Remote Sensing Fire Danger Prediction Models Applied to Northern China

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• 关键词：Fire danger ; Satellite ; Daxing’anling region
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9790
• 期：1
• 页码：624-633
• 全文大小：4,327 KB
• 参考文献：1.Lanorte, A.C., Belviso, R., Lasaponara, F., Cavalcante, F., De Santis: Satellite time series and in situ data analysis for assessing landslide susceptibility after forest fire: preliminary results focusing the case study of Pisticci (Matera, Italy). In: Computational Science and Its Applications–ICCSA 2013, 652anorte A, R Lasaponara 2012 FIRE -SAT un sistema satellitare per il monitoraggio sistematico, dinamico ed integrato degli incendi boschivi: la sperimentazione operativa nella regione Basilicata GEOmedia 16 (2013)
  2.Lasaponara, R.: Geospatial analysis from space: Advanced approaches for data processing, information extraction and interpretation. Int. J. Appl. Earth Obs. Geoinf. 20, 1–3 (2013). Lasaponara, R, Lanorte, A.: Satellite time-series analysis. Int. J. Remote Sens. 33 (15), 4649-4652 (2011)CrossRef
  3.Lanorte, A., Danese, M., Lasaponara, R., Murgante, B.: Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis. Int. J. Appl. Earth Obs. Geoinf. 20, 42–51 (2013)CrossRef
  4.Lanorte, A., Lasaponara, R., Lovallo, M., Telesca, L.: Fisher-shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbance. Int. J. Appl. Earth Obs. Geoinf. 26, 441–446 (2014)CrossRef
  5.Lasaponara, R.: Inter-comparison of AVHRR-based fire susceptibility indicators for the Mediterranean ecosystems of southern Italy. Int. J. Remote Sens. 26, 853–870 (2005)CrossRef
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  7.Cuomo, V., Lanfredi, M., Lasaponara, R., Macchiato, M.F., Simoniello, T.: Detection of interannual variation of vegetation in middle and southern Italy during 1985–1999 with 1 km NOAA AVHRR NDVI data. J. Geophys. Res.-Atmos. 106, 17863–17876 (2001)CrossRef
  8.Tian, X., Shu, L., Wang, M., Zhao, F., Chen, L.: The fire danger and fire regime for the Daxing’anling region for 1987–2010. Procedia Engineering 62, 1023–1031 (2013)CrossRef
  9.Aguado, I., Chuvieco, E., Boren, R., Nieto, H.: Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. applications in fire danger assessment. Int. J. Wildland Fire 16, 390–397 (2007)CrossRef
  10.Burgan, R., Klaver, R., Klaver, J.: Fuel models and fire potential from satellite and surface observations. Int. J. Wildland Fire 8, 159–170 (1998)CrossRef
  11.Burgan R.E., Andrews, P.L., Bradshaw, L.S., Chase, C.H., Hartford, R.A., Latham, D.J.: Current status of the wildland fire assessment system (WFAS). Fire Management Notes, vol. 27, pp. 14–17, (1997). Chuvieco E., Cocero, D., Riano, D., Martin, P., Martinez-Vega, J., de la Riva, J., et al.: Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, vol. 92, pp. 322–331, August 30 2004
  
• 作者单位：Xiaolian Li (22)
  Wiegu Song (22)
  Antonio Lanorte (23)
  Rosa Lasaponara (23)
  
  22. State Key Laboratory of Fire Science, University of Science and Technology of China, Jinzhai 96, Hefei, 230027, China
  23. CNR-IMAA, C.Da S. Loja, 85050, Tito Scalo, (PZ), Italy
  
• 丛书名：Computational Science and Its Applications – ICCSA 2016
• ISBN：978-3-319-42092-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9790

文摘

Remote sensing fire danger prediction model is applied to Northern China. This study was carried out in the Daxing’anling region, which is located in Heilongjiang Province and Inner Mongolia (50.5°–52.25° N, 122°–125.5° E), the northern China. The method integrated by dead fuel moisture content and relative greenness index, which is based on the fire potential index (FPI), was used to predict the fire danger level of the study area. The case that fire happened on the late June 2010 was used to validate the modified method. The results pointed out that the fire affected areas were located in high fire danger level on 26th, 27th, 28th June, 2010 respectively. The ROC analyses of the predicted accuracy on these days were 90.98 %, 73.79 % and 69.07 % respectively. Results from our investigation pointed out the reliability of the adopted method.