Quantifying uncertainties in earth observation-based ecosystem service assessments. (January 2019)

• Record Type: Journal Article
• Authors: Stritih, Ana; Bebi, Peter; Grêt-Regamey, Adrienne
• Is Part Of: Environmental modelling & software. Volume 111(2019)
• Page Start: 300
• Page End: 310
• Publication Date: 2019-01
• Subjects: Ecosystem services -- Earth observation -- Uncertainty -- Bayesian network -- Avalanche protection; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.09.005
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: Modelling the provision and demand of avalanche protection using a Bayesian Network. Combining EO data, models and expert knowledge, while accounting for uncertainties. Disentangling effects of uncertainties using sensitivity analyses. High uncertainty, mainly due to uncertainty about the avalanche process. Potential of EO for ES assessment limited by process understanding.