[1] B KIZILKAYA, E EVER, H Y YATBAZ et al. An effective forest fire detection framework using heterogeneous wireless multimedia sensor networks. ACM Transactions on Multimedia Computing Communications and Applications, 18, 1-21(2022).

[2] Yong ZHOU, Xiaona WANG, Sha HAO et al. The spatiotemporal dynamic characteristics of forest fires in Jilin province from 1989 to 2019. Jilin Forestry Science and Technology, 52, 29-34(2023).

[3] Xiaotong LI, Xianlin QIN, Qian LIU et al. Forest fuel type identification method based on AISA Eagle Ⅱ airborne hyperspectral data. Remote Sensing Technology and Application, 36, 544-551(2021).

[4] Z ZHU, X DENG, F ZHAO et al. How environmental factors affect forest fire occurrence in Yunnan forest region. Forests, 13, 1392(2022).

[5] Shixin LI, Fuquan ZHANG, Haifeng LIN. Research on forest fire risk evaluation based on machine learning algorithm. Journal of Nanjing Forestry University （Natural Science Edition）, 47, 49-56(2023).

[6] Y SHAO, Z FENG, L SUN et al. Mapping China’s forest fire risks with machine learning. Forests, 13, 856(2022).

[7] Liyue LIU, Zelang MIAO, Lixin X WU. Spatial-temporal variability of Amazon tropical rainforest fire based on MODIS data. Remote Sensing Technology and Application, 37, 721-730(2022).

[8] Chenhui HAN, Qian YANG, Xiaohui HE et al. Research on fire SPOT detection algorithm based on the new generation of Geostationary Meteorological Satellite. Remote Sensing Technology and Application, 38, 173-181(2023).

[9] C W SMITH, S K PANDA, U S BHATT et al. Assessing wildfire burn severity and its relationship with environmental factors： A case study in interior Alaska boreal forest. Remote Sensing, 13, 1966(2021).

[10] M C IBAN, A SEKERTEKIN. Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS： A case study of Adana and Mersin provinces， Turkey. Ecological Informatics, 69, 101647(2022).

[11] K WHITNEY, S H KIM, S JIA et al. Estimation of the relationship between satellite-derived vegetation indices and live fuel moisture towards wildfire risk in Southern California(2018).

[12] W MA, Z FENG, Z CHENG et al. Identifying forest fire driving factors and related impacts in China using random forest algorithm. Forests, 11, 507(2020).

[13] H LIANG, M ZHANG, H WANG. A neural network model for wildfire scale prediction using meteorological factors. IEEE Access, 7, 176746-176755(2019).

[14] A SHMUEL, E HEIFETZ. Global wildfire susceptibility mapping based on machine learning models. Forests, 13, 1050(2022).

[15] Z H Zhou. Ensemble learning. Encyclopedia of Biometrics, 411-416(2015).

[16] Y XIE, M PENG. Forest fire forecasting using ensemble learning approaches. Neural Computing and Applications, 31, 4541-4550(2019).

[17] A BJANES, L F R DE, P MENA. A deep learning ensemble model for wildfire susceptibility mapping. Ecological Informatics, 65, 101397(2021).

[18] J LECINA-DIAZ, A ALVAREZ, J RETANA. Extreme fire severity patterns in topographic，convective and wind-driven historical wildfires of Mediterranean pine forests. PLoS One, 9(2014).

[19] D S BIRCH, P MORGAN, C A KOLDEN et al. Vegetation， topography and daily weather influenced burn severity in central Idaho and western Montana forests. Ecosphere, 6, 1-23(2015).

[20] G K DILLON, Z A HOLDEN, P MORGAN et al. Both topography and climate affected forest and woodland burn severity in two regions of the western US， 1984 to 2006. Ecosphere, 2, 1-33(2011).

[21] A KEYSER, A L R WESTERLING. Climate drives inter-annual variability in probability of high severity fire occurrence in the western United States. Environmental Research Letters, 12(2017).

[22] D W DEERING. Rangeland reflectance characteristics measured by aircraft and spacecraft sensors(1978).

[23] Z JIANG, A R HUETE, K DIDAN et al. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833-3845(2008).

[24] F ULFA, T G ORTON, Y P DANG et al. Developing and testing remote-sensing indices to represent within-field variation of wheat yields：Assessment of the variation explained by simple models. Agronomy, 12, 384(2022).

[25] J LIU, E PATTEY, G JEGO. Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sensing of Environment, 123, 347-358(2012).

[26] N ANDELA, D C MORTON, L GIGLIO et al. The global fire atlas of individual fire size， duration， speed and direction. Earth System Science Data, 11, 529-552(2019).

[27] A KASSAMBARA. Practical guide to cluster analysis in R： unsupervised machine learning(2017).

[28] D LUCHI, A L RODRIGUES, F M VAREJAO. Sampling approaches for applying DBSCAN to large datasets. Pattern Recognition Letters, 117, 90-96(2019).

[29] X BIAN, D WU, K ZHANG et al. Variational mode decomposition weighted multiscale support vector regression for spectral determination of rapeseed oil and rhizoma alpiniae offcinarum adulterants. Biosensors, 12, 586(2022).

[30] D H WOLPERT. Stacked generalization. Neural Networks, 5, 241-259(1992).

[31] C RISK, P M A JAMES. Optimal cross‐validation strategies for selection of spatial interpolation models for the Canadian forest fire weather index system. Earth and Space Science, 9, e2021EA002019(2022).

[32] À NEBOT, F MUGICA. Forest fire forecasting using Fuzzy Logic Models. Forests, 12, 1005(2021).

[33] J BI, K P BENNETT. Regression error characteristic curves, 43-50(2003).