Yaping Chen

Dr. Yaping Chen


Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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8 April 2020 in Sustainability
Verified
Journal Article
Open access
PDF file
YCYaping Chen
Yaping Chen
BZBohong Zheng
Bohong Zheng
YHYinze Hu
Yinze Hu
The local climate zone (LCZ) has become a new tool for urban heat island research. Taking Chenzhou as the research object, eight urban spatial form elements and land cover elements are calculated respectively through ArcGIS, Skyhelios and ENVI software. The calculation results are then rasterized and clustered in ArcGIS to obtain the LCZ map at a resolution of 200 m. Afterwards, the land surface temperature (LST) of different local climate zones in the four seasons from 2017 to 2018 is further analyzed using one-way ANOVA F-test and Student’s t-test. The results suggest that: (1) by adding localized LCZ classes and applying the semi-automatic algorithm on the Arc-GIS platform, the final overall accuracy reaches 69.54%, with a kappa value of 0.67, (2) the compact middle-rise buildings (LCZ-2′) and open low-rise buildings (LCZ-6) heavily contribute to the high LST of the downtown area, while the large low-rise buildings (LCZ-8) cause the high LST regions in the eastern part of the town, (3) obvious land surface temperature differences can be detected in four seasons among different LCZ classes, with high LST in summer and autumn. Built-up LCZ classes generally revealed higher LSTs than land cover LCZs in all seasons. The findings of this study provide better understandings of the relationship between LCZ and LST, as well as important insights for urban planners on urban heat mitigation.

1 April 2020 in Sustainability
Verified
Journal Article
Open access
PDF file
YCYaping Chen
Yaping Chen
BZBohong Zheng
Bohong Zheng
YHYinze Hu
Yinze Hu
By exploring the cooling potential of tree quantity, ground albedo, green roofs and their combinations in local climate zone (LCZ)-4, LCZ-5, and LCZ-6, this study focuses on the optimum cooling level that can be achieved in open residential regions in Changsha. It designs and models 39 scenarios by integrating in situ measurement and ENVI-met numerical simulation and further compares cooling effects of various combinations of the cooling factors. The results show that (1) an increased number of trees and higher albedo are more effective compared to green roofs in reducing summer potential temperatures at street level (2 m high) in three LCZs. Negative correlations are observed in the pedestrian air temperature with trees and ground albedo; (2) the effects of cooling factors vary among different LCZ classes, with the increased 60% more trees leading to lower outdoor temperatures for LCZ-4 (0.28 °C), LCZ-5 (0.39 °C), and LCZ-6 (0.54 °C), while higher albedo of asphalt surface (increased by 0.4) is more effective in LCZ-4 (reaches to 0.68 °C) 14:00, compare to LCZ-5 (0.49 °C) and LCZ-6 (0.38 °C); (3) applying combined cooling methods can provoke air temperature reduction (up to 0.96 °C), especially when higher levels of tree quantities (increased by 60%) are coupled with cool ground materials (albedo increased by 0.4). The results can contribute useful information for improving thermal environment in existing residential regions and future residential planning.

20 August 2024 in Sustainability
Verified
Journal Article
Open access
PDF file
YCYaping Chen
Yaping Chen
CWChun Wang
Chun Wang
YHYinze Hu
Yinze Hu
This study aims to investigate the characteristics of energy consumption and outdoor thermal comfort within the high-density urban fabric of Changsha. Two different types of building (residential and office), as well as three building forms (point, slab, and enclosed) were analyzed under the local climate zone scheme. Utilizing the ENVI-met 5.6.1 and EnergyPlus 23.2.0 software, simulations were conducted to assess the thermal comfort and energy consumption of 144 architectural models. Then, multiple regression and spatial regression were applied to predict the energy consumption characteristics of the study area. The results showed the following: (1) In the high-density urban area of Changsha, the central business district and historic old town adjacent to the Xiangjiang River are identified as areas with high energy use intensity. (2) Among the residential categories, the point-types LCZ-3 and LCZ-6, as well as the slab-type LCZ-4, exhibit the lowest energy use intensity. In contrast, the enclosed office buildings, LCZ-2 and LCZ-5, are characterized by the highest energy use intensity. (3) Urban form parameters such as floor area ratio and building shape coefficient have a significant impact on EUIwinter, while EUIsummer is highly related to the normalized difference vegetation index and building shape coefficient (BSC). (4) The slab-type LCZ-4 stands out with its notably lower cooling and heating energy use intensity, coupled with excellent thermal comfort, making it particularly well-suited for the climatic conditions of Changsha.

17 November 2025 in The Journal of Supercomputing
Verified
Journal Article
Closed access
PDF file
LYLuxiu Yin
Luxiu Yin
YDYu Deng
Yu Deng
CWChun Wang
Chun Wang
YCYaping Chen
Yaping Chen
WLWei Liang
Wei Liang
Kuan-Ching LiKuan-Ching Li
Kuan-Ching Li