To fully understand ecosystem functioning under global change, we need to be able to measure the stability of ecosystem functioning at multiple spatial scales. Although a number of stability components have been established at small spatial scales, there has been little progress in scaling these measures up to the landscape. Remote sensing data holds huge potential for studying processes at landscape scales but requires quantitative measures that are comparable from experimental field data to satellite remote sensing. Here we present a methodology to extract four components of ecosystem functioning stability from satellite‐derived time series of Enhanced Vegetation Index (EVI) data. The four stability components are as follows: variability, resistance, recovery time and recovery rate in ecosystem functioning. We apply our method to the island of Ireland to demonstrate the use of remotely sensed data to identify large disturbance events in productivity. Our method uses stability measures that have been established at the field‐plot scale to quantify the stability of ecosystem functioning. This makes our method consistent with previous small‐scale stability research, whilst dealing with the unique challenges of using remotely sensed data including noise. We encourage the use of remotely‐sensed data in assessing the stability of ecosystems at a scale that is relevant to conservation and management practices.

中文翻译：

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利用遥感数据量化大规模生态系统的稳定性

为了充分了解全球变化下的生态系统功能，我们需要能够在多个空间尺度上衡量生态系统功能的稳定性。尽管已经在较小的空间尺度上建立了许多稳定组件，但是在将这些尺度扩展到景观方面进展甚微。遥感数据具有研究景观尺度过程的巨大潜力，但需要定量的措施，从实验现场数据到卫星遥感都可与之相比。在这里，我们提出一种从卫星植被增强指数（EVI）数据的时间序列中提取生态系统功能稳定性的四个组成部分的方法。四个稳定性成分如下：生态系统功能的变异性，抗性，恢复时间和恢复速率。我们将我们的方法应用于爱尔兰岛，以演示使用遥感数据识别生产力中的重大干扰事件。我们的方法使用已在现场图规模上建立的稳定性测度来量化生态系统功能的稳定性。这使我们的方法与以前的小规模稳定性研究相一致，同时解决了使用包括噪声在内的遥感数据所面临的独特挑战。我们鼓励使用遥感数据以与保护和管理实践有关的规模评估生态系统的稳定性。这使我们的方法与以前的小规模稳定性研究相一致，同时解决了使用包括噪声在内的遥感数据所面临的独特挑战。我们鼓励使用遥感数据以与保护和管理实践有关的规模评估生态系统的稳定性。这使我们的方法与以前的小规模稳定性研究相一致，同时解决了使用包括噪声在内的遥感数据所面临的独特挑战。我们鼓励使用遥感数据以与保护和管理实践有关的规模评估生态系统的稳定性。