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. Quantifying landscape heterogeneity and its organization at different scales is essential for understanding ecosystems and landscapes. Among hundreds of landscape metrics, entropy-related index represents an efficient tool to quantify and characterize landscape patterns. A recent development is Spatial Entropy index (Hs), and it has been validated as flexible and effective in landscape pattern analysis. However, the effects of changing spatial scale on Hs has not been quantified. This paper applies the fractal method to measure the spatial scale (grain size) sensitivity of Hs. Using the initial land-use data of Yanhe watershed, which is located in northwest of China, eleven different spatial scales were created in order to investigate the scale effects on Hs. A linear log–log regression model was then constructed based on the power law to calculate the coefficient of determination (COD) of the model and the fractal dimension (FD) of Hs. The result indicates that Spatial Entropy index shows a robust fractal feature, and it decreases as the spatial scale (or grain size) becomes lager in a moderate degree. In total, we believe that this study will help us to get a better understanding of Hs, and to facilitate further applications of this entropy-related index.

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

QUANTIFYING EFFECTS OF CHANGING SPATIAL SCALE ON SPATIAL ENTROPY INDEX: USE OF FRACTAL DIMENSION