{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:17:18Z","timestamp":1775593038627,"version":"3.50.1"},"reference-count":89,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China(NSFC)","award":["32171788"],"award-info":[{"award-number":["32171788"]}]},{"name":"National Natural Science Foundation of China(NSFC)","award":["BE2021716"],"award-info":[{"award-number":["BE2021716"]}]},{"name":"National Natural Science Foundation of China(NSFC)","award":["NJ2021-19"],"award-info":[{"award-number":["NJ2021-19"]}]},{"name":"Key Research and Development plan of Jiangsu Province","award":["32171788"],"award-info":[{"award-number":["32171788"]}]},{"name":"Key Research and Development plan of Jiangsu Province","award":["BE2021716"],"award-info":[{"award-number":["BE2021716"]}]},{"name":"Key Research and Development plan of Jiangsu Province","award":["NJ2021-19"],"award-info":[{"award-number":["NJ2021-19"]}]},{"name":"Jiangsu Modern Agricultural Machinery Equipment and Technology Demonstration and Promotion Project","award":["32171788"],"award-info":[{"award-number":["32171788"]}]},{"name":"Jiangsu Modern Agricultural Machinery Equipment and Technology Demonstration and Promotion Project","award":["BE2021716"],"award-info":[{"award-number":["BE2021716"]}]},{"name":"Jiangsu Modern Agricultural Machinery Equipment and Technology Demonstration and Promotion Project","award":["NJ2021-19"],"award-info":[{"award-number":["NJ2021-19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A forest fire susceptibility map generated with the fire susceptibility model is the basis of fire prevention resource allocation. A more reliable susceptibility map helps improve the effectiveness of resource allocation. Thus, further improving the prediction accuracy is always the goal of fire susceptibility modeling. This paper developed a forest fire susceptibility model based on an ensemble learning method, namely light gradient boosting machine (LightGBM), to produce an accurate fire susceptibility map. In the modeling, a subtropical national forest park in the Jiangsu province of China was used as the case study area. We collected and selected eight variables from the fire occurrence driving factors for modeling based on correlation analysis. These variables are from topographic factors, climatic factors, human activity factors, and vegetation factors. For comparative analysis, another two popular modeling methods, namely logistic regression (LR) and random forest (RF) were also applied to construct the fire susceptibility models. The results show that temperature was the main driving factor of fire in the area. In the produced fire susceptibility map, the extremely high and high susceptibility areas that were classified by LR, RF, and LightGBM were 5.82%, 18.61%, and 19%, respectively. The F1-score of the LightGBM model is higher than the LR and RF models. The accuracy of the model of LightGBM, RF, and LR is 88.8%, 84.8%, and 82.6%, respectively. The area under the curve (AUC) of them is 0.935, 0.918, and 0.868, respectively. The introduced ensemble learning method shows better ability on performance evaluation metrics.<\/jats:p>","DOI":"10.3390\/rs14174362","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4362","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["A Forest Fire Susceptibility Modeling Approach Based on Light Gradient Boosting Machine Algorithm"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6710-9391","authenticated-orcid":false,"given":"Yanyan","family":"Sun","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Fuquan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3835-6075","authenticated-orcid":false,"given":"Haifeng","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3557-5897","authenticated-orcid":false,"given":"Shuwen","family":"Xu","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103400","DOI":"10.1016\/j.firesaf.2021.103400","article-title":"Analysis of Chinese fire statistics during the period 1997\u20132017","volume":"125","author":"Luo","year":"2021","journal-title":"Fire Saf. J."},{"key":"ref_2","first-page":"63","article-title":"The current trends and challenging situations of fire incident statistics","volume":"6","author":"Rahim","year":"2015","journal-title":"Malays. J. Forensic Sci."},{"key":"ref_3","unstructured":"Bryant, S., and Preston, I. (2017). Focus on Trends in Fires and Fire-Related Fatalities, Home Office."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s40725-020-00116-5","article-title":"Satellite remote sensing contributions to wildland fire science and management","volume":"6","author":"Chuvieco","year":"2020","journal-title":"Curr. For. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1071\/WF19084","article-title":"Risk assessment for wildland fire aerial detection patrol route planning in Ontario, Canada","volume":"29","author":"McFayden","year":"2019","journal-title":"Int. J. Wildland Fire"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"118338","DOI":"10.1016\/j.foreco.2020.118338","article-title":"Relations of land cover, topography, and climate to fire occurrence in natural regions of Iran: Applying new data mining techniques for modeling and mapping fire danger","volume":"473","author":"Eskandari","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Singh, P.K., and Sharma, A. (2017, January 21\u201323). An insight to forest fire detection techniques using wireless sensor networks. Proceedings of the 2017 IEEE 4th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India.","DOI":"10.1109\/ISPCC.2017.8269757"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105856","DOI":"10.1016\/j.ecolind.2019.105856","article-title":"Evaluating the effects of forest fire on water balance using fire susceptibility maps","volume":"110","author":"Venkatesh","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ma, W., Feng, Z., Cheng, Z., Chen, S., and Wang, F. (2020). Identifying forest fire driving factors and related impacts in china using random forest algorithm. Forests, 11.","DOI":"10.3390\/f11050507"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, T., Xia, J., Zou, L., and Hong, S. (2020). Quantifying the influences of natural factors and human activities on NDVI changes in the Hanjiang river basin, China. Remote Sens., 12.","DOI":"10.3390\/rs12223780"},{"key":"ref_11","first-page":"1","article-title":"A global wildfire dataset for the analysis of fire regimes and fire behaviour","volume":"6","author":"Oom","year":"2019","journal-title":"Sci. Data"},{"key":"ref_12","unstructured":"Weixin (2022, June 01). Analytic Network Process. Available online: https:\/\/blog.csdn.net\/weixin_34116110\/article\/details\/85959973."},{"key":"ref_13","unstructured":"Xueqian, H. (2022, June 01). The Advantages and Disadvantages of Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation Method. Available online: https:\/\/zhidao.baidu.com\/question\/85314097.html."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-017-6225-7","article-title":"Forest fire risk assessment-an integrated approach based on multicriteria evaluation","volume":"189","author":"Goleiji","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1080\/09640568.2019.1594726","article-title":"GIS-based forest fire risk mapping using the analytical network process and fuzzy logic","volume":"63","author":"Feizizadeh","year":"2020","journal-title":"J. Environ. Plan. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ljubomir, G., Pamu\u010dar, D., Drobnjak, S., and Pourghasemi, H.R. (2019). Modeling the spatial variability of forest fire susceptibility using geographical information systems and the analytical hierarchy process. Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier.","DOI":"10.1016\/B978-0-12-815226-3.00015-6"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhao, P., Zhang, F., Lin, H., and Xu, S. (2021). GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing. Remote Sens., 13.","DOI":"10.3390\/rs13183704"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.firesaf.2019.01.006","article-title":"Predictive modeling of wildfires: A new dataset and machine learning approach","volume":"104","author":"Sayad","year":"2019","journal-title":"Fire Saf. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102991","DOI":"10.1016\/j.firesaf.2020.102991","article-title":"Using machine learning in physics-based simulation of fire","volume":"114","author":"Lattimer","year":"2020","journal-title":"Fire Saf. J."},{"key":"ref_20","first-page":"5612650","article-title":"Application of the artificial neural network and support vector machines in forest fire prediction in the guangxi autonomous region, China","volume":"2020","author":"Li","year":"2020","journal-title":"Discret. Dyn. Nat. Soc."},{"key":"ref_21","first-page":"68","article-title":"Neural network approach to predict forest fires using meteorological data","volume":"4","author":"Ghaly","year":"2020","journal-title":"Int. J. Acad. Eng. Res. (IJAER)"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s00704-018-2628-9","article-title":"A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data","volume":"137","author":"Tehrany","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1007\/s13753-019-00233-1","article-title":"Forest fire susceptibility modeling using a convolutional neural network for Yunnan province of China","volume":"10","author":"Zhang","year":"2019","journal-title":"Int. J. Disaster Risk Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.agrformet.2018.12.015","article-title":"Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability","volume":"266","author":"Jaafari","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1007\/s13753-017-0129-6","article-title":"Wildfire susceptibility assessment in Southern China: A comparison of multiple methods","volume":"8","author":"Cao","year":"2017","journal-title":"Int. J. Disaster Risk Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Milanovi\u0107, S., Markovi\u0107, N., Pamu\u010dar, D., Gigovi\u0107, L., Kosti\u0107, P., and Milanovi\u0107, S.D. (2020). Forest fire probability mapping in eastern Serbia: Logistic regression versus random forest method. Forests, 12.","DOI":"10.3390\/f12010005"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s12145-020-00531-z","article-title":"Object-based crop classification in Hetao plain using random forest","volume":"14","author":"Su","year":"2021","journal-title":"Earth Sci. Inf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.jclinepi.2020.03.002","article-title":"Logistic regression was as good as machine learning for predicting major chronic diseases","volume":"122","author":"Nusinovici","year":"2020","journal-title":"J. Clin. Epidemiol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhou, W.F., Wang, J.G., Deng, L.F., Yao, Y., and Liu, J.L. (2021, January 26\u201328). Terminal Temperature Prediction of Molten Steel in VD Furnace based on XGBoost and LightGBM Algorithms. Proceedings of the 2021 IEEE 40th Chinese Control Conference (CCC), Shanghai, China.","DOI":"10.23919\/CCC52363.2021.9550444"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"101084","DOI":"10.1016\/j.frl.2018.12.032","article-title":"A novel cryptocurrency price trend forecasting model based on LightGBM","volume":"32","author":"Sun","year":"2020","journal-title":"Financ. Res. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1007\/s12524-019-01047-w","article-title":"A comparative analysis of forest fire risk zone mapping methods with expert knowledge","volume":"47","author":"Yathish","year":"2019","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, X., Fan, J., Eichhorn, M.P., An, F., Chen, B., Cao, L., Zhu, Z., and Yun, T. (2020). Retrieval of aerodynamic parameters in rubber tree forests based on the computer simulation technique and terrestrial laser scanning data. Remote Sens., 12.","DOI":"10.3390\/rs12081318"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"118644","DOI":"10.1016\/j.foreco.2020.118644","article-title":"Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: A comparative analysis of VIKOR and TOPSIS","volume":"480","author":"Sari","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_34","first-page":"33","article-title":"GIS-based multi-criteria decision analysis for forest fire susceptibility mapping: A case study in Harenna forest, southwestern Ethiopia","volume":"57","author":"Suryabhagavan","year":"2016","journal-title":"Trop. Ecol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.firesaf.2017.05.007","article-title":"The effect of flow and geometry on concurrent flame spread","volume":"91","author":"Gollner","year":"2017","journal-title":"Fire Saf. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.combustflame.2017.11.025","article-title":"Fire spread across a sloping fuel bed: Flame dynamics and heat transfers","volume":"190","author":"Morandini","year":"2018","journal-title":"Combust. Flame"},{"key":"ref_37","first-page":"1","article-title":"TWI computation: A comparison of different open source GISs","volume":"4","author":"Mattivi","year":"2019","journal-title":"Open Geospat. Data, Softw. Stand."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e2021WR029871","DOI":"10.1029\/2021WR029871","article-title":"Topographic Wetness Index as a Proxy for Soil Moisture: The Importance of Flow-Routing Algorithm and Grid Resolution","volume":"57","author":"Kemppinen","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1071\/WF15121","article-title":"What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests","volume":"25","author":"Guo","year":"2016","journal-title":"Int. J. Wildland Fire"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.foreco.2015.01.011","article-title":"Quantifying influences and relative importance of fire weather, topography, and vegetation on fire size and fire severity in a Chinese boreal forest landscape","volume":"356","author":"Fang","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1007\/s10980-018-0712-2","article-title":"Drivers of forest fire occurrence in the cultural landscape of Central Europe","volume":"33","author":"Kula","year":"2018","journal-title":"Landsc. Ecol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1002\/ldr.3221","article-title":"A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China","volume":"30","author":"Hu","year":"2019","journal-title":"Land Degrad. Dev."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1071\/WF15083","article-title":"Climate change presents increased potential for very large fires in the contiguous United States","volume":"24","author":"Barbero","year":"2015","journal-title":"Int. J. Wildland Fire"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ahmed, M.R., Hassan, Q.K., Abdollahi, M., and Gupta, A. (2020). Processing of near real time land surface temperature and its application in forecasting forest fire danger conditions. Sensors, 20.","DOI":"10.3390\/s20040984"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"117723","DOI":"10.1016\/j.foreco.2019.117723","article-title":"A Bayesian network model for prediction and analysis of possible forest fire causes","volume":"457","author":"Sevinc","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yoon, D., Kim, Y.J., Lee, W.K., Choi, B.R., Oh, S.M., Lee, Y.S., Kim, J.K., and Lee, D.Y. (2020). Metabolic changes in serum metabolome of beagle dogs fed black ginseng. Metabolites, 10.","DOI":"10.3390\/metabo10120517"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1080\/02827581.2021.1918239","article-title":"Maximum entropy-based forest fire likelihood mapping: Analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya","volume":"36","author":"Banerjee","year":"2021","journal-title":"Scand. J. For. Res."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ehsani, M.R., Arevalo, J., Risanto, C.B., Javadian, M., Devine, C.J., Arabzadeh, A., Venegas-Qui\u00f1ones, H.L., Dell\u2019Oro, A.P., and Behrangi, A. (2020). 2019\u20132020 Australia fire and its relationship to hydroclimatological and vegetation variabilities. Water, 12.","DOI":"10.3390\/w12113067"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"107869","DOI":"10.1016\/j.ecolind.2021.107869","article-title":"Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area","volume":"129","author":"Mohajane","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"139561","DOI":"10.1016\/j.scitotenv.2020.139561","article-title":"Integrating multiple factors to optimize watchtower deployment for wildfire detection","volume":"737","author":"Zhang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_51","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017, January 4\u20139). Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1130\/0016-7606(1978)89<891:SVAITC>2.0.CO;2","article-title":"Systematic valley asymmetry in the central California Coast Ranges","volume":"89","author":"Dohrenwend","year":"1978","journal-title":"Geol. Soc. Am. Bull."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"144199","DOI":"10.1016\/j.scitotenv.2020.144199","article-title":"Linking environmental heterogeneity and plant diversity: The ecological role of small natural features in homogeneous landscapes","volume":"763","author":"Apostolova","year":"2021","journal-title":"Sci. Tota. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2835","DOI":"10.1007\/s10064-018-1273-y","article-title":"Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier","volume":"78","author":"Dang","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_55","first-page":"1951","article-title":"An analysis of correlation between personality and visiting place using Spearman\u2019s rank correlation coefficient","volume":"14","author":"Song","year":"2020","journal-title":"Ksii Trans. Internet Inf. Syst. (TIIS)"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"914974","DOI":"10.3389\/fpls.2022.914974","article-title":"Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework","volume":"13","author":"Sun","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., and Aryal, J. (2019). Forest fire susceptibility and risk mapping using social\/infrastructural vulnerability and environmental variables. Fire, 2.","DOI":"10.3390\/fire2030050"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e13289","DOI":"10.1111\/jfpe.13289","article-title":"Classification of oolong tea varieties based on hyperspectral imaging technology and BOSS-LightGBM model","volume":"42","author":"Ge","year":"2019","journal-title":"J. Food Process. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Rufo, D.D., Debelee, T.G., Ibenthal, A., and Negera, W.G. (2021). Diagnosis of diabetes mellitus using gradient boosting machine (LightGBM). Diagnostics, 11.","DOI":"10.3390\/diagnostics11091714"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"101984","DOI":"10.1016\/j.cose.2020.101984","article-title":"SwiftIDS: Real-time intrusion detection system based on LightGBM and parallel intrusion detection mechanism","volume":"97","author":"Jin","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1007\/s12273-020-0754-7","article-title":"Assessment and simulation of evacuation in large railway stations","volume":"Volume 14","author":"Wu","year":"2021","journal-title":"Building Simulation"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"104320","DOI":"10.1016\/j.catena.2019.104320","article-title":"Fire severity and soil erosion susceptibility mapping using multi-temporal Earth Observation data: The case of Mati fatal wildfire in Eastern Attica, Greece","volume":"187","author":"Efthimiou","year":"2020","journal-title":"Catena"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3390\/forecast2020003","article-title":"A comparison of the qualitative analytic hierarchy process and the quantitative frequency ratio techniques in predicting forest fire-prone areas in Bhutan using GIS","volume":"2","author":"Tshering","year":"2020","journal-title":"Forecasting"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Chen, J., Xu, C., Lin, S., Wu, Z., Qiu, R., and Hu, X. (2022). Is There Spatial Dependence or Spatial Heterogeneity in the Distribution of Vegetation Greening and Browning in Southeastern China?. Forests, 13.","DOI":"10.3390\/f13060840"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"126034","DOI":"10.1016\/j.jhazmat.2021.126034","article-title":"Spatial characteristics of microplastics in the high-altitude area on the Tibetan Plateau","volume":"417","author":"Feng","year":"2021","journal-title":"J. Hazard. Mater."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s10342-006-0164-0","article-title":"Characterization of forest fires in Catalonia (north-east Spain)","volume":"126","author":"Pukkala","year":"2007","journal-title":"Eur. J. For. Res."},{"key":"ref_67","unstructured":"Asori, M., Emmanuel, D., and Dumedah, G. (2020). Wildfire hazard and Risk modelling in the Northern regions of Ghana using GIS-based Multi-Criteria Decision Making Analysis. J. Environ. Earth Sci., 10."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1007\/s11069-016-2160-0","article-title":"Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China","volume":"81","author":"Pan","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"381","DOI":"10.5721\/EuJRS20164921","article-title":"Forest fire risk modeling in Uttarakhand Himalaya using TERRA satellite datasets","volume":"49","author":"Babu","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-21988-6","article-title":"ENSO modulates wildfire activity in China","volume":"12","author":"Fang","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"8881","DOI":"10.1002\/jgrd.50704","article-title":"Modeling the influence of open water surfaces on the summertime temperature and thermal comfort in the city","volume":"118","author":"Theeuwes","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1111\/gcb.13275","article-title":"A review of the relationships between drought and forest fire in the United States","volume":"22","author":"Littell","year":"2016","journal-title":"Glob. Chang. Biol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"559","DOI":"10.5194\/nhess-22-559-2022","article-title":"Past and future trends in fire weather for the UK","volume":"22","author":"Perry","year":"2022","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1029\/2019EF001210","article-title":"Observed impacts of anthropogenic climate change on wildfire in California","volume":"7","author":"Williams","year":"2019","journal-title":"Earth\u2019s Future"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Al-Fugara, A., Mabdeh, A.N., Ahmadlou, M., Pourghasemi, H.R., Al-Adamat, R., Pradhan, B., and Al-Shabeeb, A.R. (2021). Wildland fire susceptibility mapping using support vector regression and adaptive neuro-fuzzy inference system-based whale optimization algorithm and simulated annealing. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10060382"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"112694","DOI":"10.1016\/j.rse.2021.112694","article-title":"Satellite remote sensing of active fires: History and current status, applications and future requirements","volume":"267","author":"Wooster","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.apgeog.2017.05.013","article-title":"Mapping fire regimes in China using MODIS active fire and burned area data","volume":"85","author":"Chen","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"e01543","DOI":"10.1002\/ecs2.1543","article-title":"Mid-21st-century climate changes increase predicted fire occurrence and fire season length, Northern Rocky Mountains, United States","volume":"7","author":"Riley","year":"2016","journal-title":"Ecosphere"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"89","DOI":"10.5194\/gmd-12-89-2019","article-title":"Analysis fire patterns and drivers with a global SEVER-FIRE v1. 0 model incorporated into dynamic global vegetation model and satellite and on-ground observations","volume":"12","author":"Venevsky","year":"2019","journal-title":"Geosci. Model Dev."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"142844","DOI":"10.1016\/j.scitotenv.2020.142844","article-title":"Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series","volume":"764","author":"Michael","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Tedim, F., Xanthopoulos, G., and Leone, V. (2015). Forest fires in Europe: Facts and challenges. Wildfire Hazards, Risks Furthermore, Disasters, Elsevier.","DOI":"10.1016\/B978-0-12-410434-1.00005-1"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Nunes, L.J., Raposo, M.A., and Pinto Gomes, C.J. (2021). A historical perspective of landscape and human population dynamics in Guimar\u00e3es (Northern Portugal): Possible implications of rural fire risk in a changing environment. Fire, 4.","DOI":"10.3390\/fire4030049"},{"key":"ref_83","first-page":"96","article-title":"The role of big data in China\u2019s sustainable forest management","volume":"1","author":"Hasan","year":"2019","journal-title":"For. Econ. Rev."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ecoinf.2018.08.008","article-title":"GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method","volume":"48","author":"Bui","year":"2018","journal-title":"Ecol. Inf."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Jaafari, A., Avand, M., Al-Ansari, N., Dinh Du, T., Yen, H.P.H., Phong, T.V., Nguyen, D.H., Le, H.V., and Mafi-Gholami, D. (2020). Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry, 12.","DOI":"10.3390\/sym12061022"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"109321","DOI":"10.1016\/j.envres.2020.109321","article-title":"Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling","volume":"184","author":"Pourghasemi","year":"2020","journal-title":"Environ. Res."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Kalantar, B., Ueda, N., Idrees, M.O., Janizadeh, S., Ahmadi, K., and Shabani, F. (2020). Forest fire susceptibility prediction based on machine learning models with resampling algorithms on remote sensing data. Remote Sens., 12.","DOI":"10.3390\/rs12223682"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Kadir, E.A., Irie, H., and Rosa, S.L. (2019, January 22\u201325). Modeling of wireless sensor networks for detection land and forest fire hotspot. Proceedings of the 2019 International Conference on Electronics, Information, and Communication (ICEIC), Auckland, New Zealand.","DOI":"10.23919\/ELINFOCOM.2019.8706364"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"9856739","DOI":"10.34133\/2022\/9856739","article-title":"Shortwave Radiation Calculation for Forest Plots Using Airborne LiDAR Data and Computer Graphics","volume":"2022","author":"Xue","year":"2022","journal-title":"Plant Phenomics"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4362\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:22:37Z","timestamp":1760142157000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4362"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,2]]},"references-count":89,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174362"],"URL":"https:\/\/doi.org\/10.3390\/rs14174362","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,2]]}}}