Abstract
Rapid urbanization causes potential changes in the urban landscape, resulting in significant changes in land surface temperature and outdoor thermal comfort. This urban growth has a detrimental impact on the health and comfort of residents. The comfort level experienced in any given region depends on various parameters, including atmospheric temperature, relative humidity, land use and land cover (LULC). In this study, we aim to examine the spatial variation of outdoor thermal comfort in the city of Hyderabad. To achieve this, we utilize medium-resolution Landsat 8 imageries along with in situ meteorological data. The classification of LULC is carried out using the maximum likelihood method. A machine learning tool known as Support Vector Machine (SVM) is implemented, with seven environmental indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), new built-up index (NBI), LST, brightness, greenness, and wetness to predict outdoor thermal comfort (THI). The study reveals significant variations in THI across different land covers. Barren lands exhibit the highest mean THI values (27.3), followed by built-up areas (26.9), vegetation (24.1), and water bodies (20.7). These findings indicate that barren and built-up areas are associated with higher levels of discomfort, while vegetated regions and water bodies provide more neutral to moderate comfort conditions. These results also highlight distinct spatial variations in THI across different regions of the city, demonstrating the influence of the urban landscape on outdoor thermal comfort. This research is vital for identifying specific areas within cities that require targeted mitigation strategies to enhance outdoor comfort.
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References
Abir, F. A., & Saha, R. (2021). Assessment of land surface temperature and land cover variability during winter: A spatio-temporal analysis of Pabna municipality in Bangladesh. Environmental Challenges, 4, 100167. https://doi.org/10.1016/J.ENVC.2021.100167
Ahmad, A., & Quegan, S. (2012). Analysis of maximum likelihood classification on multispectral data. Applied Mathematical Sciences, 6(129), 6425–6436.
Ali, S. B., & Patnaik, S. (2018). Thermal comfort in urban open spaces: Objective assessment and subjective perception study in tropical city of Bhopal, India. Urban Climate, 24, 954–967. https://doi.org/10.1016/J.UCLIM.2017.11.006
ASHRAE Standard 55. (2017). Retrieved on 10-MAY-2023, From: https://scholar.google.com/scholar?q=ASHRAE%20Standard%2055thermal%20environmental%20conditions%20for%20human%20occupancy,%20ASHRAE%20Inc.,%201992,%20Atlanta
Baig, M. H. A., Zhang, L., Shuai, T., & Tong, Q. (2014). Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters, 5(5), 423–431. https://doi.org/10.1080/2150704X.2014.915434
Basu, T., & Das, A. (2023). Urbanization induced degradation of urban green space and its association to the land surface temperature in a medium-class city in India. Sustainable Cities and Society, 90, 104373. https://doi.org/10.1016/J.SCS.2022.104373
Binarti, F., Koerniawan, M. D., Triyadi, S., Utami, S. S., & Matzarakis, A. (2020). A review of outdoor thermal comfort indices and neutral ranges for hot-humid regions. Urban Climate, 31, 100531. https://doi.org/10.1016/J.UCLIM.2019.100531
Boccalatte, A., Fossa, M., Thebault, M., Ramousse, J., & Ménézo, C. (2023). Mapping the urban heat Island at the territory scale: An unsupervised learning approach for urban planning applied to the Canton of Geneva. Sustainable Cities and Society, 96, 104677. https://doi.org/10.1016/J.SCS.2023.104677
Buchin, O., Hoelscher, M. T., Meier, F., Nehls, T., & Ziegler, F. (2016). Evaluation of the health-risk reduction potential of countermeasures to urban heat islands. Energy and Buildings, 114, 27–37. https://doi.org/10.1016/J.ENBUILD.2015.06.038
Canty and Associates LLC. (2013). Weatherbase entry for Hyderabad.
Chen, J., Li, M., Liu, Y., Shen, C., & Hu, W. (2010). Extract residential areas automatically by new built-up index. In: 2010 18th International Conference on Geoinformatics, Geoinformatics 2010. https://doi.org/10.1109/GEOINFORMATICS.2010.5567823
Crippen, R. E. (1990). Calculating the vegetation index faster. Remote Sensing of Environment, 34(1), 71–73. https://doi.org/10.1016/0034-4257(90)90085-Z
Deevi, B., & Chundeli, F. A. (2020). Quantitative outdoor thermal comfort assessment of street: A case in a warm and humid climate of India. Urban Climate, 34, 100718. https://doi.org/10.1016/J.UCLIM.2020.100718
Farajzadeh, H., Saligheh, M., Alijani, B., & Matzarakis, A. (2015). Comparison of selected thermal indices in the northwest of Iran. Natural Environment Change, 1(1), 1–20. https://jnec.ut.ac.ir/article_55074.html
Fei, F., Wang, L., Wang, Y., Yao, W., Fukuda, H., Xiao, Y., Tian, L., & Ji, T. (2023). A new method for evaluating the synergistic effect of urban water body and vegetation in the summer outdoor thermal environment. Journal of Cleaner Production, 414, 137680. https://doi.org/10.1016/J.JCLEPRO.2023.137680
Fleiss, J. L., & Cohen, J. (2016). The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educational and Psychological Measurement, 3, 613–619. https://doi.org/10.1177/001316447303300309
Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Imran, H. M., Hossain, A., Islam, A. K. M. S., Rahman, A., Bhuiyan, M. A. E., Paul, S., & Alam, A. (2021). Impact of land cover changes on land surface temperature and human thermal comfort in Dhaka City of Bangladesh. Earth Systems and Environment, 5(3), 667–693. https://doi.org/10.1007/S41748-021-00243-4
Kikon, N., Singh, P., Singh, S. K., & Vyas, A. (2016). Assessment of urban heat islands (UHI) of Noida City, India using multi-temporal satellite data. Sustainable Cities and Society, 22, 19–28. https://doi.org/10.1016/J.SCS.2016.01.005
Kriegler, F., M. W., N. R., & R. W. (1969). Preprocessing transformations and their effect on multispectral recognition. In: Proceedings of the 6th International Symposium on Remote Sensing of Environment., pp. 97–131.
Kumar, P., & Sharma, A. (2022). Assessing the outdoor thermal comfort conditions of exercising people in the semi-arid region of India. Sustainable Cities and Society, 76, 103366. https://doi.org/10.1016/J.SCS.2021.103366
Lau, L., Calay, R. K., Yu, I., Lun, F., & Addas, A. (2023). Machine learning techniques to map the impact of urban heat island: investigating the City of Jeddah. Land, 12(6), 1159. https://doi.org/10.3390/LAND12061159
Manavvi, S., & Rajasekar, E. (2022). Evaluating outdoor thermal comfort in urban open spaces in a humid subtropical climate: Chandigarh. India. Building and Environment, 209, 108659. https://doi.org/10.1016/J.BUILDENV.2021.108659
Mijani, N., Alavipanah, S. K., Firozjaei, M. K., Arsanjani, J. J., Hamzeh, S., & Weng, Q. (2020). Modeling outdoor thermal comfort using satellite imagery: A principle component analysis-based approach. Ecological Indicators, 117, 106555. https://doi.org/10.1016/J.ECOLIND.2020.106555
Mijani, N., Alavipanah, S. K., Hamzeh, S., Firozjaei, M. K., & Arsanjani, J. J. (2019). Modeling thermal comfort in different condition of mind using satellite images: An Ordered Weighted Averaging approach and a case study. Ecological Indicators, 104, 1–12. https://doi.org/10.1016/J.ECOLIND.2019.04.069
Mushore, T. D., Odindi, J., Dube, T., & Mutanga, O. (2017). Outdoor thermal discomfort analysis in Harare, Zimbabwe in Southern Africa. South African Geographical Journal, 100(2), 162–179. https://doi.org/10.1080/03736245.2017.1339630
Norman, M. J. T. P. C. J. S. P. G. E. (1995). The ecology of tropical food crops. Cambridge University Press (pp. 149–251). Cambridge University Press.
Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12(SUPPL. 1), S27–S31. https://doi.org/10.1016/J.JAG.2009.11.002
Rajan, E. H. S., & Amirtham, L. R. (2021). Urban heat island intensity and evaluation of outdoor thermal comfort in Chennai, India. Environment, Development and Sustainability, 23(11), 16304–16324. https://doi.org/10.1007/S10668-021-01344-W
Rawal, R., Shukla, Y., Vardhan, V., Asrani, S., Schweiker, M., de Dear, R., Garg, V., Mathur, J., Prakash, S., Diddi, S., Ranjan, S. V., Siddiqui, A. N., & Somani, G. (2022). Adaptive thermal comfort model based on field studies in five climate zones across India. Building and Environment, 219, 109187. https://doi.org/10.1016/J.BUILDENV.2022.109187
Ren, J., Shi, K., Li, Z., Kong, X., Zhou, H. A., Ren, J., Shi, K., Li, Z., Kong, X., & Zhou, H. (2023). A Review on the Impacts of Urban Heat Islands on Outdoor Thermal Comfort. Buildings, 13(6), 1368. https://doi.org/10.3390/BUILDINGS13061368
Sannigrahi, S., Rahmat, S., Chakraborti, S., Bhatt, S., & Jha, S. (2017). Changing dynamics of urban biophysical composition and its impact on urban heat island intensity and thermal characteristics: The case of Hyderabad City. India. Modeling Earth Systems and Environment, 3(2), 647–667. https://doi.org/10.1007/S40808-017-0324-X/TABLES/4
Shahfahad, Naikoo, M. W., Towfiqul Islam, A. R. M., Mallick, J., & Rahman, A. (2022a). Land use/land cover change and its impact on surface urban heat island and urban thermal comfort in a metropolitan city. Urban Climate, 41, 101052. https://doi.org/10.1016/J.UCLIM.2021.101052
Sharma, R., Pradhan, L., Kumari, M., & Bhattacharya, P. (2021). Assessing urban heat islands and thermal comfort in Noida City using geospatial technology. Urban Climate, 35, 100751. https://doi.org/10.1016/J.UCLIM.2020.100751
Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
Srikanth, K., & Swain, D. (2022). Urbanization and Land surface temperature changes over Hyderabad, a semi-arid mega city in India. Remote Sensing Applications: Society and Environment, 28, 100858. https://doi.org/10.1016/J.RSASE.2022.100858
SSSSSC, Mathew, J. C., & Varghese, A. (2022). Impact of urbanization and spatio-temporal estimation of land surface temperature in a fast-growing coastal Town in Kerala, Western Coast of Peninsular India. Remote Sensing in Earth Systems Sciences, 5(4), 207–229. https://doi.org/10.1007/S41976-022-00075-4/METRICS
Sultana, S., & Satyanarayana, A. N. V. (2018). Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: Impact of urbanization. International Journal of Remote Sensing, 39(20), 6692–6730. https://doi.org/10.1080/01431161.2018.1466072
Sultana, S., & Satyanarayana, A. N. V. (2020). Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000–2018 over a sub-tropical Indian City. Sustainable Cities and Society, 52, 101846. https://doi.org/10.1016/J.SCS.2019.101846
Suping, Z., Guanglin, M., Yanwen, W., & Ji, L. (1992). Study of the relationships between weather conditions and the marathon race, and of meteorotropic effects on distance runners. International Journal of Biometeorology, 36(2), 63–68. https://doi.org/10.1007/BF01208915
Swain, D., Roberts, G. J., Dash, J., Lekshmi, K., Vinoj, V., & Tripathy, S. (2017). Impact of rapid urbanization on the City of Bhubaneswar, India. Proceedings of the National Academy of Sciences India Section A-Physical Sciences, 87(4), 845–853. https://doi.org/10.1007/S40010-017-0453-7/TABLES/1
Taleghani, M. (2018). The impact of increasing urban surface albedo on outdoor summer thermal comfort within a university campus. Urban Climate, 24, 175–184. https://doi.org/10.1016/J.UCLIM.2018.03.001
Taleghani, M., & Berardi, U. (2018). The effect of pavement characteristics on pedestrians’ thermal comfort in Toronto. Urban Climate, 24, 449–459. https://doi.org/10.1016/J.UCLIM.2017.05.007
The World’s Cities in 2018. (2018). United Nations Department of Economic and Social Affairs. https://www.un.org/en/events/citiesday/assets/pdf/the_worlds_cities_in_2018_data_booklet.pdf
Thom, E.C. (1959). The Discomfort Index. Weatherwise, 12, 57–60. http://dx.doi.org/10.1080/00431672.1959.9926960
Toy, S., Yilmaz, S., & Yilmaz, H. (2007). Determination of bioclimatic comfort in three different land uses in the city of Erzurum, Turkey. Building and Environment, 42(3), 1315–1318. https://doi.org/10.1016/J.BUILDENV.2005.10.031
Vapnik, V. N. (2000). The nature of statistical learning theory. The Nature of Statistical Learning Theory. https://doi.org/10.1007/978-1-4757-3264-1
Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483. https://doi.org/10.1016/J.RSE.2003.11.005
Xiong, J., Lian, Z., Zhou, X., You, J., & Lin, Y. (2015). Effects of temperature steps on human health and thermal comfort. Building and Environment, 94(P1), 144–154. https://doi.org/10.1016/J.BUILDENV.2015.07.032
Xu, H., Hu, X., Guan, H., & He, G. (2017). Development of a fine-scale discomfort index map and its application in measuring living environments using remotely-sensed thermal infrared imagery. Energy and Buildings, 150, 598–607. https://doi.org/10.1016/J.ENBUILD.2017.06.003
Ziaul, S., & Pal, S. (2019). Assessing outdoor thermal comfort of English Bazar Municipality and its surrounding, West Bengal, India. Adspr, 64(3), 567–580. https://doi.org/10.1016/J.ASR.2019.05.001
Acknowledgements
The first author of the manuscript, Hari Prasad, gratefully acknowledges the Indian Institute of Technology Kharagpur for conduct of the research work as a part of PhD work and providing the MHRD, Government of India, fellowship. The authors thank the USGS Earth resources observational systems (EROS) data center for freely providing Landsat imagery and Telangana state government for providing the freely accessible meteorological data used in the study.
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by PSHP and ANVS. The first draft of the manuscript was written by PSHP and correction done by ANVS. All authors read and approved the final manuscript.
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Prasad, P.S.H., Satyanarayana, A.N.V. Assessment of Outdoor Thermal Comfort Using Landsat 8 Imageries with Machine Learning Tools over a Metropolitan City of India. Pure Appl. Geophys. 180, 3621–3637 (2023). https://doi.org/10.1007/s00024-023-03328-5
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DOI: https://doi.org/10.1007/s00024-023-03328-5