Landslides and lineament mapping along the Simpang Pulai to Kg Raja highway, Malaysia

Geological structural features, such as the discontinuities that may be detected on satellite imagery as lineaments, in many cases control landslide occurrences. Lineament may represent the plane of weakness where the strength of the slope material has been reduced, eventually resulting in slope failure. The main objective of this study is to assess the relationship between lineament and landslide occurrences along the Simpang Pulai to Kg Raja highway, Malaysia. Lineament mapping was undertaken utilizing Landsat imagery and landslide distributions were identified based on field mapping and historical records. Lineament density maps of length, number and intersections were generated and compared with landslide distributions. The lineaments were also visually compared with the landslide occurrences. The results showed that there is an association between the lineaments and landslide distribution. Thus, lineament mapping is essential for the early stages of planning to prevent hazard potential from landslides

IMAGE CLASSIFICATION FOR MAPPING OIL PALM DISTRIBUTION VIA SUPPORT VECTOR MACHINE USING SCIKIT-LEARN MODULE

The world has been alarmed with the global warming effects. Global warming has been a distress towards the environment, thus shorten the Earth’s lifespan. It is a challenging task to reduce the global warming effects in a short period, knowing that the human population is increasing along with the electricity and energy demand. In order to reduce the effects, renewable energy is presented as an alternative method to produce energy in a way that will not harm the environment. Oil palm is one of the agricultural crops that produces huge amount of biomass which can be processed and used as a renewable energy source. In 2016, Malaysia has reported over 5 million hectares of land were covered by oil palm plantations. Placing Malaysia as the second largest country of oil palm producer in the world has given it an advantage to produce renewable energy source. However, there is a need to monitor the sustainability of oil palm plantations in Malaysia via effective mapping approaches. This study utilised two different platforms (open source and commercial) using a machine learning algorithm namely Support Vector Machine (SVM) to perform oil palm mapping. An open source Python programming-based technique utilising Scikit-learn module was performed to map the oil palm distribution and the result produced had an overall accuracy of 91.39%. To support and validate the efficiency of the Python programming-based image classification, a commercial remote sensing software (ENVI) was used and compared by implementing the same SVM algorithm and the result showed an overall accuracy of 98.21%

ROOFING ASSESSMENT FOR ROOFTOP RAINWATER HARVESTING ADOPTION USING REMOTE SENSING AND GIS APPROACH

Rooftop rainwater harvesting refers to the collection and storage of water from rooftops whereby the quality of harvested rainwater depend on the types of roof and the environmental conditions. This system is capable to support the water supply in almost any place either as a sole source or by reducing stress on other sources through water savings. Remote sensing and GIS have been widely used in urban environmental analysis. Thus, this study aimed to develop the roofing layer in order to assess the potential area for rooftop rainwater harvesting adoption by integrating remote sensing and GIS approach. An urban area containing various urban roofing materials and characteristics was selected. High resolution satellite imagery acquired from WorldView-3 satellite systems with 0.3 m of spatial resolution was used in order to obtain spectral and spatial information of buildings and roofs. For quality assessment, the physical and chemical parameters of the rooftop harvested rainwater were performed according to the Standard Tests for Water and Wastewater. The potential area for rooftop rainwater harvesting adoption can be identified with the detail information of the rooftops and quality assessment in geospatial environment

Sustainable bio-economy that delivers the environment-food-energy-water nexus objectives: the current status in Malaysia

Biomass is a promising resource in Malaysia for energy, fuels, and high value-added products. However, regards to biomass value chains, the numerous restrictions and challenges related to the economic and environmental features must be considered. The major concerns regarding the enlargement of biomass plantation is that it requires large amounts of land and environmental resources such as water and soil that arises the danger of creating severe damages to the ecosystem (e.g. deforestation, water pollution, soil depletion etc.). Regarded concerns can be diminished when all aspects associated with palm biomass conversion and utilization linked with environment, food, energy and water (EFEW) nexus to meet the standard requirement and to consider the potential impact on the nexus as a whole. Therefore, it is crucial to understand the detail interactions between all the components in the nexus once intended to look for the best solution to exploit the great potential of biomass. This paper offers an overview regarding the present potential biomass availability for energy production, technology readiness, feasibility study on the techno-economic analyses of the biomass utilization and the impact of this nexus on value chains. The agro-biomass resources potential and land suitability for different crops has been overviewed using satellite imageries and the outcomes of the nexus interactions should be incorporated in developmental policies on biomass. The paper finally discussed an insight of digitization of the agriculture industry as future strategy to modernize agriculture in Malaysia. Hence, this paper provides holistic overview of biomass competitiveness for sustainable bio-economy in Malaysia

A review of applying second-generation wavelets for noise removal from remote sensing data.

The processing of remotely sensed data includes compression, noise reduction, classification, feature extraction, change detection and any improvement associated with the problems at hand. In the literature, wavelet methods have been widely used for analysing remote sensing images and signals. The second-generation of wavelets, which is designed based on a method called the lifting scheme, is almost a new version of wavelets, and its application in the remote sensing field is fresh. Although first-generation wavelets have been proven to offer effective techniques for processing remotely sensed data, second-generation wavelets are more efficient in some respects, as will be discussed later. The aim of this review paper is to examine all existing studies in the literature related to applying second-generation wavelets for denoising remote sensing data. However, to make a better understanding of the application of wavelet-based denoising methods for remote sensing data, some studies that apply first-generation wavelets are also presented. In the part of hyperspectral data, there is a focus on noise removal from vegetation spectrum

An integrated user-friendly ArcMAP tool for bivariate statistical modelling in geoscience applications

Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications

Road pavement density analysis using a new non-destructive ground penetrating radar system

Density is an important parameter to determine the strength of road, and it will ensure the safety of the use as well as maintaining the quality of road pavement. In this paper, the validation of GPR mixture model based on the microwave nondestructive free space method to determine the density of road pavement typed Hot Mix Asphalt (HMA) will be presented. The frequency range of operation used is 1.7-2.6 GHz. The attenuation is a major factor for gathering the density of road pavement predictably. The existing mixture model has been used to produce simulation data for determining the predicted complex permittivity and attenuation due to various densities of road pavement. The GPR laboratory measurement is performed where the measured attenuation due to various densities was obtained. The comparison results between measurement and simulation were investigated, and the relative errors in between were calculated to see the performance of the model. The best performance of mixture model was selected in the optimization technique due to the smallest mean error. An improved attenuation formula or optimized mixture model was obtained from the optimization technique to produce the better model. The finding from the optimization process suggested that three additional constant parameters which are volume factor, permittivity factor and attenuation factor need to be included to improve the existing mixture model. The optimized mixture model is introduced as GPR mixture model in this work. The validation process at field test had been conducted to evaluate the performance of optimized GPR model and produce the error range from 3.3% and 4.7%. At the end of this project, the GPR mixture model can be used as a calibration curve where the values of predicted density of a given real road pavement can be read directly once the attenuation values are known