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.
Highlights
Quantifying the spatial information of landscape pattern structures is essential for landscape planning and land use management [1]
Comparing the R2 of MRAS modes between interaction and no interaction revealed that adopting interactions into scaling functions could improve their goodness-of-fit (R2) for 12 landscape metrics, especially NP, LSI, TE, and COHESION (Figure 3)
The results showed that most individual scaling effects were affected by other scale factors; most scaling responses of landscape metrics were inconsistent across the whole spectrum of scale, except for the resolution scale of Shannon’s diversity index (SHDI)
Summary
Quantifying the spatial information of landscape pattern structures is essential for landscape planning and land use management [1]. Landscape metrics can quantify the spatial information of landscape pattern composition, configuration, and changes of land use/land cover (LULC) at a specific scale or multiple scales [2,3]. Quantifying the characteristics of landscape pattern structures and linking them to eco-environmental processes or land use changes on the multi-scale is central to landscape metrics [7,8]. The scale is an inherent feature of landscape metrics [11], including time, space location, and the data’s scope, resolution, classification, and so on. Many researchers agree that landscape metrics are scale-dependent, and most ecological processes are driven by comprehensive multi-scale interactions [16]. Thereby, these three scales interact with each other’s scaling effect, which together affects the representation of landscape metrics
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