Abstract
Landscapes display overlapping sets of correlations in different regions at different spatial scales, and these correlations can be delineated by pattern analysis. This study identified the correlations between landscape pattern and topography at various scales and locations in urban-rural profiles from Jilin City, China, using Pearson correlation analysis and wavelet method. Two profiles, 30 km (A) and 35 km (B) in length with 0.1-km sampling intervals, were selected. The results indicated that profile A was more sensitive to the characterization of the land use pattern as influenced by topography due to its more varied terrain, and three scales (small, medium, and large) could be defined based on the variation in the standard deviation of the wavelet coherency in profile A. Correlations between landscape metrics and elevation were similar at large scales (over 8 km), while complex correlations were discovered at other scale intervals. The medium scale of cohesion and Shannon’s diversity index was 1–8 km, while those of perimeter-area fractal dimension and edge density index were 1.5–8 km and 2–8 km, respectively. At small scales, the correlations were weak as a whole and scattered due to the micro-topography and landform elements, such as valleys and hillsides. At medium scales, the correlations were most affected by local topography, and the land use pattern was significantly correlated with topography at several locations. At large spatial scales, significant correlation existed throughout the study area due to alternating mountains and plains. In general, the strength of correlation between landscape metrics and topography increased gradually with increasing spatial scale, although this tendency had some fluctuations in several locations. Despite a complex calculating process and ecological interpretation, the wavelet method is still an effective tool to identify multi-scale characteristics in landscape ecology.
Highlights
Spatiotemporal correlation is one of the fundamental processes generating the order, pattern, and diversity observed in nature [1]
This study analyzed the similarities between topographical factors and landscape metrics using the Pearson correlation coefficient, identified the scales and locations of the correlations between variables using the wavelet coherency method in urban-rural profiles, and obtained average correlation-location curves under different scale intervals
The wavelet analysis was more sensitive than the Pearson correlation coefficient for describing the correlations between landscape patterns and topography at different scales, and the topography was much more related to land use pattern in the complex area than that in the flat area
Summary
Spatiotemporal correlation is one of the fundamental processes generating the order, pattern, and diversity observed in nature [1]. Landscape patterns result from spatial heterogeneity, which in turn originates from the changes in spatial dependence [2]. It is generally believed that as the grain size or extent of the study area increases, the correlation coefficient between variables should increase [2]. This scale dependence is a key issue that needs to be considered when studying the correlations between landscape heterogeneity and its influencing factors [7]. Wu proposed that ecological disturbances, which can change the structure of a landscape pattern, will have multi-scale features at a given location [12]. A parallel study to identify the scales and locations of correlations can yield a detailed understanding of how the topography, human influences, and other factors affect the landscape pattern [13]
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