Fig 5 - uploaded by Sheriza Mohd Razali
Content may be subject to copyright.
Source publication
Forest fires are one of the major causes of the deforestation of tropical peat swamps in Malaysia. One way of trying to identify which peat swamp forests are vulnerable to forest fire is to develop a forest fire risk index. The objectives of this study were to develop both a fuel-type map and a forest fire hazard rating assessment for the peat swam...
Contexts in source publication
Context 1
... final fire hazard rating map is shown in Fig. 5. As can be seen from the map, about 49% (1,290ha) of the study area was categorized as 'low' fire hazard rating, followed by 23% (607 ha), 17% (450 ha), 10% (324 ha) and 1% (16 ha), categorized as 'high', 'moderate', 'extreme' and 'null' fire hazard rating, respectively. The data were computed as the total of burnt area (ha) divided by ...
Context 2
... area and developing specific strategies for each classification. Geometric corrections made it possible to overlay this image with that of the burnt area and to extract hazard index values for pixels within the boundary of the burned area. In this study, accuracy method was set to minimum of 30 points for each class. The resulting map is shown in Fig. 5. From this map several conclusions can be drawn (Table ...
Similar publications
Berdasarkan Penelitian di Fakultas Tarbiyah dan Keguruan Universitas Islam Kuantan Singingi alur penyelesian dosen berprestasi masih dilakukan secara manual seperti melakukan pencatatan pengajaran, Status Dosen Penelitian,Pengabdian Kepada Masyarakat dan melaksanakan Catur Dharma perguruan tinggi. Dosen merupakan tenaga pengajar yang diangkat oleh...
Background: Aging will cause loss of physical and mental function, loss of family and also loss of spouse. These type of loses gradually affect quality of life among elderly. Therefore, it is important to increase awareness among people in the community to understand about the benefits of doing physical activities regularly.
Materials and Methods:...
Although it has been run since 2012, Rapid Kuantan (RK) has not become a favorite choice for Kuantan people as public transportation. The aim of this study is to review the passenger satisfaction on information provided using Intelligent Transportation System (ITS) components by RK service, as a Public Transport in Malaysia. The method used in this...
Sustainable economic development, especially the industrial sector, is an important agenda in improving the welfare of the population. The problem that arises is that land use for industry is dominant compared to considerations of the carrying capacity. This study aims to analyze the carrying capacity of the environment, especially land and water i...
Citations
... Mahmud et al. [33] developed a user-friendly ArcView system that incorporated slope, road, elevation, and aspect attributes to produce a fire susceptibility map for the peat swamp area in Pahang. Razali et al. [34] integrated land cover, road distance, and canal buffers to create a fire hazard rating model and deliver a fire risk map for Pekan, Pahang. Ismail et al. [35] utilized factors such as moisture content, peat depth, dryness index, bulk density, water table, stand density, and species composition to generate a fire risk map for several forests in Peninsular Malaysia, including the Pekan forest reserve. ...
Forest fires pose a significant threat to ecosystems and human livelihoods. Understanding the role of climatic factors in fire occurrence is crucial for effective fire management and prevention. This study analyses the influences of temperature, precipitation, and wind speed on fire incidents in the districts of Rompin, Pekan, and Kuantan in Pahang, Malaysia. The investigation is motivated by newspaper articles dated early March 2021, which report that the fires in these districts were triggered by an extended period of hot and dry conditions, as highlighted by the Director of the Fire and Rescue Department of Pahang, Malaysia. However, no further investigation or detailed discussion has been deliberated. By examining the historical climatic data and fire incidents, this study aims to investigate the relationships between these climatic variables and fire occurrences. The results reveal that higher temperatures and lower precipitation are associated with increased fire susceptibility due to reduced soil moisture. In contrast, wind speed does not appear to impact fire spread significantly. These findings will undoubtedly provide valuable insights into the complex interactions between climatic variables and regional fire incidents, enabling policymakers and fire management authorities to develop targeted fire prevention and mitigation strategies.
... Every 5 years 1 km (Canu et al., 2017;Lee and Lim, 2010) (Fovell and Gallagher, 2018;Jahdi et al., 2014;Sakellariou et al., 2017) 8 Precipitation It is known that precipitation has an inverse relationship with wildfires and influences their speed. Daily 0.5 • (Razali et al., 2010;Tanskanen et al., 2005b;Vasilakos et al., 2009) occurrence, and resampling using the nearest neighbor approach was conducted to modify the wind data to a fixed patch size of 400 × 350. ...
... Insufficient levels of precipitation can exacerbate wildfires, causing an escalation in their spread rate (Vasilakos et al., 2009). Overall, there exists a notable inverse correlation between the likelihood of wildfire occurrence and precipitation amounts (Razali et al., 2010). The Global Precipitation Climatology Project (GPCP) (Huffman et al., 2022) was utilized to acquire daily precipitation data from Giovanni's website. ...
Wildfires represent a significant natural disaster with the potential to inflict widespread damage on both ecosystems and property. In recent years, there has been a growing interest in leveraging deep learning (DL) techniques for predicting the spread of wildfires (WS). However, existing studies have predominantly employed combined features with uniform weighting, overlooking the varying temporal resolutions they can offer (hourly, daily, and constant).As such, this study proposes a hybrid multi-temporal convolutional neural network (CNN) model called FirePred to fill this knowledge gap. In particular, 177 wildfire events were utilized along with related environmental variables between the years 2002 and 2018 in British Columbia, Canada. In pursuit of optimizing the model's performance, an exhaustive exploration of parameter configurations and settings was conducted. This involved assessing diverse combinations of loss functions, padding sizes, batch sizes, and thresholds. Notably, this rigorous analysis yielded an exceptional F1-score of 94% utilizing the most effective parameter set. In addition, to examine the versatility of our proposed model, we conducted an assessment using a dataset encompassing 10 instances of wildfires that transpired in Alaska between 2016 and 2019, as well as a wildfire occurrence in Nova Scotia during 2023. The findings revealed that the performance of the model can be influenced by regional parameters. Finally, the implementation of an uncertainty protocol discovered that the edges of the wildfire contribute the most to the uncertainty.
... The primary goal of the proposed system was to enable users with little to no experience with Geographic Information System (GIS) to produce a fire susceptibility map. Razali et al. (2010) intend to investigate the fire factors for the severe forest fire that happened in 1998 in Jalan Pekan, Pahang. The authors consider the land cover, distance to road, and canal buffers to deliver the fire risk map using ArcGIS software. ...
... We noticed that a remarkable number of hotspots are intensely focusing on the south-eastern part of Pahang. This result is very encouraging, as most of the historical forest fire incidents reported by the news (Alagesh, 2019;Astro Awani, 2018;Awang, 2021;Bernama, 2018;Malaysia Kini, 2016) and literature (Ismail et al., 2011;Jamaruppin et al., 2016;Mahmud et al., 2009;Razali et al., 2010;Setiawan et al., 2004) were materialised in the district of Pekan located in the southeastern of Pahang state. Apart from the district of Pekan, Jerantut (~231 hotspots), Termerloh (~159 hotspots), Kuala Rompin (~91 hotspots), Bentong (~81 hotspots), Bera (~79 hotspots), and Kuantan (~59 or 65 hotspots) also reveal approximate or more than 100 hotspots over the past 20 years. ...
... This is conditionally true because Malaysia is encountering hot and dry weather from February to April (Gasim et al., 2006) which subsequently results in the reduction of moisture in the soil. Additionally, the straightforward spatial analysis echoes the claims made by previous studies (Ismail et al., 2011;Jamaruppin et al., 2016;Mahmud et al., 2009;Razali et al., 2010;Setiawan et al., 2004), which assert the district of Pekan located in the south-eastern of Pahang to be a highly vulnerable fire-prone region. By recognising the soaring fire periods and locations, firefighting resources can be allocated efficiently to combat the fire to prevent or reduce the severity of each fire incidence. ...
Remote sensing imagery is one of the cost-efficient solutions to observe forest fire occurrence in a particular region. With the accessibility of more public remote sensing data, researchers and field experts can exploit the data to perform experimental analyses. In this paper, the entire state of Pahang in Malaysia has been selected to perform the fire activity trend analysis using the FIRMS hotspots public remote sensing dataset. The region is chosen because it has the largest forest reserve in Peninsular Malaysia and many fire incidences had been reported since 1998. The Google Earth Engine big data platform specifically the 20-years FIRMS hotspots dataset from 1st January 2001 to 31st December 2021 was used to observe the temporal and spatial trend of fire incidents in Pahang. The temporal analysis enacted reveals that the month from February to April every year is the occasional strong fire season. This scenario can be surmised by the hot and dry seasons encountered. On the other hand, the spatial analysis affirms the southeastern part of Pahang state, the district of Pekan to be extremely susceptible to fire when compared to other districts. Additionally, this analysis also uncovered several high-risk fire regions that are positioned in the district of Jerantut, Temerloh, Kuala Rompin, Bentong, Bera, and Kuantan. Through these analyses, early preparations to tackle the forest fire can undoubtedly inhibit or decrease the seriousness of the tragedy. In future, other datasets such as rainfall, temperature, landcover, etc. can be used jointly with the FIRMS hotspots dataset to further investigate the prominent factors which are responsible for a fire incident in the state of Pahang.
... The final component, Wetness, refers to water and soil moisture (Baig et al. 2014;Huang et al. 2010). Razali et al. (2010) stated that changes in the TC wetness component has been identified as a reliable indicator of changes in the forest, particularly damage caused by forest fires. Table 3 shows the TC coefficients (only for the components Brightness, Greenness, and Wetness used in this study) for Landsat 8 ...
In this paper, we analyse the applicability and effectiveness of satellite-retrieved Land Surface Temperature (LST) for monitoring and spatial analysis of forest fires using pre- and post-fire Landsat 8 and Aster satellite images. Firstly, LST was used for pre-fire analysis, along with other meteorological parameters to evaluate fire risk, and the results showed that the LST was highly correlated with all parameters. Besides, Google Earth Engine platform was used to evaluate forest-fire susceptibility in the region. For post-fire analysis, LST was used to monitor, analyse, and map post-fire disturbances using different image spectral transformations, and correlation analysis was performed to evaluate the strength of relationships between the parameters. High correlation coefficients were obtained between LST and selected spectral indices; and the highest correlation (0.90) was found between Difference Land Surface Temperature (dLST) – Difference Normalized Burn Ratio (dNBR). Spatial analysis done for dLST and slope showed that the post-fire LST increased in areas with a slope range of 42—55%. Then, spatial relation analysis between dLST and other indices was performed and a high correlation was found between dLST – dNBR, dLST – DI (Disturbance Index), and dLST – NDMI (Normalized Difference Moisture Index). Finally, for 4 categorical variables (namely CORINE Land Cover (CLC) map, dLST, Burn severity and Slope), the areal extent of burnt forest areas was calculated and most burnt areas were found to be related a certain degree with each category (i.e. coniferous forest class, -2 and 0.5 °C, medium–low burning intensity, 29–42% slope range, respectively).
... Razali et al. [64] proposed a fire susceptibility index considering fuel maps, road buffers, and canal buffers for a peat swamp forest in Batu Enam, Pahang. Instead of employing the NDVI vegetation index, the authors adopted Tasseled Cap (TC) transformation on a Landsat TM image retrieved on 3 April 1999 before performing supervised classification to categorise the land cover into nine distinct classes because the authors believed that TC was more effective at detecting peat swamp regions. ...
... The authors found that the overall classification accuracy of detecting land cover was 94.63%. To incorporate human activity into the proposed index, Razali et al. [64] included the road buffer (i.e., distance to road) and canal buffer parameters. They subsequently assigned a risk index to each of the class/class ranges for the fuel map, distance to road, and canal buffers. ...
The land surface of Malaysia mostly constitutes forest cover. For decades, forest fires have been one of the nation’s most concerning environmental issues. With the advent of machine learning, many studies have been conducted to resolve forest fire issues. However, the findings and results have been very case-specific. Most experiments have focused on particular regions with independent methodology settings, which has hindered the ability of others to reproduce works. Another major challenge is lack of benchmark datasets in this domain, which has made benchmark comparisons almost impossible to conduct. To our best knowledge, no comprehensive review and analysis have been performed to streamline the research direction for forest fires in Malaysia. Hence, this paper was aimed to review all works aimed to combat forest fire issues in Malaysia from 1989 to 2021. With the proliferation of publicly accessible satellite data in recent years, a new direction of utilising big data platforms has been postulated. The merit of this approach is that the methodology and experiments can be reproduced. Thus, it is strongly believed that the findings and analysis shown in this paper will be useful as a baseline to propagate research in this domain.
... Meanwhile, [5] used this technique on the coastal area of Ingrid Christensen Coast, Princess Elizabeth Land (eastern Antarctica). Meanwhile, as proven in [6,7,8,9,10], NDVI has been employed in several studies to distinguish vegetation from non-vegetation and mangrove vegetation from nonmangrove vegetation. As a result, NDVI is an excellent tool for assessing early water stress in this plantation. ...
Early examination of the water condition of the plants utilizing remote sensing technology can be used to assess the health of the vegetation in the Eucalyptus forest plantation. To preserve a sustainable wood supply and wooded region that is necessary to human life and vital wood supplies, the forested region should be protected from disease and environmental damage. Disease and environmental impacts are two of the most critical challenges in Eucalyptus forest management. To calculate the vegetation index and identify land cover in the research region, remote sensing with Catalyst Professional software based on Object Analyst (OBIA) tools was utilized. The NDVI (Normalized Difference Vegetation Index) is a valuable index for assessing early vegetation health. For atmospheric correction and haze removal, the image was first pre-processed with ATCOR tools. Second, the image was converted to NDVI using algorithm library tools. In addition, for land cover classification in the area, an OBIA based on Support Vector Machine (SVM) was utilized, followed by an accuracy assessment. Using ArcGIS software, zonal statistics were used to calculate the NDVI value for each land cover category. According to the method, the map produced roads, plantations, buildings, low-density vegetation, oil palm, and open area classifications. Based on accuracy assessment in OBIA, plantation, oil palm, and open area were all 100% accurate, whereas low-density vegetation and oil palm were 100% accurate according to the user. Producer accuracy was lowest on roads, whereas user accuracy was lowest in open areas. Non-vegetated land is difficult to classify at this site, according to the accuracy assessment results. The map improved accuracy since the study used a lower segmentation scale factor of 50, which produced fine vectors ascribed for classification. The average NDVI for oil palm area was 0.71, and 0.69 for plantation. Because it was difficult to classify open areas and roads, the NDVI for the class was low, at 0.37 and 0.22, respectively. From land use classification, the plantation was classified (37%), low-density vegetation area (28%), and oil palm (21%). Others make up only 2 to 7% of the site’s overall area. According to the study, NDVI is a useful indicator for assessing the health of vegetation in areas where NDVI values are larger than 0.70 and presents pf mixed landscape and non-vegetated features. A higher NDVI value implies that the plant is in good enough shape to conduct photosynthetic activities thus producing biomass for sustaining vegetation health. This type of inquiry can forecast more indices to produce higher accuracy of land use maps for the Eucalyptus plantation. At the same time, this type of research can assist forest managers in detecting large areas in their plantation for vegetation health assessment such as for early disease detection.
... Landsat data has been widely adopted by many researchers in Malaysia for the task of forest fire detection [14]- [24]. We would also like to highlight that Miettinen et al. [25], [26] have also produced and verified a land cover map distribution for Peninsular Malaysia and Sumatra island in the year 2015. ...
... As the works in [14], [17], [20], [21], [34], [51] had utilised the hotspots projected from AVHRR NOAA 12 and AVHRR NOAA 16 as the historical forest fire data in Malaysia, we have discovered that the hotspots archives for Southeast Asia Nation (SEA) are accessible from the Asean Specialised Meteorological Centre (ASMC) [52]. Figure 7 is a screenshot obtained from the ASMC website to access the hotspots data. ...
... Kebakaran hutan dan lahan dapat dideteksi melalui citra satelit berdasarkan data hotspot (Razali et al., 2010;Sitanggang et al., 2013). MODIS dapat digunakan untuk mendeteksi hotspot dengan resolusi spasial (ukuran piksel) 1 km 2 (Siegert & Hoffmann, 2000). ...
One of the areas in South Kalimantan that is prone to land fires is the Banjarbaru area, especially on peatlands. The fire in Banjarbaru is important because of the vital object of Syamsudin Noor Airport. Mapping of fire vulnerability was important for the Banjarbaru area, which had repeated fires throughout the year. The objective of the study was to analyze the vulnerability of forest and land fires in Banjarbaru, South Kalimantan Province. This study used Landsat 8 Oli Tirs imagery to obtain NDVI data and land cover maps from INA-Geoportal. The analysis of data used the scoring and overlay of the two maps. The level of vulnerability was dominated by the high vulnerability. The high level of vulnerability in Cempaka District was 81.9 %, in Banjarbaru Selatan District was around 99.5 %, in Banjarbaru Utara District was around 95.3 %, in Landasan Ulin District was around 94.1 % and in Lianganggang District was around 88.9 %. Land cover in the form of agriculture, plantations, and shrubs with moderate-high density caused the land to be more prone to fires.
... Despite this, peat swamp forests are being lost at a rapid pace: in Southeast Asia between 2000 and 2010, 56% were converted to plantations (Miettinen et al., 2012b), in addition to the area lost through logging and other development (Koh et al., 2011). In particular, fire is considered one of the most important drivers of land-use change and vast areas of these tropical peat swamps burn every year (Razali et al., 2010;Phua et al., 2012;Gaveau et al., 2014), especially on the island of Borneo (Langner and Siegert, 2009;Hoscilo et al., 2011;Miettinen et al., 2016). ...
... Burning has increasingly affected the peat swamp forests of Southeast Asia in the last 2 to 3 decades (Taylor, 2010) and is now claimed to be one of the most profound threats to peatland habitats (Lee, 2000;Razali et al., 2010), as well as to all rainforest ecosystems (Laurance, 2003). However, natural fires, predominantly caused by lightning strikes, have constituted an important part of the ecosystem dynamics in these tropical peat swamps (Taylor et al., 2001) by creating gaps in which succession can occur. ...
... In conjunction, the results from the three studied sediment cores strongly suggest that fire has been present in tropical peat swamp forests for thousands of years and that it is not the most prominent driver of long-term or recent changes in coastal peat swamp forest vegetation, contrary to the common concern expressed in the literature on the sustainable management of tropical peat swamp forests today (for example Razali et al., 2010;Miettinen et al., 2012c). Instead, human impact has had the most influence on internal peat swamp forest dynamics and peat swamp forest decline: with this disturbance manifesting only in the last c. ...
Tropical peat swamp forests are invaluable for their role in storing atmospheric carbon, notably in their unique below-ground reservoirs. Differing from terra firme forests, the peat-forming function of tropical swamps relies on the integrity of discrete hydrological units, in turn intricately linked to the above-ground woody, and herbaceous vegetation. Contemporary changes at a local, e.g., fire, to global level, e.g., climatic change, are impacting the integrity, and functioning of these ecosystems. In order to determine the level of impact and predict their likely future response, it is essential to understand past ecosystem disturbance, and resilience. Here, we explore the impact of burning on tropical peat swamp forests. Fires within degraded tropical peatlands are now commonplace; whilst fires within intact peat swamp forests are thought to be rare events. Yet little is known about their long-term natural fire regime. Using fossil pollen and charcoal data from three peat cores collected from Sarawak, Malaysian Borneo, we looked at the incidence and impact of local and regional fire on coastal peat swamp forests over the last 7,000 years. Palaeoecological results demonstrate that burning has occurred in these wetland ecosystems throughout their history, with peaks corresponding to periods of strengthened ENSO. However, prior to the Colonial era c. 1839 when human presence in the coastal swamp forests was relatively minimal, neither local nor regional burning significantly impacted the forest vegetation. After the mid-nineteenth century, at the onset of intensified land-use change, fire incidence elevated significantly within the peatlands. Although fire does not correlate with past vegetation changes, the long-term data reveal that it likely does correlate with the clearance of forest by humans. Our results suggest that human activity may be strongly influencing and acting synergistically with fire in the recent past, leading to the enhanced degradation of these peatland ecosystems. However, intact tropical peat swamp forests can, and did recover from local fire events. These findings support present-day concerns about the increase in fire incidence and combined impacts of fire, human disturbance and El Niño on peat swamp forests, with serious implications for biodiversity, human health and global climate change.
... A model of a potential forest fire using satellite data and the GIS was developed in China to identify areas with a high probability of forest fire [31]. In Sheriza et al. [32] five fire danger classifications were identified in developed forest fuel maps and the degree of fire danger in peat-bog forests was assessed. The vegetation types in the study area were analyzed using digital classification systems, namely, two vegetation indices: the extended vegetation index (AVI) and the Tasseled Cap (TC) conversion. ...
The vegetation cover of the Earth plays an important role in the life of mankind, whether it is natural forest or agricultural crop. The study of the variability of the vegetation cover, as well as observation of its condition, allows timely actions to make a forecast and monitor and estimate the forest fire condition. The objectives of the research were (i) to process the satellite image of the Gilbirinskiy forestry located in the basin of Lake Baikal; (ii) to select homogeneous areas of forest vegetation on the basis of their spectral characteristics; (iii) to estimate the level of forest fire danger of the area by vegetation types. The paper presents an approach for estimation of forest fire danger depending on vegetation type and radiant heat flux influence using geographic information systems (GIS) and remote sensing data. The Environment for Visualizing Images (ENVI) and the Geographic Resources Analysis Support System (GRASS) software were used to process satellite images. The area’s forest fire danger estimation and visual presentation of the results were carried out in ArcGIS Desktop software. Information on the vegetation was obtained using the analysis of the Landsat 8 Operational Land Imager (OLI) images for a typical forestry of the Lake Baikal natural area. The maps (schemes) of the Gilbirinskiy forestry were also used in the present article. The unsupervised k-means classification was used. Principal component analysis (PCA) was applied to increase the accuracy of decoding. The classification of forest areas according to the level of fire danger caused by the typical ignition source was carried out using the developed method. The final information product was the map displaying vector polygonal feature class, containing the type of vegetation and the level of fire danger for each forest quarter in the attribute table. The fire danger estimation method developed by the authors was applied to each separate quarter and showed realistic results. The method used may be applicable for other areas with prevailing forest vegetation.
