Fire risk zone analysis system using a predictive model for time and resource optimization

Author(s):  
Julio Cesar Cardenas Suca ◽  
Diego Jesus Tapia Medina ◽  
Daniel Alejandro Subauste Oliden
2021 ◽  
pp. 177-195
Author(s):  
Sk Mujibar Rahaman ◽  
Masjuda Khatun ◽  
Sanjoy Garai ◽  
Pulakesh Das ◽  
Sharad Tiwari

2019 ◽  
Vol 47 (12) ◽  
pp. 2047-2060 ◽  
Author(s):  
H. Yathish ◽  
K. V. Athira ◽  
K. Preethi ◽  
U. Pruthviraj ◽  
Amba Shetty

2005 ◽  
Vol 16 (3) ◽  
pp. 169-174 ◽  
Author(s):  
Xu Dong ◽  
Dai Li-min ◽  
Shao Guo-fan ◽  
Tang Lei ◽  
Wang Hui

Author(s):  
Elbegjargal Nasanbat ◽  
Ochirkhuyag Lkhamjav

Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.


Author(s):  
K. Pandey ◽  
S. K. Ghosh

<p><strong>Abstract.</strong> Forest fire has been regarded as one of the major reasons for the loss of biodiversity and dreadful conditions of environment. Global warming is also increasing the incidence of forest fire at an alarming rate. That’s why, one need to understand the complex biophysical parameters, which are responsible for this disaster. As it is difficult to predict forest fire, fire risk zone map can be useful for combating the forest fire. So the main aim of this study is to generate a Fire risk model to map fire risk zone using Remote Sensing &amp; GIS technique. Pauri Garhwal District, located in Uttarakhand, India, has been selected for this study as it continually faces the problem of forest fire. Landsat-8 data of 18th April, 2016 have been used for land use land cover mapping. Slope and other information have been derived from topographic maps and field information. For thematic and topographic information analysis ArcGIS and ERDAS Imagine software have been used. Forest fire risk model was generated by using AHP method, where each category was assigned subjective weight according to their sensitivity to fire. Three categories of forest fire risk ranging from very high to low were derived. The generated forest fire risk model was found to be in strong agreement with actual fire-affected sites.</p>


2004 ◽  
Vol 14 (3) ◽  
pp. 251-257 ◽  
Author(s):  
Hai-wei Yin ◽  
Fan-hua Kong ◽  
Xiu-zhen Li

2020 ◽  
Vol 20 ◽  
pp. 01003
Author(s):  
Ariesta Lestari ◽  
Katriani Puspita Ayu

Forest fire is one of environmental problem happens in Central Kalimantan. The fire does not only damage the forest ecosystem and biodiversity but also threaten the health and socio-economic of local people. Forest fire in Central Kalimantan is widely known as human-made, such as the process of shifting cultivation and land clearing. The expansion of forest into palm oil plantation is often blamed as the cause of forest fire since the forest clearing involves a massive amount of fires. Therefore, this study aims to explore whether the existence of palm oil cultivation contributes to the occurrence of forest fires. We used satellite imagery of hotspot, and overlay it with the land use data to generate the fire risk zone map using geographic information system (GIS) method. Through the map, the risk of fire can be monitored in advance to help the fire authority provide the act of mitigation. The result of this study suggested that risk mapping is vital for forest fire management to mitigate the spread of forest fire. The region to be fire-prone within the palm oil cultivation is suggested to form a preventive act through active forest-fires monitoring. In sum, this study is expected to provide a map of forest fires' risk around the cultivation area, mainly palm oil plantation, and help the fire authorities as well as stakeholders to identify the risk zone for fires prevention in the future.


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