Urban Overheating Assessment through Prediction of Surface Temperatures: A Case Study of Karachi, Pakistan

ISPRS International Journal of Geo-Information

Nauman Khalid¹  Ahsen Maqsoom¹  Bilal Aslam²  Samad Sepasgozar³  Fahim Ullah⁴ 

[1] Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt 47040, Pakistan;Department of Earth Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan;School of Built Environment, University of New South Wales, Kensington, Sydney 2052, Australia;School of Civil Engineering and Surveying, University of Southern Queensland, Springfield, Ipswich 4300, Australia;

关键词: urban overheating;    land surface temperature;    China Pakistan Economic Corridor;    Karachi city;    long short-term memory;    artificial neural network;   

DOI  :  10.3390/ijgi10080539

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown