TY - JOUR

T1 - Uncertainties in landscape analysis and ecosystem service assessment

AU - Hou, Y

AU - Burkhard, B

AU - Müller, F

PY - 2013/9

Y1 - 2013/9

N2 - Landscape analysis and ecosystem service assessment have drawn increasing concern from research and application at the landscape scale. Thanks to the continuously emerging assessments as well as studies aiming at evaluation method improvement, policy makers and landscape managers have an increasing interest in integrating ecosystem services into their decisions. However, the plausible assessments carry numerous sources of uncertainties, which regrettably tend to be ignored or disregarded by the actors or researchers. In order to cope with uncertainties and make them more transparent for landscape managers, we demonstrate them by reviewing literature, describing an example and proposing approaches for uncertainty analysis. Additionally, we conclude with potential actions to reduce the insecurities accompanying landscape analysis and ecosystem service assessments. As for landscape analysis, the fundamental uncertainty origins are landscape complexity and methodological uncertainties. Concerning the uncertainty sources of ecosystem service assessments, the complexity of the natural system, respondents' preferences and technical problems play essential roles. By analyzing the assessment process, we find that initial data uncertainty pervades the whole assessment and argue that the limited knowledge about the complexity of ecosystems is the focal origin of uncertainties. For analyzing uncertainties in assessments, we propose systems analysis, scenario simulation and the comparison method as promising strategies. To reduce uncertainties, we assume that actions should integrate continuous learning, expanding respondent numbers and sources, considering representativeness, improving and standardizing assessment methods and optimizing spatial and geobiophysical data.

AB - Landscape analysis and ecosystem service assessment have drawn increasing concern from research and application at the landscape scale. Thanks to the continuously emerging assessments as well as studies aiming at evaluation method improvement, policy makers and landscape managers have an increasing interest in integrating ecosystem services into their decisions. However, the plausible assessments carry numerous sources of uncertainties, which regrettably tend to be ignored or disregarded by the actors or researchers. In order to cope with uncertainties and make them more transparent for landscape managers, we demonstrate them by reviewing literature, describing an example and proposing approaches for uncertainty analysis. Additionally, we conclude with potential actions to reduce the insecurities accompanying landscape analysis and ecosystem service assessments. As for landscape analysis, the fundamental uncertainty origins are landscape complexity and methodological uncertainties. Concerning the uncertainty sources of ecosystem service assessments, the complexity of the natural system, respondents' preferences and technical problems play essential roles. By analyzing the assessment process, we find that initial data uncertainty pervades the whole assessment and argue that the limited knowledge about the complexity of ecosystems is the focal origin of uncertainties. For analyzing uncertainties in assessments, we propose systems analysis, scenario simulation and the comparison method as promising strategies. To reduce uncertainties, we assume that actions should integrate continuous learning, expanding respondent numbers and sources, considering representativeness, improving and standardizing assessment methods and optimizing spatial and geobiophysical data.

KW - Conservation of Natural Resources/methods

KW - Ecosystem

KW - Environmental Monitoring/methods

KW - Uncertainty

U2 - 10.1016/j.jenvman.2012.12.002

DO - 10.1016/j.jenvman.2012.12.002

M3 - Article

C2 - 23291281

VL - 127 Suppl

SP - S117-31

JO - Journal of Environmental Management

JF - Journal of Environmental Management

SN - 0301-4797

ER -