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Wavelet-Based Correlation Identification of Scales and Locations between Landscape Patterns and Topography in Urban-Rural Profiles: Case of the Jilin City, China

机译:基于小波的城乡景观格局与地形尺度和位置的相关性识别-以吉林市为例

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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.
机译:景观在不同的区域以不同的空间比例显示重叠的相关性集合,这些相关性可以通过模式分析来描绘。本研究利用皮尔逊相关分析和小波方法,确定了吉林市城乡剖面不同尺度和位置的景观格局与地形之间的相关性。选择了两个剖面,长度分别为30 km(A)和35 km(B),采样间隔为0.1 km。结果表明,剖面A由于地形变化多变而对地形影响下的土地利用模式的表征更为敏感,可以根据标准偏差的变化来定义三个尺度(小,中和大)在大尺度上(超过8 km),景观度量与海拔之间的相关性相似,而在其他尺度间隔下,则发现了复杂的相关性。凝聚力和香农多样性指数的中等规模为1-8 km,而周边区域分形维数和边缘密度指数的中等规模分别为1.5-8 km和2-8 km。在小范围内,由于微观地形和地形元素(例如山谷和山坡),相关性总体上较弱并且分散。在中等规模上,相关性受局部地形的影响最大,并且土地使用模式与多个位置的地形显着相关。在大的空间尺度上,由于山脉和平原的交替,整个研究区域存在着显着的相关性。通常,景观度量与地形之间的相关强度会随着空间规模的增加而逐渐增加,尽管这种趋势在多个位置都有一定的波动。尽管计算过程和生态解释复杂,但小波方法仍然是识别景观生态系统多尺度特征的有效工具。

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