Disturbance detection in landsat time series is influenced by tree mortality agent and severity, not by prior disturbance
Disturbance detection in landsat time series is influenced by tree mortality agent and severity, not by prior disturbance
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DOI：
10.1016/j.rse.2020.112244
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发表时间：
2021-03
影响因子：
13.5
通讯作者：
K. Rodman;R. Andrus;T. Veblen;S. J. Hart
中科院分区：
文献类型：
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作者：
K. Rodman;R. Andrus;T. Veblen;S. J. Hart
大地卫星时间序列和相关的变化探测算法对于监测全球变化对地球生态系统的影响很有用。由于LTS算法可以很容易地应用于广泛的领域，它们通常用于绘制森林结构的变化，由于野火，昆虫攻击和其他重要的树木死亡率驱动因素。但是，诸如初始森林密度、树木死亡因子和干扰严重程度（即，树木死亡率）影响表面反射率的模式，并可能影响LTS算法的准确性。虽然LTS算法被广泛用于在Landsat记录期间具有多个干扰事件历史的地区，但LTS算法在这些条件下的有效性尚未得到很好的理解。我们比较了LTS算法LandTrendr（基于陆地卫星的干扰和恢复趋势检测）的产品与一个独特的现场数据集，该数据集来自一个严重受野火和云杉甲虫（红翅大小蠹）影响的景观。2000.我们还将LandTrendr与其他常见的绘制火灾和云杉甲虫影响区域的方法进行了比较。我们发现，LandTrendr更准确地检测野火比云杉甲虫引起的树木死亡率，和死亡率代理更容易检测到，当它们发生在高严重性。令人惊讶的是，先前的云杉甲虫爆发并没有影响随后的野火的可检测性。与其他干扰映射方法相比，LandTrendr预测了c。受野火或云杉甲虫爆发影响的面积减少40%。我们的研究结果表明，干扰类型和严重程度的具体差异遗漏错误可能会产生广泛的影响干扰制图工作，利用陆地卫星数据。逐渐的、低严重程度的干扰（例如，背景树死亡率和非林分替换干扰）在森林生态系统中普遍存在，但它们可能难以使用自动LTS算法检测。在可能的情况下，应在基于LTS的制图工作中纳入考虑这些偏差的方法，包括使用多光谱集合和辅助空间数据来改进预测。然而，我们的研究结果还表明，LTS算法似乎是强大的多个干扰事件，这是很重要的，因为这些地区将增加新的收购延长陆地卫星记录的长度的地区。
Landsat time series (LTS) and associated change detection algorithms are useful for monitoring the effects of global change on Earth's ecosystems. Because LTS algorithms can be easily applied across broad areas, they are commonly used to map changes in forest structure due to wildfire, insect attack, and other important drivers of tree mortality. But factors such as initial forest density, tree mortality agent, and disturbance severity (i.e., percent tree mortality) influence patterns of surface reflectance and may influence the accuracy of LTS algorithms. And while LTS algorithms are widely used in areas with a history of multiple disturbance events during the Landsat record, the effectiveness of LTS algorithms in these conditions is not well understood. We compared products from the LTS algorithm LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) with a unique field dataset from a landscape heavily influenced by both wildfire and spruce beetles (Dendroctonus rufipennis) since c. 2000. We also compared LandTrendr to other common methods of mapping fire- and spruce beetle-affected areas. We found that LandTrendr more accurately detected wildfire than spruce beetle-induced tree mortality, and both mortality agents were more easily detected when they occurred at high severity. Surprisingly, prior spruce beetle outbreaks did not influence the detectability of subsequent wildfire. Compared to alternative disturbance mapping approaches, LandTrendr predicted a c. 40% lower area affected by wildfire or spruce beetle outbreaks. Our findings indicate that disturbance type- and severity-specific differences in omission error may have broad implications for disturbance mapping efforts that utilize Landsat data. Gradual, low-severity disturbances (e.g., background tree mortality and non-stand replacing disturbance) are pervasive in forest ecosystems, yet they can be difficult to detect using automated LTS algorithms. Whenever possible, methods to account for these biases should be incorporated in LTS-based mapping efforts, including the use of multispectral ensembles and ancillary spatial data to refine predictions. However, our findings also indicate that LTS algorithms appear to be robust in areas with multiple disturbance events, which is important because these areas will increase as new acquisitions extend the length of the Landsat record.