Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences

Abdollahi, Masoud; Dewan, Ashraf; Hassan, Quazi K.

doi:10.3390/ijgi8030143

Cited by 27 publications

(17 citation statements)

References 49 publications

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“…Lightning is also one of the important sources of nitrogen oxides in the atmosphere, and thus the occurrence of lightning has a significant influence on global atmospheric chemistry (Schumann and Huntrieser, ; Murray, ; Tost, ). In addition, the cloud to ground (CG) lightning can cause forest fires, hit people and human facilities, causing properties damage, and casualties (Flannigan and Wotton, ; Krause et al, ; Raga et al, ; Abdollahi et al, ; Holle et al, ). Therefore, research on the response of lightning to various meteorological factors and climate change remains an important research topic (Reeve and Toumi, ; Xiong et al, ; Romps et al, ; Kotroni and Lagouvardos, ; Dewan et al, ; Finney et al, ).…”

Section: Introductionmentioning

confidence: 99%

Influence of aerosols on lightning activities in central eastern parts of China

Shi

Wang

Tan

et al. 2020

Atmospheric Science Letters

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Time series data of lightning flash rates, aerosol optical depth (AOD), surface relative humidity, potential temperature, and convective available potential energy (CAPE) for 14 consecutive summers (2001-2014) over central eastern parts of China (32.5 -40 N, 100 -120 E) have been analyzed to investigate the impact of aerosol on the lightning flash rate. The Pearson correlation and the partial correlation are used to study the linear correlations between the lightning flash rate and AOD, potential temperature, surface relative humidity, and CAPE. The results show that the lightning flash rate is positively correlated (r = .64) with AOD under relatively clean conditions (AOD < 1.0), which may result from aerosol microphysical effect. In the situation of high aerosol concentration (AOD > 1.0), the correlation between AOD and lightning flash rate is not obvious (r = −.06), which may be due to the radiation effect of aerosol and the decrease of the number of large ice particles caused by excessive aerosol concentration. CAPE and surface relative humidity are both positively correlated with the lightning flash rates under relatively clean (AOD < 1.0) and relatively polluted (AOD > 1.0) conditions. Potential temperature is moderate positively correlated with the lightning flash rate under relatively clean conditions (r = .51, AOD < 1.0) but shows no significant linear relationship under relatively polluted conditions (r = .07, AOD > 1.0). K E Y W O R D Saerosol, cloud microphysics, correlation coefficient, lightning activity

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Section: Introductionmentioning

confidence: 99%

Influence of aerosols on lightning activities in central eastern parts of China

Shi

Wang

Tan

et al. 2020

Atmospheric Science Letters

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“…of weather-related natural hazards such as fire (forest fires, wildfires etc.) [16][17][18][21][22][23][24][25], flood [26][27][28][29][30][31][32], sea-level rise [33,34], landslide [35][36][37][38][39], land subsidence [40,41], drought [42,43] and erosion [44][45][46].…”

Section: Literature Review and Theoretical Foundationsmentioning

confidence: 99%

Rural Fire Risk Assessment in GIS Environment Using Fuzzy Logic and the AHP Approaches

Erdin¹,

Çağlar²

2021

Pol. J. Environ. Stud.

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Fires all around the world have caused irreparable environmental and economic losses due to the increase in global warming. Risk assessments are curial for taking measures to keep negative effects of natural hazards to a minimum and to ensure sustainability of the nature. In this regard, the aim of this study is to evaluate rural area fire risk in the seven geographical regions of Turkey. Since fire sensitive areas in rural regions are generally composed of forest areas, data on the size of forest areas, number of forest fires and temperatures are the main starting points of this study. Analysis was carried out using the Analytical Hierarchy Process and Fuzzy Logic approaches. In this study the Analytical Hierarchy Process and Fuzzy Logic approaches were integrated with the Geographical Information Systems. The results obtained from the AHP and Fuzzy Logic approaches were compared and evaluated. The results of the study show that the Aegean, Mediterranean and Marmara regions have the highest rural area fire risk levels. The fire risk assessments results obtained by the AHP and Fuzzy Logic approaches overlap on the basis of geographical regions. Integrating the AHP and Fuzzy Logic with GIS for rural area fire risk assessment provide valid, reliable and very important results in making the necessary planning and ensuring sustainability of the environment.

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“…Despite our RS-based four-day FFDFS showing a promising performance of detecting forest fires, we observed that a small number of fires (i.e., 4.35%) were identified in the low fire danger category. This situation could be improved by integrating other forest fire-related variables in this model, such as topographical parameters (e.g., slope, elevation, and aspect of the landscape) that might have influence on the conditions of vegetation [16,17,35], meteorological variables (e.g., incident solar radiation, precipitation, and wind) [16,17], and vegetation phenology to define water stress in vegetation and the fuel availability for fire occurrences [16,36,37].…”

Section: Quantitative Performance Assessment Of the New Four-day Scale Rs-based Ffdfs Modelmentioning

confidence: 99%

Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale

et al. 2019

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Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events.

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Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences

Cited by 27 publications

References 49 publications

Influence of aerosols on lightning activities in central eastern parts of China

Influence of aerosols on lightning activities in central eastern parts of China

Rural Fire Risk Assessment in GIS Environment Using Fuzzy Logic and the AHP Approaches

Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale

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