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dc.contributor.authorKleindl, William
dc.contributor.authorPowell, Scott L.
dc.contributor.authorHauer, F. Richard
dc.date.accessioned2015-12-04T19:05:28Z
dc.date.available2015-12-04T19:05:28Z
dc.date.issued2015-05
dc.identifier.citationKleindl, William, Scott L. Powell, and F. Richard Hauer. "Effect of thematic map misclassification on landscape multi-metric assessment." Environmental Monitoring & Assessment 187 (May 2015): 321. DOI:https://dx.doi.org/10.1007/s10661-015-4546-y.en_US
dc.identifier.issn0167-6369
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/9396
dc.description.abstractAdvancements in remote sensing and computational tools have increased our awareness of large-scale environmental problems, thereby creating a need for monitoring, assessment, and management at these scales. Over the last decade, several watershed and regional multi-metric indices have been developed to assist decision-makers with planning actions of these scales. However, these tools use remote-sensing products that are subject to land-cover misclassification, and these errors are rarely incorporated in the assessment results. Here, we examined the sensitivity of a landscape-scale multi-metric index (MMI) to error from thematic land-cover misclassification and the implications of this uncertainty for resource management decisions. Through a case study, we used a simplified floodplain MMI assessment tool, whose metrics were derived from Landsat thematic maps, to initially provide results that were naive to thematic misclassification error. Using a Monte Carlo simulation model, we then incorporated map misclassification error into our MMI, resulting in four important conclusions: (1) each metric had a different sensitivity to error; (2) within each metric, the bias between the error-naive metric scores and simulated scores that incorporate potential error varied in magnitude and direction depending on the underlying land cover at each assessment site; (3) collectively, when the metrics were combined into a multi-metric index, the effects were attenuated; and (4) the index bias indicated that our naive assessment model may overestimate floodplain condition of sites with limited human impacts and, to a lesser extent, either over- or underestimated floodplain condition of sites with mixed land use.en_US
dc.description.sponsorshipNational Science Foundation EPSCoR program under Grant #NSF-IIA-1443108en_US
dc.titleEffect of thematic map misclassification on landscape multi-metric assessmenten_US
dc.typeArticleen_US
mus.citation.extentfirstpage321en_US
mus.citation.journaltitleEnvironmental Monitoring & Assessmenten_US
mus.citation.volume187en_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.1007/s10661-015-4546-yen_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentLand Resources & Environmental Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.data.thumbpage4en_US


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