scholarly journals Geospatial information on geographical and human factors improved anthropogenic fire occurrence modeling in the Chinese boreal forest

2016 ◽  
Vol 46 (4) ◽  
pp. 582-594 ◽  
Author(s):  
Futao Guo ◽  
Selvaraj Selvalakshmi ◽  
Fangfang Lin ◽  
Guangyu Wang ◽  
Wenhui Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Human Factors ◽  
Boreal Forest ◽  
Forest Fire ◽  
Management Practices ◽  
Explanatory Power ◽  
Driving Factors ◽  
Fire Occurrence ◽  
Geospatial Information ◽  
Anthropogenic Fire

We applied a classic logistic regression (LR) model together with a geographically weighted logistic regression (GWLR) model to determine the relationship between anthropogenic fire occurrence and potential driving factors in the Chinese boreal forest and to test whether the explanatory power of the LR model could be increased by considering geospatial information of geographical and human factors using a GWLR model. Three tests, “all variables”, “significant variables”, and “cross-validation”, were applied to compare model performance between the LR and GWLR models. Our results confirmed the importance of distance to railway, elevation, length of fire line, and vegetation cover on fire occurrence in the Chinese boreal forest. In addition, the GWLR model performs better than the LR model in terms of model prediction accuracy, model residual reduction, and spatial parameter estimation by considering geospatial information of explanatory variables. This indicates that the global LR model is incapable of identifying underlying causal factors for wildfire modeling sufficiently. The GWLR model helped identify spatial variation between driving factors and fire occurrence, which can contribute better understanding of forest fire occurrence over large geographic areas and the forest fire management practices may be improved based on it.

scholarly journals Modeling Anthropogenic Fire Occurrence in the Boreal Forest of China Using Logistic Regression and Random Forests

Forests ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 250 ◽  
Author(s):  
Futao Guo ◽  
Lianjun Zhang ◽  
Sen Jin ◽  
Mulualem Tigabu ◽  
Zhangwen Su ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Boreal Forest ◽  
Random Forests ◽  
Fire Occurrence ◽  
Anthropogenic Fire

What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests

2016 ◽  
Vol 25 (5) ◽  
pp. 505 ◽  
Author(s):  
Futao Guo ◽  
Guangyu Wang ◽  
Zhangwen Su ◽  
Huiling Liang ◽  
Wenhui Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regional Scale ◽  
Fire Risk ◽  
Driving Factors ◽  
Fire Season ◽  
Fire Occurrence ◽  
Climate Factors ◽  
Local Factors ◽  
Risk Zones

We applied logistic regression and Random Forest to evaluate drivers of fire occurrence on a provincial scale. Potential driving factors were divided into two groups according to scale of influence: ‘climate factors’, which operate on a regional scale, and ‘local factors’, which includes infrastructure, vegetation, topographic and socioeconomic data. The groups of factors were analysed separately and then significant factors from both groups were analysed together. Both models identified significant driving factors, which were ranked in terms of relative importance. Results show that climate factors are the main drivers of fire occurrence in the forests of Fujian, China. Particularly, sunshine hours, relative humidity (fire seasonal and daily), precipitation (fire season) and temperature (fire seasonal and daily) were seen to play a crucial role in fire ignition. Of the local factors, elevation, distance to railway and per capita GDP were found to be most significant. Random Forest demonstrated a higher predictive ability than logistic regression across all groups of factors (climate, local, and climate and local combined). Maps of the likelihood of fire occurrence in Fujian illustrate that the high fire-risk zones are distributed across administrative divisions; consequently, fire management strategies should be devised based on fire-risk zones, rather than on separate administrative divisions.

scholarly journals Drought Modulated Boreal Forest Fire Occurrence and Linkage with La Nina Events in Altai Mountains, Northwest China

2020 ◽  
Author(s):  
Chunming Shi ◽  
Ying Liang ◽  
Cong Gao ◽  
Fengjun Zhao ◽  
Qiuhua Wang ◽  
...  
Keyword(s):  
Drought Stress ◽  
Boreal Forest ◽  
Forest Fire ◽  
El Niño ◽  
Boreal Forests ◽  
El Nino ◽  
Fire Frequency ◽  
Burned Area ◽  
Fire Occurrence ◽  
La Nina

Warming-induced drought stress and El Nino associated summer precipitation failure are responsible for increased forest fire intensities of tropical and temperate forests in Asia and Australia. However, both effects are unclear for boreal forests, the largest biome and carbon stock over land. Here we combined fire frequency, burned area and climate data in the Altai boreal forests, the southmost extension of Siberia boreal forest into China, and explored their link with ENSO (El Nino and South Oscillation). Surprisingly, both summer drought severity and fire occurrence have shown significant (P<0.05) teleconnections with La Nina events of the previous year, and therefore provide an important reference for forest fire prediction and prevention in Altai. Despite a significant warming trend, the increased moisture over Altai has largely offset the effect of warming-induced drought stress, and lead to an insignificant fire frequency trend in the last decades, and largely reduced burned area since the 1980s. The reduced burned area could also benefit from the fire suppression efforts and greatly increased investment in fire prevention since 1987.

Driving factors for forest fire occurrence in Durango State of Mexico: A geospatial perspective

2010 ◽  
Vol 20 (6) ◽  
pp. 491-497 ◽  
Author(s):  
Diana Avila-Flores ◽  
Marin Pompa-Garcia ◽  
Xanat Antonio-Nemiga ◽  
Dante A. Rodriguez-Trejo ◽  
Eduardo Vargas-Perez ◽  
...  
Keyword(s):  
Forest Fire ◽  
Driving Factors ◽  
Fire Occurrence

A logistic model for predicting daily people-caused forest fire occurrence in Ontario

1987 ◽  
Vol 17 (5) ◽  
pp. 394-401 ◽  
Author(s):  
D. L. Martell ◽  
S. Otukol ◽  
B. J. Stocks
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Probability Distribution ◽  
Field Test ◽  
Forest Fire ◽  
Logistic Model ◽  
Northern Region ◽  
Fire Season ◽  
Fire Occurrence ◽  
Analysis Techniques

The authors describe the development of a procedure that can be used to predict daily people-caused forest fire occurrence in the Northern Region of the province of Ontario. The procedure is based on the use of logistic regression analysis techniques to predict the probability of a fire day and the assumption that a Poisson probability distribution can be used to model daily people-caused forest fire occurrence. The results of a field test that was conducted during the summer portion of the 1984 fire season indicate the procedure works well during relatively wet periods.

scholarly journals Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Forests ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 5
Author(s):  
Slobodan Milanović ◽  
Nenad Marković ◽  
Dragan Pamučar ◽  
Ljubomir Gigović ◽  
Pavle Kostić ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Suppression ◽  
Standard Procedure ◽  
Predictive Ability ◽  
Fire Risk ◽  
Fire Season ◽  
Fire Occurrence ◽  
Probability Mapping

Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire season, from zones with a lower risk to those under higher threat. Logistic regression (LR) has been used as a standard procedure in forest fire probability mapping, but in the last decade, machine learning methods such as fandom forest (RF) have become more frequent. The main goals in this study were to (i) determine the main explanatory variables for forest fire occurrence for both models, LR and RF, and (ii) map the probability of forest fire occurrence in Eastern Serbia based on LR and RF. The most important variable was drought code, followed by different anthropogenic features depending on the type of the model. The RF models demonstrated better overall predictive ability than LR models. The map produced may increase firefighting efficiency due to the early detection of forest fire and enable resources to be allocated in the eastern part of Serbia, which covers more than one-third of the country’s area.

Modelling seasonal variation in daily people-caused forest fire occurrence

1989 ◽  
Vol 19 (12) ◽  
pp. 1555-1563 ◽  
Author(s):  
D. L. Martell ◽  
E. Bevilacqua ◽  
B. J. Stocks
Keyword(s):  
Logistic Regression ◽  
Seasonal Variation ◽  
Forest Fire ◽  
Regression Models ◽  
Northern Region ◽  
Fire Occurrence ◽  
Periodic Functions ◽  
Logistic Regression Models ◽  
Independent Test ◽  
Calendar Dates

Periodic functions of Julian calendar dates were used to incorporate seasonal variation into logistic regression models designed to predict daily people-caused forest fire occurrence in the Northern Region of the province of Ontario. Three years of independent test data were used to evaluate predictions produced by the models.

scholarly journals Developing fire danger models using logistic regression analysis for mid-hills of Himachal Pradesh

2021 ◽  
Vol 21 (4) ◽  
pp. 510-514
Author(s):  
Divya Mehta ◽  
P.K. Baweja ◽  
R.K. Aggarwal
Keyword(s):  
Logistic Regression ◽  
Wind Speed ◽  
Relative Humidity ◽  
Forest Fire ◽  
Himachal Pradesh ◽  
Fire Danger ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Fire Occurrence

The present study intended to develop a climatic fire danger model for mid-hills zone of Himachal Pradesh using ten years weather data in relation with forest fire occurrence (2007-2016). Logistic regression technique was used to determine the relationship between fire occurrence and weather parameters viz., maximum temperature (°C), relative humidity (%), and wind speed (ms-1). The model was validated by calculating area under curve (AUC), coefficient of determination (R2) and root mean square Error (RMSE), with estimated values of 88.90%, 0.705 and 0.247, respectively. The fire danger model was verified with actual fire incidences in the study area during the year 2017. Wald's test was carried out to quantify impact climatic parameters on forest fire. Wald's test value was highest for maximum temperature (40.07) followed by relative humidity (1.15) and wind speed (0.75), respectively. In future such model can be utilized for prevention of forest fire hazards in the study area.

Forest fire occurrence and climate change in Canada

2010 ◽  
Vol 19 (3) ◽  
pp. 253 ◽  
Author(s):  
B. M. Wotton ◽  
C. A. Nock ◽  
M. D. Flannigan
Keyword(s):  
Climate Change ◽  
Boreal Forest ◽  
Forest Fire ◽  
Forest Fires ◽  
General Circulation ◽  
Fire Regime ◽  
General Circulation Models ◽  
Fire Occurrence ◽  
Fire Activity ◽  
In Fire

The structure and function of the boreal forest are significantly influenced by forest fires. The ignition and growth of fires depend quite strongly on weather; thus, climate change can be expected to have a considerable impact on forest fire activity and hence the structure of the boreal forest. Forest fire occurrence is an extremely important element of fire activity as it defines the load on suppression resources a fire management agency will face. We used two general circulation models (GCMs) to develop projections of future fire occurrence across Canada. While fire numbers are projected to increase across all forested regions studied, the relative increase in number of fires varies regionally. Overall across Canada, our results from the Canadian Climate Centre GCM scenarios suggest an increase in fire occurrence of 25% by 2030 and 75% by the end of the 21st century. Results projected from fire climate scenarios derived from the Hadley Centre GCM suggest fire occurrence will increase by 140% by the end of this century. These general increases in fire occurrence across Canada agree with other regional and national studies of the impacts of climate change on fire activity. Thus, in the absence of large changes to current climatic trends, significant fire regime induced changes in the boreal forest ecosystem are likely.

scholarly journals Drought-Modulated Boreal Forest Fire Occurrence and Linkage with La Nina Events in Altai Mountains, Northwest China

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 956
Author(s):  
Chunming Shi ◽  
Ying Liang ◽  
Cong Gao ◽  
Qiuhua Wang ◽  
Lifu Shu
Keyword(s):  
Drought Stress ◽  
Boreal Forest ◽  
Forest Fire ◽  
El Niño ◽  
Boreal Forests ◽  
El Nino ◽  
Fire Frequency ◽  
Burned Area ◽  
Fire Occurrence ◽  
La Nina

Warming-induced drought stress and El Nino-associated summer precipitation failure are responsible for increased forest fire intensities of tropical and temperate forests in Asia and Australia. However, both effects are unclear for boreal forests, the largest biome and carbon stock over land. Here, we combined fire frequency, burned area, and climate data in the Altai boreal forests, the southmost extension of Siberia’s boreal forest into China, and explored their link with El Nino–Southern Oscillation (ENSO). Surprisingly, both summer drought severity and fire occurrence showed significant (p < 0.05) correlation with La Nina events of the previous year and therefore provide an important reference for forest fire prediction and prevention in Altai. Despite a significant warming trend, the increased moisture over Altai has largely offset the effect of warming-induced drought stress and led to an insignificant fire frequency trend in the last decades, resulting in largely reduced burned area since the 1980s. The reduced burned area can also be attributed to fire suppression efforts and greatly increased investment in fire prevention since 1987.

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