Operational perspective of remote sensing-based forest fire danger forecasting systems

Chowdhury, Ehsan H.; Hassan, Quazi K.

doi:10.1016/j.isprsjprs.2014.03.011

Cited by 80 publications

(50 citation statements)

References 89 publications

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“…Forest fire due to natural and anthropogenic factors (Adab et al 2013) causes economic losses to people in this region and increases the emission of carbon that influences climate change (Chowdhury and Hassan 2015). Catastrophic forest fire causes the destruction of large areas in many countries in Europe, America and Australia (Bonazountas et al 2005).…”

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

126

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: 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

126

100

View full text Add to dashboard Cite

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“…Those include: (i) meteorological variables, e.g., surface temperature (TS) [23,24], Ta [25], RH [25]; (ii) vegetation greenness, e.g., normalized difference vegetation index (NDVI) [26]; enhanced vegetation index (EVI) [27,28], relative greenness (RG) [24], visible atmospherically resistant index (VARI) [29]; (iii) surface wetness conditions, e.g., temperature-vegetation dryness index (TVDI) [30], NDVI/TS [31], TS/EVI [32]; and (iv) vegetation wetness conditions, e.g., normalized multiband drought index (NMDI) [33], normalized difference water index (NDWI) [34], normalized difference infrared index (NDII) [35,36], global vegetation moisture index (GVMI) [36]. In most of these studies, the fire danger conditions are being described either during or after the fire occurrences, meaning they cannot be used for forecasting purposes [37]. However, a limited number of studies found in the literature can be useful in forecasting.…”

Section: Introductionmentioning

confidence: 99%

Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data

Chowdhury

Hassan

2015

Remote Sensing

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Forest fires are a critical natural disturbance in most of the forested ecosystems around the globe, including the Canadian boreal forest where fires are recurrent. Here, our goal was to develop a new daily-scale forest fire danger forecasting system (FFDFS) using remote sensing data and implement it over the northern part of Canadian province of Alberta during 2009-2011 fire seasons. The daily-scale FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived four-input variables, i.e., 8-day composite of surface temperature (TS), normalized difference vegetation index (NDVI), and normalized multiband drought index (NMDI); and daily precipitable water (PW). The TS, NMDI, and NDVI variables were calculated during i period and PW during j day and then integrated to forecast fire danger conditions in five categories (i.e., extremely high, very high, high, moderate, and low) during j + 1 day. Our findings revealed that overall 95.51% of the fires fell under "extremely high" to "moderate" danger classes. Therefore, FFDFS has potential to supplement operational meteorological-based forecasting systems in between the observed meteorological stations and remote parts of the landscape. Keywords

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“…Upon generating the probabilities, they compared the forecasted number of large fires and observed ones. The result showed that both FPI 10 and FPI 1000 could predict large fires.…”

Section: Introductionmentioning

confidence: 99%

“…In general, we can divide the remote sensing-based approaches broadly into two categories, such as monitoring systems that determine the fire danger conditions during and/or after fire occurrences [5][6][7]; and forecasting systems that predict the fire danger conditions before fire occurrences [1,8,9]. In fact, the forecasting systems should be studied more extensively as the monitoring systems are not useful for the operational forest fire management purposes [10]. Some of the key studies are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

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An Advanced Forest Fire Danger Forecasting System: Integration of Remote Sensing and Historical Sources of Ignition Data

et al. 2018

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Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern region of the Canadian province of Alberta. The modified FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daily surface temperature (Ts) and precipitable water (PW), and 8-day composite of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), where we assumed that cloud-contaminant pixels would reduce the risk of fire occurrences. In addition, we generated ignition cause-specific static fire danger (SFD) maps derived using the historical human-and lightning-caused fires during the period 1961-2014. Upon incorporating different combinations of the generated SFD maps with the modified FFDFS, we evaluated their performances against actual fire spots during the 2009-2011 fire seasons. Our findings revealed that our proposed modifications were quite effective and the modified FFDFS captured almost the same amount of fires as the original FFDFS, i.e., about 77% of the detected fires on an average in the top three fire danger classes of extremely high, very high, and high categories, where about 50% of the study area fell under low and moderate danger classes. Additionally, we observed that the combination of modified FFDFS and human-caused SFD map (road buffer) demonstrated the most effective results in fire detection, i.e., 82% of detected fires on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. We believe that our developments would be helpful to manage the forest fire in order to reduce its overall impact.Keywords: human-caused static fire danger map; lightning-caused static fire danger map; normalized difference vegetation index; normalized difference water index; precipitable water; surface temperature

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Operational perspective of remote sensing-based forest fire danger forecasting systems

Cited by 80 publications

References 89 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

Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data

An Advanced Forest Fire Danger Forecasting System: Integration of Remote Sensing and Historical Sources of Ignition Data

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