Summertime Microscale Assessment and Prediction of Urban Thermal Comfort Zone Using Remote-Sensing Techniques for Kuwait

Springer Science and Business Media LLC - Tập 7 - Trang 435-456 - 2023
Ahmad E. AlDousari1, Abdulla - Al Kafy2, Milan Saha3,4, Md. Abdul Fattah5, Arpita Bakshi5, Zullyadini A. Rahaman6
1Department of Geography, Kuwait University, Kuwait City, Kuwait
2Department of Geography and the Environment, The University of Texas at Austin, Austin, USA
3Department of Urban and Regional Planning, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh
4School of Environmental Science and Management, Independent University of Bangladesh, Dhaka, Bangladesh
5Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh
6Department of Geography and Environment, Faculty of Human Sciences, Sultan Idris Education University, Tanjung Malim, Malaysia

Tóm tắt

Urbanization significantly accelerates the replacement of natural land-use and land-cover (LULC) classes, which can raise the temperature and diminish thermal comfort zone (TCZ), potentially negatively affecting the environment and human health. This study assesses and predicts the impacts of LULC change on directional shrinkage of the TCZ in Kuwait, using Landsat images and cellular automata (CA) and artificial neural networks (ANN) algorithms from 1991 to 2031. The analysis revealed a rapid urban expansion (40%) in south-east (SE), north-east (NE), and north-west (NW) directions and shrinkage of TCZ (25% area with a very uncomfortable thermal condition) in N–NW and SW directions, from 1991 to 2021. The predicted scenario showed an increase in urban areas from 44% (2021) to 47% (2026) and 52% (2031). In contrast, very uncomfortable TCZ (35% in 2026 and 40% in 2031) was found concentrated around urban areas and bare land toward N–NE and N–NW directions. The study proposes effective and sustainable strategies to mitigate the shrinkage of TCZ, including zero-soil policies, planned landscape design, artificial water bodies, and rooftop gardens. This study will be an essential tool for promoting sustainable development in Kuwait by helping urban planners and policymakers realize the impacts of urbanization and land-use change on the TCZ.

Tài liệu tham khảo

AlDousari AE, Kafy A-A, Saha M, Fattah MA et al (2022) Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait. Sustainable Cities and Society. 86. https://doi.org/10.1016/j.scs.2022.104107 Alahmad B, Vicedo-Cabrera AM, Chen K et al (2022) Climate change and health in Kuwait: temperature and mortality projections under different climatic scenarios. Environ Res Lett. https://doi.org/10.1088/1748-9326/ac7601 Al-Awadhi JM (2001) Impact of gravel quarrying on the desert environment of Kuwait. Environ Geol 41(3–4):365–371 Al-Nakeeb Y, Lyons M, Dodd LJ, Al-Nuaim A (2015) An investigation into the lifestyle, health habits and risk factors of young adults. IJERPH 12(4):4380–4394 Al-Sharif AAA, Pradhan B (2015) A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS. Geocarto Int 30(8):858–881. https://doi.org/10.1080/10106049.2014.997308 Amit S, Kafy AA, Barua L (2022) Systemic barriers to financial inclusion in the banking sector of Bangladesh. In: Endress T, Badir YF (eds) Business and management in Asia: digital innovation and sustainability. Springer, Singapore, pp 121–138 Ashtiani A, Mirzaei PA, Haghighat F (2014) Indoor thermal condition in urban heat island: comparison of the artificial neural network and regression methods prediction. Energy Build 76:597–604 Bennett KP, Mangasarian OL (1992) Robust linear programming discrimination of two linearly inseparable sets. Optim Methods Softw 1(1):23–34 Bokaie M, Zarkesh MK, Arasteh PD, Hosseini A (2016) Assessment of Urban Heat Island based on the relationship between land surface temperature and Land Use/Land Cover in Tehran. Sustain Cities Soc 23:94–104. https://doi.org/10.1016/j.scs.2016.03.009 Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Haussler D (ed) COLT ‘92: proceedings of the fifth annual workshop on computational learning theory. ACM, New York, pp 144–152 Britannica (2021) Climate of Kuwait. Britannica. [Online] Available at: https://www.britannica.com/place/Kuwait/Climate. Accessed 01 Dec 2022 Caballero CB, Ruhoff A, Biggs T (2022) Land use and land cover changes and their impacts on surface-atmosphere interactions in Brazil: a systematic review. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2021.152134 Camargo FF, Sano EE, Almeida CM, Mura JC, Almeida T (2019) A comparative assessment of machine-learning techniques for land use and land cover classification of the Brazilian tropical savanna using ALOS-2/PALSAR-2 polarimetric images. Remote Sens 11:1600 Carranza-García M, García-Gutiérrez J, Riquelme JC (2019) A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sens. https://doi.org/10.3390/rs11030274 Chen F, Kusaka H, Bornstein R et al (2011) The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems. Int J Climatol 31(2):273. https://doi.org/10.1002/joc.2158 Chen Y, Yang J, Yang R, Xiao X, Xia J (Cecilia) (2022) Contribution of urban functional zones to the spatial distribution of urban thermal environment. Build Environ 216:109000. https://doi.org/10.1016/j.buildenv.2022.109000 CIA. Central Intelligence Agency (2015) Kuwait. The World Factbook De Jong SM, Shen Y, de Vries J, Bijnaar G, van Maanen B, Augustinus P, Verweij P (2021) Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm. Int J Appl Earth Observ Geoinf 97:102293. https://doi.org/10.1016/j.jag.2020.102293 Dissanayake DMSLB, Morimoto T, Ranagalage M, Murayama Y (2019) Land-use/land-cover changes and their impact on surface urban heat islands: case study of Kandy City, Sri Lanka. Climate 7(8):1–20. https://doi.org/10.3390/cli7080099 Elmahdy S, Mohamed M, Ali T (2020) Land use/land cover changes impact on groundwater level and quality in the northern part of the United Arab Emirates. Remote Sens 12:1715. https://doi.org/10.3390/rs12111715 Eniolorunda NB, Mashi SA, Nsofor GN (2016) Toward achieving a sustainable management: characterization of land use/land cover in Sokoto Rima floodplain. Nigeria. Environ Dev Sustain 19:1855–1878. https://doi.org/10.1007/s10668-016-9831-6 Faisal AA, Kafy AA, Al Rakib A, Akter KS, Jahir DMdA, Sikdar MdS, Ashrafi TJ, Mallik S, Rahman MdM (2021) Assessing and predicting land use/land cover, land surface temperature and urban thermal field variance index using Landsat imagery for Dhaka Metropolitan area. Environ Chall 4(April):100192. https://doi.org/10.1016/j.envc.2021.100192 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. https://doi.org/10.1016/j.ecolind.2019.105798 Gumma MK, Thenkabail PS, Teluguntla PG, Oliphant A, Xiong J, Giri C, Pyla V, Dixit S, Whitbread AM (2020) Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud. Gisci Remote Sens 57(3):302–322. https://doi.org/10.1080/15481603.2019.1690780 Gunawardena KR, Wells MJ, Kershaw T (2017) Utilising green and bluespace to mitigate urban heat island intensity. Sci Total Environ 584–585:1040–1055. https://doi.org/10.1016/j.scitotenv.2017.01.158 Hamblin AL, Youngsteadt E, López-Uribe MM, Frank SD (2017) Physiological thermal limits predict differential responses of bees to urban heat-island effects. Biol Let 13(6):20170125 Hauschild T, Jentschel M (2001) Comparison of maximum likelihood estimation and chi-square statistics applied to counting experiments. Nucl Instrum Methods Phys Res Sect A 457(1–2):384–401. https://doi.org/10.1016/S0168-9002(00)00756-7 Heimhuber V, Tulbure MG, Broich M (2018) Addressing spatio-temporal resolution constraints in Landsat and MODIS-based mapping of large-scale floodplain inundation dynamics. Remote Sens Environ 211:307–320. https://doi.org/10.1016/j.rse.2018.04.016 Hussein K, Alkaabi K, Ghebreyesus D, Liaqat MU, Sharif HO (2020) Land use/land cover change along the Eastern Coast of the UAE and its impact on flooding risk. Geomat Nat Hazard Risk 11(1):112–130. https://doi.org/10.1080/19475705.2019.1707718 Imran HM, Hossain A, Shammas MI, Das MK, Islam MR, Rahman K, Almazroui M (2022) Land surface temperature and human thermal comfort responses to land use dynamics in Chittagong city of Bangladesh. Geomatics, Natural Hazards and Risk 13(1):2283–2312. https://doi.org/10.1080/19475705.2022.2114384 Islam ARMT, Salam R, Yeasmin N et al (2021) Spatiotemporal distribution of drought and its possible associations with ENSO indices in Bangladesh. Arab J Geosci 14:2681. https://doi.org/10.1007/s12517-021-08849-8 Jamali A (2019) Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran. SN Appl Sci 1:1448 Kafy A-A, Naim MNH, Subramanyam G, Faisal A-A, Ahmed NU, Al Rakib A, Kona MA, Sattar GS (2021a) Cellular Automata approach in dynamic modeling of land cover changes using RapidEye images in Dhaka, Bangladesh. Environ Chall 4:100084 Kafy AAl, Faisal AAl, Rahman MS, Islam M, Al Rakib A, Islam MA, Khan MHH, Sikdar MS, Sarker MHS, Mawa J, Sattar GS (2021b) Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh. Sustain Cities Soc. https://doi.org/10.1016/j.scs.2020.102542 Kafy A-A, Saha M, Faisal A-A, Rahaman ZA, Rahman MT, Liu D, Fattah MdA, Al Rakib A, AlDousari AE, Rahaman SN, Hasan MZ, Ahasan MAK (2022a) Predicting the impacts of land use/land cover changes on seasonal urban thermal characteristics using machine learning algorithms. Build Environ 217:109066. https://doi.org/10.1016/j.buildenv.2022.109066 Khraibut N (2022) New smart sustainable city development in Kuwait. In: World Urban Forum 11th session. Katowice, Poland. https://wuf.unhabitat.org/event/new-smart-sustainable-city-development-kuwait. Accessed 01 Dec 2022 Kusumawardani KP, Hidayati IN (2022) Analysis of urban heat island and urban ecological quality based on remote sensing imagery transformation in Semarang city. IOP Conf. Series: Earth and Environmental Science 1089:012037 Kilani, M. 2014. Building and Construction Sector in Kuwait. Flanders Investment & Trade Market Survey. Economic & Commercial Office. https://www.flandersinvestmentandtrade.com/export/sites/trade/files/market_studies/343141002161954/343141002161954_1.pdf. Accessed 01 Dec 2022 Le Treut H, Somerville R, Cubasch U, Ding Y, Mauritzen C, Mokssit A, Peterson T, Prather M (2007) Historical Overview of Climate Change. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds.) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, New York, NY, USA Lee W-S, Jung S-G (2014) The application of a prediction model on land surface temperature using artificial neural network and scenario: focused on Changwon in South Korea. J Korea Plan Assoc 49(1):263. https://doi.org/10.17208/jkpa.2014.02.49.1.263 Li Y, Zhao X (2012) An empirical study of the impact of human activity on long-term temperature change in China: a perspective from energy consumption. J Geophys Res. https://doi.org/10.1029/2012JD018132 Li X, Chen W, Cheng X, Wang LA (2016) Comparison of machine learning algorithms for mapping of complex surface-mined and agricultural landscapes using ZiYuan-3 stereo satellite imagery. Remote Sens 8:514 Ma L, Li M, Ma X, Cheng L, Du P, Liu Y (2017) A review of supervised object-based land-cover image classification. ISPRS J Photogramm Remote Sens 130:277–293 Mansour S, Al-Belushi M, Al-Awadhi T (2020) Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Policy 91:104414 Mansourmoghaddam M, Rousta I, Zamani M, Mokhtari MH, Karimi Firozjaei M, Alavipanah SK (2021) Study and prediction of land surface temperature changes of Yazd city: assessing the proximity and changes of land cover. J RS GIS Nat Resour 12(4):1–27 Mansourmoghaddam M, Rousta I, Zamani MS, Mokhtari MH, Karimi Firozjaei M, Alavipanah SK (2022) Investigating and modeling the effect of the composition and arrangement of the landscapes of Yazd City on the land surface temperature using machine learning and Landsat-8 and Sentinel-2 data. Iran J Remote Sens GIS Mondal MS, Sharma N, Garg PK, Kappas M (2016) Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. Egypt J Remote Sens Space Sci 19(2):259–272. https://doi.org/10.1016/j.ejrs.2016.08.001 Mountrakis G, Im J, Ogole C (2011a) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66(3):247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001 Mountrakis G, Im J, Ogole C (2011b) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259 Naim MdNH, Kafy AA (2021) Assessment of urban thermal field variance index and defining the relationship between land cover and surface temperature in Chattogram city: a remote sensing and statistical approach. Environ Chall 4:100107. https://doi.org/10.1016/j.envc.2021.100107 Nugroho NY, Tryadi S, Wonorahardjo S (2022) Effect of high-rise buildings on the surrounding thermal environment. Build Environ. https://doi.org/10.1016/j.buildenv.2021.108393 Onilude OO, Vaz E (2021) Urban sprawl and growth prediction for Lagos using GlobeLand30 data and cellular automata model. Sci 3(2):23. https://doi.org/10.3390/sci3020023 Pontius RG Jr, Millones M (2011) Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens 32(15):4407–4429 Ren Z, Fu Y, Dong Y, Zhang P, He X (2022) Rapid urbanization and climate change significantly contribute to worsening urban human thermal comfort: a national 183-city, 26-year study in China. Urban Clim 43:101154. https://doi.org/10.1016/j.uclim.2022.101154 Sailor DJ (2011) A review of methods for estimating anthropogenic heat and moisture emissions in the urban environment. Int J Climatol 31(2):189–199. https://doi.org/10.1002/joc.2106 Sánchez-Espinosa A, Schröder C (2019) Land use and land cover mapping in wetlands one step closer to the ground: Sentinel-2 versus Landsat 8. J Environ Manag 247:484–498. https://doi.org/10.1016/j.jenvman.2019.06.084 Saputra MH, Lee HS (2019) Prediction of land use and land cover changes for North Sumatra, Indonesia, using an artificial-neural-network-based cellular automaton. Sustainability (switz). https://doi.org/10.3390/su11113024 Saha M, Kafy A-A, Bakshim A, Faisal, A-A-, et al (2022) Modelling microscale impacts assessment of urban expansion on seasonal surface urban heat island intensity using neural network algorithms. Energy and Buildings. 275. https://doi.org/10.1016/j.enbuild.2022.112452 Shastri S, Singh P, Verma P, Kumar Rai P, Singh AP (2020) Land cover change dynamics and their impacts on thermal environment of Dadri block, Gautam budh Nagar, India. J Landsc Ecol (czech Republic) 13(2):1–13. https://doi.org/10.2478/jlecol-2020-0007 Singh P, Kikon N, Verma P (2017) Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustain Cities Soc 32:100–114. https://doi.org/10.1016/j.scs.2017.02.018 Solecki WD, Rosenzweig C, Parshall L, Pope G, Clark M, Cox J, Wiencke M (2005) Mitigation of the heat island effect in urban New Jersey. Glob Environ Change Part b: Environ Hazards 6(1):39–49. https://doi.org/10.1016/j.hazards.2004.12.002 Talukdar S, Singha P, Mahato S, Pal S, Liou YA, Rahman A (2020) Land-use land-cover classification by machine learning classifiers for satellite observations—a review. Remote Sens 12(7):1135. https://doi.org/10.3390/rs12071135 Tarek MO, Amit S, Al Kafy A (2022) Sharing Economy: Conceptualization, Motivators and Barriers, and Avenues for Research in Bangladesh. In: Rahman M, Goel R, Gomes A, Uzzaman M (Eds.) Redefining Global Economic Thinking for the Welfare of Society, IGI Global, pp. 57–74 https://doi.org/10.4018/978-1-7998-8258-9.ch004. Uddin S, Al Ghadban AN, Al Dousari A, Al Murad M, Al Shamroukh D (2010) A remote sensing classification for land-cover changes and micro-climate in Kuwait. Int J Sustain Dev Plan 5(4):367–377. https://doi.org/10.2495/SDP-V5-N4-367-377 Ullah S, Tahir AA, Akbar TA, Hassan QK, Dewan A, Khan AJ, Khan M (2019) Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the lower Himalayan region. Sustainability 11(19):5492 Vinayak B, Lee HS, Gedem S (2021) Prediction of land use and land cover changes in Mumbai city, India, using remote sensing data and a multilayer perceptron neural network-based Markov Chain model. Sustainability (switz) 13(2):1–22. https://doi.org/10.3390/su13020471 Vohra A (2021) The Middle East Is Becoming Literally Uninhabitable. [Online] Available at: https://foreignpolicy.com/2021/08/24/the-middle-east-is-becoming-literally-uninhabitable/. Accessed 21 Mar 2022 Wang M, Zhang Z, Hu T, Wang G, He G, Zhang Z, Li H, Wu Z, Liu X (2020) An efficient framework for producing Landsat-based land surface temperature data using google earth engine. IEEE J Sel Top Appl Earth Observ Remote Sens 13:4689–4701. https://doi.org/10.1109/JSTARS.2020.3014586 Yeneneh N, Elias E, Feyisa GL (2022) Detection of land use/land cover and land surface temperature change in the Suha Watershed, Northwestern Highlands of Ethiopia. Environ Chall 7:100523. https://doi.org/10.1016/j.envc.2022.100523 Zhang Z, He G, Wang M, Long T, Wang G, Zheng X, Jiao W (2016) Towards an operational method for land surface temperature retrieval from Landsat 8 data. Remote Sens Lett 7(3):279–288. https://doi.org/10.1080/2150704X.2015.1130877 Zhou W, Qian Y, Li X, Li W, Han L (2014) Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures. Landsc Ecol 29(1):153–167. https://doi.org/10.1007/s10980-013-9950-5