Disturbances have increased in Central Europe's forests, but whether changes in disturbance regimes are driven by natural or human causes remains unclear. Satellite-based remote sensing provides an important data source for quantifying forest disturbance change. Separating causes of forest disturbance is challenging, however, particularly in areas such as Central Europe where disturbance patches are small and disturbance agents interact strongly. Here we present a novel approach for the causal attribution of forest disturbance agents and illustrate its utility for 1.01 million disturbance patches mapped from Landsat data in Austria for the period 1986–2016. We gathered reference data on 2620 disturbance patches by conducting targeted field observations and structured interviews with 21 forest managers. We developed a novel indicator class characterizing the landscape context of a disturbance patch (i.e., the spatio-temporal autocorrelation of disturbance patches on the landscape), and combined it with other predictor variables describing the spectral signal, topography, and patch form of each disturbance patch. We used these predictors to identify the causal agents for disturbances mapped in Austria using Random Forest classification. Landscape context was the most important predictor of disturbance agent, improving model performance by up to 26 percentage points. Wind, bark beetles and timber harvesting were separated with an overall accuracy of 63%. Bark beetle patches were most difficult to identify correctly (producer's accuracy = 15%, user's accuracy = 30%), while regular timber harvesting was classified with highest certainty (producer's accuracy = 68%, user's accuracy = 82%). Harvesting dominates the disturbance regime of Austria's forests, with 70.5% of the disturbed area (76.7% of the disturbed patches) attributed to human causes and 29.5% (23.3%) to natural causes (wind: 23.0% [14.8%], bark beetles: 6.5% [8.5%]). Increases in disturbance since 1986 were driven by natural causes, with wind increasing by 408% and bark beetles increasing by 99% between the first and the second half of the observation period. Wind-disturbed patches were also considerably larger than those caused by bark beetles and harvesting (+102% and + 67%, respectively). Our novel approach to mapping causal agents of forest disturbance, applicable also to highly complex and interactive disturbance regimes, provides an important step towards a comprehensive monitoring and management of forest disturbances in a changing world.

中欧森林的干扰增加了，但干扰机制的变化是自然因素还是人为因素仍不清楚。基于卫星的遥感为量化森林扰动变化提供了重要的数据源。但是，要确定森林扰动的成因是具有挑战性的，尤其是在中欧等扰动斑块很小且扰动因子相互作用强烈的地区。在这里，我们介绍了森林干扰因子的因果归因的一种新方法，并举例说明了该方法对1986-2016年奥地利Landsat数据绘制的101万个干扰斑的效用。我们通过有针对性的实地观察和对21位森林管理员的结构化访谈，收集了有关2620个干扰斑块的参考数据。我们开发了一种新颖的指标类来表征干扰斑块的风景背景（即景观中干扰斑块的时空自相关），并将其与其他预测变量相结合，这些变量描述了每种干扰的频谱信号，地形和斑块形式修补。我们使用这些预测因子来确定使用随机森林分类在奥地利映射的干扰的原因。景观环境是干扰因素的最重要预测因子，可将模型性能提高多达26个百分点。风，树皮甲虫和木材采伐被分开，总体精度为63％。树皮甲虫斑块最难正确识别（生产者的准确度= 15％，用户的准确度= 30％），定期伐木被确定为最高确定性（生产商的准确度= 68％，用户的准确度= 82％）。采伐在奥地利森林的干扰制度中占主导地位，其中70.5％（占受干扰斑块的76.7％）归因于人为原因，而29.5％（占23.3％）归因于自然原因（风：23.0％[14.8％]），是树皮甲虫。 ：6.5％[8.5％]）。自1986年以来，扰动的增加是由自然原因引起的，在观察期的上半年和下半年之间，风量增加了408％，树皮甲虫增加了99％。受风扰的斑块也比由树皮甲虫和收成引起的斑块大得多（分别为+ 102％和+ 67％）。我们绘制森林干扰原因的新颖方法，也适用于高度复杂和互动的干扰机制，