A bootstrap method for assessing classification accuracy and confidence for agricultural land use mapping in Canada

Land cover and land use classifications from remote sensing are increasingly becoming institutionalized framework data sets for monitoring environmental change. As such, the need for robust statements of classification accuracy is critical. This paper describes a method to estimate confidence in classification model accuracy using a bootstrap approach. Using this method, it was found that classification accuracy and confidence, while closely related, can be used in complementary ways to provide additional information on map accuracy and define groups of classes and to inform the future reference sampling strategies. Overall classification accuracy increases with an increase in the number of fields surveyed, where the width of classification confidence bounds decreases. Individual class accuracies and confidence were non-linearly related to the number of fields surveyed. Results indicate that some classes can be estimated accurately and confidently with fewer numbers of samples, whereas others require larger reference data sets to achieve satisfactory results. This approach is an improvement over other approaches for estimating class accuracy and confidence as it uses repetitive sampling to produce a more realistic estimate of the range in classification accuracy and confidence that can be obtained with different reference data inputs.