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Dr. Xue Liu
East China Normal University, Shanghai, China

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0 urban heat island (UHI)
0 Urban expansion
0 urban sprawl
0 Human health risk assessment
0 Urban Structure

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Journal article
Published: 09 July 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The urban heat island (UHI) phenomenon, arising from rapid urbanization, has become a crucial research topic across various fields due to its adverse impacts on the ecological environment and human well-being. This study investigated the spatiotemporal patterns of summer UHI from 2001 to 2018 in Beijing-Tianjin-Hebei (BTH) urban agglomeration, and also examined the influence of natural and social factors on summer UHI by using the spatial regression model and ordinary regression model. We find that the mean summer UHI intensity in August was the highest at 0.76, followed by July and June (0.57 and 0.08, respectively). The results of spatiotemporal trend analysis reveal that the summer UHI of more than one-third of research districts and counties (68 of 200) have the significant increasing trends. The largest significant increasing trend was observed in Dongli District, Tianjin (0.17/year). Meanwhile, the summer UHI exhibited an apparent spatial pattern. Most of the high UHIs were dispersedly located in the southeast plain area, while low UHIs were mainly congregated in the northwest mountain area. For the relationships between summer UHI and influencing factors, different models have different the goodness of fit. Compared with the ordinary regression model, the spatial regression model performed better. And the optimal model indicated that the proportion of impervious surface and average temperature should take lead role for the summer UHI. The findings are of great help for understanding the features of summer UHI dynamic and provide a theoretical basis for optimizing urban agglomeration planning.

ACS Style

Li Hou; Wenze Yue; Xue Liu. Spatiotemporal patterns and drivers of summer heat island in Beijing-Tianjin-Hebei Urban Agglomeration, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.

AMA Style

Li Hou, Wenze Yue, Xue Liu. Spatiotemporal patterns and drivers of summer heat island in Beijing-Tianjin-Hebei Urban Agglomeration, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.

Chicago/Turabian Style

Li Hou; Wenze Yue; Xue Liu. 2021. "Spatiotemporal patterns and drivers of summer heat island in Beijing-Tianjin-Hebei Urban Agglomeration, China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.

Journal article
Published: 07 June 2021 in Land
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There is growing concern about the consequences of future urban expansion on carbon storage as our planet experiences rapid urbanization. While an increasing body of literature was focused on quantifying the carbon storage impact of future urban expansion across the globe, rare attempts were made from the comparative perspective on the same scale, particularly in Central Asia. In this study, Central Asian capitals, namely Ashkhabad, Bishkek, Dushanbe, Nur Sultan, and Tashkent, were used as cases. According to the potential impacts of BRI (Belt and Road Initiative) on urban expansion, baseline development scenario (BDS), cropland protection scenario (CPS), and ecological protection scenario (EPS) were defined. We then simulated the carbon storage impacts of urban expansion from 2019 to 2029 by using Google Earth Engine, the Future Land Use Simulation model, and the Integrated Valuation of Environmental Services and Tradeoffs model. We further explored the drivers for carbon storage impacts of future urban expansion in five capitals. The results reveal that Nur Sultan will experience carbon storage growth from 2019 to 2029 under all scenarios, while Ashkhabad, Bishkek, Dushanbe, and Tashkent will show a decreasing tendency. EPS and CPS will preserve the most carbon storage for Nur Sultan and the other four cities, respectively. The negative impact of future urban expansion on carbon storage will be evident in Ashkhabad, Bishkek, Dushanbe, and Tashkent, which will be relatively inapparent in Nur Sultan. The potential drivers for carbon storage consequences of future urban expansion include agricultural development in Bishkek, Dushanbe, and Tashkent, desert city development in Ashkhabad, and prioritized development of the central city and green development in Nur Sultan. We suggest that future urban development strategies for five capitals should be on the basis of differentiated characteristics and drivers for the carbon storage impacts of future urban expansion.

ACS Style

Yang Chen; Wenze Yue; Xue Liu; Linlin Zhang; Ye’An Chen. Multi-Scenario Simulation for the Consequence of Urban Expansion on Carbon Storage: A Comparative Study in Central Asian Republics. Land 2021, 10, 608 .

AMA Style

Yang Chen, Wenze Yue, Xue Liu, Linlin Zhang, Ye’An Chen. Multi-Scenario Simulation for the Consequence of Urban Expansion on Carbon Storage: A Comparative Study in Central Asian Republics. Land. 2021; 10 (6):608.

Chicago/Turabian Style

Yang Chen; Wenze Yue; Xue Liu; Linlin Zhang; Ye’An Chen. 2021. "Multi-Scenario Simulation for the Consequence of Urban Expansion on Carbon Storage: A Comparative Study in Central Asian Republics." Land 10, no. 6: 608.

Journal article
Published: 31 August 2020 in Remote Sensing
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Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition.

ACS Style

Wanliu Mao; Debin Lu; Li Hou; Xue Liu; Wenze Yue. Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China. Remote Sensing 2020, 12, 2817 .

AMA Style

Wanliu Mao, Debin Lu, Li Hou, Xue Liu, Wenze Yue. Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China. Remote Sensing. 2020; 12 (17):2817.

Chicago/Turabian Style

Wanliu Mao; Debin Lu; Li Hou; Xue Liu; Wenze Yue. 2020. "Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China." Remote Sensing 12, no. 17: 2817.

Research article
Published: 23 June 2020 in Complexity
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Extreme heat is the leading cause of heat-related mortality around the world. Extracting heat vulnerability information from the urban complexity system is crucial for urban health studies. Using heat vulnerability index (HVI) is the most common approach for urban planners to locate the places with high vulnerability for intervention and protection. Previous studies have demonstrated that HVI can play a vital role in determining which areas are at risk of heat-related deaths. Both equal weight approach (EWA) and principal component analysis (PCA) are the conventional methods to aggregate indicators to HVI. However, seldom studies have compared the differences between these two approaches in estimating HVI. In this paper, we evaluated the HVIs in Hangzhou in 2013, employing EWA and PCA, and assessed the accuracies of these two HVIs by using heat-related deaths. Our results show that both HVI maps showed that areas with high vulnerability are located in the central area while those with low vulnerability are located in the suburban area. The comparison between HVIEWA and HVIPCA shows significantly different spatial distributions, which is caused by the various weight factors in EWA and PCA. The relationship between HVIEWA and heat-related deaths performs better than the relationship between HVIPCA and deaths, implying EWA could be a better method to evaluate heat vulnerability than PCA. The HVIEWA can provide a spatial distribution of heat vulnerability at intracity to direct heat adaptation and emergency capacity planning.

ACS Style

Xue Liu; Wenze Yue; Xuchao Yang; Kejia Hu; Wei Zhang; Muyi Huang. Mapping Urban Heat Vulnerability of Extreme Heat in Hangzhou via Comparing Two Approaches. Complexity 2020, 2020, 1 -16.

AMA Style

Xue Liu, Wenze Yue, Xuchao Yang, Kejia Hu, Wei Zhang, Muyi Huang. Mapping Urban Heat Vulnerability of Extreme Heat in Hangzhou via Comparing Two Approaches. Complexity. 2020; 2020 ():1-16.

Chicago/Turabian Style

Xue Liu; Wenze Yue; Xuchao Yang; Kejia Hu; Wei Zhang; Muyi Huang. 2020. "Mapping Urban Heat Vulnerability of Extreme Heat in Hangzhou via Comparing Two Approaches." Complexity 2020, no. : 1-16.

Journal article
Published: 11 February 2020 in Journal of Cleaner Production
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Urban heat island (UHI) is a major urban ecological environment issue, and it requires to be comprehensively understood from the perspective of temporal changes. This study investigated UHI in Beijing by building a monthly land surface temperature (LST) dataset at a fine spatial resolution in the summer from 2003 to 2018 using a data fusion method. First, we generated the monthly Landsat-like LST images. We then analyzed the temporal patterns of UHI in the past 16 years. Finally, we explored the spatial patterns of UHI. We find that UHI in Beijing experienced two phases, including the enhanced UHI concentrated in the urban area from 2003 to 2009, and the mitigated UHI dispersed in the suburban area from 2010 to 2018. The results of temporal trend analysis show that sub-districts with significant decreasing trends of UHI mainly locate in the urban center, while sub-districts with significant increasing trends of UHI mainly locate in the suburban areas. Moreover, the results of spatial clusters analysis demonstrate that the sub-districts with high UHIs concentrate in the urban center, while those with low UHIs disperse in the suburban area. The 16-year fine spatial resolution LSTs from this study offer a reliable dataset for studying the UHI in Beijing. The information on spatiotemporal patterns of UHI is of great help for urban planners to design UHI mitigation strategies for sustainable urban development.

ACS Style

Xue Liu; Yuyu Zhou; Wenze Yue; Xuecao Li; Yong Liu; Debin Lu. Spatiotemporal patterns of summer urban heat island in Beijing, China using an improved land surface temperature. Journal of Cleaner Production 2020, 257, 120529 .

AMA Style

Xue Liu, Yuyu Zhou, Wenze Yue, Xuecao Li, Yong Liu, Debin Lu. Spatiotemporal patterns of summer urban heat island in Beijing, China using an improved land surface temperature. Journal of Cleaner Production. 2020; 257 ():120529.

Chicago/Turabian Style

Xue Liu; Yuyu Zhou; Wenze Yue; Xuecao Li; Yong Liu; Debin Lu. 2020. "Spatiotemporal patterns of summer urban heat island in Beijing, China using an improved land surface temperature." Journal of Cleaner Production 257, no. : 120529.

Journal article
Published: 01 June 2019 in Science of The Total Environment
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ACS Style

Wenze Yue; Xue Liu; Yuyu Zhou; Yong Liu. Impacts of urban configuration on urban heat island: An empirical study in China mega-cities. Science of The Total Environment 2019, 671, 1036 -1046.

AMA Style

Wenze Yue, Xue Liu, Yuyu Zhou, Yong Liu. Impacts of urban configuration on urban heat island: An empirical study in China mega-cities. Science of The Total Environment. 2019; 671 ():1036-1046.

Chicago/Turabian Style

Wenze Yue; Xue Liu; Yuyu Zhou; Yong Liu. 2019. "Impacts of urban configuration on urban heat island: An empirical study in China mega-cities." Science of The Total Environment 671, no. : 1036-1046.