Extraction of information from Remote sensing satellites is significant and effective tools for hazard activities such as flooding. essentially, if the field was under disaster conditions it will be so difficult to do surveying in that region. flash floods are sudden natural disaster events, which impact an extensive variety of ecological elements and applications associated with natural resources, agriculture, human activities and natural phenomena and economies. The utilization of SAR information gives much more information to control and monitor the flash floods, and additionally the information used for the damage assessment after the disasters. To recognize the region that has been flooded; a few procedures were trailed by Snap and Arc Map software. The fundamental step was flooded area in image its determined during extraction process by sets threshold estimation of radar backscatter which is taken from the calculation binary algorithm (the equitation of band math) to decide if a specified raster pixel is overflowed or not. The outcomes are indicated extend out of flash floods amid to time. On 26th of Mar Flood area was about 4.5%, however, it became around 45% in about month from the disaster beginning. This is evidence of the speed of the flood on the grounds that the nearness of high statures with Heavy rains around there and its foundation, on first of May agriculture damages, was around 1037 km2 as result appeared of this flash flood. Therefore, these outcomes are extremely helpful to make an evaluation of situations to save the people lives, property and nature from this natural disaster in the future.
A good landslide inventory map is a prerequisite for landslide hazard and risk analysis. In tropical countries, such as Malaysia, preparation of the landslide inventory is a challenging task because of the rapid growth of vegetation. Thus, it is crucial to use rapid and accurate technique and effective parameters. For this purpose, Dempster Shafer theory (DST) was applied in fusing high resolution LiDAR derived data products and Greenness index derived from orthophoto imagery. Two sites were selected, for the implementation and evaluation of the DST model; site "A" for DST implementation and site "B" for the comparison. For model implementation, vegetation index, slope and height were used as effective parameters for identifying automatic landslide detection. Two type of DST based fusions were evaluated; (greenness and height) and (greenness and slope). Furthermore, validation techniques were used to validate the accuracy are confusion matrix and area under the curve. The overall accuracy of the first and second evaluated fusions were (73.4% and 84.33%), and area under the curve were (0.76 and 0.81) respectively. Additionally, the result was compared with Random Forest (RF) based detection approach. The results showed that DST does not require a priori knowledge.
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.
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.
<p><strong>Abstract.</strong> 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%.</p>
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