Multi-approach synergic investigation between land surface temperature and land-use land-cover

Rapid urban expansion and associated land-use land-cover (LULC) change in India have emerged as a serious environmental threat that accelerates the impacts of urban heat island intensity (UHII). Three independent investigations have been conducted in this study using a series of Landsat data. The objectives of this work are: (1) To predict the near-future LULC scenario using an integrated model; (2) To understand the connection between band mean for particular LULC class with LST; (3) To analyze the temporal relationship between different types of built-up clusters and LST. The LULC and LST maps reveal that LST increases from 27.01° to 33.86°C, whereas built-up areas rise from 6.93% to 27.10% during 1988–2018, respectively. We observed that the near-future LULC scenario of KMA shows a huge expansion of built-up areas paid by decreased vegetation and open spaces. A clear significant correlation has been found between band mean and LST in all three Landsat sensors with the R2 = 0.84; p<0.02 for Landsat 5 TM, R2 = 0.91 and 0.99; p<0.01 and 0.00 for Landsat 7 ETM+, and R2 = 0.88; p<0.01 for Landsat 8 OLI in connection to our second objective. However, no agreement has been found between different built-up clusters and LST over 30 years of observation. For the first time, this study established the interconnectivity between bands of Landsat sensors and LST. The temporal relationship between different built-up clusters and LST have reviled also for the first time. Beside this, the rising rate of built-up areas was observed by the integrated model. Such alarming condition demands immediate attention to sustainable, and scientific land use regulations under new urbanism policy.

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    Source: http://Landsat.usgs.gov/Landsat8_Using_Product.php.

References

  1. Al-Ahmadi F S and Hames A S 2009 Comparison of four classification methods to extract land use and land cover from raw satellite images for some remote arid areas, Kingdom of Saudi Arabia; Earth Sci. 20(1) 167–191.

    Google Scholar 

  2. Artis D A and Carnahan W H 1982 Survey of emissivity variability in thermography of urban areas; Remote Sens. Environ. 12(4) 313–329.

    Google Scholar 

  3. Ananth P 2017 Housing for poor and the impact of IAY in rural India: Present context; Int. J. Humanities Social Sci. Res. 3(1) 54–56.

    Google Scholar 

  4. Bardhan R, Bandopadhyay S and Gupta K 2015 Rapid estimation of flood prone zones under data constraint scenario: A fuzzy modelling approach; Proc. HYDRO 2015 INTERNATIONAL viz 20th International Conference on Hydraulics, Water Resources and River Engineering, December 2015, IIT Roorkee, India

  5. Bardhan R, Debnath R and Bandopadhyay S 2016 A conceptual model for identifying the risk susceptibility of urban green spaces using geo-spatial techniques; Model. Earth Syst. Environ. 2(3) 144.

    Google Scholar 

  6. Bhatta B 2009 Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India; Int. J. Remote Sens. 30(18) 4733–4746.

    Google Scholar 

  7. Bhattacharjee S and Ghosh S K 2015 spatio-temporal change modeling of LULC: A semantic kriging approach; ISPRS Annals.

  8. Bhatti S S and Tripathi N K 2014 Built-up area extraction using Landsat 8 OLI imagery; Geosci. Remote Sens. 51(4) 445–467.

    Google Scholar 

  9. Bojesen M, Skov-Petersen H and Gylling M 2015 Forecasting the potential of Danish biogas production–spatial representation of Markov chains; Biomass Bioenergy 81 462–472.

    Google Scholar 

  10. Brown D G, Pijanowski B C and Duh J D 2000 Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA; J. Environ. Manag. 59(4) 247–263.

    Google Scholar 

  11. Campagna M, De Montis A, Isola F, Lai S, Pira C and Zoppi C (eds) 2012 Planning Support Tools: Policy analysis, implementation and evaluation; Proceedings of the Seventh International Conference on Informatics and Urban and Regional Planning, INPUT2012 FrancoAngeli.

  12. Chakraborty S, Chowdhury B R, Ghosh S, Sen P K and De U K 2019 Statistical analysis of urban regional pre-monsoon rainfall in and around Kolkata, India; J. Earth Syst. Sci. 128(3) 57.

    Google Scholar 

  13. Chander G, Markham B L and Helder D L 2009 Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors; Remote Sens. Environ. 113(5) 893–903.

    Google Scholar 

  14. Chandler T 1965 The Climate of London; W Heffer and Sons Ltd., Cambridge, England.

    Google Scholar 

  15. Chandra S, Sharma D and Dubey S K 2018 Linkage of urban expansion and land surface temperature using geospatial techniques for Jaipur City, India; Arab. J. Geosci. 11(2) 31.

    Google Scholar 

  16. Chen L, Li M, Huang F and Xu S 2013 Relationships of LST to NDBI and NDVI in Wuhan City based on Landsat ETM+ image; In: Image and Signal Processing (CISP); 6th International Congress IEEE 2 840–845.

  17. Chen W, Panahi M and Pourghasemi H R 2017a Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling; Catena 157 310–324.

    Google Scholar 

  18. Chen W, Pourghasemi H R, Kornejady A and Zhang N 2017b Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques; Geoderm. 305 314–327.

    Google Scholar 

  19. Chen X L, Zhao H M, Li P X and 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.

    Google Scholar 

  20. Cheng T and Adepeju M 2014 Modifiable temporal unit problem (MTUP) and its effect on space-time cluster detection; PLOS One 9(6) pe100465.

  21. Dasgupta A 2017 Unlocking the potential of geospatial data; Space India 20 51.

    Google Scholar 

  22. Behera M, Borate S N, Panda S N, Behera P R and Roy P S 2012 Modelling and analyzing the watershed dynamics using Cellular Automata (CA)–Markov model – A geo-information based approach; J. Earth Syst. Sci. 121(4) 1011–1024.

    Google Scholar 

  23. Drăguţ L and Blaschke T 2006 Automated classification of landform elements using object based image analysis; Geomorphology 81(3–4) 330–344.

    Google Scholar 

  24. Du P, Liu P, Xia J, Feng L, Liu S, Tan K and Cheng L 2014 Remote sensing image interpretation for urban environment analysis: methods, system and examples; Remote Sens. Basel. 6(10) 9458–9474.

    Google Scholar 

  25. Erbek F S, Özkan C and Taberner M 2004 Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities; Int. J. Remote Sens. 25(9) 1733–1748.

    Google Scholar 

  26. ERDAS 2009 ERDAS Field Guide TM—tutorial Imagine; Atlanta, Georgia

  27. Foody G M 1992 On the compensation for chance agreement in image classification accuracy assessment; Photogramm. Eng. Remote Sens. 58(10) 1459–1460.

    Google Scholar 

  28. Fu P and Weng Q 2016 A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery; Remote Sens. Environ. 175 205–214.

    Google Scholar 

  29. Ghosh S, Chatterjee N D and Dinda S 2018 Relation between urban biophysical composition and dynamics of land surface temperature in the Kolkata metropolitan area: A GIS and statistical based analysis for sustainable planning; Model. Earth Syst. Environ. 5 307–329.

    Google Scholar 

  30. Ghosh S, Singh P and Kumari M 2014 Assessment of urban sprawl and land use change dynamics, using remote sensing technique: A study of Kolkata and surrounding periphery, WB, India.

    Google Scholar 

  31. Grover A and Singh R B 2015 Analysis of urban heat island (UHI) in relation to normalized difference vegetation index (NDVI): A comparative study of Delhi and Mumbai; Environments 2(2) 125–138.

    Google Scholar 

  32. Guo G, Wu Z, Xiao R, Chen Y, Liu X and Zhang X 2015 Impacts of urban biophysical composition on land surface temperature in urban heat island clusters; Landsc. Urban Plan. 135 1–10.

    Google Scholar 

  33. Hamad R, Balzter H and Kolo K 2018 Predicting land use/land cover changes using a CA–Markov model under two different scenarios; Sustain.-Basel. 10(10) 3421.

    Google Scholar 

  34. Hiremath S, Prabhuraj D K, Lakshmikantha B P and Chakraborty S D 2013 Land use/land cover change analysis of Bangalore Urban District and its impact on land surface temperature; Indian Society of Geomatics and Indian Society of Remote Sensing, Remote Sensing and GIS for Environment with Special Emphasis on Marine and Coastal Dynamics, Visakhapatnam.

  35. Honnerová P, Martan J, Veselý Z and Honner M 2017 Method for emissivity measurement of semitransparent coatings at ambient temperature; Sci. Rep-UK 7(1) 1386.

    Google Scholar 

  36. Hu Q, Wu W, Xia T, Yu Q, Yang P, Li Z and Song Q 2013 Exploring the use of Google Earth imagery and object-based methods in land use/cover mapping; Remote Sens.-Basel. 5(11) 6026–6042.

  37. Ibrahim F and 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.

    Google Scholar 

  38. James K S 2011 India’s demographic change: Opportunities and challenges; Science 333(6042) 576–580.

    Google Scholar 

  39. Jiménez-Muñoz J C and Sobrino J A 2003 A generalized single-channel method for retrieving land surface temperature from remote sensing data; J. Geophys. Res.-Atmos. 108 (D22).

  40. Kallvetty S and Bandopadhyay S 2018 Spatial explicit modeling to understand the dynamics of landuse switch using open source satellite data; Geoplanning J. Geomatics Plann. 5(1) 1–22.

    Google Scholar 

  41. Keshtkar H and Voigt W 2016 A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models; Model. Earth Syst. Environ. 2(1) 10.

    Google Scholar 

  42. Kim W, Yeh S W, Kim J H, Kug J S and Kwon M 2011 The unique 2009–2010 El Niño event: A fast phase transition of warm pool El Niño to La Niña; Geophys. Res. Lett. 38(15).

  43. Kumar P, Husain A, Singh R B and Kumar, M 2018 Impact of land cover change on land surface temperature: A case study of Spiti Valley; J. Mt. Sci. 15(8) 1658–1670.

    Google Scholar 

  44. Li X, Mitra C, Marzen L and Yang Q 2016 Spatial and temporal patterns of wetland cover changes in East Kolkata Wetlands, India from 1972 to 2011; IJAGR 7(2) 1–13.

    Google Scholar 

  45. Lo C P and Quattrochi D A 2003 Land-use and land-cover change, urban heat island phenomenon, and health implications; Photogramm. Eng. Remote Sens. 69(9) 1053–1063.

    Google Scholar 

  46. Lombardo M A 1985 Ilha de Calor Nas Metrópoles: O Exemplo de São Paulo (in Portuguese), (first edn), Hucitec, São Paulo, Brazil, p 244.

    Google Scholar 

  47. Lv Z Q and Zhou Q G 2011 Utility of Landsat image in the study of land cover and land surface temperature change; Proc. Environ. Sci. 10 1287–1292.

    Google Scholar 

  48. Luedeling E and Buerkert A 2008 Typology of oases in northern Oman based on Landsat and SRTM imagery and geological survey data; Remote Sens. Environ. 112 1181–1195.

    Google Scholar 

  49. Mallick J, Kant Y and Bharath B D 2008 Estimation of land surface temperature over Delhi using Landsat-7 ETM+; J. Ind. Geophys. Union 12(3) 131–140.

    Google Scholar 

  50. McGee T G 2008 Managing the rural–urban transformation in East Asia in the 21st century; Sustain. Sci. 3(1) 155–167.

    Google Scholar 

  51. Mitra A 2018 Estuarine Pollution in the Lower Gangetic Delta Threats and Management; Springer, Berlin.

    Google Scholar 

  52. Mitra S 2002 Planned urbanisation through public participation: Case of the New Town, Kolkata; Economic and Political Weekly, pp. 1048–1054.

  53. Mondal A, Guha S, Lakshmi V, Kundu S, Garg R D and Govil H 2017 Evaluating the NCI technique in land use/land cover change detection using Landsat data; AGU Fall Meeting Abstracts.

  54. Monserud R A and Leemans R 1992 Comparing global vegetation maps with the Kappa statistic; Ecol. Model 62(4) 275–293, https://doi.org/10.1016/0304-3800(92)90003-W.

    Article  Google Scholar 

  55. Moulds S, Buytaert W and Mijic A 2018 A spatio-temporal land use and land cover reconstruction for India from 1960–2010; Scientific Data 5 180159.

    Google Scholar 

  56. Mukherjee J 2015 Beyond the urban: Rethinking urban ecology using Kolkata as a case study; Int. J. Sust. Dev. 7(2) 131–146.

    Google Scholar 

  57. Oke T R 1987 Boundary layer climates (2nd edn); London: Methuen (435p).

  58. Pal S and Ziaul S 2017 Detection of land use and land cover change and land surface temperature in English Bazar urban centre; Egypt J. Remote Sens. Space Sci. 20(1) 125–145.

    Google Scholar 

  59. Qin Z, Karnieli A and Berliner P 2001 A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel–Egypt border region; Int. J. Remote Sens. 22(18) 3719-3746.

    Google Scholar 

  60. Regmi R, Saha S and Balla M 2014 Geospatial analysis of land use land cover change predictive modeling at Phewa Lake Watershed of Nepal; Int. J. Curr. Eng. Tech. 4 2617–2627.

    Google Scholar 

  61. Rinner C and Hussain M 2011 Toronto’s urban heat island – Exploring the relationship between land use and surface temperature; Remote Sens.-Basel. 3(6) 1251–1265.

  62. Rose L and Devadas M D 2009 Analysis of land surface temperature and land use/land cover types using remote sensing imagery – a case in Chennai City, India; The seventh International Conference on Urban Climate, Vol 29.

  63. Roy J, Chattopadhyay S, Mukherjee S, Kanjilal M, Samajpati S and Roy S 2004 An economic analysis of demand for water quality: Case of Kolkata; Econ. Polit. Weekly, pp. 186–192.

  64. Sadhu S 2015 Identification of urban hot spots in relation to built-up surface and nature of buildings in the Kolkata Municipal Corporation (KMC) area; TTPP 451.

  65. Sahana M, Hong H and Sajjad H 2018 Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India; Sci. Total Environ. 628-629 1557–1566, https://doi.org/10.1016/jscitotenv.2018.02.170.

    Article  Google Scholar 

  66. Sang L, Zhang C, Yang J, Zhu D and Yun W 2011 Simulation of land use spatial pattern of towns and villages based on CA-Markov model; Math. Comput. Model 54(3–4) 938–943.

    Google Scholar 

  67. Sarkar S, Parihar S M and Dutta A 2016 Fuzzy risk assessment modelling of East Kolkata wetland area: A remote sensing and GIS based approach; Environ. Model. Softw. 75 105–118.

    Google Scholar 

  68. Sharma P 2017 Urbanisation and air quality: A comparative analysis of Delhi and Kolkata; Int. J. Emerging Technol. 8(1) 324–329.

    Google Scholar 

  69. Sharma R, Chakraborty A and Joshi P K 2015 Geospatial quantification and analysis of environmental changes in urbanizing city of Kolkata (India); Environ. Monit. Assess. 187(1) 4206.

    Google Scholar 

  70. Shen H, Huang L, Zhang L, Wu P and 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. 172 109–125.

    Google Scholar 

  71. Small C 2006 Comparative analysis of urban reflectance and surface temperature; Remote Sens. Environ. 104(2) 168–189.

    Google Scholar 

  72. Snyder W C, Wan Z, Zhang Y and Feng Y Z 1998 Classification-based emissivity for land surface temperature measurement from space; Int. J. Remote Sens. 19(14) 2753–2774.

    Google Scholar 

  73. Sobrino J A, Jimenez-Munoz J C and Paolini L 2004 Land surface temperature retrieval from Landsat TM 5; Remote Sens. Environ. 90(4) 434–440.

    Google Scholar 

  74. Sun J, Yang J, Zhang C, Yun W and Qu J 2013 Automatic remotely sensed image classification in a grid environment based on the maximum likelihood method; Math. Comput. Model. 58(3–4) 573–581.

    Google Scholar 

  75. Thakkar A K, Desai, V R, Patel A and Potdar M B 2017 Post-classification corrections in improving the classification of land use/land cover of arid region using RS and GIS: The case of Arjuni watershed, Gujarat, India; Egypt J. Remote Sens. Space Sci. 20(1) 79–89.

    Google Scholar 

  76. Thomas H and Laurence H M 2006 Modeling and projecting land-use and land-cover changes with a cellular automaton in considering landscape trajectories: An improvement for simulation of plausible future states; EARSeL eProc. 5 63–76.

  77. Walburg G M M E, Bauer M E, Daughtry C S T and Housley T L 1982 Effects of nitrogen nutrition on the growth, yield, and reflectance characteristics of corn canopies; Agron. J. 74(4) 677–683.

    Google Scholar 

  78. Weng Q, Lu D and Liang B 2006 Urban surface biophysical descriptors and land surface temperature variations; Photogramm. Eng. Remote Sens. 72(11) 1275–1286.

    Google Scholar 

  79. Weng Q, Lu D and Schubring J 2004 Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies; Remote Sens. Environ. 89(4) 467–483.

    Google Scholar 

  80. Xia N, Cheng L and Li M 2019 Mapping urban areas using a combination of remote sensing and geolocation data; Remote Sens.-Basel. 11(12) 1470.

  81. Xiong Y, Huang S, Chen F, Ye H, Wang C and Zhu C 2012 The impacts of rapid urbanization on the thermal environment: A remote sensing study of Guangzhou, South China; Remote Sens.-Basel. 4(7) 2033–2056.

  82. Yap D 1975 Seasonal excess urban energy and the nocturnal heat island – Toronto; Archiv für Meteorologie, Geophysik und Bioklimatologie Serie B. 23(1–2) 69–80.

    Google Scholar 

  83. Yuan D 1997 A simulation comparison of three marginal area estimators for image classification; Photogramm. Eng. Remote Sens. 53(4).

  84. Zha Y, Gao J and Ni S 2003 Use of normalized difference built-up index in automatically mapping urban areas from TM imagery; Int. J. Remote Sens. 24(3) 583–594.

    Google Scholar 

  85. Zhao S, Zhou D, Zhu C, Qu W, Zhao J, Sun Y and Liu S 2015 Rates and patterns of urban expansion in China’s 32 major cities over the past three decades; Landsc. Ecol. 30(8) 1541–1559.

    Google Scholar 

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Acknowledgements

Special thanks to the US Geological Survey, NASA, and Google earth for providing freely available satellite data. Analyses of the data were supported by the Polish National Research Centre (NCN) within the Project No 2016/21/B/ST10/02271. Sincere thanks are given to the anonymous reviewers and members of the editorial team for their comments and suggestions.

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Saha, P., Bandopadhyay, S., Kumar, C. et al. Multi-approach synergic investigation between land surface temperature and land-use land-cover. J Earth Syst Sci 129, 74 (2020). https://doi.org/10.1007/s12040-020-1342-z

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Keywords

  • Land-use land-cover (LULC)
  • land surface temperature (LST)
  • urban heat island (UHI)
  • Landsat
  • built-up clusters
  • India