Thermal stress and urban heat island effect in Jorhat urban environment as a result of changing land use and land cover

Neog, Rituraj; Hazarika, Jiten

doi:10.1007/s11600-022-00927-z

Artykuł - szczegóły

Tytuł artykułu

Thermal stress and urban heat island effect in Jorhat urban environment as a result of changing land use and land cover

Autorzy

Neog Rituraj , Hazarika Jiten

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-022-00927-z

Warianty tytułu

Języki publikacji

Abstrakty

The objective of the study is to determine the impact of land use and land cover (LULC) change on land surface temperature (LST) and thermal stress at Jorhat from 2009 to 2021. The experiment used Landsat TM (Thematic Mapper) for 2009 and OLI (Operational Land Imager)/TIRS (Thermal Infrared Sensor) for 2021 from earth.explorer.usgs.gov. Landsat data were employed to calculate the LST and LULC changes. Utilizing UTFVI (urban thermal field variance index), thermal stress over the ground surface has been computed. Thermal discomfort is computed simultaneously using the relative strain index (RSI) and net effective temperature (NET) index. Jorhat evidenced significant rise in built-up land to 281.25 hectares with reduced vegetation cover of 480.96 hectares from 2009 to 2021. These modifications caused significant rises in LST of 4.28 °C, 2.33 °C and 3.01 °C in September, October and December from 2009 to 2021. According to UTFVI from 2009 to 2021, Jorhat experienced declining ecologically excellent area with a rising proportion of ecologically worse land. In September and October 2009, the Jorhat city had just 10 days of bioclimatic discomfort and 19 days of bioclimatic comfort, as opposed to 24 and 10 days in 2021, respectively. Similarly, NET estimated 21 very hot days in October 2021, as opposed to just 9 days in 2009. Compared to 2009, there are now 6 and 4 days in December 2021 that are classified as warm or slightly hot, respectively. This leads to the conclusion that Jorhat's thermal condition is significantly impacted by changes in land use and land cover.

Słowa kluczowe

land use and land cover LST UTFVI RSI NET

użytkowanie i pokrycie terenu LST UTFVI RSI NET

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 6

Strony

2771--2783

Opis fizyczny

Bibliogr. 58 poz.

Twórcy

autor

Neog Rituraj

rituraj.neog08@gmail.com

Gargaon College, Sivasagar, India

https://orcid.org/0000-0002-2358-9992

autor

Hazarika Jiten

Department of Statistics, Dibrugarh University, Dibrugarh, India

Bibliografia

1. Alfraihat R, Mulugeta G, Gala TS (2016) Ecological evaluation of urban heat island in Chicago City, USA. J Atmos Pollut 4(1):23–29
2. Argueso D, Evans J, Fita L, Bormann K (2013) Temperature response to future urbanization and climate change. Clim Dyn. https://doi.org/10.1007/s00382-013-1789-6
3. Asghari M, Ghalhari GF, Abbasinia M, Shakeri F, Tajik R, Ghannadzadeh MJ (2020) Feasibility of relative strain index (RSI) for the assessment of heat stress in outdoor environments: case study in three different climates of Iran. Open Ecol J 13:11–18
4. Babalola OS, Akinsanola AA (2016) Change detection in land surface temperature and land use land cover over Lagos Metropolis, Nigeria. J Remote Sens GIS. https://doi.org/10.4172/2469-4134.1000171
5. Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62:241–252
6. Feng L, Zhao M, Zhou Y, Zhu L, Tian H (2020) The seasonal and annual impacts of landscape patterns on the urban thermal comfort using landsat. Ecol Indic 110:105798
7. Gohain KJ, Mohammad P, Goswami A (2021) Assessing the impact of land use land cover changes on land surface temperature over Pune city, India. Quatern Int 575–576:259–269. https://doi.org/10.1016/j.quaint.2020.04.052
8. Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM (2008) Global change and the ecology of cities. Science 319(5864):756–760
9. Guha S, Govil H, Mukherjee S (2017) Dynamic analysis and ecological evaluation of urban heat islands in Raipur city. J Appl Remote Sens 11(03):1. https://doi.org/10.1117/1.JRS.11.036020
10. Guha S, Govil H, Dey A, Gill N (2018) Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI/TIRS data in Florence and Naples city. Italy Eur J Remote Sens 51(1):667–678
11. Gupta S, Roy M (2012) Land use/land cover classification of an urban area -a case study of Burdwan municipality, India. Int J Geomat Geosci 2(4):1015–1026
12. Hegazy IR, Kaloop MR (2015) Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. Int J Sustain Built Environ 4(1):117–124. https://doi.org/10.1016/j.ijsbe.2015.02.005
13. Igun E, Williams M (2018) Impact of urban land cover change on land surface temperature. Global J Environ Sci Manag 4(1):47–58. https://doi.org/10.22034/gjesm.2018.04.01.005
14. Imran HM, Kala J, Ng AWM, Muthukumaran S (2018) An evaluation of the performance of a WRF multi-physics ensemble for heatwave events over the city of Melbourne in southeast Australia. Clim Dyn 50(7):2553–2586. https://doi.org/10.1007/s00382-017-3758-y
15. Imran HM, Kala J, Ng A, Muthukumaran S (2019a) Effectiveness of vegetated patches as Green Infrastructure in mitigating urban heat island effects during a heatwave event in the City of Melbourne. Weather Climate Extremes 25:100217. https://doi.org/10.1016/j.wace.2019.100217
16. Imran HM, Kala J, Ng A, Muthukumaran S (2019b) Impacts of future urban expansion on urban heat island effects during heatwave events in the city of Melbourne in Southeast Australia. Q J R Meteorol Soc. https://doi.org/10.1002/qj.3580
17. Imran HM, Hossain A, Islam AKMS et al (2021) Impact of land cover changes on land surface temperature and human thermal comfort in Dhaka City of Bangladesh. Earth Syst Environ 5:667–693. https://doi.org/10.1007/s41748-021-00243-4
18. Ionac N, Ciulache S (2004) Bioclimatic considerations on the Moldavian Plain (article in Romanian). In: Proceedings of the Geographic seminar “D. Cantemir”, Bucharest, Romania, 25:20–29
19. Ionac N, Ciulache S (2007) The bioclimatic stress in Dobrudja. Present Environ Sustain Dev 1:168–178
20. Jahan K, Pradhanang SM, Bhuiyan MAE (2021) Surface runoff responses to suburban growth: an integration of remote sensing, GIS, and curve number. Land 10(5):452. https://doi.org/10.3390/land10050452
21. John J, Bindu G, Srimuruganandam B, Wadhwa A, Rajan P (2020) Land use/land cover and land surface temperature analysis in Wayanad district, India, using satellite imagery. Ann GIS. https://doi.org/10.1080/19475683.2020.1733662
22. Kotharkar R, Bagade A (2017) Urban climate local climate zone classification for Indian cities: a case study of Nagpur. Urban Climate. https://doi.org/10.1016/j.uclim.2017.03.003
23. Kotharkar R, Bagade A (2017a) Evaluating urban heat island in the critical local climate zones of an Indian city. Landsc Urban Plan 169:92–104. https://doi.org/10.1016/j.landurbplan.2017.08.009
24. Kruse PW, McGlauchlin LD, McQuistan RB (1962) Elements of infrared technology: generation, transmission and detection; Wiley: New York. Wiley, NY, p 1962
25. Lai D, Guo D, Hou Y, Lin C, Chen Q (2014) Studies of outdoor thermal comfort in Northern China. Build Environ 77:110–118
26. Li ZL, Ning HW, Shi W, Sobrino JA, Wan ZB, Tang H, Yan G (2013) Land surface emissivity retrieval from satellite data. Int J Remote Sens 34(9–10):3084–3127
27. Liu L, Zhang Y (2011a) Urban heat island analysis using the landsat TM data and ASTER data: a case study in Hong Kong. Remote Sens 3:1535–1552
28. Mackey CW, Lee X, Smith RB (2012) Remotely sensing the cooling effects of city scale efforts to reduce urban heat island. Build Environ 49:348–358. https://doi.org/10.1016/j.buildenv.2011.08.004
29. Nagarajan M, Basil G (2014) Remote sensing-and GIS-based runoff modeling with the effect of land-use changes (a case study of Cochin corporation). Nat Hazards 73(3):2023–2039
30. Neinavaz E, Darvishzadeh R, Skidmore AK, Abdullah H (2019) Integration of landsat-8 thermal and visible-short wave infrared data for improving prediction accuracy of forest leaf area index. Remote Sens 11:390. https://doi.org/10.3390/rs11040390
31. Neog R (2022) Understanding the influence of COVID-19 induced lockdown on urban thermal environment of Ranchi city India. Geografisk Tidsskrift-Danish J Geogr. https://doi.org/10.1080/00167223.2022.2053999
32. Neog R, Acharjee S, Hazarika J (2021) Spatiotemporal analysis of road surface temperature (RST) and building wall temperature (BWT) and its relation to the traffic volume at Jorhat urban environment, India. Environ Dev Sustain 23:10080–10092. https://doi.org/10.1007/s10668-020-01047-8
33. Ng E, Cheng V (2012) Urban human thermal comfort in hot and humid Hong Kong. Energy Build 55:51–65
34. Nichol JE (2005) Remote sensing of urban heat islands by day and night. Photogramm Eng Remote Sens 19:1639–1649
35. Nikolopoulou M, Baker N, Steemers K (2001) Thermal comfort in outdoor urban spaces: understanding the human parameter. Sol Energy 70:227–235
36. Nzoiwu CP, Agulue EI, Mbah S, Igboanugo CP (2017) Impact of land use/land cover change on surface temperature condition of Awka Town Nigeria. J Geogr Inf Syst 09(06):763–776. https://doi.org/10.4236/jgis.2017.96047
37. Patra S, Sahoo S, Mishra P, Mahapatra SC (2018) Impacts of urbanization on land use/cover changes and its probable implications on local climate and groundwater level. J Urban Manag 7(2):70–84. https://doi.org/10.1016/j.jum.2018.04.006
38. "Provisional Population Totals, Census of India 2011, Urban Agglomerations/Cities having population 1 lakh and above" (PDF). Ministry of Home Affairs, Office of the Registrar General & Census Commissioner, India—Government of India. 2011. 1016/jwace.2019.100217
39. Rahman MA, Franceschi E, Pattnaik N et al (2022) Spatial and temporal changes of outdoor thermal stress: influence of urban land covers types. Sci Rep 12:671. https://doi.org/10.1038/s41598-021-04669-8
40. Rawat JS, Biswas V, Kumar M (2013) Changes in land use/cover using geospatial techniques: a case study of Ramnagar town area, district Nainital, Uttarakhand, India. Egypt J Remote Sens Space Sci 16(1):111–117. https://doi.org/10.1016/j.ejrs.2013.04.002
41. Rupanarayan and Verma P (2018) Assessment of Relationship between vegetation and land surface temperature of selected Tehsil in Dist-Raipur, Chhattisgarh, India using GIS & remote sensing Technique. Int J Sci Res 10.21275/SR20524183558
42. Schwarz N, Schlink U, Franck U, Großmann K (2012) Relationship of land surface and air temperatures and its implications for quantifying urban island indicators-an application for the city of Leipzig (Germany). Ecol Ind 18:693–704. https://doi.org/10.1016/j.ecolind.2012.01.001
43. Sekertekin A, Bonafoni S (2020) Land surface temperature retrieval from landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sens 12(2):294. https://doi.org/10.3390/rs12020294
44. Shahfahad TS, Rihan M et al (2022) Modelling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities. Environ Dev Sustain 24:3762–3790. https://doi.org/10.1007/s10668-021-01587-7
45. Shapiro Y, Pandolf KB, Goldman RF (1982) Predicting sweat loss response to exercise, environment and clothing. Eur J Appl Physiol Springer London 48:83–96
46. Shukla A, Jain K (2019) Modeling urban growth trajectories and spatiotemporal pattern: a case study of Lucknow City, India. J Indian Soc Remote Sens 47(1):139–152. https://doi.org/10.1007/s12524-018-0880-1
47. Sobrino JA, Raissouni N, Li ZL (2001) A comparative study of land surface emissivity retrieval from NOAA data. Remote Sens Environ 75(2):256–266
48. Sobrino JA, Munoz JC, Paolini L (2004) Land surface temperature retrieval from Landsat TM5. Remote Sens Environ 9:434–440
49. Sobrino JA, Jimenez-Muoz JC, Soria G, Romaguera M, Guanter L, Moreno J, Plaza A, Martinez P (2008) Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Trans Geosci Remote Sens 46:316–327
50. Taleghani M, Kleerekoper L, Tenpierik M, van den Dobbelsteen A (2015) Outdoor thermal comfort within five different urban forms in the Netherlands. Build Environ 83:65–78
51. Unger J (1999) Comparisons of urban and rural bio-climatological conditions in the case of a Central-European city. Int J Biometeorol 43:139–144
52. United Nations Department of Economic and Social Affairs PD (2018) World Urbanization 632 Prospects: the 2018 revision, Methodology. Working Paper No. ESA/P/WP.252
53. Van De Griend AA, Owe M (1993) On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Int J Remote Sens 14:1119–1131. https://doi.org/10.1080/01431169308904400
54. Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86:370–384. https://doi.org/10.1016/S0034-4257(03)00079-8
55. Wakode HB, Baier K, Jha R, Azzam R (2014) Analysis of urban growth using Landsat TM/ETM data and GIS-a case study of Hyderabad. India Arab J Geosci 7(1):109–121. https://doi.org/10.1007/s12517-013-0843-3
56. Wang SLL (2012) Chapter 8—land-surface temperature and thermal infrared emissivity. In: Wang SLL (ed) Advanced remote sensing. Academic Press, Boston, FL, USA, pp 235–271
57. 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
58. Zhang Y (2006) Land surface temperature retrieval from CBERS-02 IRMSS thermal infrared data and its applications in quantitative analysis of urban heat island effect. J Remote Sens 10:789–797

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-72285926-a697-47b6-864b-c4eddaf4e76b