Uncertainty evaluation approach based on Shannon entropy for upscaled land use/cover maps
Yunduo Lu, Peijun Sun, Linna Linghu, Meng Zhang
Abstract
ABSTRACTUnderstanding the scale of land use/cover (LULC) map and its impacts on representing LULC is central to address earth observation issues. However, there is an absence of quantitative uncertainty evaluation of upscaled maps to be used over decades. An approach based on the Shannon entropy theory was then proposed to tackle this issue by reporting categorical heterogeneity information contained in upscaled pixels. The Majority Rule-Based aggregation algorithm was performed to generate upscaled maps at different widely used scales using a national LU map. The results reveal that substantial uncertainties inevitably exist in the upscaled maps. Additionally, the analysts demonstrate that the proposed approach can-and-indeed accurately provide spatially uncertain information of upscaled maps. These findings suggest that this approach is necessary for users to most effectively use these maps in earth observation models and should be extensively used in the future work.