Uncertainty assessment in multitemporal land use/cover mapping with classification system semantic heterogeneity

Abstract

Land use/cover (LUC) data are commonly relied on to provide land surface information in a variety of applications. However, the exchange and joint use of LUC information from different datasets can be challenging due to semantic differences between common classification systems (CSs). In this paper, we propose an uncertainty assessment schema to capture the semantic translation uncertainty between heterogeneous LUC CSs and evaluate the data label uncertainty of multitemporal LUC mapping results caused by uncertainty propagation. The semantic translation uncertainty between CSs is investigated using a dynamic semantic reference system (DSRS) model and semantic similarity analysis. An object-based unsupervised change detection algorithm is adopted to determine the probability of changes in land patches, and novel uncertainty metrics are proposed to estimate the patch label uncertainty in LUC maps. The proposed uncertainty assessment schema was validated via experiments on four LUC datasets, and the results confirmed that semantic uncertainty had great impact on data reliability and that the uncertainty metrics could be used in the development of uncertainty controls in multitemporal LUC mapping by referring to uncertainty assessment results. We anticipate our findings will be used to improve the applicability and interoperability of LUC data products.