Forest fires are the main threat to the sustainability of forests around the world destroying the valuable forest biodiversity and nearby infrastructure. Forest covers provide shading, cooling the environment, and lower thermal absorption. However, the changes in forest cover due to forest fires increase the land surface temperature (LST) locally. This study is aimed to analyze the relationship between forest fires and LST using Landsat-8 OLI/TIRS imagery in Kuala Baram, Sarawak. More than 1500ha of forests and plantations have been destroyed after 26 days of fires, deteriorating the air quality to hazardous levels, damaging the nearby house and infrastructure. The differenced normalized burn ratio (dNBR) between pre-fire and post-fire NBR was used to estimate the burn severity categories in Kuala Baram, Sarawak. It was found that the burn severity ranges from moderate-low to moderate-high levels. The correlation between forest fire and LST in Kuala Baram, Sarawak in June 2019 and September 2019 was calculated using Pearson’s correlation coefficients. It indicates the strongest negative relationship between NBR and LST in pre-fires (R2 =-0.985) and post-fires (R2 =-0.996). The findings from this study showed that forest fires also affecting the land surface temperature of Kuala Baram, Sarawak along with the physical damages that have been diminished from the forest fires occurrences.
The world was shocked by an unprecedented outbreak caused by coronavirus disease 2019 (COVID-19). In Malaysia, it started with the largest number of COVID-19 cases with the first wave of infection on 25 January 2020. The objectives of this paper are to obtain the perspective of the respondents about the need for web-mapping in the form of mapping the geospatial data in Malaysia and to visualize the current online datasets of COVID-19 disease case clusters. The study area would cover the entire Malaysia since a rapidly increasing number of citizens were affected by this virus. To be specific, this study focused on the active clusters of COVID-19 in Malaysia. The data were freely shared in real-time by referring to the Ministry of Health (MOH) channel. The hotspots map were explored using the Map Editor by Cloud GIS. The approach has been illustrated using a dataset of whole Malaysia which are locally transmitted confirmed cases in four phases of COVID-19 wave in Malaysia. This study is significant to raise public awareness of the virus, especially among Malaysian citizens. It can provide an accurate estimation of the cluster tracking of the COVID-19 system by using geospatial technology. Therefore, people are more concerned and followed all the Standard Operating Procedure (SOP) provided by the government to prevent the spread of COVID-19.
The Sun's radiated energy is an important source in realizing the green technology concept construction. When interacting with the atmosphere and objects on the Earth's surface incoming solar radiation (insolation) will create insolation patterns that are ambiguous and as a result need to be investigated further. This paper explores the insolation pattern and ambiguities against topographic surfaces in the context of direct, diffuse, and reflectance irradiance. The topography is modeled from LiDAR data as Digital Surface Model (DSM) and Digital Terrain Model (DTM). The generated DSM and DTM were converted to Triangular Irregular Network (TIN) format within the Arc GIS environment before the insolation pattern could be visualized. The slope and aspect of the topography has an impact on the insolation which is the emphasis of this paper. The main outcome from the study is the insolation map and plots of relationship between the insolation and surface aspect. The findings from this study should contribute to the sustainable practices of green building technology.
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