scholarly journals Forest Fire Risk Zone Mapping of Eravikulam National Park in India

2021 ◽  
Vol 43 (1) ◽  
Author(s):  
G.S. Pradeep ◽  
Megha K. Prasad ◽  
Sekhar L. Kuriakose ◽  
R.S. Ajin ◽  
Valeria-Ersilia Oniga ◽  
...  
Keyword(s):  
Forest Fire ◽  
Roc Curve ◽  
National Park ◽  
Slope Angle ◽  
Fire Risk ◽  
Mitigation Measures ◽  
Slope Aspect ◽  
Anthropogenic Factors ◽  
Risk Zone ◽  
Forest Fire Risk

Forest fire is one of the most common natural hazards occurring in the Western Ghats region of Kerala and is one of the reasons for forest degradation. This natural disaster causes considerable damage to the biodiversity of this region during the dry fire season. The area selected for the present study, Eravikulam National Park, which is predominantly of grassland vegetation, is also prone to forest fires. This study aims to delineate the forest fire risk zones in Eravikulam National Park using remote sensing (RS) data and geographic information system (GIS) techniques. In the present study, methods such as Analytic Hierarchy Process (AHP) and Frequency Ratio (FR) were used to derive the weights, and the results were compared. We have used seven factors, i.e. land cover types, normalized difference vegetation index, normalized difference water index, slope angle, slope aspect, distance from the settlement, and distance from the road to prepare the fire risk zone map. The area of the prepared risk zone maps is divided into three zones, namely low, moderate, and high. From the study, it was found that the fire occurring in this area is due to natural as well as anthropogenic factors. The prepared forest fire risk zone maps are validated using the fire incidence data for the period from January 2003 to June 2019 collected from the records of the Forest Survey of India. The investigation revealed that 72% and 24% of the fire incidences occurred in the high risk zone of the maps prepared using the AHP and FR methods, respectively, which ascertained the superiority of the AHP method over the FR method for forest fire risk zone mapping. The receiver operating characteristic (ROC) curve analysis gives an area under the ROC curve (AUC) value of 0.767 and 0.567 for the AHP and FR methods, respectively. The risk zone maps will be useful for staff of the forest department, planners, and officials of the disaster management department to take effective preventive and mitigation measures.

scholarly journals Utilisation of GIS and remote sensing for forest fire risk zone mapping at Tram Chim National Park

2015 ◽  
Vol 18 (4) ◽  
pp. 221-235
Author(s):  
Minh Thanh Vu ◽  
Hien Thi Thu Le
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Rainy Season ◽  
National Park ◽  
Fire Risk ◽  
Dry Season ◽  
Risk Zone ◽  
Gis And Remote Sensing ◽  
Risk Zones ◽  
Forest Fire Risk

Identification of areas of high fire risk is extremely important task in fire prevention and fire fighting. This study focuses on utilizing GIS and remote sensing to predict highest forest fire risk zones at Tram Chim National Park. Forest fire risk index was calculated based on forest-fire causing factors. The factors consist of landcover density and types, distance to water and settlements, surface temperature and leaf wetness index. And then, two forest fire risk maps were completed, one of them represented the fire risk in the rainy season in 2013, the other performed the fire risk in the dry season 2014. High fire risk zones locate mostly at the edge of the park where the bionass is rich and are near settlements. According to this fire risk computing, in the rainy season, area of high fire risk zone was 1,014.65 ha, about 14 % natural areas of Tram Chim National Park. In additional, in the dry season, high forest fire risk zones was 3,344.65 ha, and there is no safety zone. Results of the research contribute to the forest protecting at Tram Chim National Park and over the country.

Forest Fire Risk Zone Mapping in Tropical Forests of Saranda, Jharkhand, Using FAHP Technique

2021 ◽  
pp. 177-195
Author(s):  
Sk Mujibar Rahaman ◽  
Masjuda Khatun ◽  
Sanjoy Garai ◽  
Pulakesh Das ◽  
Sharad Tiwari
Keyword(s):  
Tropical Forests ◽  
Forest Fire ◽  
Fire Risk ◽  
Risk Zone ◽  
Forest Fire Risk

scholarly journals Mapping Forest Fire Risk—A Case Study in Galicia (Spain)

2020 ◽  
Vol 12 (22) ◽  
pp. 3705
Author(s):  
Ana Novo ◽  
Noelia Fariñas-Álvarez ◽  
Joaquín Martínez-Sánchez ◽  
Higinio González-Jorge ◽  
José María Fernández-Alonso ◽  
...  
Keyword(s):  
Forest Fire ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fire Risk ◽  
Fire Regimes ◽  
Slope Aspect ◽  
Fire Weather Index ◽  
Novel Approach ◽  
Fuel Model ◽  
Forest Fire Risk

The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation.

A Comparative Analysis of Forest Fire Risk Zone Mapping Methods with Expert Knowledge

2019 ◽  
Vol 47 (12) ◽  
pp. 2047-2060 ◽  
Author(s):  
H. Yathish ◽  
K. V. Athira ◽  
K. Preethi ◽  
U. Pruthviraj ◽  
Amba Shetty
Keyword(s):  
Comparative Analysis ◽  
Forest Fire ◽  
Expert Knowledge ◽  
Fire Risk ◽  
Risk Zone ◽  
Forest Fire Risk

Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China

2005 ◽  
Vol 16 (3) ◽  
pp. 169-174 ◽  
Author(s):  
Xu Dong ◽  
Dai Li-min ◽  
Shao Guo-fan ◽  
Tang Lei ◽  
Wang Hui
Keyword(s):  
Forest Fire ◽  
Satellite Images ◽  
Fire Risk ◽  
Risk Zone ◽  
Baihe Forestry Bureau ◽  
Forest Fire Risk

scholarly journals MODELING OF PARAMETERS FOR FOREST FIRE RISK ZONE MAPPING

2018 ◽  
Vol XLII-5 ◽  
pp. 299-304 ◽  
Author(s):  
K. Pandey ◽  
S. K. Ghosh
Keyword(s):  
Forest Fire ◽  
Risk Model ◽  
Fire Risk ◽  
Landsat 8 ◽  
Biophysical Parameters ◽  
Risk Zone ◽  
Subjective Weight ◽  
Other Information ◽  
Erdas Imagine ◽  
Forest Fire Risk

<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>

RS and GIS-based forest fire risk zone mapping in da hinggan mountains

2004 ◽  
Vol 14 (3) ◽  
pp. 251-257 ◽  
Author(s):  
Hai-wei Yin ◽  
Fan-hua Kong ◽  
Xiu-zhen Li
Keyword(s):  
Forest Fire ◽  
Fire Risk ◽  
Risk Zone ◽  
Rs And Gis ◽  
Da Hinggan Mountains ◽  
Forest Fire Risk

scholarly journals Mapping the Forest Fire Risk Zones Using Artificial Intelligence with Risk Factors Data

2022 ◽  
Author(s):  
Volkan Sevinc
Keyword(s):  
Artificial Intelligence ◽  
High Risk ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
Geographical Information ◽  
Data Set ◽  
Risk Zone ◽  
Risk Zones ◽  
Forest Fire Risk

Abstract Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of nine human-induced factors, three natural factors, and a temperature factor causing forest fires. Moreover, an artificial intelligence method, k-means, clustering algorithm was employed in preparation of the fire risk zone map. Turkey was selected as the study area as there are social and economic varieties among its zones. Therefore, the forestry zones in Turkey were separated into three groups as low, moderate, and high-risk categories and a map was provided for these risk zones. The map reveals that the forestry zones on the west coast of Turkey are under high risk of forest fire while the moderate risk zones mostly exist in the southeastern zones. The zones located in the interior parts, in the east, and on the north coast of Turkey have comparatively lower forest fire risks.

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