Student | Zhejiang A and F Unversity | China
Zihao Huang is currently pursuing a Ph.D. in Forestry at Zhejiang Agriculture & Forest University (ZAFU), focusing on remote sensing, land cover change, carbon cycle dynamics, and machine learning. With a strong academic background in geographic information science and forest management, he has dedicated his research to addressing complex environmental issues through innovative methods. His contributions to understanding forest ecosystems and carbon storage patterns are widely recognized, including a series of impactful publications in renowned journals such as Remote Sensing and IEEE Transactions on Geoscience and Remote Sensing.
Profile
Education
Zihao Huang’s academic journey began at Nanjing Tech University, where he earned his B.S. in Geographic Information Science (2014-2018). His commitment to environmental studies led him to Zhejiang Agriculture & Forest University (ZAFU), where he completed his M.S. in Forest Management in 2021 with high academic distinction (GPA: 3.68/4.0). Currently, as a Ph.D. candidate at ZAFU (expected 2025), he maintains a stellar GPA of 3.87/4.0. Throughout his academic tenure, he has been the recipient of multiple Graduate Studies Scholarships (2018-2022), further cementing his academic excellence.
Experience
Zihao’s extensive research experience has honed his expertise in various critical aspects of forestry and remote sensing. As a Research Assistant at ZAFU (2018-present), he has worked under the mentorship of Dr. Huaqiang Du and collaborated with several experts, contributing to multiple groundbreaking projects. His notable projects include the simulation of land use/land cover (LUCC) changes, estimation of forest age, and the exploration of the effects of forest cover change on above-ground carbon storage. Utilizing advanced techniques like artificial neural networks, random forests, and Google Earth Engine, he has contributed significantly to the understanding of forest dynamics in Zhejiang Province, China.
Research Interests
Zihao Huang’s primary research interests encompass remote sensing, land cover change, carbon cycle dynamics, and machine learning. His work focuses on simulating spatial patterns of land use and land cover change, assessing the impact of climate and human activities on forest ecosystems, and quantifying the carbon storage potential of subtropical forests. By combining satellite imagery, machine learning models, and carbon cycle simulations, his research aims to enhance the accuracy of carbon flux estimates and develop more effective forest management strategies.
Awards
Zihao Huang appears to be an outstanding candidate for the Best Researcher Award based on his extensive academic and research accomplishments. His research spans multiple important and cutting-edge fields such as remote sensing, land cover change, carbon cycle, and machine learning, with a particular focus on environmental and ecological systems in China.
Publications Top Note
Huang, Z., Mao, F., Du, H.*, Li, X. (in preparation). Improving the land cover classification in Zhejiang Province and analyzing the effect of land cover change on above-ground carbon storage.
Huang, Z., Li, X., Du, H.*, Zou, W., Zhou, G., Mao, F. (Minor Revision). An algorithm of forest age estimation based on the forest disturbance and recovery detection. IEEE Transactions on Geoscience and Remote Sensing.
Huang, Z., Du, H.*, Li, X., Mao, F. (2022). Simulating future LUCC by coupling climate change and human effects based on multi-phase remote sensing data. Remote Sensing, 14(7): 1698.
Huang, Z., Du, H.*, Li, X., Zhang, M., Mao, F. (2020). Spatiotemporal LUCC simulation under different RCP scenarios based on the BPNN_CA_Markov model: A case study of bamboo forest in Anji County. ISPRS International Journal of Geo-Information, 9(12): 718.
Mao, F., Du, H.*, Zhou, G., Huang, Z. (2023). Land use and cover in subtropical East Asia and Southeast Asia from 1700 to 2018. Global and Planetary Change, 226: 104157.
Xu, Y., Li, X., Du, H., Mao, F., Zhou, G., Huang, Z. (2023). Improving extraction phenology accuracy using SIF coupled with the vegetation index and mapping the spatiotemporal pattern of bamboo forest phenology. Remote Sensing of Environment, 297: 113785.
Zhang, X., Jiao, H., Chen, G.*, Shen, J., Huang, Z., Luo, H. (2022). Forest damage by super typhoon Rammasun and post-disturbance recovery using Landsat imagery and machine-learning methods. Remote Sensing, 14: 3826.
Conclusion
Zihao Huang’s research trajectory showcases his unwavering commitment to improving the scientific understanding of forest ecosystems and environmental sustainability. His proficiency in integrating machine learning with remote sensing techniques has enabled him to tackle complex issues surrounding land cover changes and carbon cycle dynamics. With numerous publications and a strong academic background, he continues to make significant strides in his field. As he nears the completion of his Ph.D. program, Zihao Huang’s research promises to contribute significantly to the scientific community’s efforts in combating climate change and managing forest resources sustainably.