Modeling the forest fire risk by incorporating a new human activity factor from nighttime light data

References

• Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32. doi: 10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Cao X, Hu Y, Zhu X, Shi F, Zhuo L, Chen J. 2019. A simple self-adjusting model for correcting the blooming effects in DMSP-OLS nighttime light images. Remote Sens Environ. 224:401–411. doi: 10.1016/j.rse.2019.02.019.
   Web of Science ®Google Scholar
• Chan JC-W, Paelinckx D. 2008. Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens Environ. 112(6):2999–3011. doi: 10.1016/j.rse.2008.02.011.
   Web of Science ®Google Scholar
• Chen B, Jin Y. 2022. Spatial patterns and drivers for wildfire ignitions in California. Environ Res Lett. 17(5):055004. doi: 10.1088/1748-9326/ac60da.
   Web of Science ®Google Scholar
• Chen F, Du Y, Niu S, Zhao J. 2015. Modeling forest lightning fire occurrence in the daxinganling mountains of northeastern china with MAXENT. Forests. 6(12):1422–1438. doi: 10.3390/f6051422.
   Web of Science ®Google Scholar
• Chen J, Wang X, Yu Y, Yuan X, Quan X, Huang H. 2022. Improved prediction of forest fire risk in central and northern china by a time-decaying precipitation model. Forests. 13(3):480. doi: 10.3390/f13030480.
   Web of Science ®Google Scholar
• Chuvieco E, Cocero D, Riaño D, Martin P, Martínez-Vega J, de la Riva J, Pérez F. 2004. Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens Environ. 92(3):322–331., doi: 10.1016/j.rse.2004.01.019.
   Web of Science ®Google Scholar
• Cui L, Luo C, Yao C, Zou Z, Wu G, Li Q, Wang X. 2022. The influence of climate change on forest fires in Yunnan Province, Southwest China detected by GRACE satellites. Remote Sens. 14(3):712. doi: 10.3390/rs14030712.
   Google Scholar
• Elvidge CD, Baugh KE, Kihn EA, Kroehl HW, Davis ER, Davis CW. 1997. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. Int J Remote Sens. 18(6):1373–1379. doi: 10.1080/014311697218485.
   Web of Science ®Google Scholar
• Elvidge CD, Zhizhin M, Ghosh T, Hsu F-C, Taneja J. 2021. Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sens. 13(5):922. doi: 10.3390/rs13050922.
   Google Scholar
• Ganteaume A, Camia A, Jappiot M, San-Miguel-Ayanz J, Long-Fournel M, Lampin C. 2013. A review of the main driving factors of forest fire ignition over Europe. Environ Manage. 51(3):651–662. doi: 10.1007/s00267-012-9961-z.
   PubMed Web of Science ®Google Scholar
• Guo F, Su Z, Tigabu M, Yang X, Lin F, Liang H, Wang G. 2017a. Spatial modelling of fire drivers in urban-forest ecosystems in China. Forests. 8(6):180. doi: 10.3390/f8060180.
   Web of Science ®Google Scholar
• Guo F, Su Z, Wang G, Sun L, Lin F, Liu A. 2016a. Wildfire ignition in the forests of southeast China: identifying drivers and spatial distribution to predict wildfire likelihood. Appl Geogr. 66:12–21. doi: 10.1016/j.apgeog.2015.11.014.
   Web of Science ®Google Scholar
• Guo F, Su Z, Wang G, Sun L, Tigabu M, Yang X, Hu H. 2017b. Understanding fire drivers and relative impacts in different Chinese forest ecosystems. Sci Total Environ. 605-606:411–425. doi: 10.1016/j.scitotenv.2017.06.219.
   PubMed Web of Science ®Google Scholar
• Guo F, Wang G, Su Z, Liang H, Wang W, Lin F, Liu A. 2016b. What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests. Int J Wildland Fire. 25(5):505–519. doi: 10.1071/WF15121.
   Web of Science ®Google Scholar
• Hong H, Tsangaratos P, Ilia I, Liu J, Zhu A-X, Xu C. 2018. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Sci Total Environ. 630:1044–1056. doi: 10.1016/j.scitotenv.2018.02.278.
   PubMed Web of Science ®Google Scholar
• Kalabokidis KD, Koutsias N, Konstantinidis P, Vasilakos C. 2007. Multivariate analysis of landscape wildfire dynamics in a Mediterranean ecosystem of Greece. Area. 39(3):392–402. doi: 10.1111/j.1475-4762.2007.00756.x.
   Web of Science ®Google Scholar
• Kim SJ, Lim C-H, Kim GS, Lee J, Geiger T, Rahmati O, Son Y, Lee W-K. 2019. Multi-temporal analysis of forest fire probability using socio-economic and environmental variables. Remote Sens. 11(1):86. doi: 10.3390/rs11010086.
   Google Scholar
• Kolanek A, Szymanowski M, Raczyk A. 2021. Human activity affects forest fires: the impact of anthropogenic factors on the density of forest fires in Poland. Forests. 12(6):728. doi: 10.3390/f12060728.
   Web of Science ®Google Scholar
• Lan F, Gong X, Da H, Wen H. 2020. How do population inflow and social infrastructure affect urban vitality? Evidence from 35 large-and medium-sized cities in China. Cities. 100:102454. doi: 10.1016/j.cities.2019.102454.
   Web of Science ®Google Scholar
• Li Y, Feng Z, Chen S, Zhao Z, Wang F. 2020. Application of the artificial neural network and support vector machines in forest fire prediction in the Guangxi Autonomous Region, China. Discrete Dyn Nat Soc. 2020:1–14. doi: 10.1155/2020/5612650.
   Web of Science ®Google Scholar
• Li Z, Bi S, Hao S, Cui Y. 2022. Aboveground biomass estimation in forests with random forest and Monte Carlo-based uncertainty analysis. Ecol Indic. 142:109246. doi: 10.1016/j.ecolind.2022.109246.
   Web of Science ®Google Scholar
• Liu D, Sun K. 2019. Random forest solar power forecast based on classification optimization. Energy. 187:115940. doi: 10.1016/j.energy.2019.115940.
   Web of Science ®Google Scholar
• Liu J, Xiao B, Li Y, Wang X, Bie Q, Jiao J. 2021. Simulation of dynamic urban expansion under ecological constraints using a long short term memory network model and cellular automata. Remote Sens. 13(8):1499. doi: 10.3390/rs13081499.
   Google Scholar
• Liu Q, Shan Y, Shu L, Sun P, Du S. 2018. Spatial and temporal distribution of forest fire frequency and forest area burnt in Jilin Province, Northeast China. J for Res. 29(5):1233–1239. doi: 10.1007/s11676-018-0605-x.
   Google Scholar
• Lu D, Wang Y, Yang Q, Su K, Zhang H, Li Y. 2021. Modeling spatiotemporal population changes by integrating DMSP-OLS and NPP-VIIRS nighttime light data in Chongqing, China. Remote Sens. 13(2):284. doi: 10.3390/rs13020284.
   Google Scholar
• Lv Q, Liu H, Wang J, Liu H, Shang Y. 2020. Multiscale analysis on spatiotemporal dynamics of energy consumption CO2 emissions in China: utilizing the integrated of DMSP-OLS and NPP-VIIRS nighttime light datasets. Sci Total Environ. 703:134394. doi: 10.1016/j.scitotenv.2019.134394.
   PubMed Web of Science ®Google Scholar
• Ma W, Feng Z, Cheng Z, Chen S, Wang F. 2020. Identifying forest fire driving factors and related impacts in China using random forest algorithm. Forests. 11(5):507. doi: 10.3390/f11050507.
   Web of Science ®Google Scholar
• Martínez J, Vega-Garcia C, Chuvieco E. 2009. Human-caused wildfire risk rating for prevention planning in Spain. J Environ Manage. 90(2):1241–1252. doi: 10.1016/j.jenvman.2008.07.005.
   PubMed Web of Science ®Google Scholar
• Milanović S, Marković N, Pamučar D, Gigović L, Kostić P, Milanović SD. 2020. Forest fire probability mapping in eastern Serbia: logistic regression versus random forest method. Forests. 12(1):5. doi: 10.3390/f12010005.
   Web of Science ®Google Scholar
• Mourão PR, Martinho VD. 2014. The choices of the fire—Debating socioeconomic determinants of the fires observed at Portuguese municipalities. Forest Policy Econ. 43:29–40. doi: 10.1016/j.forpol.2014.01.007.
   Web of Science ®Google Scholar
• Naderpour M, Rizeei HM, Ramezani F. 2021. Forest fire risk prediction: a spatial deep neural network-based framework. Remote Sens. 13(13):2513. doi: 10.3390/rs13132513.
   Google Scholar
• Nechaev D, Zhizhin M, Poyda A, Ghosh T, Hsu F-C, Elvidge C. 2021. Cross-sensor nighttime lights image calibration for DMSP/OLS and SNPP/VIIRS with residual U-net. Remote Sens. 13(24):5026. doi: 10.3390/rs13245026.
   Google Scholar
• Nikhil S, Danumah JH, Saha S, Prasad MK, Rajaneesh A, Mammen PC, Ajin R, Kuriakose SL. 2021. Application of GIS and AHP method in forest fire risk zone mapping: a study of the Parambikulam tiger reserve, Kerala, India. J Geovis Spat Anal. 5(1):14. doi: 10.1007/s41651-021-00083-w.
   Google Scholar
• Pourtaghi ZS, Pourghasemi HR, Aretano R, Semeraro T. 2016. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecol Indic. 64:72–84. doi: 10.1016/j.ecolind.2015.12.030.
   Web of Science ®Google Scholar
• Pourtaghi ZS, Pourghasemi HR, Rossi M. 2015. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environ Earth Sci. 73(4):1515–1533. doi: 10.1007/s12665-014-3502-4.
   Web of Science ®Google Scholar
• Probst P, Boulesteix A-L. 2017. To tune or not to tune the number of trees in random forest. J Mach Learn Res. 18:6673–6690. doi: 10.48550/arXiv.1705.05654.
   Web of Science ®Google Scholar
• Puri K, Areendran G, Raj K, Mazumdar S, Joshi P. 2011. Forest fire risk assessment in parts of Northeast India using geospatial tools. J For Res. 22(4):641–647. doi: 10.1007/s11676-011-0206-4.
   Google Scholar
• Rihan, W., Zhao, J., Zhang, H., Guo, X., Ying, H., Deng, G., Li, H., 2019. Wildfires on the Mongolian Plateau: identifying drivers and spatial distributions to predict wildfire probability. Remote Sens, 20. 11, 2361. doi: 10.3390/rs11202361.
   Google Scholar
• Sannigrahi S, Pilla F, Basu B, Basu AS, Sarkar K, Chakraborti S, Joshi PK, Zhang Q, Wang Y, Bhatt S, et al. 2020. Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using Remote Sens approaches. Sci Total Environ. 725:138331. doi: 10.1016/j.scitotenv.2020.138331.
   PubMed Web of Science ®Google Scholar
• Su Z, Hu H, Wang G, Ma Y, Yang X, Guo F. 2018. Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China. Geomat Nat Haz Risk. 9(1):1207–1229. doi: 10.1080/19475705.2018.1505667.
   Web of Science ®Google Scholar
• Su Z, Tigabu M, Cao Q, Wang G, Hu H, Guo F. 2019. Comparative analysis of spatial variation in forest fire drivers between boreal and subtropical ecosystems in China. For Ecol Manage. 454:117669. doi: 10.1016/j.foreco.2019.117669.
   Web of Science ®Google Scholar
• Tan M, Li X, Li S, Xin L, Wang X, Li Q, Li W, Li Y, Xiang W. 2018. Modeling population density based on nighttime light images and land use data in China. Appl Geogr. 90:239–247. doi: 10.1016/j.apgeog.2017.12.012.
   Web of Science ®Google Scholar
• Vadrevu KP, Badarinath K. 2009. Spatial pattern analysis of fire events in Central India–A case study. Geocarto Int. 24(2):115–131. doi: 10.1080/10106040802230837.
   Google Scholar
• Vallejo-Villalta I, Rodríguez-Navas E, Márquez-Pérez J. 2019. Mapping forest fire risk at a local scale—a case study in Andalusia (Spain). Environments. 6(3):30. doi: 10.3390/environments6030030.
   Web of Science ®Google Scholar
• Vigna I, Besana A, Comino E, Pezzoli A. 2021. Application of the socio-ecological system framework to forest fire risk management: a systematic literature review. Sustainability. 13(4):2121. doi: 10.3390/su13042121.
   Web of Science ®Google Scholar
• Wang L, Qu JJ. 2007. NMDI: a normalized multi‐band drought index for monitoring soil and vegetation moisture with satellite Remote Sens. Geophys Res Lett. 34: L20405. doi: 10.1029/2007gl031021.
   Web of Science ®Google Scholar
• Woo H, Chung W, Graham JM, Lee B. 2017. Forest fire risk assessment using point process modelling of fire occurrence and Monte Carlo fire simulation. Int J Wildland Fire. 26(9):789–805. doi: 10.1071/WF17021.
   Web of Science ®Google Scholar
• Wu Z, He HS, Yang J, Liu Z, Liang Y. 2014. Relative effects of climatic and local factors on fire occurrence in boreal forest landscapes of northeastern China. Sci Total Environ. 493:472–480. doi: 10.1016/j.scitotenv.2014.06.011.
   PubMed Web of Science ®Google Scholar
• Wu Z, Li M, Wang B, Quan Y, Liu J. 2021. Using artificial intelligence to estimate the probability of forest fires in Heilongjiang, northeast China. Remote Sens. 13(9):1813. doi: 10.3390/rs13091813.
   Google Scholar
• Xiong Q, Luo X, Liang P, Xiao Y, Xiao Q, Sun H, Pan K, Wang L, Li L, Pang X. 2020. Fire from policy, human interventions, or biophysical factors? Temporal–spatial patterns of forest fire in southwestern China. For Ecol Manage. 474:118381. doi: 10.1016/j.foreco.2020.118381.
   Web of Science ®Google Scholar
• Yang D, Luan W, Qiao L, Pratama M. 2020. Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery. Appl Energy. 268:114696. doi: 10.1016/j.apenergy.2020.114696.
   Web of Science ®Google Scholar
• You W, Lin L, Wu L, Ji Z, Yu J, Zhu J, Fan Y, He D., 2017. Geographical information system-based forest fire risk assessment integrating national forest inventory data and analysis of its spatiotemporal variability. Ecol Indic. 77:176–184. doi: 10.1016/j.ecolind.2017.01.042.
   Web of Science ®Google Scholar
• Zeng A, Yang S, Zhu H, Tigabu M, Su Z, Wang G, Guo F. 2022. Spatiotemporal Dynamics and Climate Influence of Forest Fires in Fujian Province, China. Forests. 13(3):423. doi: 10.3390/f13030423.
   Web of Science ®Google Scholar
• Zhang X, Lan M, Ming J, Zhu J, Lo S. 2023. Spatiotemporal Heterogeneity of Forest Fire Occurrence Based on Remote Sensing Data: an Analysis in Anhui, China. Remote Sens. 15(3):598. doi: 10.3390/rs15030598.
   Google Scholar
• Zheng Z, Gao Y, Yang Q, Zou B, Xu Y, Chen Y, Yang S, Wang Y, Wang Z. 2020. Predicting forest fire risk based on mining rules with ant-miner algorithm in cloud-rich areas. Ecol Indic. 118:106772. doi: 10.1016/j.ecolind.2020.106772.
   Web of Science ®Google Scholar
• Zhong Y, Lin A, He L, Zhou Z, Yuan M. 2020. Spatiotemporal dynamics and driving forces of urban land-use expansion: a case study of the Yangtze River economic belt, China. Remote Sens. 12(2):287. doi: 10.3390/rs12020287.
   Google Scholar