Methodology for credibility assessment of historical global LUCC datasets

Land use-induced land cover change (LUCC) is an important anthropogenic driving force of global change that has influenced, and is still influencing, many aspects of regional and global environments. Accurate historical global land use/cover datasets are essential for a better understanding of the impacts of LUCC on global change. However, there are not only evident inconsistencies in current historical global land use/cover datasets, but inaccuracies in the data in these global dataset revealed by historical record-based reconstructed regional data throughout the world. A focus in historical LUCC and global change research relates to how the accuracy of historical global land cover datasets can be improved. A methodology for assessing the credibility of existing historical global land cover datasets that addresses temporal as well as spatial changes in the amount and distribution of land cover is therefore needed. Theoretically, the credibility of a global land cover dataset could be assessed by comparing similarities or differences in the data according to actual land cover data (the “true value”). However, it is extremely difficult to obtain historical evidence for assessing the credibility of historical global land cover datasets, which cannot be verified through field sampling like contemporary global land cover datasets. We proposed a methodological framework for assessing the credibility of global land cover datasets. Considering the types and characteristics of the available evidence used for assessments, we outlined four methodological approaches: (1) accuracy assessment based on regional quantitative reconstructed land cover data, (2) rationality assessment based on regional historical facts, (3) rationality assessment based on expertise, and (4) likelihood assessment based on the consistency of multiple datasets. These methods were illustrated through five case studies of credibility assessments of historical cropland cover data. This framework can also be applied in assessments of other land cover types, such as forest and grassland.