Remote Sensing Technology and Application, Volume. 39, Issue 5, 1261(2024)
Prediction of Forest Burned Area based on MODIS-EVI2 and Ensemble Learning
[1] KIZILKAYA B, EVER E, YATBAZ H Y 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] ZHOU Yong, WANG Xiaona, HAO Sha 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] LI Xiaotong, QIN Xianlin, LIU Qian 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] ZHU Z, DENG X, ZHAO F et al. How environmental factors affect forest fire occurrence in Yunnan forest region. Forests, 13, 1392(2022).
[5] LI Shixin, ZHANG Fuquan, LIN Haifeng. Research on forest fire risk evaluation based on machine learning algorithm. Journal of Nanjing Forestry University (Natural Science Edition), 47, 49-56(2023).
[6] SHAO Y, FENG Z, SUN L et al. Mapping China’s forest fire risks with machine learning. Forests, 13, 856(2022).
[7] LIU Liyue, MIAO Zelang, WU Lixin X. Spatial-temporal variability of Amazon tropical rainforest fire based on MODIS data. Remote Sensing Technology and Application, 37, 721-730(2022).
[8] HAN Chenhui, YANG Qian, HE Xiaohui 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] SMITH C W, PANDA S K, BHATT U S 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] IBAN M C, SEKERTEKIN A. 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] WHITNEY K, KIM S H, JIA S et al. Estimation of the relationship between satellite-derived vegetation indices and live fuel moisture towards wildfire risk in Southern California(2018).
[12] MA W, FENG Z, CHENG Z et al. Identifying forest fire driving factors and related impacts in China using random forest algorithm. Forests, 11, 507(2020).
[13] LIANG H, ZHANG M, WANG H. A neural network model for wildfire scale prediction using meteorological factors. IEEE Access, 7, 176746-176755(2019).
[14] SHMUEL A, HEIFETZ E. Global wildfire susceptibility mapping based on machine learning models. Forests, 13, 1050(2022).
[15] Zhou Z H. Ensemble learning. Encyclopedia of Biometrics, 411-416(2015).
[16] XIE Y, PENG M. Forest fire forecasting using ensemble learning approaches. Neural Computing and Applications, 31, 4541-4550(2019).
[17] BJANES A, DE L F R, MENA P. A deep learning ensemble model for wildfire susceptibility mapping. Ecological Informatics, 65, 101397(2021).
[18] LECINA-DIAZ J, ALVAREZ A, RETANA J. Extreme fire severity patterns in topographic,convective and wind-driven historical wildfires of Mediterranean pine forests. PLoS One, 9(2014).
[19] BIRCH D S, MORGAN P, KOLDEN C A et al. Vegetation, topography and daily weather influenced burn severity in central Idaho and western Montana forests. Ecosphere, 6, 1-23(2015).
[20] DILLON G K, HOLDEN Z A, MORGAN P 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] KEYSER A, WESTERLING A L R. Climate drives inter-annual variability in probability of high severity fire occurrence in the western United States. Environmental Research Letters, 12(2017).
[22] DEERING D W. Rangeland reflectance characteristics measured by aircraft and spacecraft sensors(1978).
[23] JIANG Z, HUETE A R, DIDAN K et al. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833-3845(2008).
[24] ULFA F, ORTON T G, DANG Y P 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] LIU J, PATTEY E, JEGO G. 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] ANDELA N, MORTON D C, GIGLIO L et al. The global fire atlas of individual fire size, duration, speed and direction. Earth System Science Data, 11, 529-552(2019).
[27] KASSAMBARA A. Practical guide to cluster analysis in R: unsupervised machine learning(2017).
[28] LUCHI D, RODRIGUES A L, VAREJAO F M. Sampling approaches for applying DBSCAN to large datasets. Pattern Recognition Letters, 117, 90-96(2019).
[29] BIAN X, WU D, ZHANG K 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] WOLPERT D H. Stacked generalization. Neural Networks, 5, 241-259(1992).
[31] RISK C, JAMES P M A. 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] NEBOT À, MUGICA F. Forest fire forecasting using Fuzzy Logic Models. Forests, 12, 1005(2021).
[33] BI J, BENNETT K P. Regression error characteristic curves, 43-50(2003).
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Junchen FENG, Hao DONG, Peng HAN, Yuanbin LI, Jingyu LIU, Yunhong DING. Prediction of Forest Burned Area based on MODIS-EVI2 and Ensemble Learning[J]. Remote Sensing Technology and Application, 2024, 39(5): 1261
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Received: Nov. 13, 2022
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Published Online: Jan. 7, 2025
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