Evaluating the impact of urbanization on the urban heat islands through integrated radius and non-linear regression approach.

التفاصيل البيبلوغرافية
العنوان:	Evaluating the impact of urbanization on the urban heat islands through integrated radius and non-linear regression approach.
المؤلفون:	Bindajam AA; Department of Architecture, College of Architecture and Planning, King Khalid University, Abha, Kingdom of Saudi Arabia., Hang HT; Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, India. hlhangstac@gmail.com., Alshayeb MJ; Department of Architecture, College of Architecture and Planning, King Khalid University, Abha, Kingdom of Saudi Arabia., Shohan AAA; Department of Architecture, College of Architecture and Planning, King Khalid University, Abha, Kingdom of Saudi Arabia., Mallick J; Department of Civil Engineering, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia.
المصدر:	Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 Jul; Vol. 31 (31), pp. 44120-44135. Date of Electronic Publication: 2024 Jun 27.
نوع المنشور:	Journal Article
اللغة:	English
بيانات الدورية:	Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE
أسماء مطبوعة:	Publication: <2013->: Berlin : Springer Original Publication: Landsberg, Germany : Ecomed
مواضيع طبية MeSH:	Urbanization*, Saudi Arabia ; Humans ; Nonlinear Dynamics ; Hot Temperature ; Cities ; Environmental Monitoring
مستخلص:	Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km ² , an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km ² , peripheral expansion of 85.40 km ² , and infilling of 3.80 km ² . At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73 °C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50 °C from 36.58 km ² in 1990 to 133.52 km ² in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km ² in the period 1990-2000 to 80.86 km ² in the period 2000-2020. Peripheral areas also grew significantly from 36.42 to 96.27 km ² , contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization. (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
References:	Bala R, Prasad R, Yadav VP (2021) Quantification of urban heat intensity with land use/land cover changes using Landsat satellite data over urban landscapes. Theoret Appl Climatol 145:1–12. https://doi.org/10.1007/s00704-021-03636-0. (PMID: 10.1007/s00704-021-03636-0) Ge G, Shi Z, Zhu Y, Yang X, Hao Y (2020) Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: performance assessment of four machine learning algorithms. Glob Ecol Conserv 22:e00971. https://doi.org/10.1016/j.gecco.2020.e00971. (PMID: 10.1016/j.gecco.2020.e00971) He BJ (2022) Urban morphology, urban ventilation and urban heat island mitigation: a methodological framework. Climate change and environmental sustainability. Springer International Publishing, Cham, pp 131–136. https://doi.org/10.1007/978-3-030-71011-1_11. (PMID: 10.1007/978-3-030-71011-1_11) He BJ (2023) Cause-related injustice, process-related injustice, effect-related injustice and regional heat action planning priorities: an empirical study in Yangtze River Delta and Chengdu-Chongqing urban agglomerations. Landsc Urban Plan 237:104800. https://doi.org/10.1016/j.landurbplan.2023.104800. (PMID: 10.1016/j.landurbplan.2023.104800) He BJ (2024) Innovate green building for urban heat mitigation and adaptation. PLOS Clim 3(2):e0000352. https://doi.org/10.1371/journal.pclm.0000352. (PMID: 10.1371/journal.pclm.0000352) He BJ, Zhao ZQ, Shen LD, Wang HB, Li LG (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. https://doi.org/10.1016/j.scs.2018.10.009. (PMID: 10.1016/j.scs.2018.10.009) Kerr YH, Lagouarde JP, Nerry F, Ottlé C (2004) Land surface temperature retrieval techniques and applications. Therm Remote Sens Land Surf Process 33–109. https://doi.org/10.1201/9780203612364.ch3. Kim SW, Brown RD (2021) Urban heat island (UHI) intensity and magnitude estimations: a systematic literature review. Sci Total Environ 779:146389. https://doi.org/10.1016/j.scitotenv.2021.146389. (PMID: 10.1016/j.scitotenv.2021.146389) Kundu D, Pandey AK (2020) World urbanisation: trends and patterns. In: Developing national urban policies: ways forward to green and smart cities (pp 13–49). Springer International Publishing. https://doi.org/10.1007/978-3-030-42488-9_2. Li H, Zhou Y, Jia G, Zhao K, Dong J (2022) Quantifying the response of surface urban heat island to urbanization using the annual temperature cycle model. Geosci Front 13(1):101141. https://doi.org/10.1016/j.gsf.2021.101141. (PMID: 10.1016/j.gsf.2021.101141) Liu H, He BJ, Gao S, Zhan Q, Yang C (2023) Influence of non-urban reference delineation on trend estimate of surface urban heat island intensity: a comparison of seven methods. Remote Sens Environ 296:113735. https://doi.org/10.1016/j.rse.2023.113735. (PMID: 10.1016/j.rse.2023.113735) Morabito M, Crisci A, Guerri G, Messeri A, Congedo L, Munafò M (2021) Surface urban heat islands in Italian metropolitan cities: tree cover and impervious surface influences. Sci Total Environ 751:142334. https://doi.org/10.1016/j.scitotenv.2020.142334. (PMID: 10.1016/j.scitotenv.2020.142334) Mshana GD, Mbilinyi B, Msigula PH (2022) Characterization and quantification of urban expansion and impact on urban planning practice in Morogoro Municipality, Tanzania. J Sci Res Rep 28(8):57–71. https://doi.org/10.9734/jsrr/2022/v28i830515. (PMID: 10.9734/jsrr/2022/v28i830515) Naikoo MW, Islam ARMT, Mallick J, Rahman A (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. (PMID: 10.1016/j.uclim.2021.101052) Nikam BR, Ibragimov F, Chouksey A, Garg V, Aggarwal SP (2016) Retrieval of land surface temperature from Landsat 8 TIRS for the command area of Mula irrigation project. Environ Earth Sci 75:1–17. https://doi.org/10.1007/s12665-016-5790-2. (PMID: 10.1007/s12665-016-5790-2) Rana VK, Suryanarayana TMV (2020) Performance evaluation of MLE, RF and SVM classification algorithms for watershed scale land use/land cover mapping using sentinel 2 bands. Remote Sens Appl: Soc Environ 19:100351. https://doi.org/10.1016/j.rsase.2020.100351. (PMID: 10.1016/j.rsase.2020.100351) Shahfahad, Talukdar S, Ali R, Nguyen KA, Naikoo MW, Liou YA, Rahman A (2022) Monitoring drought pattern for pre-and post-monsoon seasons in a semi-arid region of western part of India. Environ Monit Assess 194(6):396. https://doi.org/10.1007/s10661-022-09959-4. (PMID: 10.1007/s10661-022-09959-4) Shahfahad, Talukdar S, Rihan M, Hang HT, Bhaskaran S, Rahman A (2021) 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 1-29. https://doi.org/10.1007/s10668-021-01632-8. Shih HC, Stow DA, Chang KC, Roberts DA, Goulias KG (2022) From land cover to land use: applying random forest classifier to Landsat imagery for urban land-use change mapping. Geocarto Int 37(19):5523–5546. https://doi.org/10.1080/10106049.2021.1930846. (PMID: 10.1080/10106049.2021.1930846) Ulpiani G (2021) On the linkage between urban heat island and urban pollution island: three-decade literature review towards a conceptual framework. Sci Total Environ 751:141727. https://doi.org/10.1016/j.scitotenv.2020.141727. (PMID: 10.1016/j.scitotenv.2020.141727) Verma P, Raghubanshi AS (2019) Rural development and land use land cover change in a rapidly developing agrarian South Asian landscape. Remote Sens Appl: Soc Environ 14:138–147. https://doi.org/10.1016/j.rsase.2019.02.002. (PMID: 10.1016/j.rsase.2019.02.002) Wang L, Lu Y, Yao Y (2019) Comparison of three algorithms for the retrieval of land surface temperature from Landsat 8 images. Sensors 19(22):5049. https://doi.org/10.3390/s19225049. (PMID: 10.3390/s19225049) Zhang X, Estoque RC, Murayama Y, Ranagalage M (2021) Capturing urban heat island formation in a subtropical city of China based on Landsat images: implications for sustainable urban development. Environ Monit Assess 193:1–13. https://doi.org/10.1007/s10661-020-08798-8. (PMID: 10.1007/s10661-020-08798-8) Zhao Z, Sharifi A, Dong X, Shen L, He BJ (2021) Spatial variability and temporal heterogeneity of surface urban heat island patterns and the suitability of local climate zones for land surface temperature characterization. Remote Sens 13(21):4338. https://doi.org/10.3390/rs13214338. (PMID: 10.3390/rs13214338)
معلومات مُعتمدة:	R.G.P2/349/44 Deanship of Scientific Research, King Khalid University
فهرسة مساهمة:	Keywords: Land use and land cover (LULC) changes; Landscape metrics; Sustainable urban planning; Urban expansion; Urban heat island (UHI)
تواريخ الأحداث:	Date Created: 20240627 Date Completed: 20240716 Latest Revision: 20240716
رمز التحديث:	20240716
DOI:	10.1007/s11356-024-34051-w
PMID:	38935284
قاعدة البيانات:	MEDLINE

Find this article in full text from Springer Verlag.

Full Text Finder

الوصف
تدمد:	1614-7499
DOI:	10.1007/s11356-024-34051-w