Machine Learning and Citizen Science Approaches for Monitoring the Changing Environment

• Main Author: Zhou, Sulong
• Format: Dissertation
• Language: English
• Published: ProQuest Dissertations & Theses 01-01-2021
• Subjects: Artificial intelligence; Computer science; Environmental science
• ISBN: 9798728268833

This 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.Freshwater lakes supply a large amount of inland 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 of a 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.Natural 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 real time 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, LDA 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 assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multi-label references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders, and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.Citizen science and volunteered geographic information (VGI) are in high demand to encourage broader engagement of professional and unprofessional volunteers with their passions, knowledge, and experience. The prosperity of internet and mobile devices create new and increasing opportunities to sustain and maximize the contribution of spatial data from citizen science and VGI. More attentions have been paid on the quality and reliability of data collected from citizen science and VGI rather than the development process of applications and platforms for data collection. However, the questions on development are also dominant factors to affect the crowdsourcing data quality. For instance, what are the characteristics and functionalities of different user interfaces for VGI creation onsmart devices? How can stakeholders select relevant VGI for their specific tasks and needs? To addresses these questions, we introduced two pilot studies as the demonstration on how to select and develop mobile apps to collect field observations according to different research purposes. The case studies included detailed and valuable insights in the comparison metric of 3-tier architecture from both user and developer perspective. Additionally, we also shared the complete process to customize popular participatory mobile applications, Surver123 and AppStudio, provided recommendations for different stakeholders, and found that having clear requirements and being familiar with functionalities of the app development tools were two prominent features to develop and improve customized applications.