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

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.

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

10.21203/rs.3.rs-1130891/v1 ◽

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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Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal

Geomatics Natural Hazards and Risk ◽

10.1080/19475705.2020.1853251 ◽

2020 ◽

Vol 11 (1) ◽

pp. 2569-2586

Author(s):

Ashok Parajuli ◽

Ambika Prasad Gautam ◽

Sundar Prasad Sharma ◽

Krishna Bahadur Bhujel ◽

Gagan Sharma ◽

...

Keyword(s):

Remote Sensing ◽

Forest Fire ◽

Fire Risk ◽

Risk Mapping ◽

Gis And Remote Sensing ◽

Forest Fire Risk

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Forest fire risk assessment model using Remote sensing and GIS techniques in Tujiin nars national park, Mongolia

Proceedings of the Mongolian Academy of Sciences ◽

10.5564/pmas.v61i01.1557 ◽

2021 ◽

pp. 10-19

Author(s):

Munkh-Erdene Altangerel ◽

Amarsaikhan Damdinsuren ◽

Jargaldalai Enkhtuya ◽

Nyamjargal Erdenebaatar

Keyword(s):

Remote Sensing ◽

Risk Assessment ◽

Forest Fire ◽

National Park ◽

Fire Risk ◽

Assessment Model ◽

Research Approach ◽

Main Concern ◽

Fire Risk Assessment ◽

Forest Fire Risk

Forest is an important natural resource that should be carefully protected and rationally managed. In recent years, deforestation and forest land degradation have become the main concern for forest specialists as well as policy and decision-makers dealing with environment issues. It has been found that much of the existing forests have been destroyed, mainly by shifting cultivation, timber preparation, legal and illegal logging, and forest fires. To protect and conserve the deteriorating forest, it is very important to conduct forest-related risk assessment and map the outcomes in a spatial domain. The aim of this research is to conduct a forest fire risk assessment mapping of Tujiin Nars National Park using geographic information system (GIS) and remote sensing (RS) techniques. The research approach is based on an empirical model. It includes three parameters (i.e. geomorphology, vegetation cover combustibility and human activity) that are crucial for the forest fire assessment. The results of the study can be used for different decision making processes.

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Forest Fire Risk Zone Mapping of Eravikulam National Park in India

Croatian journal of forest engineering ◽

10.5552/crojfe.2022.1137 ◽

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.

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