Abstract
The accuracy of a map is dependent on the reference dataset used in its construction. Classification analyses used in thematic mapping can, for example, be sensitive to a range of sampling and data quality concerns. With particular focus on the latter, the effects of reference data quality on land cover classifications from airborne thematic mapper data are explored. Variations in sampling intensity and effort are highlighted in a dataset that is widely used in mapping and modelling studies; these may need accounting for in analyses. The quality of the labelling in the reference dataset was also a key variable influencing mapping accuracy. Accuracy varied with the amount and nature of mislabelled training cases with the nature of the effects varying between classifiers. The largest impacts on accuracy occurred when mislabelling involved confusion between similar classes. Accuracy was also typically negatively related to the magnitude of mislabelled cases and the support vector machine (SVM), which has been claimed to be relatively insensitive to training data error, was the most sensitive of the set of classifiers investigated, with overall classification accuracy declining by 8% (significant at 95% level of confidence) with the use of a training set containing 20% mislabelled cases.
Highlights
Maps are widely used in scientific research
This paper aims to explore the sensitivity of mapping methods to error and uncertainty in the reference datasets used in map derivation
The classifications based upon the original, assumed to be error-free, training set showed that the classification from support vector machine (SVM) (89.11%) was slightly more accurate than all of the other classifications; the accuracy of the classification from the discriminant analysis, relevance vector machine (RVM), and sparse multinomial logistic regression (SMLR) were 86.88%, 88.0%, and 88.67%, respectively
Summary
Maps are widely used in scientific research. Their accuracy can, be critical, with the effect of map error being dramatic in a range of applications (e.g., [1]). The estimated value of ecosystem services for the conterminous USA determined using the National Land Cover Database (2006) changes from $1118 billion/y to $600 billion/y after adjustment for the known error in the maps used [2]. The databases may contain errors of varying nature and magnitude such as mislabelling arising from confusion between classes [3], which may vary regionally if, for example, the skills and expertise of data collectors vary These various sources of error (e.g., mislabelled cases) and uncertainty (e.g., ambiguous class membership) may degrade mapping and the effect may vary between mapping methods. It focuses on thematic mapping such as species distribution maps and land cover
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