LST; LULC; surface urban heat island intensity (SUHII); thermal heat; Contribution indices; Ecological conditions; Land use and land cover; Negative correlation; Positive correlations; Surface urban heat islands; Unsupervised classification; Urban thermal environment; Environmental Science (all); Earth and Planetary Sciences (all); General Earth and Planetary Sciences; General Environmental Science
Abstract :
[en] This study investigates the relationship of urban thermal environment (UTE) with various influential factors as well as ecological conditions. The relation between LST and land use and land cover (LULC) changes was explored in terms of remote-sensing (RS) based indices; heat effect contribution index (HECI), Urban thermal field variance index (UTFVI), Surface urban heat island intensity (SUHII), Normal Difference Built-up Index (NDBI), and Normal Difference Vegetation Index (NDVI). LULC maps were classified using the unsupervised classification technique and made error matrix to determine the accuracy. Results revealed that the vegetated area in Faisalabad decreased by 230km2 due to an expansion in the urban area of 124-320km2 during the period 1992-2014. An average LST in the rural buffers is increasing rapidly as compare to urban buffer and varied over the eight years with a range of 0.68-2.57 (°C). After 2007, SUHII's linear trend was negative because rural temperatures were still rising. Based on HECI, we found that urban expansion mainly led to increase in LST. UTFVI has shown poor ecological conditions in all urban buffers. In addition, there is a positive correlation between LST and NDBI, while NDVI indicates a negative correlation with LST.
Disciplines :
Space science, astronomy & astrophysics
Author, co-author :
Dilawar, Adil; State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Chen, Baozhang; State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China ; University of Chinese Academy of Sciences, Beijing, China ; School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Jiangsu, China ; Jiangsu Center for Collaborative innovation of Geographical Information Resources Development and Application, Nanjing, China
Trisurat, Yongyut; Faculty of Forestry, Kasetsart University, Bangkok, Thailand
Tuankrua, Venus; Faculty of Forestry, Kasetsart University, Bangkok, Thailand
Arshad, Arfan; University of Chinese Academy of Sciences, Beijing, China ; Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China ; Department of Irrigation and Drainage, Faculty of Agricultural Engineering, University of Agriculture Faisalabad, Faisalabad, Pakistan
Hussain, Yawar ; Université de Liège - ULiège > Département de géologie > Géologie de l'environnement ; Environmental Engineering and Earth Science Department, Clemson University, Clemson, United States
Measho, Simon; State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China ; University of Chinese Academy of Sciences, Beijing, China
Guo, Lifeng; State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China ; University of Chinese Academy of Sciences, Beijing, China
Kayiranga, Alphonse; State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China ; University of Chinese Academy of Sciences, Beijing, China
Zhang, Huifang; State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China ; University of Chinese Academy of Sciences, Beijing, China
Wang, Fei; State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China ; University of Chinese Academy of Sciences, Beijing, China
Sun, Shaobo; Institute of Surface-Earth System Science, Tianjin University, Tianjin, China
Language :
English
Title :
Spatiotemporal shifts in thermal climate in responses to urban cover changes: a-case analysis of major cities in Punjab, Pakistan
NSCF - National Natural Science Foundation of China [CN] State Key Laboratory of Resources and Environmental Information System [CN]
Funding text :
The First author wants to thank the United States Geological Survey (USGS) https://earthexplorer.usgs.gov/ for Landsat bands images from 1992 to 2014 and the Chinese Government Scholarship Council (CSC) to support for Ph.D. study.
Ahmed S., 2018. Assessment of urban heat islands and impact of climate change on socioeconomic over Suez Governorate using remote sensing and GIS techniques. Egypt J Remote Sens and Space Science. 21 (1): 15–25.
Amiri R, Weng Q, Alimohammadi A, Alavipanah SK., 2009. Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. " Remote Sens Environ. 113 (12): 2606–2617.
Arshad A, Zhang W, Zaman MA, Dilawar A, Sajid Z., 2019. Monitoring the impacts of spatio-temporal land-use changes on the regional climate of city Faisalabad, Pakistan. Ann Gis. 25 (1): 57–70.
Asmala A., 2012. Analysis of maximum likelihood classification on multispectral data. Appl Math Sci. 6 (129-132): 6425–6436.
Balaghi R, Tychon B, Eerens H, Jlibene M., 2008. Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. Int J Appl Earth Obs Geoinf. 10 (4): 438–452.
Baumert K, Pershing J., 2004. Climate data: insights and observations. Pew Center on Global Climate Change. USA.
Bokaie M, Zarkesh MK, Arasteh PD, Hosseini A., 2016. Assessment of urban heat island based on the relationship between land surface temperature and land use/land cover in Tehran. Sustain Cities Soc. 23: 94–104.
Cai D, Fraedrich K, Guan Y, Guo S, Zhang C., 2017. Urbanization and the thermal environment of Chinese and US-American cities. Sci Total Environ. 589: 200–211.
Cai X, Sharma BR., 2010. Integrating remote sensing, census and weather data for an assessment of rice yield, water consumption and water productivity in the Indo-Gangetic river basin. Agric Water Manage. 97 (2): 309–316.
Cao W, Huang L, Liu L, Zhai J, Wu D., 2019. Overestimating impacts of urbanization on regional temperatures in developing megacity: Beijing as an example. Adv Meteorol. 2019: 1–15. "
Chapin FS, Sturm M, Serreze MC, McFadden JP, Key JR, Lloyd AH, McGuire AD, Rupp TS, Lynch AH, Schimel JP, et al. 2005. Role of land-surface changes in Arctic summer warming. Science. 310 (5748): 657–660.
Chen X-L, Zhao H-M, Li P-X, Yin Z-Y., 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens Environ. 104 (2): 133–146.
Chung U, Choi J, Yun JI., 2004. Urbanization effect on the observed change in mean monthly temperatures between 1951–1980 and 1971–2000 in Korea. Clim Change. 66 (1/2): 127–136.
Cui L, Shi J., 2012. Urbanization and its environmental effects in Shanghai, China. Urban Clim. 2: 1–15.
Dai Z, Guldmann J-M, Hu Y., 2018. Spatial regression models of park and land-use impacts on the urban heat island in central Beijing. Sci Total Environ. 626: 1136–1147.
Dempewolf J, Adusei B, Becker-Reshef I, Hansen M, Potapov P, Khan A, Barker B., 2014. Wheat yield forecasting for Punjab Province from vegetation index time series and historic crop statistics. " Remote Sensing. 6 (10): 9653–9675.
Department of Economics and Social Affairs, U. N. 2019. U.N. World Population Prospects.
Fan H, Yu Z, Yang G, Liu TY, Liu TY, Hung CH, Vejre H., 2019. How to cool hot-humid (Asian) cities with urban trees? An optimal landscape size perspective. Agric Meteorol. 265: 338–348.
Fu P, Weng Q., 2018. Responses of urban heat island in Atlanta to different land-use scenarios. Theor Appl Climatol. 133 (1/2): 123–135.
Goovaerts P., 1997. Geostatistics for natural resources evaluation, Oxford University Press on Demand, Michigan, USA.
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R., 2017. Google Earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ. 202: 18–27.
Guha S, Govil H, Dey A, Gill N., 2018. Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. Eur J Remote Sens. 51 (1): 667–678.
Gunawardena K, Wells M, Kershaw T., 2017. Utilising green and bluespace to mitigate urban heat island intensity. Sci Total Environ. 584-585: 1040–1055.
He C, Shi P, Xie D, Zhao Y., 2010. Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sens Lett. 1 (4): 213–221.
He B-J, Zhao Z-Q, Shen L-D, Wang H-B, Li L-G., 2019. An approach to examining performances of cool/hot sources in mitigating/enhancing land surface temperature under different temperature backgrounds based on landsat 8 image. Sustain Cities Soc. 44: 416–427.
Houghton. 2001. Climate Change 2001. The scientific basis. New York: Cambridge University Press.
Huang Q, Huang J, Yang X, Fang C, Liang Y., 2019. Quantifying the seasonal contribution of coupling urban land use types on Urban Heat Island using Land Contribution Index: a case study in Wuhan, China. Sustain Cities Soc. 44: 666–675.
Ibrahim F, Rasul G., 2017. Urban land use land cover changes and their effect on land surface temperature: Case study using Dohuk City in the Kurdistan Region of Iraq. Climate. 5 (1): 13.
Jain M, Dimri A, Niyogi D., 2017. Land-air interactions over urban-rural transects using satellite observations: analysis over Delhi, India from 1991–2016. Remote Sens. 9 (12): 1283.
Jamei Y, Rajagopalan P, Sun QC., 2019. Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Sci Total Environ. 659: 1335–1351.
Jones P, Lister D, Li Q., 2008. Urbanization effects in large‐scale temperature records, with an emphasis on China. J Geophys Res. 113 (D16122)
Kaplan G, Avdan U, Avdan ZY., 2018. Urban heat island analysis using the landsat 8 satellite data: a case study in Skopje, Macedonia. Multidiscip Digital Publis Instit Proc. 2 (7): 358.
Karakus CB, Kavak KS, Cerit O., 2014. Determination of variations in land cover and land use by remote sensing and geographic information systems around the city of Sivas (Turkey). Fresenius Environ Bull. 23 (3): 667–677.
Karakuş CB., 2019. The impact of land use/land cover (LULC) changes on land surface temperature in sivas city center and its surroundings and assessment of urban heat island. Asia-Pacific J Atmos Sci. 55 (4): 669–616.
Kumar D, Shekhar S., 2015. Statistical analysis of land surface temperature-vegetation indexes relationship through thermal remote sensing. Ecotoxicol Environ Saf. 121: 39–44.
Landsat N., 2016. Landsat Project Science Office.
Landsat. 2019. Science data users handbook. U.S Geological Survey (USGS) Landsat Project Science Office, Sioux Falls, USA.
Li W, Bai Y, Chen Q, He K, Ji X, Han C., 2014. Discrepant impacts of land use and land cover on urban heat islands: A case study of Shanghai, China. Ecol Indic. 47: 171–178.
Lim H, Jafri M, Abdullah K, Alsultan S., 2012. Application of a simple mono window land surface temperature algorithm from Landsat ETM over Al Qassim, Saudi Arabia. Sains Malaysiana. 41 (7): 841–846.
Li Y-B, Shi T, Yang Y-J, Wu B-W, Wang L-B, Shi C-E, Guo J-X, Ji C-L, Wen H-Y., 2015. Satellite-based investigation and evaluation of the observational environment of meteorological stations in Anhui province, China. Pure Appl Geophys. 172 (6): 1735–1749.
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.
Liu W, Ji C, Zhong J, Jiang X, Zheng Z., 2007. Temporal characteristics of the Beijing urban heat island. Theor Appl Climatol. 87 (1-4): 213–221.
Liu K, Su H, Li X, Wang W, Yang L, Liang H., 2016. Quantifying spatial–temporal pattern of urban heat island in Beijing: An improved assessment using land surface temperature (LST) time series observations from LANDSAT, MODIS, and Chinese new satellite GaoFen-1. IEEE J Sel Top Appl Earth Observations Remote Sensing. 9 (5): 2028–2042.
Liu L, Zhang Y., 2011. Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sens. 3 (7): 1535–1552.
Liu F, Zhang X, Murayama Y, Morimoto T., 2020. Impacts of land cover/use on the urban thermal environment: a comparative study of 10 megacities in China. Remote Sens. 12 (2): 307.
Li Y-y, Zhang H, Kainz W., 2012. Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis, China: using time-series of Landsat TM/ETM + data. Int J Appl Earth Obs Geoinf. 19: 127–138.
Li H, Zhou Y, Li X, Meng L, Wang X, Wu S, Sodoudi S., 2018. A new method to quantify surface urban heat island intensity. Sci Total Environ. 624: 262–272.
Li X, Zhou W, Ouyang Z, Xu W, Zheng H., 2012. Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China. Landscape Ecol. 27 (6): 887–898.
Lu D, Song K, Zang S, Jia M, Du J, Ren C., 2015. The effect of urban expansion on urban surface temperature in Shenyang, China: an analysis with landsat imagery. Environ Model Assess. 20 (3): 197–210.
Matthew MW, Adler-Golden SM, Berk A, Richtsmeier SC, Levine RY, Bernstein LS, Acharya PK, Anderson GP, Felde GW, Hoke ML., 2000. Status of atmospheric correction using a MODTRAN4-based algorithm. Algorithms for multispectral, hyperspectral, and ultraspectral imagery VI, International Society for Optics and Photonics.
Mumtaz, Faisal, Yu Tao, Gerrit de Leeuw, Limin Zhao, Cheng Fan, Abdelrazek Elnashar, Barjeece Bashir, Gengke Wang, LingLing Li, and Shahid Naeem. 2020. ‘Modeling Spatio-Temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST)’, Remote Sensing, 12: 2987.
Nichol JE., 1994. A GIS-based approach to microclimate monitoring in Singapore's high-rise housing estates. Photogramm Eng Remote Sens. 60 (10): 1225–1232.
Nimish G, Bharath H, Lalitha A., 2020. Exploring temperature indices by deriving relationship between land surface temperature and urban landscape. Remote Sens Appl: Soc Environ. 18: 100299.
Pan J., 2016. Area delineation and spatial-temporal dynamics of urban heat island in Lanzhou City, China using remote sensing imagery. J Indian Soc Remote Sens. 44 (1): 111–127.
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.
Pramanik S, Punia M., 2020. Land use/land cover change and surface urban heat island intensity: source–sink landscape-based study in Delhi, India. Environ Dev Sustain. 22 (8): 7331–7326. "
Quintano C, Fernández-Manso A, Calvo L, Marcos E, Valbuena L., 2015. Land surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems. Int J Appl Earth Obs Geoinf. 36: 1–12.
Raynolds MK, Comiso JC, Walker DA, Verbyla D., 2008. Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI. Remote Sens Environ. 112 (4): 1884–1894.
Renard F, Alonso L, Fitts Y, Hadjiosif A, Comby J., 2019. Evaluation of the effect of urban redevelopment on surface urban heat islands. Remote Sens. 11 (3): 299.
Rizwan AM, Dennis LY, Chunho L., 2008. A review on the generation, determination and mitigation of urban heat Island. J Environ Sci. 20 (1): 120–128.
Sadiq Khan, Muhammad, Sami Ullah, Tao Sun, Arif UR Rehman, and Liding Chen. 2020. ‘Land-Use/Land-Cover Changes and Its Contribution to Urban Heat Island: A Case Study of Islamabad, Pakistan’, Sustainability, 12: 3861.
Sahana M, Dutta S, Sajjad H., 2019. Assessing land transformation and its relation with land surface temperature in Mumbai city, India using geospatial techniques. Int J Urban Sci. 23 (2): 205–225.
Sajjad SH, Batool R, Qadri ST, Shirazi SA, Shakrullah K., 2015. The long-term variability in minimum and maximum temperature trends and heat island of Lahore city, Pakistan. Sci Int. 27 (2): 1321–1325.
Saleem, Muhammad Sajid, Sajid Rashid Ahmad, and Muhammad Asif Javed. 2020. ‘Impact assessment of urban development patterns on land surface temperature by using remote sensing techniques: a case study of Lahore, Faisalabad and Multan district’, Environmental Science and Pollution Research, 27: 39865–78.
Santamouris M, Ban-Weiss G, Osmond P, Paolini R, Synnefa A, Cartalis C, Muscio A, Zinzi M, Morakinyo TE, Ng E, et al. 2018. Progress in urban greenery mitigation science–assessment methodologies advanced technologies and impact on cities. J Civil Eng Manage. 24 (8): 638–671.
Sekertekin A, Kutoglu SH, Kaya S., 2016. Evaluation of spatio-temporal variability in land surface temperature: a case study of Zonguldak, Turkey. Environ Monit Assess. 188 (1): 30
Shah B, Ghauri B., 2015. Mapping urban heat island effect in comparison with the land use, land cover of Lahore district. Pak J Meteorol. 11 (22): 37–48.
Shalaby A, Tateishi R., 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Appl Geogr. 27 (1): 28–41.
Shi T, Huang Y, Wang H, Shi C-E, Yang Y-J., 2015. Influence of urbanization on the thermal environment of meteorological station: satellite-observed evidence. Adv Clim Change Res. 6 (1): 7–15.
Simwanda M, Ranagalage M, Estoque RC, Murayama Y., 2019. Spatial analysis of surface urban heat islands in four rapidly growing African cities. Remote Sens. 11 (14): 1645.
Sobrino JA, Jiménez-Muñoz JC, Paolini L., 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ. 90 (4): 434–440.
Song X-P, Hansen MC, Stehman SV, Potapov PV, Tyukavina A, Vermote EF, Townshend JR., 2018. Author correction: global land change from 1982 to 2016. " Nature. 563 (7732): E26–E26.
Svirejeva-Hopkins A, Schellnhuber HJ, Pomaz VL., 2004. Urbanised territories as a specific component of the global carbon cycle. Ecol Modell. 173 (2-3): 295–312.
Tan J, Yu D, Li Q, Tan X, Zhou W., 2020. Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China. Sci Rep. 10 (1): 1–9.
Tran DX, Pla F, Latorre-Carmona P, Myint SW, Caetano M, Kieu HV., 2017. Characterizing the relationship between land use land cover change and land surface temperature. ISPRS J Photogramm Remote Sens. 124: 119–132.
Tran H, Uchihama D, Ochi S, Yasuoka Y., 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities. Int J Appl Earth Obs Geoinf. 8 (1): 34–48.
Ullah S, Tahir AA, Akbar TA, Hassan QK, Dewan A, Khan AJ, Khan M., 2019. Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the Lower Himalayan Region. Sustainability. 11 (19): 5492.
Wang J, Huang B, Fu D, Atkinson PM, Zhang X., 2016. Response of urban heat island to future urban expansion over the Beijing–Tianjin–Hebei metropolitan area. Appl Geogr. 70: 26–36.
Weng Q, Lu D., 2008. A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States. Int J Appl Earth Obs Geoinf. 10 (1): 68–83.
Weng Q, Lu D, Schubring J., 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ. 89 (4): 467–483.
Wu Z, Zhang Y., 2018. Spatial variation of urban thermal environment and its relation to green space patterns: Implication to sustainable landscape planning. Sustainability. 10 (7): 2249.
Xie M, Wang Y, Chang Q, Fu M, Ye M., 2013. Assessment of landscape patterns affecting land surface temperature in different biophysical gradients in Shenzhen, China. Urban Ecosyst. 16 (4): 871–886.
Yamamoto Y, Ishikawa H., 2020. Influence of urban spatial configuration and sea breeze on land surface temperature on summer clear-sky days. Urban Clim. 31: 100578.
Yang C, He X, Yan F, Yu L, Bu K, Yang J, Chang L, Zhang S., 2017. Mapping the influence of land use/land cover changes on the urban heat island effect—a case study of Changchun, China. Sustainability. 9 (2): 312.
Yang J, Wang Y, August P., 2004. Estimation of land surface temperature using spatial interpolation and satellite-derived surface emissivity. J Env Inform. 4 (1): 40–44.
Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME., 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sens Environ. 98 (2-3): 317–328.
Zareie S, Khosravi H, Nasiri A., 2016. Derivation of land surface temperature from Landsat Thematic Mapper (TM) sensor data and analyzing relation between land use changes and surface temperature. " Solid Earth. Discuss: 1–15.
Zareie S, Khosravi H, Nasiri A, Dastorani M., 2016. Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran. Solid Earth. 7 (6): 1551–1564.
Zhang Y, Balzter H, Liu B, Chen Y., 2017. Analyzing the impacts of urbanization and seasonal variation on land surface temperature based on subpixel fractional covers using Landsat images. IEEE J Sel Top Appl Earth Observ Remote Sens. 10 (4): 1344–1356.
Zhang H, Qi Z-f, Ye X-y, Cai Y-b, Ma W-c, Chen M-n., 2013. Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China. Appl Geogr. 44: 121–133.
Zhang K, Wang R, Shen C, Da L., 2010. Temporal and spatial characteristics of the urban heat island during rapid urbanization in Shanghai, China. Environ Monit Assess. 169 (1-4): 101–112.
Zhou D, Bonafoni S, Zhang L, Wang R., 2018. Remote sensing of the urban heat island effect in a highly populated urban agglomeration area in East China. Sci Total Environ. 628 / 629: 415–429.
Zhou G, Wang H, Chen W, Zhang G, Luo Q, Jia B., 2020. Impacts of Urban land surface temperature on tract landscape pattern, physical and social variables. Int J Remote Sens. 41 (2): 683–703.
Zhou D, Zhao S, Liu S, Zhang L, Zhu C., 2014. Surface urban heat island in China's 32 major cities: spatial patterns and drivers. Remote Sens Environ. 152: 51–61.data a collection of facts from which conclusions may be drawn More (Definitions, Synonyms, Translation)
