Prediction and Selection of Appropriate Landscape Metrics and Optimal Scale Ranges Based on Multi-Scale Interaction Analysis

Abstract

Landscape metrics are widely used in landscape planning and land use management. Understanding how landscape metrics respond with scales can provide more accurate prediction information; however, ignoring the interference of multi-scale interaction may lead to a severe systemic bias. In this study, we quantitatively analyzed the scaling sensitivity of metrics based on multi-scale interaction and predict their optimal scale ranges. Using a big data method, the multivariate adaptive regression splines model (MARS), and the partial dependence model (PHP), we studied the scaling relationships of metrics to changing scales. The results show that multi-scale interaction commonly exists in most landscape metric scaling responses, making a significant contribution. In general, the scaling effects of the three scales (i.e., spatial extent, spatial resolution, and classification of land use) are often in a different direction, and spatial resolution is the primary driving scale in isolation. The findings show that only a few metrics are highly sensitive to the three scales throughout the whole scale spectrum, while the other metrics are limited within a certain threshold range. This study confirms that the scaling-sensitive scalograms can be used as an application guideline for selecting appropriate landscape metrics and optimal scale ranges.