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Physical sea surface parameters such as irradiance reflectance are most commonly used in ocean color remote sensing. Algorithms are developed to relate irradiance reflectance (R) and quantities such as chlorophyll and suspended sediment concentration. The study interpreted how many independent parameters of ALOS satellite data collected can be found to assess the sediment concentration in Langkawi Island, Malaysia by using principal component analysis (PCA). PCA is a technique designed for reducing the dimensionality of multivariate set of data. This method can reduce the variables which are highly correlated with one another without losing any information. In order to extract the reflectance quantities, all satellite data sets have been atmospherically corrected using ATCOR-2. PCA and band ratios were applied on the four independent bands (band 1-4). The result from independent band show that the PC1 and PC2 composed mostly of band 2(RAV2) and band 3 (RAV3) reflectances, while the band ratios show that t...

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 results are accurate and can be used in early warning of the population to comply with air quality standards.

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

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 (

Mangrove mapping is crucial for the policy maker to have more proper planning of land use of a country or nation while helping to preserve the mangrove area. The unique mangrove ecosystem need to be conserved as mangrove trees has many applications for human being not only in their forestry products such as timber and charcoal but also serve as a strong barrier from the attack of wave, erosion and tsunami to inland area near the seashore or coastal region. The aim of this paper is to compare the accuracy of the mangrove map produced from different remote sensing techniques. Thailand Earth Observation System (THEOS) satellite data of Penang Island with date 29 January 2010 was utilized for the image processing analysis. All the pre-processing, classification, validation and post-classification analysis were done by using Geomatica version 10.3.2 software package. The results obtained show that Artificial Neural Network (ANN) with classification accuracy of 93.5% can increase the overall accuracy by 2.0% as compare to Maximum Likelihood Classification method (91.5%). This study indicates that ANN approach which has the highest accuracy and kappa coefficient is more reliable used for mangrove mapping at generic level.

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.

Spectral reflectance properties of mangrove plants play an important role in the interpretation and analysis of mangrove species in coastal region. In this study, reflectance of several mangrove species at Matang Mangrove Forest Reserve will be measured and investigated. The four selected mangrove species include Rhizophora apiculata, Rhizophora mucronata, Acrostichum speciosum and Acrostichum aureum. The reflectance value will be obtained by using a handheld spectroradiometer. The reflectance response of the mangrove species from visible to near infrared wavelength were analyzed with the aid of Viewspec Pro software. In the visible spectrum portion, Acrostichum speciosum recorded the highest reflectance value of 16% amongst the four mangrove species. However, highest reflectance value of 53% was recorded by Rhizophora apiculata in the Near Infra Red (NIR) region. Understanding of reflectance characteristic in different mangrove canopy is crucial and necessary in order to study on the mangrove ecosystem.

Remote sensing is a very useful method in application of establishing land cover map. It has been widely used for detecting and analyzing land cover/use feature in our environment especially in the developing country like Malaysia. The objective of this study is to investigate the use of quad polarization ALOS-PALSAR data for land cover/use classification. Speckle filtering was an improvement over the unfiltered ALOS-PALSAR data. The use of Mean texture measure was found to be advantageous. A maximum likelihood decision rule is utilized to determine different land-cover classes such as paddy, forest, oil palm, water, and urban. The ALOS-PALSAR data training areas were chosen based on optical satellite imagery. Ground reference data from sites throughout the study area were collected for validation. The land cover information was extracted from the digital data using PCI Geomatica 10.3.2 software package. The high accuracies indicated that the land cover/use can be mapped accurately using ALOS PALSAR data.

The 2004 Asian tsunami was the worst tsunami disaster in the history. The tsunami caused by the huge Sumatra-Andaman earthquake devastated the shores of Indian Ocean on December 26, 2004. The present of tsunami carry a large portion of possibility in the damaging the environmental as well as in degradation of coastal area of Aceh, Indonesia. Remote sensing data have been widely used in the environmental studies like land cover change, flood observation, environmental pollution (air and water) monitoring. The objective of this study was to test the feasibility study for mapping and monitoring the surface water in the coastal area of Aceh by using Landsat TM satellite data after the disaster of tsunami hit on 2004. The implemented algorithm is based on the reflectance model, which is a function of the inherent optical characteristic of water, and it was related to the concentration of its constituents. In this study, the algorithm used for monitoring water quality in the coastal area of Aceh was based on the algorithm developed from the study on Penang Island in Malaysia. Previously algorithm revealed that it had given a high of correlation coefficient (R) and low root means square deviations (RMSD) values. Finally, the total suspended solid (TSS) map was generated based on the algorithm developed. The study shows that the Landsat TM satellite data has the potentiality to be used for sediment studies over a large study area.