Abstract
Considerable economic losses and ecological damage can be caused by forest fires, and compared to suppression, prevention is a much smarter strategy. Accordingly, this study focuses on developing a novel framework to assess forest fire risks and policy decisions on forest fire management in China. This framework integrated deep learning algorithms, geographic information, and multisource data. Compared to conventional approaches, our framework featured timesaving, easy implementation, and importantly, the use of deep learning that vividly integrates various factors from the environment and human activities. Information on 96,594 forest fire points from 2001 to 2019 was collected on Moderate Resolution Imaging Spectroradiometer (MODIS) fire hotspots from 2001 to 2019 from NASA's Fire Information Resource Management System. The information was classified into factors such as topography, climate, vegetation, and society. The prediction of forest fire risk was generated using a fully connected network model, and spatial autocorrelation used to analyze the spatial aggregation correlation of active fire hotspots in the whole area of China. The results show that high accuracy prediction of fire risks was achieved (accuracy 87.4%, positive predictive value 87.1%, sensitivity 88.9%, area under curve (AUC) 94.1%). Based on this, it was found that Chinese forest fire risk shows significant autocorrelation and agglomeration both in seasons and regions. For example, forest fire risk usually raises dramatically in spring and winter, and decreases in autumn and summer. Compared to the national average, Yunnan Province, Guangdong Province, and the Greater Hinggan Mountains region of Heilongjiang Province have higher fire risks. In contrast, a large region in central China has been recognized as having a long-term, low risk of forest fires. All forest risks in each region were recorded into the database and could contribute to the forest fire prevention. The successful assessment of forest fire risks in this study provides a comprehensive knowledge of fire risks in China over the last 20 years. Deep learning showed its advantage in integrating multiple factors in predicting forest fire risks. This technical framework is expected to be a feasible evaluation tool for the occurrence of forest fires in China.
Similar content being viewed by others
Data Availability Statement
The data used to support the findings of this study are available from the corresponding author upon request.
References
Adab H, Kanniah KD, Solaimani K (2013) Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Nat Hazards 65(3):1723–1743
Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Essen BCV, Awwal AAS, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3):292
Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27(2):93–115
Astiani D, Curran LM, Burhanuddin TM, Gusmayanti E (2018) Fire-driven biomass and peat carbon losses and post-fire soil co2 emission in a west kalimantan peatland forest. J Trop For Sci 30(4):570–575
Banerjee P (2021) Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya. Scand J Forest Res 36:275–288
Bisong E (2019) Building machine learning and deep learning models on Google Cloud Platform: a comprehensive guide for beginners. Apress, Berkeley, pp 581–598. [Google Scholar] [CrossRef]
Bo M, Mercalli L, Pognant F, Cat BD, Clerico M (2020) Urban air pollution, climate change and wildfires: the case study of an extended forest fire episode in northern Italy favoured by drought and warm weather conditions. Energy Rep 6:781–786
Brown KJ, Hebda NJ, Conder N, Golinksi KG, Hawkes B, Schoups G, Hebda RJ (2017) Changing climate, vegetation, and fire disturbance in a sub-boreal pine-dominated forest, British Columbia. Canada Can J For Res 47(5):615–627
Deng O, Li Y, Feng Z, Zhang D (2012) Model and zoning of forest fire risk in Heilongjiang province based on spatial Logistic. Trans Chin Soc Agr Eng 28(8):200–205
Eugenio FC, dos Santos AR, Fiedler NC, Ribeiro GA, da Silva AG, dos Santos ÁB, Paneto GG, Schettino VR (2016) Applying GIS to develop a model for forest fire risk: a case study in Espírito Santo, Brazil. J Environ Manage 173:65–71
Fang K, Yao Q, Guo Z, Zheng B, Du J, Qi F, Yan P, Li J, Ou T, Liu J, Ou T, Liu J, He M, Trouet V (2021) ENSO modulates wildfire activity in China. Nat Commun 12(1):1764
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
Gao JF (2015) Middle and long term plan discussion of key problems to forest fire prevention in China. For Invent Plan 40(1):4 ((in Chinese))
Gao C, Lin HL, Hu HQ, Song H (2020) A review of models of forest fire occurrence prediction in China. Chin J Appl Ecol 31 (09):3227–3240. (in Chinese). https://doi.org/10.13287/j.1001-9332.202009.014.
Garcia C, Woodard P, Titus S, Adamowicz W, Lee B (1995) A logit model for predicting the daily occurrence of human caused forest fires. Int J Wildland Fire 5(2):101–111
Ghobadi GJ, Gholizadeh B, Dashliburun OM (2012) Forest fire risk zone mapping from geographic information system in northern forests of Iran (Case study, Golestan province). Int J Agr Crop Sci 4(12):818–824
Gholamnia K, Gudiyangada T, Ghorbanzadeh O, Blaschke T (2020) Comparisons of diverse machine learning approaches for wildfire susceptibility mapping. Symmetry 12(4):604
Gigliarano C, Figini S, Muliere P (2014) Making classifier performance comparisons when ROC curves intersect. Comput Stat Data Anal 77:300–312
Giglio L, Descloitres J, Justice CO, Kaufman YJ (2003) An enhanced contextual fire detection algorithm for MODIS. Remote Sens Environ 87(2–3):273–282
Guo FT, Su ZW, Wang GY, Sun L, Lin FF, Liu AQ (2016) Wildfire ignition in the forests of southeast China: identifying drivers and spatial distribution to predict wildfire likelihood. Appl Geogr 66:12–21
Heo JP, Im CG, Ryu KH, Sung SW, Yoo C, Yang DR (2022) Shallow fully connected neural network training by forcing linearization into valid region and balancing training rates. Processes 10(6):1157
Holden ZA, Jolly WM (2011) Modeling topographic influences on fuel moisture and fire danger in complex terrain to improve wildland fire management decision support. Forest Ecol Manag 262(12):2133–2141
Hong HY, Tsangaratos P, Ilia I, Liu JZ, Zhu AX, Chong X (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
Jaafari A, Davood MG, Eric KZ (2017) A Bayesian modeling of wildfire probability in the Zagros Mountains. Iran Ecol Inform 39:32–44
Jahdi R, Salis M, Darvishsefat AA, Urdiroz FA, Etemad V, Mostafavi MA, Lozano OM, Spano D (2015) Calibration of FARSITE fire area simulator in Iranian northern forests. Nat Hazards Earth Sys 15:443–459
Justice CO, Giglio L, Korontzi S, Owens J, Morisette JT, Roy D, Descloitres J, All Ea Ume S, Petitcolin F, Kaufman Y (2002) The MODIS fire products. Remote Sens Environ 83(1–2):244–262
KöHl M, Lasco R, Cifuentes M, Jonsson Ö, Korhonen KT, Mundhenk P, Djn J, Stinson G (2015) Changes in forest production, biomass and carbon: results from the 2015 UN FAO Global Forest Resource Assessment. Forest Ecol Manag 352(352):21–34
Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436
Li XH, LV D (2021) Elaborate forecast about fire risk grade in forest and grassland of inner mongolia based on intelligent grid. Meteorol Environ Res 12(5):39–42
Li P, Li WJ, Feng ZM, Xiao CW, Liu YY (2019) Spatiotemporal dynamics of active fire frequency in Southeast Asia with the FIRMS Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) data. Resources Sci 41(8):1526–1540 ((in Chinese))
Li YD, Feng ZK, Chen SL, Zhao ZY, Wang FG (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 2020:5612650
Liang HL, Wang WH, Guo FT, Lin FF, Lin YR (2017) Comparing the application of logistic and geographically weighted logistic regression models for Fujian forest fire forecasting. Acta Ecol Sin 37(12):4128–4144 ((in Chinese))
Linn R, Reisner J, Colman JJ, Winterkamp J (2002) Studying wildfire behavior using FIRETEC. Int J Wildland Fire 11(4):233–246
Liyan S, Zhou LX, Liu ML, Yu Y (2019) Research on forest fire prediction method based on deep learning. J For Eng 4(03):132–136. https://doi.org/10.13360/j.issn.2096-1359.2019.03.020
Long TT, Yin JY, Ou CR, Yang Q, Li Y, Wang QH (2021) Comprehensive assessment and spatial pattern study on forest fire risk in Yunnan Province. Chin Safety Sci J 31(9):167–173 ((in Chinese))
Lopes AMG, Cruz MG, Viegas DX (2002) FireStation—an integrated software system for the numerical simulation of fire spread on complex topography. Environ Modell Softw 17:269–285
Ma W, Feng Z, Cheng Z, Chen S, Wang F (2020a) Identifying forest fire driving factors and related impacts in china using random forest algorithm. Forests 11(5):507
Ma WY, Feng ZK, Cheng ZX, Wang FG (2020b) Study on forest fire drivers and distribution pattern in Shanxi Province. J Central South Univ For Sci Tech 40(9):57–69 ((in Chinese))
Mohajane M, Costache R, Karimi F, Bao PQ, Essahlaoui A, Nguyen H, Laneve G, Oudija F (2021) Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecol Indic 129:107869
Morales-Hidalgo D, Oswalt SN, Somanathan E (2015) Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. Forest Ecol Manag 352:68–77
Naderpour M, Rizeei HM, Ramezani F (2021) Forest fire risk prediction: a spatial deep neural network-based framework. Remote Sens 13(13):2513
Oliveira S, Oehler F, San-Miguel-Ayanz J, Camia A, Pereira JMC (2012) Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest. Forest Ecol Manag 275:117–129
Overmars KP, de Koning GHJ, Veldkamp A (2003) Spatial autocorrelation in multi-scale land use models. Ecol Model 164(2–3):257–270
Pan P, Sun YJ, Ouyang XZ, Rao JF, Feng RQ, Yang ZY (2019) Spatial variation of carbon density in Pinus massoniana forest in Jiangxi Province. China Chin J Appl Ecol 30(6):1885–1892 ((in Chinese))
Parente J, Amraoui M, Menezes I, Pereira MG (2019) Drought in Portugal: Current regime, comparison of indices and impacts on extreme wildfires. Sci Total Environ 685 (OCT.1):150–173.
Pastor E, Zárate L, Planas E, Arnaldos J (2003) Mathematical models and calculation systems for the study of wildland fire behaviour. Prog Energ Combust 29(2):139–153
Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS (2008) Evaluating the added predictive ability of a new marker: from area ussnder the ROC curve to reclassification and beyond. Stat Med 27(2):173–181
Pham BT, Abolfazl J, Mohammadtaghi NA, Tran DD, Hoang PHY, Tran VP et al (2020) Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry 12(6):1022
Prasad VK, Badarinath K, Eaturu A (2008) Biophysical and anthropogenic controls of forest fires in the Deccan Plateau. India J Environ Manage 86(1):1–13
Qiu M, Zuo Q, Wu Q, Yang Z, Zhang J (2022) Water ecological security assessment and spatial autocorrelation analysis of prefectural regions involved in the Yellow River Basin. Sci Rep-UK 12(1):5105
Rishickesh R, Shahina A, Khan N (2019) Predicting forest fires using supervised and ensemble machine learning algorithms. Int J Recent Tech Eng 2 8(2):3697–3705.
Sachdeva S, Bhatia T, Verma AK (2018) GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping. Nat Hazards 92:1399–1418
Sayad YO, Mousannif H, Al Moatassime H (2019) Predictive modeling of wildfires: a new dataset and machine learning approach. Fire Safety J 104 (MAR.):130–146.
Sebastián-López A, Salvador-Civil R, Gonzalo-Jiménez J, Sanmiguel-Ayanz J (2008) Integration of socio-economic and environmental variables for modelling long-term fire danger in Southern Europe. Eur J Forest Res 127(2):149–163
Sevinc V, Kucuk O, Goltas M (2020) A Bayesian network model for prediction and analysis of possible forest fire causes. Forest Ecol Manag 457:117723
Shakesby RA (2011) Post-wildfire soil erosion in the Mediterranean: review and future research directions. Earth-Sci Rev 105(3–4):71–100
Shao Y, Feng Z, Sun L, Yang X, Li Y, Xu B, Chen Y (2022) Mapping China’s forest fire risks with machine learning. Forests 13(6):856
Shu LF, Zhang XL, Dai X, Tian XR, Wang MY (2003) Forest fire research (II): fire forecast. World For Res 16(4):34–37 ((in Chinese))
Sun L, Shang ZC, Hu HQ (2012) Application of a Poisson regression model and anegative binomial regression model in the forest fire forecasting. Scientia Silvae Sinicae 48(5):126–129 ((in Chinese))
Sun T, Zhang W, Chen W, Tang X, Qin Q (2013) Mountains forest fire spread simulator based on geo-cellular automaton combined with Wang Zhengfei velocity model. IEEE J-Stars 6:1971–1987
Sun JX, Zhong CH, He HW, Hugeman G, Li H (2021) Continuous remote sensing monitoring and changes of land desertification in China from 2000 to 2015. J Northeast For Univ 49(3):87–92 ((in Chinese))
Suryabhagavan KV, Alemu B (2016) GIS-based multi-criteria decision analysis for forest fire susceptibility mapping: a case study in Harenna forest, southwestern Ethiopia. Trop Ecol 57(1):33–43
Tien BD, Le KTT, Nguyen VC, Le HD, Revhaug I (2016) Tropical forest fire susceptibility mapping at the Cat Ba National Park area, Hai Phong city, Vietnam, using gis-based kernel lo-gistic regression. Remote Sens 8(4):347–347
Tien BD, Bui QT, Nguyen QP, Pradhan B, Nampak H, Trinh PT (2017) 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. Agr Forest Meteorol 233 (Complete):32–44.
Tuyen TT, Jaafari A, Yen HPH, Nguyen-Thoi T, Phong TV, Nguyen HD, Van Le H, Phuong TTM, Nguyen SH, Prakash I et al (2021) Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm. Ecol Inform 63:101292
Verde JC, Zêzere JL (2010) Assessment and validation of wildfire susceptibility and hazard in Portugal. Nat Hazards Earth Sys 10(3):485–497
Wang Y, Fang ZC, Niu RQ, Peng L (2021) Landslide susceptibility analysis based on deep learning. J Geo-Inf Sci 23 (12):2244–2260. (in Chinese). https://doi.org/10.12082/dqxxkx.2021.210057.
Wotton BM, Nock CA, Flannigan MD (2010) Forest fire occurrence and climate change in Canada. Int J Wildland Fire 19(3):253–271
Wu ZW, He HS, Keane RE, Zhu ZL, Shan YL (2020) Current and future patterns of forest fire occurrence in China. Int J Wildland Fire 29(2):104
Xie Y, Peng M (2019) Forest fire forecasting using ensemble learning approaches. Neural Comput Appl 31(9):4541–4550. https://doi.org/10.1007/s00521-018-3515-0
Yi K, Bao Y, Zhang J (2017) Spatial distribution and temporal variability of open fire in China. Int J Wildland Fire 26(2):122–135
Yin BC, Wang WT, Wang LC (2015) Review of Deep Learning. J Bjing Univ Tech 41(1):48–59 ((in Chinese))
Zeng C, Zeng Z, Cao ZY, Zou Q, Yu CX (2021) Forest fire dynamic monitoring based on time series and multisource satellite images: A case study of the Muli county forest areas in Sichuan province. Remote Sens Tech Appl 36(03):521–532 ((in Chinese))
Zhang JJ, Fu WJ, Du Q, Zhang GJ, Jiang PK (2014a) Spatial variability characteristics of carbon densities in the forest litter in Zhejiang province. Sci Silvae Sinicae 50(2):8–13 ((in Chinese))
Zhang ZX, Xu MX, Liu J, Li Q (2014b) Spatial variation reasonable sampling number of soil organic carbon under different geomorphic types on the loess plateau. J Nat Resour 29(12):2103–2113 ((in Chinese))
Zhang Y, Lee JD, Wainwright MJ, Jordan M I (2017) On the learnability of fully-connected neural networks. Paper presented at 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, Fort Lauderdale, United States.
Zhao P, Zhang F, Lin H, Xu S (2021) GIS-Based forest fire risk model: a case study in Laoshan National Forest Park. Nanjing Remote Sens 13(18):3704
Zhong M, Fan W, Liu T, Li P (2003) Statistical analysis on current status of China forest fire safety. Fire Safety J 38(3):257–269
Acknowledgements
We would like to thank the editors and reviewers for their valuable opinions and suggestions that improved this research.
Author information
Authors and Affiliations
Contributions
YZ, ZW, ZF, LS, XY, JZ, and TM collected and processed the data. ZW and YS designed the experiment, performed the analysis, and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Project funding: This research was funded by the Key R & D Projects in Hainan Province (ZDYF2021SHFZ256) and Natural Science Foundation of Hainan University, grant numbers KYQD (ZR)21,115.
Corresponding editor: Yu Lei.
The online version is available at http://www.springerlink.com
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shao, Y., Wang, Z., Feng, Z. et al. Assessment of China’s forest fire occurrence with deep learning, geographic information and multisource data. J. For. Res. 34, 963–976 (2023). https://doi.org/10.1007/s11676-022-01559-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11676-022-01559-1