DOI QR코드

DOI QR Code

Quantifying how urban landscape heterogeneity affects land surface temperature at multiple scales

  • Rahimi, Ehsan (Environmental Sciences Research Institute, Shahid Beheshti University) ;
  • Barghjelveh, Shahindokht (Environmental Sciences Research Institute, Shahid Beheshti University) ;
  • Dong, Pinliang (Department of Geography and the Environment, University of North Texas)
  • Received : 2021.08.11
  • Accepted : 2021.10.19
  • Published : 2021.12.31

Abstract

Background: Landscape metrics have been widely applied to quantifying the relationship between land surface temperature and urban spatial patterns and have received acceptable verification from landscape ecologists but some studies have shown their inaccurate results. The objective of the study is to compare landscape metrics and texture-based measures as alternative indices in measuring urban heterogeneity effects on LST at multiple scales. Results: The statistical results showed that the correlation between urban landscape heterogeneity and LST increased as the spatial extent (scale) of under-study landscapes increased. Overall, landscape metrics showed that the less fragmented, the more complex, larger, and the higher number of patches, the lower LST. The most significant relationship was seen between edge density (ED) and LST (r = - 0.47) at the sub-region scale. Texture measures showed a stronger relationship (R2 = 34.84% on average) with LST than landscape metrics (R2 = 15.33% on average) at all spatial scales, meaning that these measures had a greater ability to describe landscape heterogeneity than the landscape metrics. Conclusion: This study suggests alternative measures for overcoming landscape metrics shortcomings in estimating the effects of landscape heterogeneity on LST variations and gives land managers and urban planners new insights into urban design.

Keywords

References

  1. Asgarian A, Amiri BJ, Sakieh Y. Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosyst. 2015;18(1):209-22. https://doi.org/10.1007/s11252-014-0387-7.
  2. Bao T, Li X, Zhang J, Zhang Y, Tian S. Assessing the distribution of urban green spaces and its anisotropic cooling distance on urban heat island pattern in Baotou, China. ISPRS Int J Geo Inf. 2016;5(2):12. https://doi.org/10.3390/ijgi5020012.
  3. Bolliger, J., Wagner, H. H., Turner, M. G., 2007, Identifying and quantifying landscape patterns in space and time, in Kienast, F., Wildi, O. and Ghosh, S., eds., A Changing World: Challenges for Landscape Research, Landscape Series. Berlin: Springer, pp. 177-194. https://link.springer.com/book/10.1007%2F978-1-4020-4436-6.
  4. Cadenasso ML, Pickett ST, Schwarz K. Spatial heterogeneity in urban ecosystems: reconceptualizing land cover and a framework for classification. Front Ecol Environ. 2007;5(2):80-8. https://doi.org/10.1890/1540-9295(2007)5[80:SHIUER]2.0.CO;2.
  5. Chen A, Yao L, Sun R, Chen L. How many metrics are required to identify the effects of the landscape pattern on land surface temperature? Ecol Indic. 2014;45:424-33. https://doi.org/10.1016/j.ecolind.2014.05.002.
  6. Chong I-G, Jun C-H. Performance of some variable selection methods when multicollinearity is present. Chemom Intel Lab Syst. 2005;78(1-2):103-12. https://doi.org/10.1016/j.chemolab.2004.12.011.
  7. Coburn C, Roberts AC. A multiscale texture analysis procedure for improved forest stand classification. Int J Remote Sensing. 2004;25(20):4287-308. https://doi.org/10.1080/0143116042000192367.
  8. Cockx K, Van de Voorde T, Canters F. Quantifying uncertainty in remote sensingbased urban land-use mapping. Int J Appl Earth Observ Geoinform. 2014;31:154-66. https://doi.org/10.1016/j.jag.2014.03.016.
  9. Connors JP, Galletti CS, Chow WT. Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landsc Ecol. 2013;28(2):271-83. https://doi.org/10.1007/s10980-012-9833-1.
  10. Cushman SA, Huettmann F. Spatial complexity, informatics, and wildlife conservation: Springer; 2010. https://doi.org/10.1007/978-4-431-87771-4.
  11. Dormann CF. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Glob Ecol Biogeogr. 2007;16(2):129-38. https://doi.org/10.1111/j.1466-8238.2006.00279.x.
  12. Essa W, van der Kwast J, Verbeiren B, Batelaan O. Downscaling of thermal images over urban areas using the land surface temperature-impervious percentage relationship. Int J Appl Earth Observ Geoinform. 2013;23:95-108. https://doi.org/10.1016/j.jag.2012.12.007.
  13. Estoque RC, Murayama Y, Myint SW. Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia. Sci Total Environ. 2017;577:349-59. https://doi.org/10.1016/j.scitotenv.2016.10.195.
  14. Fan C, Myint S. A comparison of spatial autocorrelation indices and landscape metrics in measuring urban landscape fragmentation. Landscape Urban Planning. 2014;121:117-28. https://doi.org/10.1016/j.landurbplan.2013.10.002.
  15. Fan C, Myint SW, Zheng B. Measuring the spatial arrangement of urban vegetation and its impacts on seasonal surface temperatures. Prog Phys Geography. 2015;39(2):199-219. https://doi.org/10.1177/0309133314567583.
  16. Ferreira PA, Boscolo D, Lopes LE, Carvalheiro LG, Biesmeijer JC, da Rocha PLB, et al. Forest and connectivity loss simplify tropical pollination networks. Oecologia. 2020;192(2):577-90. https://doi.org/10.1007/s00442-019-04579-7.
  17. Frazier AE, Kedron P. Landscape metrics: past progress and future directions. Curr Landscape Ecol Reports. 2017;2(3):63-72. https://doi.org/10.1007/s40823-017-0026-0.
  18. Gluch R. Urban growth detection using texture analysis on merged Landsat TM and SPOT-P data. Photogram Eng Remote Sensing. 2002;68(12):1283-8.
  19. Guo G, Wu Z, Chen Y. Complex mechanisms linking land surface temperature to greenspace spatial patterns: Evidence from four southeastern Chinese cities. Sci Total Environ. 2019;674:77-87. https://doi.org/10.1016/j.scitotenv.2019.03.402.
  20. Haralick RM, Shanmugam K. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;6(6):610-21. https://doi.org/10.1109/TSMC.1973.4309314.
  21. Hofmann S, Everaars J, Schweiger O, Frenzel M, Bannehr L, Cord AF. Modelling patterns of pollinator species richness and diversity using satellite image texture. PLoS One. 2017;12(10):e0185591. https://doi.org/10.1371/journal.pone.0185591.
  22. Jaeger JA. Landscape division, splitting index, and effective mesh size: new measures of landscape fragmentation. Landsc Ecol. 2000;15(2):115-30. https://doi.org/10.1023/A:1008129329289.
  23. Kedron PJ, Frazier AE, Ovando-Montejo GA, Wang J. Surface metrics for landscape ecology: A comparison of landscape models across ecoregions and scales. Landsc Ecol. 2018;33(9):1489-504. https://doi.org/10.1007/s10980-018-0685-1.
  24. Kong F, Yin H, James P, Hutyra LR, He HS. Effects of spatial pattern of greenspace on urban cooling in a large metropolitan area of eastern China. Landscape Urban Planning. 2014;128:35-47. https://doi.org/10.1016/j.landurbplan.2014.04.018.
  25. Li C, Zhao J, Thinh NX, Yang W, Li Z. Analysis of the spatiotemporally varying effects of urban spatial patterns on land surface temperatures. Journal of Environ Eng Landscape Manag. 2018;26(3):216-31. https://doi.org/10.3846/jeelm.2018.5378.
  26. Li H, Wu J. Use and misuse of landscape indices. Landsc Ecol. 2004;19(4):389-99. https://doi.org/10.1023/B:LAND.0000030441.15628.d6.
  27. Li J, Narayanan RM. Integrated spectral and spatial information mining in remote sensing imagery. IEEE Trans Geosci Remote Sensing. 2004;42(3):673-85. https://doi.org/10.1109/TGRS.2004.824221.
  28. Li J, Song C, Cao L, Zhu F, Meng X, Wu J. Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China. Remote Sens Environ. 2011;115(12):3249-63. https://doi.org/10.1016/j.rse.2011.07.008.
  29. Li X, Zhou W, Ouyang Z, Xu W, Zheng H. Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China. Landsc Ecol. 2012;27(6):887-98. https://doi.org/10.1007/s10980-012-9731-6.
  30. Liu D, Hao S, Liu X, Li B, He S, Warrington D. Effects of land use classification on landscape metrics based on remote sensing and GIS. Environ Earth Sci. 2013;68(8):2229-37. https://doi.org/10.1007/s12665-012-1905-7.
  31. Liu H, Weng Q. Seasonal variations in the relationship between landscape pattern and land surface temperature in Indianapolis, USA. Environ Monit Assess. 2008;144(1):199-219. https://doi.org/10.1007/s10661-007-9979-5.
  32. Lu L, Weng Q, Xiao D, Guo H, Li Q, Hui W. Spatiotemporal variation of surface urban heat islands in relation to land cover composition and configuration: a multi-scale case study of Xi'an, China. Remote Sens (Basel). 2020;12(17):2713. https://doi.org/10.3390/rs12172713.
  33. Ma Q, Wu J, He C. A hierarchical analysis of the relationship between urban impervious surfaces and land surface temperatures: spatial scale dependence, temporal variations, and bioclimatic modulation. Landsc Ecol. 2016;31(5):1139-53. https://doi.org/10.1007/s10980-016-0356-z.
  34. Maimaitiyiming M, Ghulam A, Tiyip T, Pla F, Latorre-Carmona P, Hali U, et al. Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation. ISPRS J Photogram Remote Sensing. 2014;89:59-66. https://doi.org/10.1016/j.isprsjprs.2013.12.010.
  35. Mallick J, Rahman A, Singh CK. Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using nighttime ASTER satellite data in highly urbanizing city, Delhi-India. Adv Space Res. 2013;52(4):639-55. https://doi.org/10.1016/j.asr.2013.04.025.
  36. Masoudi M, Tan PY. Multi-year comparison of the effects of spatial pattern of urban green spaces on urban land surface temperature. Landscape Urban Planning. 2019;184:44-58. https://doi.org/10.1016/j.landurbplan.2018.10.023.
  37. Masoudi M, Tan PY, Liew SC. Multi-city comparison of the relationships between spatial pattern and cooling effect of urban green spaces in four major Asian cities. Ecol Indic. 2019;98:200-13. https://doi.org/10.1016/j.ecolind.2018.09.058.
  38. McGarigal K, Cushman SA, Neel MC, Ene E. FRAGSTATS: spatial pattern analysis program for categorical maps; 2002.
  39. Myint SW, Brazel A, Okin G, Buyantuyev A. Combined effects of impervious surface and vegetation cover on air temperature variations in a rapidly expanding desert city. GIScience Remote Sensing. 2010;47(3):301-20. https://doi.org/10.2747/1548-1603.47.3.301.
  40. Neel MC, McGarigal K, Cushman SA. Behavior of class-level landscape metrics across gradients of class aggregation and area. Landsc Ecol. 2004;19(4):435-55. https://doi.org/10.1023/B:LAND.0000030521.19856.cb.
  41. Nole G, Lasaponara R, Lanorte A, Murgante B. Quantifying urban sprawl with spatial autocorrelation techniques using multi-temporal satellite data. Int J Agric Environ Inform Sys. 2014;5(2):19-37. https://doi.org/10.4018/IJAEIS.2014040102.
  42. Osborne PE, Alvares-Sanches T. Quantifying how landscape composition and configuration affect urban land surface temperatures using machine learning and neutral landscapes. Comput Environ Urban Systems. 2019;76:80-90. https://doi.org/10.1016/j.compenvurbsys.2019.04.003.
  43. Park Y, Guldmann J-M. Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: an alternative to patch metrics? Ecol Indic. 2020;109:105802. https://doi.org/10.1016/j.ecolind.2019.105802.
  44. Rocchini D, Foody GM, Nagendra H, Ricotta C, Anand M, He KS, et al. Uncertainty in ecosystem mapping by remote sensing. Comput Geosci. 2013;50:128-35. https://doi.org/10.1016/j.cageo.2012.05.022.
  45. Sahana M, Ahmed R, Sajjad H. Analyzing land surface temperature distribution in response to land use/land cover change using split window algorithm and spectral radiance model in Sundarban Biosphere Reserve, India. Model Earth Syst Environ. 2016;2(2):81. https://doi.org/10.1007/s40808-016-0135-5.
  46. Saura S, Castro S. Scaling functions for landscape pattern metrics derived from remotely sensed data: Are their subpixel estimates really accurate? ISPRS J Photogram Remote Sensing. 2007;62(3):201-16. https://doi.org/10.1016/j.isprsjprs.2007.03.004.
  47. Shao G, Wu J. On the accuracy of landscape pattern analysis using remote sensing data. Landsc Ecol. 2008;23(5):505-11. https://doi.org/10.1007/s10980-008-9215-x.
  48. Simova P, Gdulova K. Landscape indices behavior: a review of scale effects. Appl Geography. 2012;34:385-94. https://doi.org/10.1016/j.apgeog.2012.01.003.
  49. Sodoudi S, Shahmohamadi P, Vollack K, Cubasch U, Che-Ani A. Mitigating the urban heat island effect in megacity Tehran. Adv Meteorol. 2014;2014:1-19. https://doi.org/10.1155/2014/547974.
  50. Song J, Du S, Feng X, Guo L. The relationships between landscape compositions and land surface temperature: Quantifying their resolution sensitivity with spatial regression models. Landscape Urban Planning. 2014;123:145-57. https://doi.org/10.1016/j.landurbplan.2013.11.014.
  51. St-Louis V, Pidgeon AM, Kuemmerle T, Sonnenschein R, Radeloff VC, Clayton MK, et al. Modelling avian biodiversity using raw, unclassified satellite imagery. Phil Trans Royal Soc London B Biol Sci. 2014;369(1643):20130197. https://doi.org/10.1098/rstb.2013.0197.
  52. Tuanmu MN, Jetz W. A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling. Glob Ecol Biogeogr. 2015;24(11):1329-39. https://doi.org/10.1111/geb.12365.
  53. Turner, M. G., Gardner, R. H., O'neill, R. V., 2001, Landscape ecology in theory and practice (Vol.401). New York: Springer.
  54. Tuttle EM, Jensen RR, Formica VA, Gonser RA. Using remote sensing image texture to study habitat use patterns: a case study using the polymorphic white-throated sparrow (Zonotrichia albicollis). Glob Ecol Biogeogr. 2006;15(4):349-57. https://doi.org/10.1111/j.1466-822X.2006.00232.x.
  55. Wood EM, Pidgeon AM, Radeloff VC, Keuler NS. Image texture predicts avian density and species richness. PLoS One. 2013;8(5):e63211. https://doi.org/10.1371/journal.pone.0063211.
  56. Wu C, Li J, Wang C, Song C, Haase D, Breuste J, et al. Estimating the Cooling Effect of Pocket Green Space in High Density Urban Areas in Shanghai, China. Front Environ Sci. 2021;9:181. https://doi.org/10.3389/fenvs.2021.657969.
  57. Wu J, Shen W, Sun W, Tueller PT. Empirical patterns of the effects of changing scale on landscape metrics. Landsc Ecol. 2002;17(8):761-82. https://doi.org/10.1023/A:1022995922992.
  58. Wu Q, Tan J, Guo F, Li H, Chen S. Multi-scale relationship between land surface temperature and landscape pattern based on wavelet coherence: the case of metropolitan Beijing, China. Remote Sens (Basel). 2019;11(24):3021. https://doi.org/10.3390/rs11243021.
  59. Xiao R-B, Ouyang Z-Y, Zheng H, Li W-F, Schienke EW, Wang X-K. Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. J Environ Sci. 2007;19(2):250-6. https://doi.org/10.1016/S1001-0742(07)60041-2.
  60. Xie M, Wang Y, Chang Q, Fu M, Ye M. Assessment of landscape patterns affecting land surface temperature in different biophysical gradients in Shenzhen, China. Urban Ecosyst. 2013;16(4):871-86. https://doi.org/10.1007/s11252-013-0325-0.
  61. Yue W, Xu J, Tan W, Xu L. The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data. Int J Remote Sensing. 2007;28(15):3205-26. https://doi.org/10.1080/01431160500306906.
  62. Zhang X, Zhong T, Feng X, Wang K. Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing. Int J Remote Sensing. 2009;30(8):2105-18. https://doi.org/10.1080/01431160802549252.
  63. Zhang Y, Odeh IO, Ramadan E. Assessment of land surface temperature in relation to landscape metrics and fractional vegetation cover in an urban/peri-urban region using Landsat data. Int J Remote Sensing. 2013;34(1):168-89. https://doi.org/10.1080/01431161.2012.712227.
  64. Zheng B, Myint SW, Fan C. Spatial configuration of anthropogenic land cover impacts on urban warming. Landscape Urban Planning. 2014;130:104-11. https://doi.org/10.1016/j.landurbplan.2014.07.001.
  65. Zhibin R, Haifeng Z, Xingyuan H, Dan Z, Xingyang Y. Estimation of the relationship between urban vegetation configuration and land surface temperature with remote sensing. J Indian Soc Remote Sensing. 2015;43(1):89-100. https://doi.org/10.1007/s12524-014-0373-9.
  66. Zhou W, Huang G, Cadenasso ML. Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape Urban Planning. 2011;102(1):54-63. https://doi.org/10.1016/j.landurbplan.2011.03.009.
  67. Zhou W, Wang J, Cadenasso ML. Effects of the spatial configuration of trees on urban heat mitigation: A comparative study. Remote Sens Environ. 2017;195:1-12. https://doi.org/10.1016/j.rse.2017.03.043.