[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121906-en":3,"doc-seo-121906-105":30,"detail-sidebar-cat-0-en-105":95},{"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},121906,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning and Citizen Science Approaches for Monitoring the Changing Environment - Dissertation","This dissertation combines machine learning with citizen science to address environmental monitoring needs in complex, heterogeneous settings, focusing on inundation and hurricane events. It develops radar-based lake surface extraction workflows, evaluating the Otsu image segmentation method and automating threshold selection using Sentinel-1 SAR on Google Earth Engine to support timely conservation-relevant monitoring. It also proposes a guided LDA framework to mine tweet streams during Hurricane Laura, integrating prior knowledge and validation signals to uncover temporal latent topics and enable multi-label situational awareness extraction. Pilot studies further investigate mobile app design for VGI collection and data quality considerations.","Machine Learning and Citizen Science Approaches for Monitoring the Changing  \nEnvironment  \nby  \nSulong Zhou  \na dissertation submitted in partial fulfillment of  \nthe requirements for the degree of  \nDoctor of Philosophy  \n(Environment and Resources)  \nat the  \nUniversity of Wisconsin-Madison  \n2021  \nDate of final oral examination: 12/16/2020  \nThe dissertation is approved by the following members of the Final Oral Committee: Janet Silbernagel, Professor, Planning and Landscape Architecture  \nSong Gao, Assistant Professor, Geography  \nQunying Huang, Associate Professor, Geography  \nSteve Ventura, Professor, Soil Science  \nDavid Hart, Assistant Director, Sea Grant Institute  \n©2021 – Sulong Zhou  \nall rights reserved.  \ni  \nMachine Learning and Citizen Science Approaches for Monitoring the Changing Environment  \nAbstract  \nThis dissertation will combine new tools and methodologies to answer pressing questions regarding inundation area and hurricane events in complex, heterogeneous changing environments. In addition to remote sensing approaches, citizen science and machine learning are both emerging fields that harness advancing technology to answer environmental management and disaster response questions.  \nFreshwater lakes supply a large amount ofinland water resources to sustain local and regional developments. However, some lake systems depend upon great fluctuation in water surface area. Poyang lake, the largest freshwater lake in China, undergoes dramatic seasonal and interannual variations. Timely monitoring of Poyang lake surface provides essential information on variation of water occurrence for its ecosystem conservation. Application of histogram-based image segmentation in radar imagery has been widely used to detect water surface of lakes. Still, it is challenging to select the optimal threshold. Here, we analyze the advantages and disadvantages ofa segmentation algorithm, the Otsu Method, from both mathematical and application perspectives. We implement the Otsu Method and provide reusable scripts to automatically select a threshold for surface water extraction using Sentinel-1 synthetic aperture radar (SAR) imagery on Google Earth Engine, a cloud-based platform that accelerates processing of Sentinel-1 data and auto-threshold computation. The optimal thresholds for each January from 2017 to 2020 are −14 .88, −16 .93, −16 .96 and −16 .87 respectively, and the overall accuracy achieves 92% after rectification. Furthermore, our study contributes to the update of temporal and spatial variation of Poyang lake, confirming that its surface water area fluctuated annually and tended to shrink both in the center and boundary of the lake on each January from 2017 to 2020 .  \nNatural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve realtime situation awareness (SA) information from tweets, the most effective way to mine text is using Natural Language Processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high performing supervised models require domain knowledge to specify categories and involve costly labeling tasks. This research proposes a guided Latent Dirichlet Allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model,  \nii  \nLDA topics visualization and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilized tweet information because they only assi","cbCaisdUSGeqUN8J","https://ap.wps.com/l/cbCaisdUSGeqUN8J","pdf",8927533,1,97,"English","en",105,"# Abstract\n## Radar-based lake water extraction\n## Tweet mining for Hurricane Laura situational awareness\n## Mobile app pilots for volunteered geographic information (VGI)","[{\"question\":\"What research problems does the dissertation address?\",\"answer\":\"The dissertation targets inundation-area monitoring and hurricane-event analysis in changing, heterogeneous environments, using remote sensing plus machine learning and citizen-science-style data collection.\"},{\"question\":\"How is radar imagery used to monitor lake surface water?\",\"answer\":\"It applies histogram-based image segmentation to radar imagery, analyzes the Otsu thresholding method, and implements automated threshold selection for Sentinel-1 SAR on Google Earth Engine.\"},{\"question\":\"What method is proposed for extracting information from tweets during Hurricane Laura?\",\"answer\":\"It introduces a guided Latent Dirichlet Allocation (LDA) workflow that integrates prior knowledge and validation from reports to reveal temporal latent topics and support multi-label situational awareness extraction.\"},{\"question\":\"How do the pilot studies relate to citizen science and VGI?\",\"answer\":\"They demonstrate how stakeholders can select and develop mobile apps for field observations, comparing a 3-tier architecture and outlining customization processes for participatory tools to improve data collection outcomes.\"}]","Machine Learning and Citizen Science Approaches for Monitoring the Changing Environment - 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