Abstract
Global Land Cover 30m (GlobeLand30) can be usually used for environmental change studies, land resource management, sustainable development, and many other fields. However, this land cover dataset only provides a 30m resolution. For some cases, Ecology system and Climate Change, etc., data with coarser resolutions may still be needed. To solve this problem, the spatial aggregation of the catergories data is necessary. Current spatial aggregations approaches can generally divided into two classes, i.e. majority rule-based aggregation and random rule-based aggregation. This study aims to evaluate these two methods for the effective of the spatial aggregation for GlobeLand30 data with consideration of some measures, i.e. Cover type Proportion, Perimeter-Area Fractal Dimension (PAFRAC), Aggregation Index (AI), and Landscape Shape Index (LSI). The result demonstrated that random rule-based aggregation maintains land cover diversity and category proportion, but landscape pattern can lead to disaggregated which reflected from PLAND and AI indexs scalogram. In contrast, majority rule-based aggregation keeps spatial patterns better than random rules.
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