Giles M. Foody

TL;DR: It is likely that it is unlikely that a single standardized method of accuracy assessment and reporting can be identified, but some possible directions for future research that may facilitate accuracy assessment are highlighted.

TL;DR: This work provides practitioners with a set of “good practice” recommendations for designing and implementing an accuracy assessment of a change map and estimating area based on the reference sample data.

TL;DR: In this paper, the authors compare the accuracy of thematic maps derived by image classification analyses in remote sensing studies using the kappa coefficient of agreement derived for each map, which is a subjective assessment of the observed difference in accuracy but should be undertaken in a statistically rigorous fashion.

TL;DR: Although each classifier could yield a very accurate classification, > 90% correct, the classifiers differed in the ability to correctly label individual cases and so may be suitable candidates for an ensemble-based approach to classification.

TL;DR: In this article, an error-adjusted estimator of area can be easily produced once an accuracy assessment has been performed and an error matrix constructed, which can then be incorporated into an uncertainty analysis for applications using land change area as an input (e.g., a carbon flux model).