Abstract
Urban land change plays an essential role in the development of country’s economy. Rapid and unplanned urban growth increases land surface temperature (LST) and reduces carbon storage (CS) by replacing the natural land use/land cover (LULC). This paper aims to monitor and predict the changes in urban growth patterns on LST and CS for the winter season in Guangzhou from 1989 to 2021. The integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model is used to assess the existing (1989-2021) scenario of CS. The Cellular Automata-Artificial Neural Network (CA-ANN) and Long Short Term Memory (LSTM) based Whale Optimization Algorithm (WOA) are used to predict the future LULC, CS and LST scenarios. The results indicate that an increase in urban areas by 63% causes an upsurge of high LST (≥34 °C) areas by 652 km2 and a decrease in carbon storage by 56,943.607 t from 1989 to 2021. In addition, the results demonstrate that 93% of lower temperature areas have higher CS capacity (65%), whereas 80% of higher temperature areas demonstrate a low CS capacity (89%). This study can provide effective mitigating measures for designing smart cities and valuable guidelines for ensuring environmental-friendly green cities.
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Ao Wang: Conceptualization, Methodology, Writing - original draft, Writing - review & editing. Maomao Zhang: Conceptualization, Methodology, Writing - original draft, Writing - review & editing. Abdulla - Al Kafy: Methodology, Writing - review & editing. Bin Tong, Daoqing Hao and Yanfei Feng: Writing - review & editing.
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Wang, A., Zhang, M., Kafy, A . et al. Predicting the impacts of urban land change on LST and carbon storage using InVEST, CA-ANN and WOA-LSTM models in Guangzhou, China. Earth Sci Inform 16, 437–454 (2023). https://doi.org/10.1007/s12145-022-00875-8
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DOI: https://doi.org/10.1007/s12145-022-00875-8