Developing a Spatiotemporal Model to Forecast Land Surface Temperature: A Way Forward for Better Town Planning

Umer Khalil, Umar Azam, Bilal Aslam, Israr Ullah, Aqil Tariq (), Qingting Li () and Linlin Lu
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Umer Khalil: ITC Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat, 997514 AE Enschede, The Netherlands
Umar Azam: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Islamabad 47040, Pakistan
Bilal Aslam: School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
Israr Ullah: Division of Earth Sciences and Geography, RWTH Aachen University, 52062 Aachen, Germany
Aqil Tariq: Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, 775 Stone Boulevard, Starkville, MS 39762, USA
Qingting Li: Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Linlin Lu: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Sustainability, 2022, vol. 14, issue 19, 1-21

Abstract: The change in the local climate is attributed primarily to rapid urbanization, and this change has a strong influence on the adjacent areas. Lahore is one of the fast-growing metropolises in Pakistan, representing a swiftly urbanizing cluster. Anthropogenic materials sweep the usual land surfaces owing to the rapid urbanization, which adversely influences the environment causing the Surface Urban Heat Island (SUHI) effect. For the analysis of the SUHI effect, the parameter of utmost importance is the Land Surface Temperature (LST). The current research aimed to develop a model to forecast the LST to evaluate the SUHI effect on the surface of the Lahore district. For LST prediction, remote sensing data from Advanced Spaceborne Thermal Emission and the Reflection Radiometer Global Digital Elevation Model and Moderate-Resolution Imaging Spectroradiometer sensor are exploited. Different parameters are used to develop the Long Short-Term Memory (LSTM) model. In the present investigation, for the prediction of LST, the input parameters to the model included 10 years of LST data (2009 to 2019) and the Enhanced Vegetation Index (EVI), road density, and elevation. Data for the year 2020 are used to validate the outcomes of the LSTM model. An assessment of the measured and model-forecasted LST specified that the extent of mean absolute error is 0.27 K for both periods. In contrast, the mean absolute percentage error fluctuated from 0.12 to 0.14%. The functioning of the model is also assessed through the number of pixels of the research area, classified based on the error in the forecasting of LST. The LSTM model is contrasted with the Artificial Neural Network (ANN) model to evaluate the skill score factor of the LSTM model in relation to the ANN model. The skill scores computed for both periods expressed absolute values, which distinctly illustrated the efficiency of the LSTM model for better LST prediction compared to the ANN model. Thus, the LST prediction for evaluating the SUHI effect by the LSTM model is practically acceptable.

Keywords: Land Surface Temperature; Urban Heat Island; EVI; Road Density; DEM; Long Short-Term Memory; ANN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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