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Improving the detection of wildfire disturbances in space and time based on indicators extracted from MODIS data: a case study in northern Portugal

机译:基于MODIS数据提取的指标,改善空间和时间的野火扰动的检测 - 葡萄牙北部的案例研究

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Wildfires constitute an important threat to human lives and livelihoods worldwide, as well as a major ecological disturbance. However, available wildfire databases often provide incomplete or inaccurate information, namely regarding the timing and extension of fire events. In this study, we described a generic framework to compare, rank and combine multiple remotely-sensed indicators of wildfire disturbances, in order to not only select the best indicators for each specific case, as well as to provide multi-indicator consensus approaches that can be used to detect wildfire disturbances in space and time. For this end, we compared the performance of different remotely-sensed variables to discriminate burned areas, by applying a simple change-point analysis procedure on time-series of MODIS imagery for the northern half of Portugal, without external information (e.g. active fire maps). Overall, our results highlight the importance of adopting a multi-indicator consensus approach for mapping and detecting wildfire disturbances at a regional scale, that allows to profit from spectral indices capturing different aspects of the Earth's surface, and derived from distinct regions of the electromagnetic spectrum. Finally, we argue that the framework here described can be used: (i) in a wide variety of geographical and environmental contexts; (ii) to support the identification of the best possible remotely-sensed functional indicators of wildfire disturbance; and (iii) for improving and complementing incomplete wildfire databases.
机译:野火对全世界人类生命和生计以及重大生态障碍构成了重要威胁。但是,可用的野火数据库通常提供不完整或不准确的信息,即关于火灾事件的时序和延长。在这项研究中,我们描述了一个通用框架来比较,等级和结合多个远程感测的野火扰动指标,以便不仅为每个特定情况选择最佳指标,以及提供可以的多指示达成的方法用于在空间和时间内检测野火扰动。为此,我们将不同远程感测变量的性能进行了比较,通过对葡萄牙北半部分的Modis Imagery的时间序列,无需外部信息来对烧毁区域进行辨别区域的性能,而无需外部信息(例如主动火地图)。总体而言,我们的结果突出了采用多指示互联方法来映射和检测区域规模的野火扰动的重要性,这允许从捕获地球表面的不同方面的光谱指标中获利,并从电磁谱的不同区域中获取。 。最后,我们争辩说,这里描述的框架可以使用:(i)在各种地理和环境上下文中; (ii)支持识别野火障碍最佳的远程感官功能指标; (iii)用于改进和补充不完整的野火数据库。

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