[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123023-en":3,"doc-seo-123023-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123023,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Natural Hazards Monitoring Using Multiple Remote Sensing Techniques and Machine Learning Algorithms - Doctor of Philosophy Dissertation","Natural hazards often fit into two categories according to their onset speed and spatial extent: rapid onset and slow onset hazards, each posing substantial risks in densely populated urban areas. Satellite-based remote sensing provides critical information for prevention and mitigation. This dissertation presents both long- and short-term monitoring workflows that combine multiple remote sensing techniques with advanced machine-learning algorithms, illustrated through a 2021 Texas winter storm and Tianjin land subsidence.","Natural Hazards Monitoring Using Multiple Remote Sensing Techniques and Machine  \nLearning Algorithms  \nby  \nXiao Yu  \nA dissertation submitted to the Department of Earth and Atmospheric Sciences, College of Natural Sciences and Mathematics in partial fulfillment of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nin Geology  \nChair of Committee: Guoquan Wang  \nCommittee Member: Shuhab D. Khan  \nCommittee Member: Jiajia Sun  \nCommittee Member: Hongyi Li  \nUniversity of Houston  \nDecember 2022  \nCopyright 2022, Xiao Yu  \nACKNOWLEDGMENTS  \nFirst and foremost, I want to express my great and sincere thanks to my supervisor, Professor Guoquan Wang. I am incredibly grateful for his support and help over the past four years. It is a precious opportunity to study at the University of Houston as [a Ph.D. in](a Ph.D. in)[ ](a Ph.D. in)[Dr.Wang](Dr.Wang)’[s](s) group. He always encourages us to explore as much as possible and guides us to learn by ourselves.  \nI also want to say a lot of thanks to my co-supervisor, Professor Xie Hu. Her professional and patient guidance finally helps me get off the hook of many research problems. I could not have finished my publication without Dr. Hu’s consistent guidance. She is not only a teacher but a good friend. The saying goes,“Give a man a fish, and you feed him for a day. Teach aman to fish, and you feed him for a lifetime.” Thanks to Dr. Hu for your teaching me how to fish, I did learn how to work hard to be an InSAR expert.  \nI also thank my dissertation committee members for their helpful suggestions and comments on my research work: Prof. Shuhab D. Khan, Prof. Jiajia Sun, and Prof. Hongyi Li. Thanks to Dr. Lorenzo Colli for his advice on the thesis, especially in the machine learning analysis. I appreciate their time and advice in each dissertation status meeting. Their valuable advice, continuous support, and patience help me improve the final dissertation finally.  \nMy great appreciation also extends to all my group members. We Dr. Wang’s warriors fought together over the past years to do field trips, take classes, study to use new software, and share ideas. Thanks to our big brother Dr. Xiong Lin and his wife, Dr. Jia Liu, for their help and for being with me when I had problems. Thanks to Dr. Yuhao Liu for a lot of help with the research work, especially the data processing. Thanks to Gonzalo Agudelo for his accompanying and advice for writing and presentation. Thanks to Kuan for his working  \ntogether and being there to help whenever I had trouble. Thanks to them, I enjoyed the best group time in the past four years.  \nI also need to say thank you to all my good friends. It was so hard for me at the start of my [Ph.D. life](Ph.D. life), but their video calls and encouragement helped me gradually get through the most challenging. Thanks to my best friend Huili Qiao for accompanying me during my dark period. Thanks to my besties in the U.S. too: Yina Wu, Yinan Li, Bin Yan, and Jiazheng Zhang, and my brothers: Dr. Chang Liu and Dr. DJ. I appreciate your friendship, your every call, and all your love.  \nLast but not least, my family deserves endless gratitude. Thanks to my parents, Mr. & Mrs. Yu, for your support and devotion since my childhood. They are the best parents. It is my dear parents that teach me to be honest, unremitting, and happy every day. Thanks to my sister Huan’s support too. My family is my biggest motivation to move forward and finally finish the Ph.D. dissertation. To my family, I would give everything. I love you forever. It is our family’s achievement together to get the degree. I’m so proud that we finally did it!  \nFinally, all for the memory of my mom in heaven. Forever love to you.  \nABSTRACT  \nNatural hazards often fit into two categories according to the speed and extent: rapid onset and slow onset hazards. Both could pose considerable risks in urban areas with dense populations. Satellite-based remote sensing techniques provide essential information ","cbCaiofPQOSAr20a","https://ap.wps.com/l/cbCaiofPQOSAr20a","pdf",6295305,1,169,"English","en",105,"# Abstract\n# Rapid Onset Hazard Monitoring: 2021 Texas Winter Storm\n## Differential coherence from Sentinel-1 SAR imagery\n## Machine-learning estimation of snow depth\n# Slow Onset Hazard Monitoring: Tianjin Land Subsidence\n## Effects of South-to-North Water Diversion project\n## Multi-source data integration (InSAR, GPS, groundwater)\n## Principal Component Analysis for controlling factors","[{\"question\":\"How does the dissertation address rapid onset natural hazards?\",\"answer\":\"It uses Sentinel-1 SAR differential coherence to characterize surface disturbance from the 2021 Texas winter storm, then applies machine-learning methods to estimate statewide snow depth.\"},{\"question\":\"How does the dissertation monitor slow onset hazards like land subsidence?\",\"answer\":\"It quantifies Tianjin subsidence impacts using Sentinel-1A/B InSAR (2014–2021), GPS (2010–2021), and groundwater data, focusing on changes associated with the South-to-North Water Diversion project.\"},{\"question\":\"What role does Principal Component Analysis (PCA) play in the subsidence study?\",\"answer\":\"PCA is used to highlight the primary controlling factors responsible for recent land subsidence by analyzing displacement results in a high-dimensional setting.\"}]","Natural Hazards Monitoring Using Multiple Remote Sensing Techniques and Machine Learning Algorithms - 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