Many important ecological phenomena occur on large spatial scales and/or are unplanned and thus do not easily fit within analytical frameworks that rely on randomization, replication, and interspersed a priori controls for statistical comparison. Analyses of such large‐scale, natural experiments are common in the health and econometrics literature, where techniques have been developed to derive insight from large, noisy observational data sets. Here, we apply a technique from this literature, synthetic control, to assess landscape change with remote sensing data. The basic data requirements for synthetic control include (1) a discrete set of treated and untreated units, (2) a known date of treatment intervention, and (3) time series response data that include both pre‐ and post‐treatment outcomes for all units. Synthetic control generates a response metric for treated units relative to a no‐action alternative based on prior relationships between treated and unexposed groups. Using simulations and a case study involving a large‐scale brush‐clearing management event, we show how synthetic control can intuitively infer treatment effect sizes from satellite data, even in the presence of confounding noise from climate anomalies, long‐term vegetation dynamics, or sensor errors. We find that accuracy depends on the number and quality of potential control units, highlighting the importance of selecting appropriate control populations. Although we consider the synthetic control approach in the context of natural experiments with remote sensing data, we expect the methodology to have wider utility in ecology, particularly for systems with large, complex, and poorly replicated experimental units.

中文翻译：

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评估生态学中的自然实验：使用合成控件评估遥感土地的处理方式

许多重要的生态现象在较大的空间尺度上发生和/或未经计划，因此不容易放入依赖于随机化，复制和散布先验控制以进行统计比较的分析框架之内。在健康和计量经济学文献中，对此类大型自然实验的分析是很常见的，在这些文献中，已经开发出了从大量嘈杂的观测数据集中获取洞察力的技术。在这里，我们运用文献中的一种综合控制技术，利用遥感数据评估景观变化。综合控制的基本数据要求包括（1）一组离散的已治疗和未治疗单位，（2）已知的治疗干预日期，以及（3）时间序列响应数据，其中包括所有患者的治疗前和治疗后结果单位。综合控制基于已处理组和未暴露组之间的先验关系，为已处理单元生成相对于无作用替代方案的响应度量。通过模拟和涉及大规模刷子清除管理事件的案例研究，我们展示了即使存在由于气候异常，长期植被动态变化或传感器错误。我们发现准确性取决于潜在控制单位的数量和质量，突出了选择适当控制人群的重要性。尽管我们在具有遥感数据的自然实验的背景下考虑了综合控制方法，但我们希望该方法在生态学中具有更广泛的用途，尤其是对于具有大型，复杂，