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Landscape Ecology

, Volume 33, Issue 7, pp 1133–1145 | Cite as

Application of partial least squares regression in detecting the important landscape indicators determining urban land surface temperature variation

  • Yanxu Liu
  • Jian Peng
  • Yanglin Wang
Research Article

Abstract

Context

Revealing the interaction between landscape pattern and urban land surface temperature (LST) can provide insight into mitigating thermal environmental risks. However, there is no consensus about the key landscape indicators influencing LST.

Objectives

This study sought to identify the key landscape indicators influencing LST considering a large number of landscape pattern variables and multiple scales.

Methods

This study applied ordinary least squares regression and partial least squares regression to explore a combination of landscape metrics and identify the key indicators influencing LST. A total of 49 Landsat images of the main city of Shenzhen, China were examined at 13 spatial scales.

Results

The landscape composition indicators derived from biophysical proportion, a new metric developed in this study, more effectively determined LST variation than those derived from land cover proportion. Area-related landscape configuration indicators independently characterized LST variation, but did not give much more new information beyond that given by land cover proportion. Shape-related landscape configuration indicators were effective in combination with land cover proportion, but their importance was uncertain when temporal and spatial scales varied.

Conclusions

The influence of landscape configuration on LST exists but should not be overestimated. Comparison of numerous variables at multiple spatiotemporal scales can help identify the influence of multiple landscape characteristics on LST variation.

Keywords

Land cover proportion Biophysical parameters Landscape configuration Partial least squares regression 

Notes

Acknowledgements

This research was financially supported by National Natural Science Foundation of China (41671182).

Supplementary material

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References

  1. Abdi H (2010) Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip Rev 2(1):97–106CrossRefGoogle Scholar
  2. Ai L, Shi ZH, Yin W, Huang X (2015) Spatial and seasonal patterns in stream water contamination across mountainous watersheds: linkage with landscape characteristics. J Hydrol 523:398–408CrossRefGoogle Scholar
  3. Chen AL, Yao L, Sun RH, Chen LD (2014a) How many metrics are required to identify the effects of the landscape pattern on land surface temperature? Ecol Indic 45:424–433CrossRefGoogle Scholar
  4. Chen AL, Yao XA, Sun RH, Chen LD (2014b) Effect of urban green patterns on surface urban cool islands and its seasonal variations. Urban Urban Green 13(4):646–654CrossRefGoogle Scholar
  5. Deng CB, Wu CS (2012) BCI: a biophysical composition index for remote sensing of urban environments. Remote Sens Environ 127:247–259CrossRefGoogle Scholar
  6. Du SH, Xiong ZQ, Wang YC, Guo L (2016) Quantifying the multilevel effects of landscape composition and configuration on land surface temperature. Remote Sens Environ 178:84–92CrossRefGoogle Scholar
  7. Eriksson L, Hermens JLM, Johansson E, Verhaar HJM, Wold S (1995) Multivariate-analysis of aquatic toxicity data with PLS. Aquat Sci 57(3):217–241CrossRefGoogle Scholar
  8. Estoque RC, Murayama Y, Myint SW (2017) Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia. Sci Total Environ 577:349–359CrossRefPubMedGoogle Scholar
  9. Feyisa GL, Meilby H, Fensholt R, Proud SR (2014) Automated Water Extraction Index: a new technique for surface water mapping using Landsat imagery. Remote Sens Environ 140:23–35CrossRefGoogle Scholar
  10. Geladi P, Kowalski BR (1986) Partial least-squares regression - a tutorial. Anal Chim Acta 185:1–17CrossRefGoogle Scholar
  11. Gustafson EJ (1998) Quantifying landscape spatial pattern: what is the state of the art? Ecosystems 1(2):143–156CrossRefGoogle Scholar
  12. Lee X, Goulden ML, Hollinger DY, Barr A, Black TA, Bohrer G, Bracho R, Drake B, Goldstein A, Gu L, Katul G (2011) Observed increase in local cooling effect of deforestation at higher latitudes. Nature 479(7373):384–387CrossRefPubMedGoogle Scholar
  13. Li XX, Li WW, Middel A, Harlan SL, Brazel AJ, Turner BL (2016) Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: combined effects of land composition and configuration and cadastral-demographic-economic factors. Remote Sens Environ 174:233–243CrossRefGoogle Scholar
  14. Li JX, Song CH, Cao L, Zhu FG, Meng XL, Wu JG (2011) Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sens Environ 115(12):3249–3263CrossRefGoogle Scholar
  15. Li Y, Zhao MS, Motesharrei S, Mu QZ, Kalnay E, Li SC (2015) Local cooling and warming effects of forests based on satellite observations. Nat Commun 6Google Scholar
  16. Li XM, Zhou WQ, Ouyang ZY (2013) Relationship between land surface temperature and spatial pattern of greenspace: what are the effects of spatial resolution? Landsc Urban Plan 114:1–8CrossRefGoogle Scholar
  17. Liu Y, Peng J, Wang Y (2017) Diversification of land surface temperature change under urban landscape renewal: a case study in the main city of Shenzhen, China. Remote Sens-Basel 9(9):919CrossRefGoogle Scholar
  18. Liu ZH, Wang YL, Li ZG, Peng J (2013) Impervious surface impact on water quality in the process of rapid urbanization in Shenzhen, China. Environ Earth Sci 68(8):2365–2373CrossRefGoogle Scholar
  19. Ma Y, Kuang YQ, Huang NS (2010) Coupling urbanization analyses for studying urban thermal environment and its interplay with biophysical parameters based on TM/ETM plus imagery. Int J Appl Earth Obs 12(2):110–118CrossRefGoogle Scholar
  20. McGarigal K, Marks BJ (1995) Spatial pattern analysis program for quantifying landscape structure. Gen. Tech. Rep. PNW-GTR-351. US Department of Agriculture, Forest Service, Pacific Northwest Research StationGoogle Scholar
  21. Monk WA, Wilbur NM, Curry RA, Gagnon R, Faux RN (2013) Linking landscape variables to cold water refugia in rivers. J Environ Manag 118:170–176CrossRefGoogle Scholar
  22. Peng J, Wang YL, Zhang Y, Wu JS, Li WF, Li Y (2010) Evaluating the effectiveness of landscape metrics in quantifying spatial patterns. Ecol Indic 10(2):217–223CrossRefGoogle Scholar
  23. Peng J, Xie P, Liu YX, Ma J (2016) Urban thermal environment dynamics and associated landscape pattern factors: a case study in the Beijing metropolitan region. Remote Sens Environ 173:145–155CrossRefGoogle Scholar
  24. Peng SS, Piao SL, Zeng ZZ, Ciais P, Zhou L, Li LZ, Zeng H (2014) Afforestation in China cools local land surface temperature. Proc Natl Acad Sci USA 111(8):2915–2919CrossRefPubMedGoogle Scholar
  25. Pickett STA, Cadenasso ML (1995) Landscape Ecology—spatial heterogeneity in ecological-systems. Science 269(5222):331–334CrossRefPubMedGoogle Scholar
  26. Rozenstein O, Paz-Kagan T, Salbach C, Karnieli A (2015) Comparing the effect of preprocessing transformations on methods of land-use classification derived from spectral soil measurements. IEEE J-Stars 8(6):2393–2404Google Scholar
  27. Shen HF, Huang LW, Zhang LP, Wu PH, Zeng C (2016) Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: a 26-year case study of the city of Wuhan in China. Remote Sens Environ 172:109–125CrossRefGoogle Scholar
  28. Sun RH, Chen LD (2017) Effects of green space dynamics on urban heat islands: mitigation and diversification. Ecosyst Serv 23:38–46CrossRefGoogle Scholar
  29. Townshend JRG, Goff TE, Tucker CJ (1985) Multitemporal dimensionality of images of normalized difference vegetation index at continental scales. IEEE Trans Geosci Remote 23(6):888–895CrossRefGoogle Scholar
  30. Turner MG, O’Neill RV, Gardner RH, Milne BT (1989) Effects of changing spatial scale on the analysis of landscape pattern. Landscape Ecol 3(3–4):153–162CrossRefGoogle Scholar
  31. Uuemaa E, Mander U, Marja R (2013) Trends in the use of landscape spatial metrics as landscape indicators: a review. Ecol Indic 28:100–106CrossRefGoogle Scholar
  32. Weng QH (2003) Fractal analysis of satellite-detected urban heat island effect. Photogramm Eng Rem Sens 69(5):555–566CrossRefGoogle Scholar
  33. Wilson EH, Sader SA (2002) Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens Environ 80(3):385–396CrossRefGoogle Scholar
  34. Wu JG (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecol 19(2):125–138CrossRefGoogle Scholar
  35. Wu JG (2013) Landscape sustainability science: ecosystem services and human well-being in changing landscapes. Landscape Ecol 28(6):999–1023CrossRefGoogle Scholar
  36. Wu ZF, Kong FH, Wang YN, Sun RH, Chen LD (2016) The impact of greenspace on thermal comfort in a residential quarter of Beijing, China. Int J Environ Res Public Health 13(12):1217CrossRefPubMedCentralGoogle Scholar
  37. Xiao RB, Ouyang ZY, Zheng H, Li WF, Schienke EW, Wang XK (2007) Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. J Environ Sci 19(2):250–256CrossRefGoogle Scholar
  38. Xie MM, Wang YL, Chang Q, Fu MC, Ye MT (2013) Assessment of landscape patterns affecting land surface temperature in different biophysical gradients in Shenzhen, China. Urban Ecosyst 16(4):871–886CrossRefGoogle Scholar
  39. Yu WJ, Zhou WQ (2017) The spatiotemporal pattern of urban expansion in China: a comparison study of three urban megaregions. Remote Sens-Basel 9(1):45CrossRefGoogle Scholar
  40. Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 24(3):583–594CrossRefGoogle Scholar
  41. Zhou WQ, Huang GL, Cadenasso ML (2011) Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landsc Urban Plan 102(1):54–63CrossRefGoogle Scholar
  42. Zhou WQ, Pickett STA, Cadenasso ML (2017a) Shifting concepts of urban spatial heterogeneity and their implications for sustainability. Landscape Ecol 32(1):15–30CrossRefGoogle Scholar
  43. Zhou WQ, Qian YG, Li XM, Li WF, Han LJ (2014) Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures. Landscape Ecol 29(1):153–167CrossRefGoogle Scholar
  44. Zhou WQ, Wang J, Cadenasso ML (2017b) Effects of the spatial configuration of trees on urban heat mitigation: a comparative study. Remote Sens Environ 195:1–12CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  2. 2.Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental SciencesPeking UniversityBeijingChina

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