Quantifying fire trends in boreal forests with Landsat time series and self-organized criticality
Quantifying fire trends in boreal forests with Landsat time series and self-organized criticality
复制标题
利用Landsat时间序列和自组织临界性量化北方森林火灾趋势
DOI：
10.1016/j.rse.2019.111525
复制
发表时间：
2020-02
影响因子：
13.5
通讯作者：
A. Kato;D. Thau;A. Hudak;G. Meigs;L. Moskal
中科院分区：
文献类型：
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作者：
A. Kato;D. Thau;A. Hudak;G. Meigs;L. Moskal
北方森林在全球范围内广泛分布，储存了大量的碳，但最近的气候变化导致了干旱和火灾活动的增加。本研究的目的是利用跨越空间和时间多个尺度的数据来量化火灾规模和频率的趋势。我们使用谷歌Earth Engine上的多时相Landsat图像合成，并与加拿大公园管理局的参考火灾地图进行验证。我们还通过自组织临界性（SOC）的概念解释了火灾的一般趋势。我们的研究地点是伍德布法罗国家公园，这是加拿大的一个火灾热点，因为经常被闪电点燃。相对差分归一化烧伤比（RdNBR）是我们评估的最准确的基于landsat的烧伤严重程度指标（生产者的准确率为52.2%，用户的准确率为87.6%）。与手工绘制的火灾地图相比，基于landsat的火灾严重程度地图在火灾规模和频率的对数-对数比尺上提供了更好的线性关系。1990年以来的陆地卫星火灾趋势符合幂律分布，斜率为1.9，这与卫星火灾周长形状的分形维数有关。烧伤程度马赛克中的未烧伤斑块和低烧伤斑块影响幂律斜率和相关的分形维数。本研究展示了一种多尺度和多数据集技术，用于量化偏远地区的一般火灾趋势和变化的火灾周期，并建立了一个基线数据库，用于评估未来的火灾活动。通过幂律测试临界性有助于量化当代火灾制度的紧急趋势，这可以为规定火灾和其他管理活动的战略应用提供信息。自然资源管理者可以利用这项研究的信息来了解当地生态系统对大型火灾事件的适应性，以及在最近火灾活动增加的背景下生态系统的稳定性。
Boreal forests are globally extensive and store large amounts of carbon, but recent climate change has led to drier conditions and increasing fire activity. The objective of this study is to quantify trends in fire size and frequency using data spanning multiple scales in space and time. We use multi-temporal Landsat image compositing on Google Earth Engine and validate results with reference fire maps from the Canadian Park Service. We also interpret general fire trends through the concept of Self-Organized Criticality (SOC). Our study site is Wood Buffalo National Park, which is a fire hot spot in Canada due to frequent lightning ignitions. The relativize differenced normalized burn ratio (RdNBR) was the most accurate Landsat-based burn severity metric we evaluated (52.2% producer's accuracy, 87.6% user's accuracy). The Landsat-based burn severity maps provided a better fit for a linear relationship on the log-log scale of fire size and frequency than a manually drawn fire map. Landsat-based fire trends since 1990 conformed to a power-law distribution with a slope of 1.9, which is related to fractal dimensions of the satellite-based fire perimeter shapes. The unburned and low-severity patches within the burn severity mosaic influenced the power-law slope and associated fractal dimensionality. This study demonstrates a multi-scale and multi-dataset technique to quantify general fire trends and changing fire cycles in remote locations and establishes a baseline database for assessing future fire activity. Testing criticality by power laws helps to quantify emergent trends of contemporary fire regimes, which could inform the strategic application of prescribed fire and other management activities. Natural resource managers can utilize information from this study to understand local ecosystem adaptability to large fire events and ecosystem stability in the context of recent increasing fire activity.