Abstract
Urbanization has led to the rapid and large-scale changes in land use and land cover and has affected the spatial distribution of land surface temperature (LST) in urban areas. Studying the LST pattern and their spatial heterogeneity characteristics at different scales can help understand the dynamic mechanism of the thermal landscape and provide insights into urban ecological planning. We utilized transfer matrixes, landscape metrics, and spatial autocorrelation analyses to study the transfer of LST classes, changes in the LST pattern, and changes in LST clusters with varying grain sizes by taking the central urban districts of Hangzhou City in China as a case study. Results indicate that (1) the transfer proportion of the LST classes increased, except for high-temperature class, and each LST class shifted to the adjacent dominant LST class with the increase in grain size. (2) The landscape metrics remarkably changed as the grain size increased, indicating that the LST pattern was scale-dependent. As the grain size increased, the small patches gradually merged into large patches; the fragmentation, complexity, and ductility of the urban thermal landscapes decreased; and the shape of the patches became simple and regular. (3) The LST pattern exhibited a positive spatial autocorrelation. The area of low–low cluster decreased, whereas that of non-significant clusters substantially increased with the grain size. The area of high–high cluster remained steady when the grain size exceeded 90 m. (4) Patch density, mean patch fractal dimension, clumpiness index, and contagion index exhibited predictable responses to changing grain size, whereas Shannon’s diversity and Shannon’s evenness indexes showed erratic responses, indicating that the diversity and evenness of the LST pattern were not scale-dependent. (5) The suitable domain of scale for the analysis of LST pattern was (60, 120), and the optimal grain size was 120 m. The selection of domains of scale and optimal grain size need to be determined according to the changes in thermal landscape patterns at different grain sizes and regional environments.
Similar content being viewed by others
References
Alhamad MN, Alrababah MA, Feagin RA, Gharaibeh A (2011) Mediterranean drylands: the effect of grain size and domain of scale on landscape metrics. Ecol Indic 11(2):611–621. https://doi.org/10.1016/j.ecolind.2010.08.007
Anniballe R, Bonafoni S, Pichierri M (2014) Spatial and temporal trends of the surface and air heat island over Milan using MODIS data. Remote Sens Environ 150:163–171. https://doi.org/10.1016/j.rse.2014.05.005
Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27(2):93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Anselin L (2013) Spatial econometrics: methods and models, vol 4. Springer Science & Business Media, Berlin
Argañaraz JP, Entraigas I (2014) Scaling functions evaluation for estimation of landscape metrics at higher resolutions. Ecol Inform 22:1–12. https://doi.org/10.1016/j.ecoinf.2014.02.004
Asgarian A, Amiri BJ, Sakieh Y (2015) Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosyst 18(1):209–222. https://doi.org/10.1007/s11252-014-0387-7
Barbieri T, Despini F, Teggi S (2018) A multi-temporal analyses of land surface temperature using Landsat-8 data and open source software: The case study of Modena, Italy. Sustainability 10 (5):1678. https://doi.org/10.3390/su10051678
Bu R, Hu Y, Chang Y, Li X, He H (2005) A correlation analysis on landscape metrics. Acta Ecol Sin 25(10):2764–2775. https://doi.org/10.3321/j.issn:1000-0933.2005.10.044
Chen A, Yao L, Sun R, Chen L (2014) How many metrics are required to identify the effects of the landscape pattern on land surface temperature? Ecol Indic 45:424–433. https://doi.org/10.1016/j.ecolind.2014.05.002
Chou YH (1991) Map resolution and spatial autocorrelation. Geogr Anal 23(3):228–246. https://doi.org/10.1111/j.1538-4632.1991.tb00236.x
Chun B, Guldmann JM (2014) Spatial statistical analysis and simulation of the urban heat island in high-density central cities. Landsc Urban Plan 125:76–88. https://doi.org/10.1016/j.landurbplan.2014.01.016
Corry RC, Lafortezza R (2007) Sensitivity of landscape measurements to changing grain size for fine-scale design and management. Landsc Ecol Eng 3(1):47–53. https://doi.org/10.1007/s11355-006-0015-7
Dark SJ, Bram D (2007) The modifiable areal unit problem (MAUP) in physical geography. Prog Phys Geogr 31(5):471–479. https://doi.org/10.1177/0309133307083294
Deng C, Wu C (2013) Examining the impacts of urban biophysical compositions on surface urban heat island: a spectral unmixing and thermal mixing approach. Remote Sens Environ 131:262–274. https://doi.org/10.1016/j.rse.2012.12.020
Fan C, Myint S (2014) A comparison of spatial autocorrelation indices and landscape metrics in measuring urban landscape fragmentation. Landsc Urban Plan 121:117–128. https://doi.org/10.1016/j.landurbplan.2013.10.002
Fialkowski M, Bitner A (2008) Universal rules for fragmentation of land by humans. Landsc Ecol 23(9):1013–1022. https://doi.org/10.1007/s10980-008-9268-x
Göttsche FM, Olesen FS, Trigo I, Bork-Unkelbach A, Martin M (2016) Long term validation of land surface temperature retrieved from MSG/SEVIRI with continuous in-situ measurements in Africa. Remote Sens 8(5):410. https://doi.org/10.3390/rs8050410
Guo G, Chen Y, Wei J, Wu Z, Rong X (2012) Impacts of grid sizes on urban heat island pattern analysis. Acta Ecol Sin 32:3764–3772. https://doi.org/10.5846/stxb201107181068
Guo G, Wu Z, Xiao R, Chen Y, Liu X, Zhang X (2015) Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landsc Urban Plan 135:1–10. https://doi.org/10.1016/j.landurbplan.2014.11.007
Guo G, Zhou X, Wu Z, Xiao R, Chen Y (2016) Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou, China. Environ Model Softw 84:427–439. https://doi.org/10.1016/j.envsoft.2016.06.021
Kindu M, Schneider T, Teketay D, Knoke T (2013) Land use/land cover change analysis using object-based classification approach in Munessa-Shashemene landscape of the Ethiopian highlands. Remote Sens 5(5):2411–2435. https://doi.org/10.3390/rs5052411
Kuang W, Liu Y, Dou Y, Chi W, Chen G, Gao C, Yang T, Liu J, Zhang R (2015) What are hot and what are not in an urban landscape: quantifying and explaining the land surface temperature pattern in Beijing, China. Landsc Ecol 30(2):357–373. https://doi.org/10.1007/s10980-014-0128-6
Lai LW, Cheng WL (2009) Air quality influenced by urban heat island coupled with synoptic weather patterns. Sci Total Environ 407(8):2724–2733. https://doi.org/10.1016/j.scitotenv.2008.12.002
Lam NSN, Quattrochi DA (1992) On the issues of scale, resolution, and fractal analysis in the mapping sciences. Prof Geogr 44(1):88–98. https://doi.org/10.1111/j.0033-0124.1992.00088.x
Levin SA (1992) The problem of pattern and scale in ecology. Ecology 73(6):1943–1967. https://doi.org/10.1007/978-1-4615-1769-6_15
Li H, Calder CA, Cressie N (2007) Beyond Moran’s I: testing for spatial dependence based on the spatial autoregressive model. Geogr Anal 39(4):357–375. https://doi.org/10.1111/j.1538-4632.2007.00708.x
Li J, Song C, Cao L, Zhu F, Meng X, Wu J (2011) Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sens Environ 115(12):3249–3263. https://doi.org/10.1016/j.rse.2011.07.008
Li X, Zhou W, Ouyang Z (2013) Relationship between land surface temperature and spatial pattern of greenspace: what are the effects of spatial resolution? Landsc Urban Plan 114:1–8. https://doi.org/10.1016/j.landurbplan.2013.02.005
Li J, Zheng X, Zhang C, Chen Y (2018) Impact of land-use and land-cover change on meteorology in the Beijing–Tianjin–Hebei Region from 1990 to 2010. Sustainability 10(1):176. https://doi.org/10.3390/su10010176
Liu H, Weng Q (2009) An examination of the effect of landscape pattern, land surface temperature, and socioeconomic conditions on WNV dissemination in Chicago. Environ Monit Assess 159:143–161. https://doi.org/10.1016/10.1007/s10661-008-0618-6
Madanian M, Soffianian AR, Koupai SS, Pourmanafi S, Momeni M (2018) Analyzing the effects of urban expansion on land surface temperature patterns by landscape metrics: a case study of Isfahan city, Iran. Environ Monit Assess 190(4):189. https://doi.org/10.1007/s10661-018-6564-z
Majumdar DD, Biswas A (2016) Quantifying land surface temperature change from LISA clusters: an alternative approach to identifying urban land use transformation. Landsc Urban Plan 153:51–65. https://doi.org/10.1016/j.landurbplan.2016.05.001
McGarigal K, Marks BJ (1995) FRAGSTATS: spatial analysis program for quantifying landscape structure. USDA Forest Service General Technical Report PNW-GTR, 351
McGarigal K, Cushman SA, Neel MC, Ene E (2002) FRAGSTATS: spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: www.umass.edu/landeco/research/fragstats/fragstats.html. Accessed 18 April 2020
Moran PA (1950) Notes on continuous stochastic phenomena. Biometrika 37(1/2):17–23. https://doi.org/10.2307/2332142
Nichol JE, Fung WY, Lam KS, Wong MS (2009) Urban heat island diagnosis using ASTER satellite images and ‘in situ’ air temperature. Atmos Res 94(2):276–284. https://doi.org/10.1016/j.atmosres.2009.06.011
Nie Q, Xu J (2015) Understanding the effects of the impervious surfaces pattern on land surface temperature in an urban area. Front Earth Sci 9(2):276–285. https://doi.org/10.1007/s11707-014-0459-2
Oke TR (1982) The energetic basis of the urban heat island. Q J R Meteorol Soc 108(455):1–24. https://doi.org/10.1002/qj.49710845502
Peng J, Jia J, Liu Y, Li H, Wu J (2018) Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas. Remote Sens Environ 215:255–267. https://doi.org/10.1016/j.rse.2018.06.010
Rad HD, Assarehzadegan MA, Goudarzi G et al (2019) Do Conocarpus erectus airborne pollen grains exacerbate autumnal thunderstorm asthma attacks in Ahvaz, Iran? Atmos Environ 213:311–325. https://doi.org/10.1016/j.atmosenv.2019.06.010
Teng M, Zeng L, Zhou Z, Wang P, Xiao W, Dian Y (2016) Responses of landscape metrics to altering grain size in the Three Gorges Reservoir landscape in China. Environ Earth Sci 75(13):1055–1013. https://doi.org/10.1007/s12665-016-5605-6
Tian P, Cao L, Li J, Pu R, Shi X, Wang L, Liu R, Xu H, Tong C, Zhou Z, Shao S (2019) Landscape grain effect in Yancheng coastal wetland and its response to landscape changes. Int J Environ Res Public Health 16(12):2225. https://doi.org/10.3390/ijerph16122225
Tobler WR (1970) A computer model simulation of urban growth in the Detroit region. Econ Geogr 46:234–240. https://doi.org/10.2307/143141
Vargo J, Stone B, Habeeb D, Liu P, Russell A (2016) The social and spatial distribution of temperature-related health impacts from urban heat island reduction policies. Environ Sci Pol 66:366–374. https://doi.org/10.1016/j.envsci.2016.08.012
Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384. https://doi.org/10.1016/s0034-4257(03)00079-8
Wang H, Zhang Y, Tsou J, Li Y (2017) Surface urban heat island analysis of Shanghai (China) based on the change of land use and land cover. Sustainability 9(9):1538. https://doi.org/10.3390/su9091538
Wheatley M (2010) Domains of scale in forest-landscape metrics: implications for species-habitat modeling. Acta Oecol 36(2):259–267. https://doi.org/10.1016/j.actao.2009.12.003
Wongsai N, Wongsai S, Huete A (2017) Annual seasonality extraction using the cubic spline function and decadal trend in temporal daytime MODIS LST data. Remote Sens 9(12):1254. https://doi.org/10.3390/rs9121254
Wu J (1999) Hierarchy and scaling: extrapolating information along a scaling ladder. Can J Remote Sens 25(4):367–380. https://doi.org/10.1080/07038992.1999.10874736
Wu J (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landsc Ecol 19(2):125–138. https://doi.org/10.1023/b:land.0000021711.40074.ae
Wu J, Shen W, Sun W, Tueller PT (2002) Empirical patterns of the effects of changing scale on landscape metrics. Landsc Ecol 17(8):761–782. https://doi.org/10.1023/a:1022995922992
Xue Y, Fung T, Tsou J (2014) Urban thermal landscape characterization and analysis. IOP Conf Ser: Earth Environ Sci 17(1):012164. https://doi.org/10.1088/1755-1315/17/1/012164
Yi J, Tian Y, Zhu L, Gao Y, Wang B (2008) Remote sensing-based research of urban thermodynamic landscape heterogeneity and spatial scale effect. Proc. SPIE 7083. https://doi.org/10.1117/12.795911
Yu X, Guo X, Wu Z (2014) Land surface temperature retrieval from Landsat 8 TIRS—comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sens 6(10):9829–9852. https://doi.org/10.3390/rs6109829
Yuan S, Zhu C, Yang L, Xie F (2019) Responses of ecosystem services to urbanization-induced land use changes in ecologically sensitive suburban areas in Hangzhou, China. Int J Environ Res Public Health 16(7):1124. https://doi.org/10.3390/ijerph16071124
Zhang Y, Odeh IO, Ramadan E (2013) 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 Sens 34(1):168–189. https://doi.org/10.1080/01431161.2012.712227
Zhang X, Estoque RC, Murayama Y (2017) An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables. Sustain Cities Soc 32:557–568. https://doi.org/10.1016/j.scs.2017.05.005
Zhao XF, Deng L, Wang H, Hua L, Chen F (2014) Landscape classifications for landscape metrics-based assessment of urban heat island: a comparative study. In: IOP conference series: earth and environmental science IOP Publishing https://doi.org/10.1088/1755-1315/17/1/012155
Zhao H, Ren Z, Tan J (2018) The spatial patterns of land surface temperature and its impact factors: spatial non-stationarity and scale effects based on a geographically-weighted regression model. Sustainability 10(7):2242. https://doi.org/10.3390/su10072242
Funding
This research is supported by the National Natural Science Foundation of China (No. 41871181), the Project of Human Social Science of the Ministry of Education of China (No. 18YJA630136, 19YJA630099), and the Philosophy and Social Sciences Study Foundation of Zhejiang Province (No. 19NDJC015Z).
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editor: Marcus Schulz
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yuan, S., Xia, H. & Yang, L. How changing grain size affects the land surface temperature pattern in rapidly urbanizing area: a case study of the central urban districts of Hangzhou City, China. Environ Sci Pollut Res 28, 40060–40074 (2021). https://doi.org/10.1007/s11356-020-08672-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-020-08672-w