Urban overheating assessment through prediction of surface temperatures: a case study of Karachi, Pakistan

Aslam, Bilal and Maqsoom, Ahsen and Khalid, Nauman and Ullah, Fahim ORCID: https://orcid.org/0000-0002-6221-1175 and Sepasgozar, Samad (2021) Urban overheating assessment through prediction of surface temperatures: a case study of Karachi, Pakistan. ISPRS International Journal of Geo-Information, 10 (8):539. pp. 1-22.

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Abstract

Global climate has been radically affected by the urbanization process in recent years. Karachi, Pakistan’s economic hub, is also showing signs of swift urbanization. Owing to the construction of infrastructure projects under the China-Pakistan Economic Corridor (CPEC) and associated urbanization, Karachi’s climate has been significantly affected. The associated replacement of natural surfaces by anthropogenic materials results in urban overheating and increased local temperatures leading to serious health issues and higher air pollution. Thus, these temperature changes and urban overheating effects must be addressed to minimize their impact on the city’s population. For analyzing the urban overheating of Karachi city, LST (land surface temperature) is assessed in the current study, where data of the past 20 years (2000–2020) is used. For this purpose, remote sensing data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors were utilized. The long short-term memory (LSTM) model was utilized where the road density (RD), elevation, and enhanced vegetation index (EVI) are used as input parameters. Upon comparing estimated and measured LST, the values of mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) are 0.27 K, 0.237, and 0.15% for January, and 0.29 K, 0.261, and 0.13% for May, respectively. The low MAE, MSE, and MAPE values show a higher correlation between the predicted and observed LST values. Moreover, results show that more than 90% of the pixel data falls in the least possible error range of −1 K to +1 K. The MAE, MSE and MAPE values for Support Vector Regression (SVR) are 0.52 K, 0.453 and 0.18% and 0.76 K, 0.873, and 0.26%. The current model outperforms previous studies, shows a higher accuracy, and depicts greater reliability to predict the actual scenario. In the future, based on the accurate LST results from this model, city planners can propose mitigation strategies to reduce the harmful effects of urban overheating and associated Urban Heat Island effects (UHI).


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -)
Date Deposited: 16 Aug 2021 08:54
Last Modified: 16 Aug 2021 08:54
Uncontrolled Keywords: urban overheating; land surface temperature; China Pakistan Economic Corridor; Karachi city; long short-term memory; artificial neural network; urban heat island
Fields of Research (2008): 12 Built Environment and Design > 1299 Other Built Environment and Design > 129999 Built Environment and Design not elsewhere classified
09 Engineering > 0909 Geomatic Engineering > 090903 Geospatial Information Systems
Fields of Research (2020): 33 BUILT ENVIRONMENT AND DESIGN > 3399 Other built environment and design > 339999 Other built environment and design not elsewhere classified
40 ENGINEERING > 4013 Geomatic engineering > 401302 Geospatial information systems and geospatial data modelling
Identification Number or DOI: https://doi.org/10.3390/ijgi10080539
URI: http://eprints.usq.edu.au/id/eprint/43215

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