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

Abstract. 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 (H_s), and it has been validated as flexible and effective in landscape pattern analysis. However, the effects of changing spatial scale on H_s has not been quantified. This paper applies the fractal method to measure the spatial scale (grain size) sensitivity of H_s. 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 H_s. 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 H_s. 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 H_s, and to facilitate further applications of this entropy-related index.