Forest fire risk zone mapping from satellite imagery and GIS

Jaiswal, Rajeev Kumar; Mukherjee, Saumitra; Raju, K.; Saxena, Rajesh

doi:10.1016/s0303-2434(02)00006-5

Cited by 354 publications

(288 citation statements)

References 3 publications

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“…This data was then averaged for the study period (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) to generate the LST factor for input into the model. Topographic characteristics are also very important factors for fire spread (Jaiswal et al 2002). Topographic parameters (i.e.…”

Section: Data Usedmentioning

confidence: 99%

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Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data

Matin

Chitale

Murthy

et al. 2017

Int. J. Wildland Fire

142

100

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Abstract. Forest fire is one of the key drivers of forest degradation in Nepal. Most of the forest fires are human-induced and occur during the dry season, with ,89% occurring in March, April and May. The inaccessible mountainous terrain and narrow time window of occurrence complicate suppression efforts. In this paper, forest fire patterns are analysed based on historical fire incidence data to explore the spatial and temporal patterns of forest fires in Nepal. Three main factors are involved in the ignition and spread of forest fires, namely fuel availability, temperature and ignition potential. Using these factors a spatially distributed fire risk index was calculated for Nepal based on a linear model using weights and ratings. The input parameters for the risk assessment model were generated using remote sensing based land cover, temperature and active fire data, and topographic data. A relative risk ranking was also calculated for districts and village development committees (VDCs). In total, 18 out of 75 districts were found with high risk of forest fires. The district and VDC level fire risk ranking could be utilised by the Department of Forest for prioritisation, preparedness and resource allocation for fire control and mitigation.Additional keywords: fire behaviour, fire danger, fire management, fire risk.

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Section: Data Usedmentioning

confidence: 99%

“…Geospatial models have been used in some parts of the world to map fire risk indices (Jaiswal et al 2002;Saglam et al 2008;Adab et al 2013;Mohammadi et al 2014;Sivrikaya et al 2014). These models integrate various factors related to vegetation, topography and proximity of forests to human activities to estimate risk.…”

Section: Introductionmentioning

confidence: 99%

Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data

Matin

Chitale

Murthy

et al. 2017

Int. J. Wildland Fire

142

100

View full text Add to dashboard Cite

show abstract

“…Several approaches were discussed in the literature on forest fire hazard mapping. Most of them were applied multilayer evaluation system provided from different data sources such as remote sensing, climate stations and pre-designed maps (road, settlement, land use) (Jaiswal et al 2002;Adab et al 2013). In a multilayer assessment system, weighting the each layer is the most important issue.…”

Section: Discussionmentioning

confidence: 99%

“…Multi-criteria evaluation techniques using GIS tools might be useful in multiple data assessment. However, weighting the inputs is major problem in such kinds of techniques because of subjective ranking (Jaiswal et al 2002). Weight of evidence analyses can be used for weighting the input variables according to fire occurrence and location to solve this problem (Dickson et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Şatır

Berberoğlu

Dönmez

2015

Geomatics, Natural Hazards and Risk

177

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Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R D ¡0.43), tree cover (R D 0.93) and temperature (R D 0.42) were strongly correlated with forest fire probability in the USB region. KEYWORDSForest fire probability and hazard; landscape feature; weighting; artificial neural network; Mediterranean region; fire weather index

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“…GIS technology is also been used increasingly in all aspects of wild land fire management recently [36]. In this research, the likelihood of forest fire was calculated based on vegetation type, elevation, and slope [37][38][39]. Different vegetation types were assigned different scores, for example, coniferous forest was assigned 5 and waterbody was assigned 0.…”

Section: Safety Condition (A3)mentioning

confidence: 99%

Campgrounds Suitability Evaluation Using GIS-based Multiple Criteria Decision Analysis: A Case Study of Kuerdening, China

Wang¹,

Yang²,

Liu³

et al. 2016

Open Geosciences

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Abstract:The main objective of this study was to evaluate the suitability and select the most appropriate areas for building campgrounds in Kuerdening, China. To achieve this aim, AHP and GIS-based weighted overlay methods were adopted. AHP was used to determine the weights of the indexes, and ArcGIS 10 was used to calculate and map the campground suitability. In pursuit of minimum environmental effects and sustainable development, this paper identifies four factors to evaluate the suitability of areas for building campgrounds: natural environment condition, landscape condition, safety condition and infrastructure condition. The final outcome of this study was the suitability map for building campgrounds. This research not only provides a theoretical guide for the construction of campgrounds in this area but also provides a scientific and efficient workflow to evaluate the appropriateness of other areas. The result is reasonable and operable for camping facilities development and also useful for managers and planners working in local governments as well as investors.

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Forest fire risk zone mapping from satellite imagery and GIS

Cited by 354 publications

References 3 publications

Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data

Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Campgrounds Suitability Evaluation Using GIS-based Multiple Criteria Decision Analysis: A Case Study of Kuerdening, China

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