An hierarchical information entropy model for coverage estimation of coastal areas based on an adaptive Discrete Global Grid System

Firstly, by virtue of extending the architecture of standard topographic map, this paper sets forth an adaptive Discrete Global Grid System (DGGS) based on the principle of quadtree subdivision, which can serve the task of representing unbalanced distribution of environmental features. Secondly, because the information entropy of the coverage at coastal areas is treated as a decisive factor for the adaptive mechanism of representing the hierarchical features, the grids in the DGGS are classified into 4 types including Absolute Land, Absolute Sea, Preferable Land, and Preferable Sea. The grids attributed to Preferable Land and Preferable Sea are regarded as the uncertain ones with ambiguous genus. According to the statistical amount of each grid type, a fuzzy estimation method for the computation of uncertain coverage entropy has been designed. The next, oriented to the hierarchical structure in DGGS, a progressive method for entropy computation is given out. Using part of one topographic map data, an experiment about the computation of hierarchical entropies is been examined. In the end, this paper discusses the fuzzy reconciliation problem arising from the uncertainty and inconsistency of multi-source data.