张颖，新葡的京集团35222vip博雅博士后获得者

合作导师：黄清华教授

研究领域：地震红外遥感 统计地震学 热红外遥感 城市遥感

学习经历：

2012年-2016年 武汉大学遥感信息工程学院 工学学士

2016年-2021年 中国科学院空天信息创新研究院 理学博士

2019年-2020年 苏黎世联邦理工学院 访问学者 联合培养博士

代表作：

Zhang, Y., & Meng, Q. (2019). A statistical analysis of TIR anomalies extracted by RSTs in relation to an earthquake in the Sichuan area using MODIS LST data. Nat. Hazards Earth Syst. Sci., 19, 535-549

Zhang, Y., Meng, Q., Ouillon, G., Sornette, D., Ma, W., Zhang, L., Zhao, J., Qi, Y., & Geng, F. (2021). Spatially variable model for extracting TIR anomalies before earthquakes: Application to Chinese Mainland. Remote Sensing of Environment, 267, 112720

Zhang, Y., Meng, Q., Ouillon, G., Zhang, L., Hu, D., Ma, W., & Sornette, D. (2021). Long-Term Statistical Evidence Proving the Correspondence between TIR Anomalies and Earthquakes is Still Absent. Eur. Phys. J. Special Topics, 133-150

Zhang, Y., Meng, Q., Wang, Z., Lu, X., & Hu, D. (2021). Temperature Variations in Multiple Air Layers before the Mw 6.2 2014 Ludian Earthquake, Yunnan, China. Remote Sensing, 13, 884

Meng, Q., & Zhang, Y. (2021). Discovery of Spatial-temporal Causal Interactions Between Thermal and Methane Anomalies Associated with the Wenchuan Earthquake. Eur. Phys. J. Special Topics, 247-261

Yang, X., C. Wang, X. Xi, Y. Wang, Y. Zhang, and G. Zhou (2021), Footprint Size Design of Large-Footprint Full-Waveform LiDAR for Forest and Topography Applications: A Theoretical Study, IEEE Transactions on Geoscience and Remote Sensing, 59(11), 9745-9757, doi:10.1109/TGRS.2021.3054324.

Hu, D., Q. Meng, L. Zhang, and Y. Zhang (2020), Spatial quantitative analysis of the potential driving factors of land surface temperature in different “Centers” of polycentric cities: A case study in Tianjin, China, Science of The Total Environment, 706, 135244, doi:https://doi.org/10.1016/j.scitotenv.2019.135244.

Meng, Q., D. Hu, Y. Zhang, X. Chen, L. Zhang, and Z. Wang (2022), Do industrial parks generate intra-heat island effects in cities? New evidence, quantitative methods, and contributing factors from a spatiotemporal analysis of top steel plants in China, Environmental Pollution, 292, 118383, doi:https://doi.org/10.1016/j.envpol.2021.118383.