Ecosystem service (ES) assessments are widely promoted as a tool to support decision-makers in ecosystem management, and the mapping of ES is increasingly supported by the spatial data on ecosystem properties provided by Earth Observation (EO). However, ES assessments are often associated with high levels of uncertainty, which affects their credibility. We demonstrate how different types of information on ES (including EO data, process models, and expert knowledge) can be integrated in a Bayesian Network, where the associated uncertainties are quantified. The probabilistic approach is used to map the provision and demand of avalanche protection, an important regulating service in mountain regions, and to identify the key sources of uncertainty. The model outputs show high uncertainties, mainly due to uncertainties in process modelling. Our results demonstrate that the potential of EO to improve the accuracy of ES assessments cannot be fully utilized without an improved understanding of ecosystem processes.

广泛推广了生态系统服务（ES）评估，将其作为支持决策者进行生态系统管理的工具，而地球观测（EO）提供的有关生态系统特性的空间数据也日益支持对ES的绘图。但是，ES评估通常与高度不确定性相关联，这会影响其可信度。我们演示了如何将有关ES的不同类型的信息（包括EO数据，过程模型和专家知识）整合到贝叶斯网络中，在贝叶斯网络中量化相关的不确定性。概率方法用于绘制雪崩保护的提供和需求（雪崩保护是山区的重要调节服务），并确定不确定性的关键来源。模型输出显示出很高的不确定性，这主要是由于过程建模中的不确定性所致。