Abstract
Mounting human population and fast urban expansion has driven the ecosystem degradation within the past 3 decades by reducing permeable cultivable land surface for the construction and thus increasing land surface temperature (LST) and creating urban heat islands (UHI). It is well known that temporal assessment of land surface temperature and UHI are inevitable for city planning and ecosystem monitoring. Landsat-derived LST is widely used for urban temperature studies including the study of the urban heat island (UHI). In this study, remote sensing (RS) techniques have been used for the estimation and forecasting of LST and identification of UHI in one of the fast-growing cities of Tamil Nadu state, India, using Autoregressive Integrated Moving Average (ARIMA) model. Satellite data between the period 2008 and 2018 was used in the model study. Analysis of the images and model results show that there is a progressive increasing trend of LST in built-up areas. LST values obtained from model study exhibited a negative relationship with land use and land cover (LULC) for Chennai city and surrounding area. LST maps developed from the model study depicted growing UHI hotspots in the southeastern and western parts of the city where the development of the city is fast. The present study would help in forecasting the LST of a city and in identifying UHI hotspots for proper urban planning.
Similar content being viewed by others
References
Abdikan S, Balik Sanli F, Sunar F, Ehlers M (2014) A comparative data-fusion analysis of multi-sensor satellite images. Int J Digital Earth 7:671–687. https://doi.org/10.1080/17538947.2012.748846
Ahmad A, Quegan S (2012) Analysis of maximum likelihood classification on multispectral data. Appl Math Sci 6:6425–6436
Amirtham LR (2016) Urbanization and its impact on urban heat Island intensity in Chennai Metropolitan Area, India. Indian J Sci Technol 9:1–8. https://doi.org/10.17485/ijst/2016/v9i5/87201
Amirtham L, Monsingh D (2008) Assessing the intensity of urban heat island effect in the hot humid city of Chennai. Int J Des Manuf Technol 2:92–99
Amirtham LR, Devadas MD, Perumal M (2009) Mapping of micro-urban heat islands and land cover changes: a case in Chennai City, India. Int J Clim Change: Impacts Responses 1:71–84. https://doi.org/10.18848/1835-7156/CGP/v01i02/37258
Arnfield AJ (2003) Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int J Climatol 23:1–26. https://doi.org/10.1002/joc.859
Becerril-Piña R, Díaz-Delgado C, Mastachi-Loza CA, González-Sosa E (2016) Integration of remote sensing techniques for monitoring desertification in Mexico. Hum Ecol Risk Assess: Int J 22:1323–1340. https://doi.org/10.1080/10807039.2016.1169914
Berlanga-Robles CA, Ruiz-Luna A (2002) Land use mapping and change detection in the coastal zone of northwest Mexico using remote sensing techniques. J Coast Res 18:514–522
Bernard J, Musy M, Calmet I, Bocher E, Kéravec P (2017) Urban heat island temporal and spatial variations: empirical modeling from geographical and meteorological data. Build Environ 125:423–438. https://doi.org/10.1016/j.buildenv.2017.08.009
Binh T, Vromant N, Hung NT, Hens L, Boon E (2005) Land cover changes between 1968 and 2003 in Cai Nuoc, Ca Mau peninsula, Vietnam. Environ Dev Sustain 7:519–536. https://doi.org/10.1007/s10668-004-6001-z
Bonan G (2015) Ecolog climatology: concepts and applications. Cambridge University Press. https://doi.org/10.1017/CBO9781107339200
Bongaarts J (2020) United Nations Department of Economic and Social Affairs, Population Division World Family Planning 2020: Highlights United Nations Publications
Brandsma T, Wolters D (2012) Measurement and statistical modeling of the urban heat island of the city of Utrecht (the Netherlands). J Appl Meteorol 51:1046–1060. https://doi.org/10.1175/JAMC-D-11-0206.1
Cadenasso ML, Pickett ST, Schwarz K (2007) Spatial heterogeneity in urban ecosystems: reconceptualizing land cover and a framework for classification. Front Ecol Environ 5:80–88. https://doi.org/10.1890/1540-9295(2007)5[80:SHIUER]2.0.CO;2
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 104:133–146
Chun B, Guldmann J-M (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
Clinton N, Gong P (2013) MODIS detected surface urban heat islands and sinks: global locations and controls. Remote Sens Environ 134:294–304. https://doi.org/10.1016/j.rse.2013.03.008
Díaz BM, Blackburn GA (2003) Remote sensing of mangrove biophysical properties: evidence from a laboratory simulation of the possible effects of background variation on spectral vegetation indices. Int J Remote Sens 24:53–73. https://doi.org/10.1080/01431160305012
Farina AJ (2012) Exploring the relationship between land surface temperature and vegetation abundance for urban heat island mitigation in Seville, Spain. Lund University, Dissertation
Gajendran C, Jayapriya S, Yohannan D, Victor O, Jacob C (2013) Assessment of groundwater quality in Tirunelveli District. Tamil Nadu India Int J Environ Sci 3:1874–1880
Gamba P, Dell’Acqua F, Dasarathy BV (2005) Urban remote sensing using multiple data sets: past, present, and future. Inf Fusion 6:319–326. https://doi.org/10.1016/j.inffus.2005.02.007
Gartland LM (2012) Heat islands: understanding and mitigating heat in urban areas. Routledge
Giri CP, Kratzschmar E, Ofren RS, Pradhan D, Shrestha S (1996) Assessing land use/land cover dynamics in two identified “hot spot” areas: Oudomxay Province of Lao PDR and Mekong Delta of Vietnam. In Proceeding of The 17th Asian Conference on Remote Sensing, Srilanka.
Gopalakrishnan SG, Rama Krishna TVBPS, Sharan M (2003) Some signatures of Urban Heat Patches in southern India. Proc Indian Natl Sci Acad 5:603–614
Gordon A, Simondson D, White M, Moilanen A, Bekessy SA (2009) Integrating conservation planning and landuse planning in urban landscapes. Landsc Urban Plan 91:183–194. https://doi.org/10.1016/j.landurbplan.2008.12.011
Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM (2008) Global change and the ecology of cities. J Dermatol Sci 319:756–760. https://doi.org/10.1126/science.1150195
Grimmond SU (2007) Urbanization and global environmental change: local effects of urban warming. Geogr J 173:83–88. https://doi.org/10.1111/j.1475-4959.2007.232_3.x
Howard L (1833) The climate of London: deduced from meteorological observations made in the metropolis and at various places around it (vol 3). Harvey and Darton, London
Hua LJ, Ma ZG, Guo WD (2008) The impact of urbanization on air temperature across China. Theor Appl Climatol 93:179–194. https://doi.org/10.1007/s00704-007-0339-8
Jansson Å (2013) Reaching for a sustainable, resilient urban future using the lens of ecosystem services. Ecol Econ 86:285–291. https://doi.org/10.1016/j.ecolecon.2012.06.013
Jat MK, Garg PK, Khare D (2008) Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. Int J Appl Earth Obs Geoinf 10:26–43. https://doi.org/10.1016/j.jag.2007.04.002
Jayanthi N (1991) Heat island study over Madras city and neighbourhood. Mausam 42:83–88
Kandrika S, Ravisankar T (2011) Multi-temporal satellite imagery and data fusion for improved land cover information extraction. Int J Image Data Fusion 2:61–73. https://doi.org/10.1080/19479832.2010.518166
Kato S, Yamaguchi Y (2005) Analysis of urban heat-island effect using ASTER and ETM+ Data: separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux. Remote Sens Environ 99:44–54. https://doi.org/10.1016/j.rse.2005.04.026
Lillo-Saavedra M, Gonzalo C (2006) Spectral or spatial quality for fused satellite imagery? A trade-off solution using the wavelet à trous algorithm. Int J Remote Sens 27:1453–1464. https://doi.org/10.1080/01431160500462188
Liu X, Yetik IS (2010) A maximum likelihood classification method for image segmentation considering subject variability. In: 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI), 2010. IEEE, 125-128
Lu Y, Michaels JE (2009) Feature extraction and sensor fusion for ultrasonic structural health monitoring under changing environmental conditions. IEEE Sensors J 9:1462–1471. https://doi.org/10.1109/JSEN.2009.2019339
Masek JG, Vermote EF, Saleous NE, Wolfe R, Hall FG, Huemmrich KF, Gao F, Kutler J, Lim TK (2006) A Landsat surface reflectance dataset for North America, 1990-2000. IEEE Geosci Remote Sens Lett 3:68–72. https://doi.org/10.1109/LGRS.2005.857030
Maske, PGKSJ, Behere A, Kachare SD (1978) Characteristics of heat islands at Pune. IMD India
Miura T, Huete AR, Yoshioka H (2000) Evaluation of sensor calibration uncertainties on vegetation indices for MODIS. IEEE Trans Geosci Remote Sens 38:1399–1409. https://doi.org/10.1109/36.843034
Mohan M, Kikegawa Y, Gurjar B, Bhati S, Kolli NR (2013) Assessment of urban heat island effect for different land use–land cover from micrometeorological measurements and remote sensing data for megacity Delhi. Theor Appl Climato 112:647–658. https://doi.org/10.1007/s00704-012-0758-z
Muttitanon W, Tripathi N (2005) Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. Int J Remote Sens 26:2311–2323
O’Loughlin J, Witmer FD, Linke AM, Laing A, Gettelman A, Dudhia J (2012) Climate variability and conflict risk in East Africa, 1990–2009. Proc Natl Acad Sci 109:18344–18349. https://doi.org/10.1073/pnas.1205130109
Oh JW, Ngarambe J, Duhirwe PN, Yun GY, Santamouris M (2020) Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea. Sci Rep 10:1–13. https://doi.org/10.1038/s41598-020-60632-z
Padmanaban R, Bhowmik AK, Cabral P (2017a) A remote sensing approach to environmental monitoring in a reclaimed mine area. ISPRS Int J Geo-inf 6:401. https://doi.org/10.3390/ijgi6120401
Padmanaban R, Bhowmik AK, Cabral P, Zamyatin A, Almegdadi O, Wang S (2017b) Modelling urban sprawl using remotely sensed data: A case study of Chennai city, Tamilnadu. Entropy 19:163. https://doi.org/10.3390/e19040163
Padmanaban R, Karuppasamy S, Narayanan R (2018) Assessment of pollutant level and forecasting water pollution of Chennai Coastal, Tamilnadu using R. Indian Journal of Geo-Marine Sciences 47:1420–1429
Padmanaban R, Bhowmik AK, Cabral P (2019) Satellite image fusion to detect changing surface permeability and emerging urban heat islands in a fast-growing city. PLoS One 14:e0208949. https://doi.org/10.1371/journal.pone.0208949
Painho M (2015) Urban Agent Based Model of Urban SlumDharavi, Mumbai, India. Int J Earth Sci Eng 10:1110–1117
Panda SS, Ames DP, Panigrahi S (2010) Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sens 2:673–696. https://doi.org/10.3390/rs2030673
Patz JA, Campbell-Lendrum D, Holloway T, Foley JA (2005) Impact of regional climate change on human health. Nature 438:310–317. https://doi.org/10.1038/nature04188
Pongrácz R, Bartholy J, Dezső Z (2010) Application of remotely sensed thermal information to urban climatology of Central European cities. Phys Chem Earth, Parts A/B/C 35:95–99. https://doi.org/10.1016/j.pce.2010.03.004
Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55:95–107
Sarvestani MS, Ibrahim AL, Kanaroglou P (2011) Three decades of urban growth in the city of Shiraz, Iran: A remote sensing and geographic information systems application. Cities 28:320–329. https://doi.org/10.1016/j.cities.2011.03.002
Schwarz N, Lautenbach S, Seppelt R (2011) Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures. Remote Sens Environ 115:3175–3186
Seto KC, Güneralp B, Hutyra LR (2012) Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc Natl Acad Sci 109:16083–16088. https://doi.org/10.1073/pnas.1211658109
Sharma KP, Jain SC, Garg PK (1984) Monitoring landuse and landcover changes using landsat images. J Indian Soc Photo-Interpretation Remote Sens 12:65–70
Stow DA, Chen DM (2002) Sensitivity of multitemporal NOAA AVHRR data of an urbanizing region to land-use/land-cover changes and misregistration. Remote Sens Environ 80:297–307. https://doi.org/10.1016/S0034-4257(01)00311-X
Su Y-F, Foody GM, Cheng K-S (2012) Spatial non-stationarity in the relationships between land cover and surface temperature in an urban heat island and its impacts on thermally sensitive populations. Landsc Urban Plan 107:172–180. https://doi.org/10.1016/j.landurbplan.2012.05.016
Sun Y (2008) Retrieval and application of land surface temperature. Geo Utexas Edu 1:1–27
Sundersingh SD (1990) Effect of heat islands over urban Madras and measures for its mitigation Energy Buildings 15:245-252. 15:245–252. https://doi.org/10.1016/0378-7788(90)90136-7
Ulivieri C, Cannizzaro G (1985) Land surface temperature retrievals from satellite measurements. Acta Astronautica 12:977–985. https://doi.org/10.1016/0094-5765(85)90026-8
USGS (2016) Landsat-8 Data User Handbook. United States Geological Survey
Ustaoglu B, Cigizoglu H, Karaca M (2008) Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods. Meteorol Appl Sci Tech Weather and Clim 15:431–445. https://doi.org/10.1002/met.83
Wan Z (1999) MODIS land-surface temperature algorithm theoretical basis document (LST ATBD). Institute for Computational Earth System Science. University of California, Santa Barbara
Wang H, Huang J, Zhou H, Zhao L, Yuan Y (2019a) An integrated variational mode decomposition and ARIMA model to forecast air temperature. Sustainability 11:4018. https://doi.org/10.3390/su11154018
Wang W, Liu K, Tang R, Wang S (2019b) Remote sensing image-based analysis of the urban heat island effect in Shenzhen, China. Phys Chem Earth, Parts A/B/C 110:168–175. https://doi.org/10.1016/j.pce.2019.01.002
Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89:467–483. https://doi.org/10.1016/j.rse.2003.11.005
Zhao L, Lee X, Smith RB, Oleson K (2014) Strong contributions of local background climate to urban heat islands. Nature 511:216–219. https://doi.org/10.1038/nature13462
Zhou D, Zhao S, Zhang L, Liu S (2016) Remotely sensed assessment of urbanization effects on vegetation phenology in China’s 32 major cities. Remote Sens Environ 176:272–281. https://doi.org/10.1016/j.rse.2016.02.010
Zhou D, Xiao J, Bonafoni S, Berger C, Deilami K, Zhou Y, Frolking S, Yao R, Qiao Z, Sobrino J (2019) Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives. Remote Sens 11:48. https://doi.org/10.3390/rs11010048
Zhu L, Tateishi R (2006) Fusion of multisensor multitemporal satellite data for land cover mapping. Int J Remote Sens 27:903–918. https://doi.org/10.1080/0143116031000139818
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no competing interests.
Additional information
Responsible Editor: Biswajeet Pradhan
Rights and permissions
About this article
Cite this article
Kesavan, R., Muthian, M., Sudalaimuthu, K. et al. ARIMA modeling for forecasting land surface temperature and determination of urban heat island using remote sensing techniques for Chennai city, India. Arab J Geosci 14, 1016 (2021). https://doi.org/10.1007/s12517-021-07351-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-021-07351-5