| dc.description.abstract | Background
The urban heat island (UHI), an essential topic in studying urban thermal environments, has drawn increasing attention in recent years. However, research on underlying mechanisms and drivers influencing the spatial variation of surface UHI (SUHI) and the comparisons between SUHI and canopy layer heat island (CLHI) for a complex topographic region like Taiwan have yet to be reported. Therefore, this study aims to achieve three primary objectives: (1) to explore the diurnal, seasonal, and spatial characteristics of SUHI intensity (SUHII) and its associated determinants across eleven chosen Taiwanese cities using land surface temperature (LST) data from MODIS; (2) to generate a monthly air temperature (Ta) dataset (at four overpass times and daily mean) with a resolution of 1 km employing machine learning (ML) models covering Taiwan from 2003 to 2020; and (3) to perform a comparative analysis of the spatiotemporal patterns between the SUHII and CLHI intensity (CLHII).
Methodology
This work underwent four main stages of processing, including the definition of the urban zones (stage 1), investigation of characteristics and driving factors of SUHI (stage 2), Ta estimation using ML algorithms (stage 3), and comparison between SUHI and CLHI (stage 4), to fulfill the research objectives. More specifically, in stage 1, the ISA map generated using the Random Forest algorithm was first created using the built-up area from the LULC map and the moving window technique. Next, this study used ISA percentage, water class, and elevation data to define different zones. In stage 2, UHI intensity was computed for eleven Taiwanese cities using the MODIS LST data and the refined urban zones. Next, Pearson′s correlation method and stepwise multiple regression were utilized to investigate the factors associated with the SUHII spatiotemporal patterns. In stage 3, monthly Ta datasets (daily average and four overpass times) were first developed for Taiwan, covering the period from 2003 to 2020. These datasets were developed with a spatial resolution of 1 km, utilizing ML models that integrated a range of variables, including in situ measurements, remotely sensed data, and auxiliary information. In stage 4, first, the Ta datasets created in stage 3 were used to compute the CLHI. Then, this study compared SUHII and CLHII in three aspects: (1) the differences in intensities at diurnal, seasonal, or annual scales; (2) differences in spatial distribution using the SPAtial EFficiency (SPAEF) and (3) the relationships between SUHII and CLHII employing Pearson′s correlation analysis.
Results
The main findings of this investigation include:
(1) As for the diurnal, seasonal, and spatial aspects of SUHII and its related determinants, this work′s findings reveal that SUHII tends to be greater during the daytime than at nighttime. Furthermore, northern cities of Taiwan exhibit higher SUHII intensities than southern cities. The SUHII experiences notable seasonal fluctuations, with more significant variability observed during the daytime than at nighttime. The daytime spatial distribution of SUHII is significantly influenced by factors such as the Normalized Difference Latent Heat Index (NDLI), vegetative activity, built-up intensity (BI), and human-induced emission. Conversely, nighttime SUHII is closely associated with BI, nighttime light signal, and vegetative activity. The findings from this research indicate that the factors considered explain a more significant portion of the daytime SUHII variation compared to nighttime, indicating the more complicated underlying mechanism for nighttime SUHII.
(2) As for the Ta estimation using ML models, the Extreme Gradient Boosting (XGB) regressor outperformed the other two models, demonstrating the highest level of accuracy. The model identified eight key variables through Recursive Feature Elimination (RFE). Results from variance importance analysis highlighted nighttime LST as the most critical predictor, subsequently daytime LST and month. The resultant monthly Ta dataset performed satisfactorily in a 5-fold cross-validation (5-CV) setting (R2 of 0.986, RMSE of 0.639 °C, and MAE of 0.477 °C). Notably, the XGB model consistently excelled across all four seasons and demonstrated remarkable accuracy over various time frames, including months, years, and subsets. Furthermore, this study′s XGB model yielded outstanding 5-CV performances, with R2 (MAE) of 0.981 (0.57), 0.980 (0.60), 0.986 (0.46), and 0.986 (0.45°C) at 10:30, 13:30, 22:30, and 01:30, respectively.
(3) As for the comparison of two kinds of UHIs, while this study observed a higher annual SUHII in the day than at night over all cities, an opposite pattern was found for CLHII. From a seasonal perspective, the nighttime SUHII-CLHII difference shows weak seasonal variation. In contrast, significant seasonal variations were observed for the daytime SUHII-CLHII difference, with high values for warm and low values for cold seasons, respectively. As for spatial distribution comparison, the SUHIs are relatively similar to CLHIs at night (high SPAEF) but more different in the daytime (low SPAEF). Besides, correlation analysis shows significant and positive correlations between SUHII and CLHII across cities.
Conclusion
This study′s findings provide crucial information on urban heat islands in Taiwan, such as the spatiotemporal patterns of SUHI and its driving forces and the spatiotemporal variations of the difference between CLHI and SUHII in terms of intensities and spatial distribution. Thus, this work formed a scientific basis for developing SUHI prediction and heat mitigation strategies. Also, this study suggests that the monthly Ta datasets (four overpass times and daily mean) with high accuracy can serve as a crucial factor in environmental research and diverse applications. | en_US |