Analysis of the influence of parameter and scale uncertainties on a local multi-criteria land use evaluation model

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Abstract

Land use evaluation involves careful consideration of several environmental factors and their relative importance quantified by factor weights. Local multi-criteria evaluation provides a mechanism for computing factor (criteria) weights within local neighborhoods that capture spatial heterogeneity and contribute to more accurate evaluation results. The accuracy of results, however, is tempered by the potential uncertainty of criteria weights. The paper presents a spatially explicit approach to uncertainty and sensitivity analysis of local criteria weights and modeling scale on the variability of model output. The efficacy of the approach is presented on the example of Environmental Benefit Index (EBI) model used by the U.S. Department of Agriculture Conservation Reserve Program (CRP) to select environmentally sensitive agricultural areas for conservation. The uncertainty analysis resulted in identifying robust areas for CRP selection characterized by high suitability and low uncertainty. The sensitivity analysis focused on the next-best group of candidates characterized by high suitability and high uncertainty. The results show that there is a relationship between spatial heterogeneity, data representation scale, and the level of uncertainty in the results of EBI model. The sensitivity of model output can be attributed to both the uncertainty of criteria weights and the modeling scale. A potential practical value of this approach is the improved analytical support for land suitability evaluation requiring a consideration of sub-optimal land units (high suitability/high uncertainty). Also, this approach can guide modelling effort by allowing the analyst to visualize spatial distribution and patterns of model output uncertainty and focus data collection on influential model input factors.

Keywords

GIS Local multi-criteria evaluation Uncertainty analysis Global sensitivity analysis Scale effect 
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Notes

Acknowledgements

This research was supported in part by the National Science Foundation Geography and Spatial Sciences Program Grant No. BCS-1263071. Any opinion, findings, conclusions, and recommendations expressed in this paper are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of GeographySan Diego State UniversitySan DiegoUSA
  2. 2.Department of GeographyUniversity of California, Santa BarbaraSanta BarbaraUSA
  3. 3.Institute of Geoecology and GeoinformationAdam Mickiewicz UniversityPoznanPoland

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