Wavelet-Based Correlation Identification of Scales and Locations between Landscape Patterns and Topography in Urban-Rural Profiles: Case of the Jilin City, China
Abstract
:1. Introduction
- (1)
- The effectiveness and adaptability of the Pearson correlation coefficient and wavelet coherency methods in studying the relationship between landscape heterogeneity and topography were analyzed.
- (2)
- The sensitivity of landscape metrics in describing the influence of topography on the landscape pattern, especially the locally correlated features at different scales and locations in an urban-rural profile, were analyzed using the wavelet coherency method.
2. Materials and Methods
2.1. Data
2.2. Pearson Correlation Coefficient
2.3. Continuous Wavelet Transform
2.4. Wavelet Coherency Analysis
2.5. Test of Significance
3. Results
3.1. Overall Pearson Correlation
3.2. The Wavelet Coherencies between Topography Factors and Landscape Indices
4. Discussion
5. Conclusions
- 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.
- Landscape metrics should not be chosen randomly to study ecological phenomena. This is because some landscape metrics were not sensitive to the terrain variation (e.g., the proximity index), while other selected metrics displayed obvious multi-scale features in describing the influence of the 3D terrain (mainly the elevation and slope) on the landscape pattern.
- The multi-location correlation characteristics at different scales were measured along urban-rural profiles. For example, the influence of traffic lines on the land use distribution within a neighborhood space was indicated only at medium scales in the A2 region, while the correlation was extremely weak at smaller scales and extremely strong at larger scales for the total study area.
- The variation of the correlation period between topography and landscape metrics, especially the decrease shown in the A3 region, can be seen as important evidence that the main influencing factors of landscape pattern gradually changed from human influences to topography.
- The identification of multiple scales and locations using the wavelet method supplies us with new insights into the relationship between landscape heterogeneity and topography. However, this kind of method is still too complex, and the ecological interpretation of wavelet coherencies needs to be supplemented as well as have a more accurate division strategy of scale.
Author Contributions
Funding
Conflicts of Interest
References
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Landscape Metrics | Metrics Calculation Formula | Instructions |
---|---|---|
ED | E is the total length of patch edges in landscape. A is the total landscape area. | |
SHDI | Pk equals the plane area of class k, divided by the landscape area. | |
PAFRAC | is the perimeter of patch ij, and is the area of patch ij. m is the number of class i and n is the number of patches belong to the class i. N is the total number of patches in landscape. | |
Cohesion index | is the perimeter of patch ij, and is the area of patch ij. | |
Proximity index | is the area of patch ijk within a specified neighborhood. is the closest distance between patch ijk and patch ijk based on edge-to-edge distance, and k is the number of patches. |
Landscape Metric | DEM | Slope | Aspect |
---|---|---|---|
Proximity_A | −0.161 * | −0.204 * | 0.008 |
PAFRAC_A | −0.612 * | −0.416 * | −0.172 * |
Cohesion_A | 0.661 * | 0.427 * | 0.193 * |
ED_A | −0.672 * | −0.495 * | −0.166 * |
SHDI_A | −0.753 * | −0.462 * | −0.195 * |
Proximity_B | −0.103 ** | −0.030 | −0.002 |
PAFRAC_B | 0.230 * | −0.022 | 0.015 |
Cohesion_B | −0.249 * | 0.001 | 0.001 |
ED_B | 0.273 * | −0.021 | 0.020 |
SHDI_B | 0.234 * | 0.113 ** | −0.069 |
Scale Division | PAFRAC (km) | Cohesion (km) | ED (km) | SHDI (km) |
---|---|---|---|---|
Small Scales | 0.25–1.5 | 0.25–1 | 0.25–2 | 0.25–1 |
Medium Scales | 1.5–8 | 1–8 | 2–8 | 1–8 |
Large Scales | >8 | >8 | >8 | >8 |
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Wu, Q.; Guo, F.; Li, H. Wavelet-Based Correlation Identification of Scales and Locations between Landscape Patterns and Topography in Urban-Rural Profiles: Case of the Jilin City, China. Remote Sens. 2018, 10, 1653. https://doi.org/10.3390/rs10101653
Wu Q, Guo F, Li H. 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. https://doi.org/10.3390/rs10101653
Chicago/Turabian StyleWu, Qiong, Fengxiang Guo, and 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. https://doi.org/10.3390/rs10101653