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Scale dependence is one of the major characteristics of landscape. Urban landscape is highly affected by human activities with a multi-scale structure, which makes the multi-scale identification of urban structure an obligation for urban spatial studies. Although there have been many previous studies on urban landscape structure, most of them have been conducted on a single scale, and the multi-scale effects of landscape patterns were rarely involved. Two-dimensional wavelet transforms can link spatial structures to scale and spatial locations, and maybe an effective method for the multi-scale analysis of landscape. In this paper, we applied two-dimensional discrete wavelet transform and wavelet variance to analyze the multi-scale spatial structure characteristics and the nested hierarchical structure of the metropolitan Beijing area. The results indicated that the spatial distribution and configuration of the patches were highly scattered at small scales, and the urban landscape exhibited a relatively complicated structure. At medium scales, a combination of the polycentric and sectorial structure was identified due to the prominence of dominant patches within each administrative district. At larger scales, the urban landscape pattern exhibits typical concentric ring characteristics. Two characteristic scales were detected by the wavelet variance in the south-north direction of the main urban zones, scale 4 (112m) and 8 (1792m) in Dongcheng District, scale 3 (56m) and 6 (448m) in Xicheng District, which were corresponding to the extent of middle-small blocks and large blocks respectively. One characteristic scale was detected in each of the suburb areas (Chaoyang, Haidian, and Fengtai District). The spatial structure of the main urban zones is more complex than that of the suburb areas, and it presents a typical hierarchical structure in the south-north direction. In general, the spatial structure of Beijing metropolitan area appears polycentric and concentric ring structure at large scales, the main urban area has nested hierarchies at different characteristic scales, and the wavelet method can effectively identify multi-scale characteristics of urban spatial structure.
Qiong Wu; Jinxiang Tan; Fengxiang Guo; Hongqing Li; Shengbo Chen; Sheng Jiang. Multi-Scale Identification of Urban Landscape Structure Based on Two-Dimensional Wavelet Analysis: The Case of Metropolitan Beijing, China. Ecological Complexity 2020, 43, 100832 .
AMA StyleQiong Wu, Jinxiang Tan, Fengxiang Guo, Hongqing Li, Shengbo Chen, Sheng Jiang. Multi-Scale Identification of Urban Landscape Structure Based on Two-Dimensional Wavelet Analysis: The Case of Metropolitan Beijing, China. Ecological Complexity. 2020; 43 ():100832.
Chicago/Turabian StyleQiong Wu; Jinxiang Tan; Fengxiang Guo; Hongqing Li; Shengbo Chen; Sheng Jiang. 2020. "Multi-Scale Identification of Urban Landscape Structure Based on Two-Dimensional Wavelet Analysis: The Case of Metropolitan Beijing, China." Ecological Complexity 43, no. : 100832.
The relationship between urban landscape pattern and land surface temperature (LST) is one of the core issues in urban thermal environment research. Although previous studies have shown a significant correlation between LST and landscape pattern, most were conducted at a single scale and rarely involve multi-scale effects of the landscape pattern. Wavelet coherence can relate the correlation between LST and landscape pattern to spatial scale and location, which is an effective multi-scale correlation method. In this paper, we applied wavelet coherence and Pearson correlation coefficient to analyze the multi-scale correlations between landscape pattern and LST, and analyzed the spatial pattern of the urban thermal environment during the urbanization of Beijing from 2004 to 2017 by distribution index of high-temperature center (HTC). The results indicated that the HTC of Beijing gradually expands from the main urban zone and urban function extended zone to the new urban development zone and far suburb zone, and develops from monocentric to polycentric spatial pattern. Land cover types, such as impervious surfaces and bare land, have a positive contribution to LST, while water and vegetation play a role in mitigating LST. The wavelet coherence and Pearson correlation coefficients showed that landscape composition and spatial configuration have significant effects on LST, but landscape composition has a greater effect on LST in Beijing metropolitan area. Landscape composition indexes (NDBI and NDVI) showed significant multi-scale characteristics with LST, especially at larger scales, which has a strong correlation on the whole transect. There was no significant correlation between the spatial configuration indexes (CONTAG, DIVISION, and LSI) and LST at smaller scales, only at larger scales near the urban area has a significant correlation. With the increase of the scale, Pearson correlation coefficient calculated by spatial rectangle sampling and wavelet coherence coefficient have the same trend, although it had some fluctuations in several locations. However, the wavelet coherence coefficient diagram was smoother and less affected by position and rectangle size, which more conducive to describe the correlation between landscape pattern index and LST at different scales and locations. In general, wavelet coherence provides a multi-scale method to analyze the relationship between landscape pattern and LST, helping to understand urban planning and land management to mitigate the factors affecting urban thermal environment.
Qiong Wu; Jinxiang Tan; Fengxiang Guo; Hongqing Li; Shengbo Chen. Multi-Scale Relationship between Land Surface Temperature and Landscape Pattern Based on Wavelet Coherence: The Case of Metropolitan Beijing, China. Remote Sensing 2019, 11, 3021 .
AMA StyleQiong Wu, Jinxiang Tan, Fengxiang Guo, Hongqing Li, Shengbo Chen. Multi-Scale Relationship between Land Surface Temperature and Landscape Pattern Based on Wavelet Coherence: The Case of Metropolitan Beijing, China. Remote Sensing. 2019; 11 (24):3021.
Chicago/Turabian StyleQiong Wu; Jinxiang Tan; Fengxiang Guo; Hongqing Li; Shengbo Chen. 2019. "Multi-Scale Relationship between Land Surface Temperature and Landscape Pattern Based on Wavelet Coherence: The Case of Metropolitan Beijing, China." Remote Sensing 11, no. 24: 3021.
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.
Qiong Wu; Fengxiang Guo; Hongqing Li. Wavelet-Based Correlation Identification of Scales and Locations between Landscape Patterns and Topography in Urban-Rural Profiles: Case of the Jilin City, China. Remote Sensing 2018, 10, 1653 .
AMA StyleQiong Wu, Fengxiang Guo, Hongqing Li. Wavelet-Based Correlation Identification of Scales and Locations between Landscape Patterns and Topography in Urban-Rural Profiles: Case of the Jilin City, China. Remote Sensing. 2018; 10 (10):1653.
Chicago/Turabian StyleQiong Wu; Fengxiang Guo; Hongqing Li. 2018. "Wavelet-Based Correlation Identification of Scales and Locations between Landscape Patterns and Topography in Urban-Rural Profiles: Case of the Jilin City, China." Remote Sensing 10, no. 10: 1653.
Qiong Wu; Fengxiang Guo; Hongqing Li; Jingyu Kang. Measuring landscape pattern in three dimensional space. Landscape and Urban Planning 2017, 167, 49 -59.
AMA StyleQiong Wu, Fengxiang Guo, Hongqing Li, Jingyu Kang. Measuring landscape pattern in three dimensional space. Landscape and Urban Planning. 2017; 167 ():49-59.
Chicago/Turabian StyleQiong Wu; Fengxiang Guo; Hongqing Li; Jingyu Kang. 2017. "Measuring landscape pattern in three dimensional space." Landscape and Urban Planning 167, no. : 49-59.