Abstract
Rapid changes in land use and land cover (LULC) have ecological and environmental effects in metropolitan areas. Since the 1990s, Saudi Arabia’s cities have undergone tremendous urban growth, causing urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, etc. This study evaluates the variance and heterogeneity in land surface temperature (LST) because of LULC changes in Abha-Khamis Mushyet, Saudi Arabia, from 1990 to 2020. The research aims to determine the impact of urban biophysical parameters on the High–High (H–H) LST cluster using geospatial, statistical, and machine learning techniques. The support vector machine (SVM) was used to map LULC. The land surface temperature (LST) has been derived using the mono-window algorithm (MWA). The local indicator of spatial associations (LISA) model was implemented on the spatiotemporal LST maps to identify LST clusters. Also, the parallel coordinate plot (PCP) approach was employed to examine the relationship between LST clusters and urban biophysical variables as a proxy of LULC. LULC maps show that urban areas rose by > 330% between 1990 and 2020. Built-up areas had an 83.6% transitional probability between 1990 and 2020. In addition, vegetation and agricultural land have been transformed into built-up areas by 17.9% and 21.8% respectively between 1990 and 2020. Uneven LULC changes in terms of built-up areas lead to increased LST hotspots. High normalized difference built-up index (NDBI) was linked to LST hotspots but not normalized difference water index (NDWI) or normalized difference vegetation index (NDVI). This research could help policymakers develop mitigation strategies for urban heat islands.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
“The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.”
References
Abrar R, Sarkar SK, Nishtha KT et al (2022) Assessing the spatial mapping of heat vulnerability under urban heat island (UHI) effect in the Dhaka metropolitan area. Sustain 14:4945. https://doi.org/10.3390/SU14094945
Abulibdeh A (2021) Analysis of urban heat island characteristics and mitigation strategies for eight arid and semi-arid gulf region cities. Environ Earth Sci 80:1–26. https://doi.org/10.1007/S12665-021-09540-7/TABLES/8
Addas A (2022) Exploring the pattern of use and accessibility of urban green spaces: evidence from a coastal desert megacity in Saudi Arabia. Environ Sci Pollut Res 29:55757–55774. https://doi.org/10.1007/S11356-022-19639-4/FIGURES/5
Al-Aklabi A, Al-Khulaidi AW, Hussain A, Al-Sagheer N (2016) Main vegetation types and plant species diversity along an altitudinal gradient of Al Baha region, Saudi Arabia. Saudi J Biol Sci 23:687–697. https://doi.org/10.1016/J.SJBS.2016.02.007
Allen MA, Roberts DA, McFadden JP (2021) Reduced urban green cover and daytime cooling capacity during the 2012–2016 California drought. Urban Clim 36:100768. https://doi.org/10.1016/J.UCLIM.2020.100768
Almazroui M (2020) Changes in temperature trends and extremes over Saudi Arabia for the period 1978-2019. Adv Meteorol 2020. https://doi.org/10.1155/2020/8828421
AlQadhi S, Mallick J, Talukdar S et al (2021) Quantification of urban sprawl for past-to-future in Abha City, Saudi Arabia. CMES 129. https://doi.org/10.32604/cmes.2021.016640
Alqurashi AF, Kumar L (2017) An assessment of the impact of urbanization and land use changes in the fast-growing cities of Saudi Arabia. Geocarto Int 34:78–97. https://doi.org/10.1080/10106049.2017.1367423
Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27:93–115. https://doi.org/10.1111/J.1538-4632.1995.TB00338.X
Anselin L, Syabri I, Kho Y (2010) GeoDa: an introduction to spatial data analysis. Handb Appl Spat Anal 73–89. https://doi.org/10.1007/978-3-642-03647-7_5
Artis DA, Carnahan WH (1982) Survey of emissivity variability in thermography of urban areas. Remote Sens Environ 12:313–329. https://doi.org/10.1016/0034-4257(82)90043-8
Ayanlade A, Aigbiremolen MI, Oladosu OR (2021) Variations in urban land surface temperature intensity over four cities in different ecological zones. Sci Rep 111 11:1–17. https://doi.org/10.1038/s41598-021-99693-z
Bindajam AA, Mallick J (2020) Impact of the spatial configuration of streets networks on urban growth: A case study of Abha City, Saudi Arabia. Sustainability 12(5):1856
Bindajam AA, Mallick J, AlQadhi S et al (2020) Impacts of vegetation and topography on land surface temperature variability over the semi-arid mountain cities of Saudi Arabia. Atmos 11:762. https://doi.org/10.3390/ATMOS11070762
Congalton RG, Green K (2008) Assessing the accuracy of remotely sensed data : principles and practices. CRC Press,Taylor & Francis
Cortes C, Vapnik V, Saitta L (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1007/BF00994018
Cui F, Rafiq H, Yuan X, He H, Yang T, Kuang W, Piet T, Philippe DM (2021) Quantifying the response of surface urban heat island to urban greening in global north megacities. Sci Total Environ 801:149553. https://doi.org/10.1016/j.scitotenv.2021.149553
Das Majumdar D, 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
Detommaso M, Gagliano A, Marletta L, Nocera F (2021) Sustainable urban greening and cooling strategies for thermal comfort at pedestrian level. Sustain 13:3138. https://doi.org/10.3390/SU13063138
Dewan A, Kiselev G, Botje D, Mahmud GI, Bhuian MH, Hassan QK (2021) Surface urban heat island intensity in five major cities of Bangladesh: Patterns, drivers and trends. Sustainable Cities and Society 71:102926
Dimoudi A, Zoras S, Kantzioura A et al (2014) Use of cool materials and other bioclimatic interventions in outdoor places in order to mitigate the urban heat island in a medium size city in Greece. Sustain Cities Soc 13:89–96. https://doi.org/10.1016/J.SCS.2014.04.003
Ergun SJ, Khan MU, Rivas MF (2021) Factors affecting climate change concern in Pakistan: are there rural/urban differences? Environ Sci Pollut Res 28:34553–34569. https://doi.org/10.1007/S11356-021-13082-7/TABLES/8
Fan C, Myint SW, Kaplan S et al (2017) Understanding the impact of urbanization on surface urban heat islands—a longitudinal analysis of the oasis effect in subtropical desert cities. Remote Sens 9:672. https://doi.org/10.3390/RS9070672
Fu P, 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. https://doi.org/10.1016/J.RSE.2015.12.040
Giannini MB, Belfiore OR, Parente C, Santamaria R (2015) Land surface temperature from Landsat 5 TM images: comparison of different methods using airborne thermal data. J Eng Sci Technol Rev 8:83–90
Gioia A, Paolini L, Malizia A, Oltra-Carrió R, Sobrino JA (2014) Size matters: vegetation patch size and surface temperature relationship in foothills cities of northwestern Argentina. Urban ecosystems 17(4):1161–1174
Gohain KJ, Mohammad P, Goswami A (2021) Assessing the impact of land use land cover changes on land surface temperature over Pune city, India. Quat Int 575–576:259–269. https://doi.org/10.1016/J.QUAINT.2020.04.052
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. 101080/2279725420181474494 51:667–678. https://doi.org/10.1080/22797254.2018.1474494
Hang HT, Rahman A (2018) Characterization of thermal environment over heterogeneous surface of National Capital Region (NCR), India using LANDSAT-8 sensor for regional planning studies. Urban Clim 24:1–18. https://doi.org/10.1016/J.UCLIM.2018.01.001
Howard L (1818) The climate of London. https://www.urban-climate.org/documents/LukeHoward_Climate-of-London-V1.pdf. Accessed 18 Jul 2021
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/FIGURES/13
IPCC (2018) https://www.ipcc.ch/2018/1/. Accessed 26 Aug 2022
Jiang J, Tian G (2010) Analysis of the impact of land use/land cover change on land surface temperature with remote sensing. Procedia Environ Sci 2:571–575. https://doi.org/10.1016/J.PROENV.2010.10.062
Jimenez-Munoz JC, Sobrino JA, Skokovic D et al (2014) Land surface temperature retrieval methods from landsat-8 thermal infrared sensor data. IEEE Geosci Remote Sens Lett 11:1840–1843. https://doi.org/10.1109/LGRS.2014.2312032
Kumari B, Shahfahad, Tayyab M et al (2020) Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arab J Geosci 1319 13:1–19. https://doi.org/10.1007/S12517-020-06068-1
Lemonsu A, Viguié V, Daniel M, Masson V (2015) Vulnerability to heat waves: impact of urban expansion scenarios on urban heat island and heat stress in Paris (France). Urban Clim 14:586–605. https://doi.org/10.1016/J.UCLIM.2015.10.007
Leng P, Song X, Duan SB, Li ZL (2016) A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data. Int J Appl Earth Obs Geoinf 52:338–348. https://doi.org/10.1016/J.JAG.2016.07.004
Li X, Wang Y, Li J, Lei B (2016) Physical and socioeconomic driving forces of land-use and land-cover changes: A Case Study of Wuhan City, China. Discrete Dynamics in Nature and Society
Lu XY, Chen X, Zhao XL et al (2021) Assessing the impact of land surface temperature on urban net primary productivity increment based on geographically weighted regression model. Sci Rep 111 11:1–14. https://doi.org/10.1038/s41598-021-01757-7
Ma X, Peng S (2022) Research on the spatiotemporal coupling relationships between land use/land cover compositions or patterns and the surface urban heat island effect. Environ Sci Pollut Res 2926 29:39723–39742. https://doi.org/10.1007/S11356-022-18838-3
Mallick J, Rahman A (2012) Impact of population density on the surface temperature and micro-climate of Delhi. Curr Sci 102(12):1708–1713
Mallick J, Singh CK, Shashtri S et al (2012) Land surface emissivity retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surfaces of Delhi city. Int J Appl Earth Obs Geoinf 19:348–358. https://doi.org/10.1016/J.JAG.2012.06.002
Mallick J, Khan RA, Ahmed M, Alqadhi SD, Alsubih M, Falqi I, Hasan MA (2019) Modeling groundwater potential zone in a semi-arid region of Aseer using fuzzy-AHP and geoinformation techniques. Water 11(12):2656
Mallick J, Bindajam AA, AlQadhi S et al (2020) A comparison of four land surface temperature retrieval method using TERRA-ASTER satellite images in the semi-arid region of Saudi Arabia. Geocarto Int 1–25. https://doi.org/10.1080/10106049.2020.1790675
Mallick J, Alqadhi S, Talukdar S et al (2021) A novel technique for modeling ecosystem health condition: a case study in Saudi Arabia. Remote Sens 13:2632. https://doi.org/10.3390/RS13132632
Mallick J, Alsubih M, Ahmed M et al (2022) Assessing the spatiotemporal heterogeneity of terrestrial temperature as a proxy to microclimate and its relationship with urban hydro-biophysical parameters. Front Ecol Evol 10:312. https://doi.org/10.3389/FEVO.2022.878375/BIBTEX
Masoudi M, Tan PY, Liew SC (2019) Multi-city comparison of the relationships between spatial pattern and cooling effect of urban green spaces in four major Asian cities. Ecol Indic 98:200–213. https://doi.org/10.1016/J.ECOLIND.2018.09.058
Mathew A, Khandelwal S, Kaul N (2016) Spatial and temporal variations of urban heat island effect and the effect of percentage impervious surface area and elevation on land surface temperature: study of Chandigarh city, India. Sustain Cities Soc 26:264–277. https://doi.org/10.1016/J.SCS.2016.06.018
Matlhodi B, Kenabatho PK, Parida BP, Maphanyane JG (2021) Analysis of the future land use land cover changes in the Gaborone dam catchment using CA-Markov model: implications on water resources. Remote Sens 13:2427. https://doi.org/10.3390/RS13132427
McFeeters SK (2007) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int J Remote Sens 17:1425–1432. https://doi.org/10.1080/01431169608948714
Meyer D, Wien FT (2001) Support vector machines. R News 1(3):23–26
Moffett KB, Makido Y, Shandas V (2019) Urban-rural surface temperature deviation and intra-urban variations contained by an urban growth boundary. Remote Sens 11:2683. https://doi.org/10.3390/RS11222683
Mohammad P, Goswami A, Chauhan S, Nayak S (2022) Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India. Urban Clim 42:101116. https://doi.org/10.1016/J.UCLIM.2022.101116
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259. https://doi.org/10.1016/J.ISPRSJPRS.2010.11.001
Müller KR, Mika S, Rätsch G et al (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Networks 12:181–201. https://doi.org/10.1109/72.914517
Naikoo MW, Rihan M, IshtiaqueShahfahad M (2020) Analyses of land use land cover (LULC) change and built-up expansion in the suburb of a metropolitan city: spatio-temporal analysis of Delhi NCR using landsat datasets. J Urban Manag 9:347–359. https://doi.org/10.1016/J.JUM.2020.05.004
Niu L, Tang R, Jiang Y, Zhou X (2020) Spatiotemporal patterns and drivers of the surface urban heat island in 36 major cities in China: a comparison of two different methods for delineating rural areas. Sustain 12(478):478. https://doi.org/10.3390/SU12020478
Nuissl H, Siedentop S (2021) Urbanisation and land use change. In: Weith T, Barkmann T, Gaasch N, Rogga S, Strauß C, Zscheischler J (eds) Sustainable land management in a European context. Human-Environment Interactions, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-030-50841-8_5
Nurwanda A, Honjo T (2020) The prediction of city expansion and land surface temperature in Bogor City, Indonesia. Sustain Cities Soc 52:101772. https://doi.org/10.1016/J.SCS.2019.101772
Pal S, Ziaul S (2017) Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt J Remote Sens Sp Sci 20:125–145. https://doi.org/10.1016/J.EJRS.2016.11.003
Qiao Z, Liu L, Qin Y et al (2020) The impact of urban renewal on land surface temperature changes: a case study in the main city of Guangzhou. China. Remote Sens 12:794. https://doi.org/10.3390/RS12050794
Rouse JW Jr, Haas RH, Deering DW, Schell JA, Harlan JC (1974) Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation (No. E75-10354)
Sarif MO, Gupta RD (2022) Evaluation of seasonal ecological vulnerability using LULC and thermal state dynamics using Landsat and MODIS data: a case study of Prayagraj City, India (1987–2018). Environ Sci Pollut Res 2022:1–34. https://doi.org/10.1007/S11356-022-21225-7
Schwaab J, Meier R, Mussetti G et al (2021) The role of urban trees in reducing land surface temperatures in European cities. Nat Commun 121 12:1–11. https://doi.org/10.1038/s41467-021-26768-w
Shahfahad, Talukdar S, Rihan M et al (2021a) 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(3):3762–3790. https://doi.org/10.1007/S10668-021-01587-7
Shahfahad Rihan M, Naikoo MW et al (2021b) Urban heat island dynamics in response to land-use/land-cover change in the coastal city of Mumbai. J Indian Soc Remote Sens 49(9):2227–2247. https://doi.org/10.1007/S12524-021-01394-7
Shahfahad NMW, Towfiqul Islam ARM et al (2022) Land use/land cover change and its impact on surface urban heat island and urban thermal comfort in a metropolitan city. Urban Clim 41:101052. https://doi.org/10.1016/J.UCLIM.2021.101052
Shahfahad, Kumari B, Tayyab M et al (2020) Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arab J Geosci 1319 13:1–19. https://doi.org/10.1007/S12517-020-06068-1
Shakir Khan M, Suhail M, Alharbi T (2018) Evaluation of urban growth and land use transformation in Riyadh using Landsat satellite data. Arab J Geosci 1118 11:1–13. https://doi.org/10.1007/S12517-018-3896-5
Shi D, Yang X (2015) Support vector machines for land cover mapping from remote sensor imagery. In: Monitoring and modeling of global changes: a geomatics perspective. Springer, Dordrecht. pp 265–279
Singh RB, Grover A, Zhan J (2014) Inter-seasonal variations of surface temperature in the urbanized environment of Delhi Using Landsat thermal data. Energies 7:1811–1828. https://doi.org/10.3390/EN7031811
Sinha S, Sharma LK, Nathawat MS (2015) Improved land-use/land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. Egypt J Remote Sens Sp Sci 18:217–233. https://doi.org/10.1016/J.EJRS.2015.09.005
Sobrino JA, Jiménez-Munoz JC, El-Kharraz J et al (2010) Single-channel and two-channel methods for land surface temperature retrieval from DAIS data and its application to the Barrax site. Int J Remote Sens 25:215–230. https://doi.org/10.1080/0143116031000115210
Song H, Kim Y, Kim Y (2019) A patch-based light convolutional neural network for land-cover mapping using Landsat-8 images. Remote Sens 11:114. https://doi.org/10.3390/RS11020114
Sussman HS, Dai A, Roundy PE (2021) The controlling factors of urban heat in Bengaluru, India. Urban Clim 38:100881. https://doi.org/10.1016/J.UCLIM.2021.100881
Talukdar S, Singha P, Mahato S et al (2020) Land-use land-cover classification by machine learning classifiers for satellite observations—a review. Remote Sens 12:1135. https://doi.org/10.3390/RS12071135
Tomlinson CJ, Chapman L, Thornes JE, Baker C (2011) Remote sensing land surface temperature for meteorology and climatology: a review. Meteorol Appl 18:296–306. https://doi.org/10.1002/MET.287
Trlica A, Hutyra LR, Schaaf CL et al (2017) Albedo, land cover, and daytime surface temperature variation across an urbanized landscape. Earth’s Futur 5:1084–1101. https://doi.org/10.1002/2017EF000569
United Nations (2018) World urbanization prospects: the 2018 revision. Department of Economic and Social Affairs Population Division
Wentz EA, Anderson S, Fragkias M et al (2014) Supporting global environmental change research: a review of trends and knowledge gaps in urban remote sensing. Remote Sens 6:3879–3905. https://doi.org/10.3390/RS6053879
Zawadzka JE, Harris JA, Corstanje R (2021) A simple method for determination of fine resolution urban form patterns with distinct thermal properties using class-level landscape metrics. Landsc Ecol 36:1863–1876. https://doi.org/10.1007/S10980-020-01156-9/FIGURES/5
Zha Y, Gao J, Ni S (2010) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 24:583–594. https://doi.org/10.1080/01431160304987
Zhao J, Zhao X, Liang S et al (2020) Assessing the thermal contributions of urban land cover types. Landsc Urban Plan 204:103927. https://doi.org/10.1016/J.LANDURBPLAN.2020.103927
Zhou L, Tian Y, Baidya Roy S et al (2012) Impacts of wind farms on land surface temperature. Nat Clim Chang 27 2:539–543. https://doi.org/10.1038/nclimate1505
Zhou D, Xiao J, Bonafoni S et al (2018) Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives. Remote Sens 2019 11:48. https://doi.org/10.3390/RS11010048
Acknowledgements
The authors are thankful to the USGS Earth Explorer for making the Landsat data freely available.
Funding
“Funding for this research was given under award numbers R.G.P1/319/43 by the Deanship of Scientific Research; King Khalid University, Ministry of Education, Kingdom of Saudi Arabia.”
Author information
Authors and Affiliations
Contributions
Conceptualization, AAB, JM, ST; data curation, AAB, JM, ST; formal analysis, JM, ST; funding acquisition, AAB; methodology, JM, ST, AAB; project administration, JM; resources, S; software, JM, ST; supervision, JM; validation: ST, JM; writing—original draft, JM, ST; writing—review and editing, AR.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
“Not applicable”.
Consent for publication
“Not applicable”.
Competing interests
“The authors declare no competing interests.”
Additional information
Responsible editor: Philippe Garrigues
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Bindajam, A.A., Mallick, J., Talukdar, S. et al. Modeling the spatiotemporal heterogeneity of land surface temperature and its relationship with land use land cover using geo-statistical techniques and machine learning algorithms. Environ Sci Pollut Res 30, 106917–106935 (2023). https://doi.org/10.1007/s11356-022-23211-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-022-23211-5