Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables

As most of the forest fires in South Korea are related to human activity, socio-economic factors are critical in estimating their probability. To estimate and analyze how human activity is influencing forest fire probability, this study considered not only environmental factors such as precipitation, elevation, topographic wetness index, and forest type, but also socio-economic factors such as population density and distance from urban area. The machine learning Maximum Entropy (Maxent) and Random Forest models were used to predict and analyze the spatial distribution of forest fire probability in South Korea. The model performance was evaluated using the receiver operating characteristic (ROC) curve method, and models’ outputs were compared based on the area under the ROC curve (AUC). In addition, a multi-temporal analysis was conducted to determine the relationships between forest fire probability and socio-economic or environmental changes from the 1980s to the 2000s. The analysis revealed that the spatial distribution was concentrated in or around cities, and the probability had a strong correlation with variables related to human activity and accessibility over the decades. The AUC values for validation were higher in the Random Forest result compared to the Maxent result throughout the decades. Our findings can be useful for developing preventive measures for forest fire risk reduction considering socio-economic development and environmental conditions.

[1]  David P. Roy,et al.  Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome , 2017, Int. J. Digit. Earth.

[2]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[3]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[4]  A. Syphard,et al.  Location, timing and extent of wildfire vary by cause of ignition , 2015 .

[5]  Douglas G. Woolford,et al.  A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain , 2010 .

[6]  Lazaros S. Iliadis,et al.  A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation , 2005, Environ. Model. Softw..

[7]  C. Lim,et al.  Effect of National-Scale Afforestation on Forest Water Supply and Soil Loss in South Korea, 1971–2010 , 2017 .

[8]  M. Wiesmeier,et al.  Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem , 2011, Plant and Soil.

[9]  O. Sass,et al.  Using multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests , 2014 .

[10]  Andrew Aldersley,et al.  Global and regional analysis of climate and human drivers of wildfire. , 2011, The Science of the total environment.

[11]  M. Schwartz,et al.  Using species distribution models to predict new occurrences for rare plants , 2009 .

[12]  Sergi Costafreda-Aumedes,et al.  Human-caused fire occurrence modelling in perspective: a review , 2017 .

[13]  Shoji Ohga,et al.  Predicting the potential impact of climate change on people-caused forest fire occurrence in-South Korea , 2012 .

[14]  F. Moreira,et al.  Regional variations in wildfire susceptibility of land-cover types in Portugal: implications for landscape management to minimize fire hazard , 2009 .

[15]  B. R. Ramesh,et al.  Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India , 2012 .

[16]  Carlos J. Novillo,et al.  Spatial modelling of socioeconomic data to understand patterns of human-caused wildfire ignition risk in the SW of Madrid (central Spain) , 2010 .

[17]  C. Skinner,et al.  Probability of tree survival after wildfire in an interior pine forest of northern California: Effects of thinning and prescribed fire , 2007 .

[18]  Scott L. Stephens,et al.  Scientific and social challenges for the management of fire-prone wildland–urban interfaces , 2009 .

[19]  Víctor Urrea,et al.  Letter to the Editor: Stability of Random Forest importance measures , 2011, Briefings Bioinform..

[20]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[21]  Zohre Sadat Pourtaghi,et al.  Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques , 2016 .

[22]  B. Bridges.  The Seoul Olympics: Economic Miracle Meets the World , 2008 .

[23]  Donald McKenzie,et al.  Climatic Change, Wildfire, and Conservation , 2004 .

[24]  S. Jeon,et al.  Assessing Climate Change Impact on Forest Habitat Suitability and Diversity in the Korean Peninsula , 2018 .

[25]  F. Mouillot,et al.  Contribution of human and biophysical factors to the spatial distribution of forest fire ignitions and large wildfires in a French Mediterranean region , 2017 .

[26]  S. Pimm,et al.  Reserves Protect against Deforestation Fires in the Amazon , 2009, PloS one.

[27]  Alexandra D. Syphard,et al.  Predicting spatial patterns of fire on a southern California landscape , 2008 .

[28]  D. Mercer,et al.  Planning to Reduce Risk: The Wildfire Management Overlay in Victoria, Australia , 2009 .

[29]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[30]  P. Danielsson.  Euclidean distance mapping , 1980 .

[31]  T. McKee,et al.  THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME SCALES , 1993 .

[32]  Sunil Kumar,et al.  Regional modeling of large wildfires under current and potential future climates in Colorado and Wyoming, USA , 2016, Climatic Change.

[33]  Zohre Sadat Pourtaghi,et al.  Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran , 2015, Environmental Earth Sciences.

[34]  Nicolas Barbier,et al.  The global biogeography of semi‐arid periodic vegetation patterns , 2008 .

[35]  H. Rii,et al.  Urbanization and its impact on Seoul, Korea , 2002 .

[36]  D. Riaño,et al.  Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms , 2016, PloS one.

[37]  Niels Raes,et al.  Minimum required number of specimen records to develop accurate species distribution models , 2016 .

[38]  Keith M. Reynolds,et al.  A method for mapping fire hazard and risk across multiple scales and its application in fire management. , 2010 .

[39]  H. Pourghasemi,et al.  Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran , 2016 .

[40]  Y. Choi,et al.  Complex Relationships of the Effects of Topographic Characteristics and Susceptible Tree Cover on Burn Severity , 2018 .

[41]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[42]  Yun Jin-il,et al.  A Spatial Interpolation Model for Daily Minimum Temperature over Mountainous Regions , 2000 .

[43]  Matt P. Plucinski,et al.  The timing of vegetation fire occurrence in a human landscape , 2014 .

[44]  E. Chuvieco,et al.  Human-caused wildfire risk rating for prevention planning in Spain. , 2009, Journal of environmental management.

[45]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[46]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[47]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[48]  Biswajeet Pradhan,et al.  A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area , 2017 .

[49]  Y. Shimabukuro,et al.  Interactions between rainfall, deforestation and fires during recent years in the Brazilian Amazonia , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[50]  Examining Spatial Variation in the Effects of Japanese Red Pine (Pinus densiflora) on Burn Severity Using Geographically Weighted Regression , 2017 .

[51]  J. San-Miguel-Ayanz,et al.  Analysis of large fires in European Mediterranean landscapes: Lessons learned and perspectives , 2013 .

[52]  Avi Bar Massada,et al.  Wildfire ignition-distribution modelling: a comparative study in the Huron-Manistee National Forest, Michigan, USA , 2013 .

[53]  P. Hernandez,et al.  Predicting species distributions in poorly-studied landscapes , 2008, Biodiversity and Conservation.

[54]  Hwa-Woon Lee,et al.  Numerical Experiment on the Variation of Atmospheric Circulation due to Wild Fire , 2013 .

[55]  S. Costafreda-Aumedes,et al.  Improving fire season definition by optimized temporal modelling of daily human-caused ignitions. , 2018, Journal of environmental management.

[56]  K. Hirsch,et al.  Fire-smart forest management: A pragmatic approach to sustainable forest management in fire-dominated ecosystems , 2001 .

[57]  Y. Igarashi,et al.  Wet and dry deposition patterns of plutonium in Daejeon, Korea. , 2004, The Science of the total environment.

[58]  M. Harmon,et al.  Can fuel-reduction treatments really increase forest carbon storage in the western US by reducing future fire emissions? , 2012 .

[59]  Trevor Hastie,et al.  Overview of Supervised Learning , 2001 .

[60]  M. Moritz,et al.  Environmental controls on the distribution of wildfire at multiple spatial scales , 2009 .

[61]  Si-Young Lee,et al.  Estimating the spatial pattern of human-caused forest fires using a generalized linear mixed model with spatial autocorrelation in South Korea , 2012, Int. J. Geogr. Inf. Sci..

[62]  M. Becht,et al.  Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows , 2014 .

[63]  Woo-Kyun Lee,et al.  Can satellite-based data substitute for surveyed data to predict the spatial probability of forest fire? A geostatistical approach to forest fire in the Republic of Korea , 2019, Geomatics, Natural Hazards and Risk.

[64]  Giovanni B. Crosta,et al.  Techniques for evaluating the performance of landslide susceptibility models , 2010 .

[65]  S. Ly,et al.  What limits fire? An examination of drivers of burnt area in Southern Africa , 2008 .

[66]  Richard A. Minnich,et al.  Mapping probability of fire occurrence in San Jacinto Mountains, California, USA , 1993 .

[67]  Jong‐Jin Baik,et al.  Daily maximum urban heat island intensity in large cities of Korea , 2004 .

[68]  I. A. Nalder,et al.  Spatial interpolation of climatic Normals: test of a new method in the Canadian boreal forest , 1998 .

[69]  Miguel B. Araújo,et al.  sdm: a reproducible and extensible R platform for species distribution modelling , 2016 .

[70]  Myoung-Soo Won,et al.  Relationship between landscape structure and burn severity at the landscape and class levels in Samchuck, South Korea , 2009 .

[71]  Wouter Dorigo,et al.  A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1) , 2017 .

[72]  Dieu Tien Bui,et al.  Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression , 2016, Remote. Sens..

[73]  Alan A. Ager,et al.  A review of recent advances in risk analysis for wildfire management , 2013 .

[74]  R. Nicholls,et al.  A global analysis of human settlement in coastal zones , 2003 .

[75]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[76]  A. Markandya,et al.  Economic viability of the national-scale forestation program: The case of success in the Republic of Korea , 2018 .

[77]  E. Box,et al.  Forest Vegetation of Northeast Asia , 2003, Geobotany.

[78]  J. Pereira,et al.  Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest , 2012 .

[79]  Kenneth P. Davis.  Forest Fire: Control and Use , 1959 .