首页> 外文会议>International Conference on Computational Science and Its Applications >Low Cost Pre-operative Fire Monitoring from Fire Danger to Severity Estimation Based on Satellite MODIS, Landsat and ASTER Data: The Experience of FIRE-SAT Project in the Basilicata Region (Italy)
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Low Cost Pre-operative Fire Monitoring from Fire Danger to Severity Estimation Based on Satellite MODIS, Landsat and ASTER Data: The Experience of FIRE-SAT Project in the Basilicata Region (Italy)

机译:基于卫星MODIS,LANDSAT和ASTER数据的严重估计,低成本预算法监测到严重程度估算:巴斯利卡塔(意大利)在巴斯利卡塔(意大利)的火灾坐飞机经验

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This paper presents the results we obtained in the context of the FIRE-SAT project focused on the use of satellite data for pre-operational monitoring of fire danger and fire effects in the Basilicata Region. The use of satellite data was manyfold, to obtain: (i) fuel property (type and loading) maps, mainly obtained from satellite Landsat TM data, (ii) fuel moisture estimation (mainly from MODIS), (iii) fire danger/susceptibility indices as well as (iv) post fire effects including fire severity and vegetation recovery assessment. Results obtained during the first year of project (2008) suggested that the integrated model identified the main fire danger zones by means of the integration of fuel types with daily fuel moisture and Greenness maps. MODIS multitemporal data analyses enable us to dynamically estimate fire severity as well as to map fire affected areas and evaluate the vegetation recovery capability over time. The pre-operative use of the integrated model, carried out within the framework of the FIRE-SAT project funded by the Basilicata Region, pointed out that the system enables us to timely monitor spatial and temporal variations of fire susceptibility and promptly provide useful information on both fire severity and post fire regeneration capability.
机译:本文介绍了我们在Fire-SAT项目的背景下获得的结果,重点是在Basilicata地区使用卫星数据进行卫星数据进行预防危险和火灾效应。使用卫星数据的许多折叠,获得:(i)燃料属性(类型和装载)地图,主要从卫星Landsat TM数据获得(ii)燃油湿度估计(主要来自MODIS),(iii)火灾危险/易感性指数以及(iv)后消防效应,包括火灾严重程度和植被恢复评估。项目第一年获得的结果(2008)建议综合模型通过将燃料类型与日常燃料水分和绿色地图的整合确定了主要的火灾危险区域。 Modis Multi二模型数据分析使我们能够动态估计火灾严重性以及地图射击受影响的区域,并随着时间的推移评估植被恢复能力。在Basilicata地区资助的Fire-SAT项目框架内进行的综合模型的综合性使用指出,该系统使我们能够及时监控火灾敏感性的空间和时间变化,并及时提供有用的信息既有火灾严重程度和消防后的再生能力。

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