Effects of changing scales on landscape patterns and spatial modeling under urbanization
Abstract
Spatial scale is an eternal topic in landscape pattern related analysis. This paper examined the spatial scale effect of landscape pattern changes and their relationships with urbanization indicators in Qingdao using a series of sampling blocks. The results indicated that, with the increasing block scale, the mean patch density and aggregation within a block decreased, whereas the diversity increased. Furthermore, the expanding scale amplified the mean change ratio of landscape metrics and eliminated local drastic changes and regional variation trends along an urban-to-rural gradient, which would be obvious at a finer block scale. Meanwhile, the adjusted R2 of GWR (Geographically Weighted Regression) models increased with an increasing block size, especially when the block scale changed from 1 km to 5 km. Odd-numbered block scales performed better than even-numbered block scales.
Keyword : spatial scale, block size, urbanization, landscape patterns, geographically weighted regression (GWR)
How to Cite
Yang, J., Li, S., Xu, J., Wang, X., & Zhang, X. (2020). Effects of changing scales on landscape patterns and spatial modeling under urbanization. Journal of Environmental Engineering and Landscape Management, 28(2), 62-73. https://doi.org/10.3846/jeelm.2020.12081
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Defense Meteorological Satellite Program (DMSP). (n.d.). National Centers for Environmental Information. http://ngdc.noaa.gov/eog/dmsp.html
Deng, J. S., Wang, K., Hong, Y., & Qi, J. G. (2009). Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landscape and Urban Planning, 92(3–4), 187–198. https://doi.org/10.1016/j.landurbplan.2009.05.001
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., & Lautenbach, S. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
Dormann, C. F., McPherson, J. M., Araújo, M. B., Bivand, R., Bolliger, J., Carl, G., Davies, R. G., Hirzel, A., Jetz, W., Daniel Kissling, W., Kühn, I., Ohlemüller, R., Peres‐Neto, P. R., Reineking, B., Schröder, B., Schurr, F. M., & Wilson, R. (2007). Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. Ecography, 30(5), 609–628. https://doi.org/10.1111/j.2007.0906-7590.05171.x
Du, S., Wang, Q., & Guo, L. (2014). Spatially varying relationships between land-cover change and driving factors at multiple sampling scales. Journal of Environmental Management, 137, 101–110. https://doi.org/10.1016/j.jenvman.2014.01.037
Elvidge, D. C., Ziskin, D., Baugh, E. K., Tuttle, T. B., Ghosh, T., Pack, W. D., & Zhizhin, M. (2009). A fifteen year record of global natural gas flaring derived from satellite data. Energies, 2(3), 595–622. https://doi.org/10.3390/en20300595
Feng, Y., & Liu, Y. (2015). Fractal dimension as an indicator for quantifying the effects of changing spatial scales on landscape metrics. Ecological Indicators, 53, 18–27. https://doi.org/10.1016/j.ecolind.2015.01.020
Feng, Y., Liu, Y., & Tong, X. (2018). Spatiotemporal variation of landscape patterns and their spatial determinants in Shanghai, China. Ecological Indicators, 87, 22–32. https://doi.org/10.1016/j.ecolind.2017.12.034
Fotheringham, A. S., Brunsdon, C., & Charlton, M. E. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Wiley.
Gao, J., & Li, S. (2011). Detecting spatially non-stationary and scale-dependent relationships between urban landscape fragmentation and related factors using Geographically Weighted Regression. Applied Geography, 31(1), 292–302. https://doi.org/10.1016/j.apgeog.2010.06.003
Herold, M., Goldstein, N. C., & Clarke, K. C. (2003). The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sensing of Environment, 86(3), 286–302. https://doi.org/10.1016/S0034-4257(03)00075-0
Herold, M., Scepan, J., & Clarke, K. C. (2002). The Use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environment and Planning A: Economy and Space, 34(8), 1443–1458. https://doi.org/10.1068/a3496
Hou, L., Wu, F., & Xie, X. (2020). The spatial characteristics and relationships between landscape pattern and ecosystem service value along an urban-rural gradient in Xi’an city, China. Ecological Indicators, 108, 105720. https://doi.org/10.1016/j.ecolind.2019.105720
Jiao, M., Hu, M., & Xia, B. (2019). Spatiotemporal dynamic simulation of land-use and landscape-pattern in the Pearl River Delta, China. Sustainable Cities and Society, 49, 101581. https://doi.org/10.1016/j.scs.2019.101581
Koch, J., Dorning, M. A., Van Berkel, D. B., Beck, S. M., Sanchez, G. M., Shashidharan, A., Smart, L. S., Zhang, Q., Smith, J. W., & Meentemeyer, R. K. (2019). Modeling landowner interactions and development patterns at the urban fringe. Landscape and Urban Planning, 182, 101–113. https://doi.org/10.1016/j.landurbplan.2018.09.023
Li, C., & Zhao, J. (2019). Investigating the spatiotemporally varying correlation between urban spatial patterns and ecosystem services: A case study of Nansihu Lake Basin, China. ISPRS International Journal of Geo-Information, 8(8), 346. https://doi.org/10.3390/ijgi8080346
Li, B., Chen, D., Wu, S., Zhou, S., Wang, T., & Chen, H. (2016). Spatio-temporal assessment of urbanization impacts on ecosystem services: Case study of Nanjing City, China. Ecological Indicators, 71, 416–427. https://doi.org/10.1016/j.ecolind.2016.07.017
Li, F., Zheng, W., Wang, Y., Liang, J., Xie, S., Guo, S., Li, X., & Yu, C. (2019). Urban green space fragmentation and urbanization: A spatiotemporal perspective. Forests, 10(4), 333. https://doi.org/10.3390/f10040333
Li, H., Peng, J., Yanxu, L., & Yi’na, H. (2017). Urbanization impact on landscape patterns in Beijing City, China: A spatial heterogeneity perspective. Ecological Indicators, 82(Suppl C), 50–60. https://doi.org/10.1016/j.ecolind.2017.06.032
Li, S., Zhao, Z., Miaomiao, X., & Wang, Y. (2010). Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression. Environmental Modelling & Software, 25(12), 1789–1800. https://doi.org/10.1016/j.envsoft.2010.06.011
Liu, Z., He, C., Zhang, Q., Huang, Q., & Yang, Y. (2012). Extracting the dynamics of urban expansion in China using DMSPOLS nighttime light data from 1992 to 2008. Landscape and Urban Planning, 106(1), 62–72. https://doi.org/10.1016/j.landurbplan.2012.02.013
Luck, M., & Wu, J. (2002). A gradient analysis of urban landscape pattern: A case study from the Phoenix metropolitan region, Arizona, USA. Landscape Ecology, 17(4), 327–339. https://doi.org/10.1023/A:1020512723753
Luo, X., & Peng, Y. (2016). Scale effects of the relationships between urban heat islands and impact factors based on a geographically-weighted regression model. Remote Sensing, 8(9), 760. https://doi.org/10.3390/rs8090760
Mann, S. (2009). Institutional causes of urban and rural sprawl in Switzerland. Land Use Policy, 26(4), 919–924. https://doi.org/10.1016/j.landusepol.2008.11.004
Marull, J., Font, C., & Boix, R. (2015). Modelling urban networks at mega-regional scale: Are increasingly complex urban systems sustainable? Land Use Policy, 43, 15–27. https://doi.org/10.1016/j.landusepol.2014.10.014
Min, M., Lin, C., Duan, X., Jin, Z., & Zhang, L. (2019). Spatial distribution and driving force analysis of urban heat island effect based on raster data: A case study of the Nanjing metropolitan area, China. Sustainable Cities and Society, 50, 101637. https://doi.org/10.1016/j.scs.2019.101637
Peng, W., Wang, X., Li, X., & He, C. (2018). Sustainability evaluation based on the emergy ecological footprint method: A case study of Qingdao, China, from 2004 to 2014. Ecological Indicators, 85, 1249–1261. https://doi.org/10.1016/j.ecolind.2017.12.020
Riitters, K. H., O’Neill, R. V., Hunsaker, C. T., Wickham, J. D., Yankee, D. H., Timmins, S. P., & Jackson, B. L. (1995). A factor analysis of landscape pattern and structure metrics. Landscape Ecology, 10(1), 23–39. https://doi.org/10.1007/BF00158551
Shen, S., Yue, P., & Fan, C. (2019). Quantitative assessment of land use dynamic variation using remote sensing data and landscape pattern in the Yangtze River Delta, China. Sustainable Computing: Informatics and Systems, 23, 111–119. https://doi.org/10.1016/j.suscom.2019.07.006
Shen, W., Jenerette, G. D., Wu, J., & Gardner, R. H. (2004). Evaluating empirical scaling relations of pattern metrics with simulated landscapes. Ecography, 27(4), 459–469. https://doi.org/10.1111/j.0906-7590.2004.03799.x
Su, S., Jiang, Z., Zhang, Q., & Zhang, Y. (2011). Transformation of agricultural landscapes under rapid urbanization: A threat to sustainability in Hang-Jia-Hu region, China. Applied Geography, 31(2), 439–449. https://doi.org/10.1016/j.apgeog.2010.10.008
Su, S., Ma, X., & Xiao, R. (2014). Agricultural landscape pattern changes in response to urbanization at ecoregional scale. Ecological Indicators, 40, 10–18. https://doi.org/10.1016/j.ecolind.2013.12.013
Sun, X., Crittenden, J. C., Li, F., Lu, Z., & Dou, X. (2018). Urban expansion simulation and the spatio-temporal changes of ecosystem services, a case study in Atlanta Metropolitan area, USA. Science of the Total Environment, 622–623, 974–987. https://doi.org/10.1016/j.scitotenv.2017.12.062
Tan, M., Li, X., Xie, H., & Lu, C. (2005). Urban land expansion and arable land loss in China – a case study of Beijing–Tianjin–Hebei region. Land Use Policy, 22(3), 187–196. https://doi.org/10.1016/j.landusepol.2004.03.003
Tian, P., Cao, L., Li, J., Pu, R., Shi, X., Wang, L., & Shao, S. (2019). Landscape grain effect in Yancheng Coastal Wetland and its response to landscape changes. International Journal of Environmental Research and Public Health, 16(12), 2225. https://doi.org/10.3390/ijerph16122225
Torres, A., Jaeger, J. A. G., & Alonso, J. C. (2016). Multi-scale mismatches between urban sprawl and landscape fragmentation create windows of opportunity for conservation development. Landscape Ecology, 31(10), 2291–2305. https://doi.org/10.1007/s10980-016-0400-z
Wang, K., Zhang, C., Chen, H., Yue, Y., Zhang, W., Zhang, M., Qi, X., & Fu, Z. (2019). Karst landscapes of China: Patterns, ecosystem processes and services. Landscape Ecology, 34(12), 2743–2763. https://doi.org/10.1007/s10980-019-00912-w
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