column ozone WFMD version 1.0 with spatial resolution 1° × 1.25° were acquired through SCIAMACHY. Analysis for trend of five selected sites exhibit strong seasonal variation in atmospheric ozone concentrations, where there is a significant difference between northeast monsoon and southwest monsoon. The highest ozone values occurred over industrial and congested urban zones (280.97 DU) on August at Bayan Lepas. The lowest ozone values were observed during northeast monsoon on December at Subang (233.08 DU). In addition, the local meteorological factors also bring an impact on the atmospheric ozone. During northeast monsoon, with the higher rate of precipitation, higher relative humidity, low temperature, and less sunlight hours let to the lowest ozone concentrations. Inversely, the highest ozone concentrations observed during southwest monsoon, with the low precipitation rate, lower relative humidity, higher temperature, and more sunlight hours. Back trajectories analysis is carried out, in order to trace the path of the air parcels with high ozone concentration event, suggesting cluster of trajectory (from southwest of the study area) caused by the anthropogenic sources associated with biogenic emissions from large tropical forests, which can make important contribution to regional and global pollution.

Land cover/use mapping using multi-band imageries captured by Cropcam Unmanned Aerial Vehicle Autopilot (UAV) over Penang Island, Malaysia

The problem of difficulty in obtaining cloud-free scene at the Equatorial region from satellite platforms can be overcome by using airborne imagery. Airborne digital imagery has proved to be an effective tool for land cover studies. Airborne digital camera imageries were selected in this present study because of the airborne digital image provides higher spatial resolution data for mapping a small study area. The main objective of this study is to classify the RGB bands imageries taken from a low-altitude Cropcam UAV for land cover/use mapping over USM campus, penang Island, Malaysia. A conventional digital camera was used to capture images from an elevation of 320 meter on board on an UAV autopilot. This technique was cheaper and economical compared with other airborne studies. The artificial neural network (NN) and maximum likelihood classifier (MLC) were used to classify the digital imageries captured by using Cropcam UAV over USM campus, Penang Islands, Malaysia. The supervised classifier was chosen based on the highest overall accuracy (<80%) and Kappa statistic (<0.8). The classified land cover map was geometrically corrected to provide a geocoded map. The results produced by this study indicated that land cover features could be clearly identified and classified into a land cover map. This study indicates the use of a conventional digital camera as a sensor on board on an UAV autopilot can provide useful information for planning and development of a small area of coverage.

Chlorophyll a Concentration of Fresh Water Phytoplankton Analysed by Algorithmic based Spectroscopy

Phytoplankton are microscopic single-celled plants that play an important role in the ecosystem as a major primary producers through photosynthesis. The main objective of this study is to investigate properties of reflection and chlorophyll a concentration of phytoplankton in fresh water. The reflection of phytoplankton is taken using spectroradiometer to observe the relationship between the reflectance and the wavelengths of phytoplankton. Tasik Harapan is selected as a study area. This lake is located in Universiti Sains Malaysia (USM), Penang, Malaysia. In this study, the water samples collected are filtered using phytoplankton net and analyzed in the laboratory to determine the reflectance and chlorophyll a concentration of phytoplankton. The water samples taken were prior to culture in the medium known as Bolds Basal Medium (BBM) before the reflections are taken so that accurate reflection measurements of phytoplankton can be acquired. Two spectrometers are used in this study, firstly, spectroradiometer, which was used to measure the reflectance of phytoplankton and secondly, spectrophotometer, which was used to measure the concentration of chlorophyll a. The algorithms that related between chlorophyll a concentration and reflectance of phytoplankton are used. In this study, three regions also are focused, which are the red band, green band and blue band. These bands are related in analyzing phytoplankton. Reflectance of each band specified referred to the concentration of chlorophyll a for calibration algorithm. Finally, three wavelengths of 438 nm, 550 nm and 675 nm were selected. The selection of these three wavelengths were found to be strongly correlated to phytoplankton and chlorophyll a. The water sampling for validation was also taken from the same lake and was analyzed using the same algorithm. The best results are obtained in this study, which are evidenced by the good correction coefficient, R2 in the analysis results using the developed algorithm.

A feed-forward Hopfield neural network algorithm (FHNNA) with a colour satellite image for water quality mapping

There are many techniques that have been given for water quality problem, but the remote sensing techniques have proven their success, especially when the artificial neural networks are used as mathematical models with these techniques. Hopfield neural network is one type of artificial neural networks which is common, fast, simple, and efficient, but it when it deals with images that have more than two colours such as remote sensing images. This work has attempted to solve this problem via modifying the network that deals with colour remote sensing images for water quality mapping. A Feed-forward Hopfield Neural Network Algorithm (FHNNA) was modified and used with a satellite colour image from type of Thailand earth observation system (THEOS) for TSS mapping in the Penang strait, Malaysia, through the classification of TSS concentrations. The new algorithm is based essentially on three modifications: using HNN as feed-forward network, considering the weights of bitplanes, and non-self-architecture or zero diagonal of weight matrix, in addition, it depends on a validation data. The achieved map was colour-coded for visual interpretation. The efficiency of the new algorithm has found out by the higher correlation coefficient (R=0.979) and the lower root mean square error (RMSE=4.301) between the validation data that were divided into two groups. One used for the algorithm and the other used for validating the results. The comparison was with the minimum distance classifier. Therefore, TSS mapping of polluted water in Penang strait, Malaysia, can be performed using FHNNA with remote sensing technique (THEOS). It is a new and useful application of HNN, so it is a new model with remote sensing techniques for water quality mapping which is considered important environmental problem.

The identification of geothermal with geographic information system and remote sensing in distric of Dolok Marawa

The potency of the Indonesian geothermal resources able to supply 40% of world’s demand on the geothermal resources. These resources are spread over 251 locations at 33 provinces having the total potential energy of 27.149 MW. One of these geothermal resources is Tinggi Raja located at Distric of Dolok Marawa, Simalungun Regency, North Sumatera 449385 E – 473025 E and 324105 N - 349545 N. This paper reports the study on mapping of the prospect of geothermal resource area by utilizing a remote sensing. The remote sensing consisted of Landsat 8 OLI which was published on February 8th 2015 and June 29th 2015 with Path 129 Row 58 as input data for ENVI 4.7 and ArcGIS 10 as mapping tools. Calculated land surface temperature (LST) was essential for mapping and calculating a geothermal resources. In this study, land surface temperature was used as the Thermal Infrared images obtained from the thermal infrared remote sensor. The highest achieved LST was 310.889587 K. The obtained LST distribution indicated the lo...

Discrimination of mangrove species in Matang Mangrove Forest Reserve, Perak using in-situ measurement of hyperspectral leaf reflectance

Studies of mangrove species’s reflectance characteristic are important in order to have a deep understanding of mangrove vegetation. In this paper, the significant wavelengths which can be used to separate the six mangrove species at Matang Mangrove Forest Reserve (MMFR), Perak were examined. The investigated mangrove species comprise of Rhizophora apiculata, Acrostichum aurem, Acrostichum speciosum, Acanthus ilicifolius, Ceriops tagal and Sonneratia ovata. In-situ spectral reflectance data of six mangrove species’s leaf were obtained using ASD FieldSpec3 spectroradiometer and were statistically tested using SPSS program. First, wavelengths which exhibited significant differences (P value<0.05) among the mean reflectance of six mangrove species were identified using a series of one way ANOVA. Second, the identified wavelengths were further analyzed using canonical stepwise discriminant analysis and 26 significant wavelengths were obtained which can be utilized to distinguish among the six mangrove species...