Remote Sensing Fire Danger Prediction Models Applied to Northern China
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
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State Key Laboratory of Fire Science, University of Science and Technology of China, Jinzhai 96, Hefei 230027, China 2 CNR-IMAA, C.Da S. Loja, 85050 Tito Scalo, (PZ), Italy [email protected] Abstract. 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. Keywords: Fire danger
Satellite Daxing’anling region
1 Introduction Fire represents one of the main disturbances of vegetation covers and ecosystems, bringing profound transformations at different temporal and spatial scales which affect landscapes and environments. Forest fire is a primary process that influences the vegetation causing alteration in its structure and composition, soil erosion, changes in nutrient levels, micro-climate, hydrology, vegetation succession, etc. Climate change is expected to bring increased temperatures, prolonged droughts and heat waves that will further aggravate the risks of forest fires in the Mediterranean regions, as well as in alpine ecosystems and boreal forests, with severe environmental and economic consequences. Moreover, other climate change impacts that could add damaged or dead wood to the forest fuel load (for example, as a result of insect outbreaks, ice storms or high winds) may increase the risk of fire activity. Satellite remote sensing can provide a useful support for natural and cultural heritage and in the case of fire monitoring the ARGON Laboratory of the IMAA-CNR developed operative tools [1–7] systematically adopted by the Protezione Civile of the Basilicata Region.
© Springer International Publishing Switzerland 2016 O. Gervasi et al. (Eds.): ICCSA 2016, Part V, LNCS 9790, pp. 624–633, 2016. DOI: 10.1007/978-3-319-42092-9_47
Remote Sensing Fire Danger Prediction Models
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Focusing on fire research in China, it is important to remind that over the years many studies [see 8] and references therein quoted] have been conducted for the characterization of the fire regime in northeastern China, as in the Heilongjiang Province and the Daxing’anling region, investigations have been also conducted on fire frequency in Mongolian pine (Pinussylvestris), in Huzhong Forest China (Northestern China) in Great Xing’an Montains and in Great Hing Mountains as well as on forest fire management policy and its effects on fuel and fire dange
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