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Uncertainties in landscape analysis and ecosystem service assessment
1Institute for Natural Resource Conservation, Department of Ecosystem Management, Christian-Albrechts-Universität zu Kiel, Olshausenstrasse 75, Kiel, Germany. yhou@ecology.uni-kiel.de
Journal of Environmental Management
|January 8, 2013
Summary
Uncertainties in landscape analysis and ecosystem service assessments are often overlooked. This study reviews uncertainty sources, proposes analysis methods, and suggests actions to improve reliability for better landscape management decisions.
Area of Science:
- Environmental science
- Ecosystem management
- Geospatial analysis
Background:
- Landscape analysis and ecosystem service assessments are increasingly vital for policy and management.
- Integrating ecosystem services into decision-making is a growing trend.
- Significant uncertainties in these assessments are often ignored, hindering effective application.
Purpose of the Study:
- To identify and review sources of uncertainty in landscape analysis and ecosystem service assessment.
- To propose methods for analyzing and managing these uncertainties.
- To suggest actions for reducing uncertainties in landscape-related studies.
Main Methods:
- Literature review to identify uncertainty origins.
- Analysis of the ecosystem service assessment process.
- Proposal of uncertainty analysis strategies (systems analysis, scenario simulation, comparison method).
Main Results:
- Key uncertainty sources include landscape complexity, methodological limitations, natural system complexity, respondent preferences, and technical issues.
- Initial data uncertainty is pervasive throughout the assessment process.
- Limited knowledge of ecosystem complexity is a primary source of uncertainty.
Conclusions:
- Addressing uncertainties is crucial for transparent and reliable landscape management.
- Proposed methods like systems analysis and scenario simulation can aid in understanding uncertainties.
- Reducing uncertainties requires continuous learning, improved data, standardized methods, and broader respondent engagement.
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