Improving Hydrologic Connectivity Delineation Based on High-Resolution DEMs and Geospatial Artificial Intelligence

Author

Di Wu, Southern Illinois University CarbondaleFollow

Date of Award

8-1-2024

Degree Name

Doctor of Philosophy

Department

Environmental Resources & Policy

First Advisor

Li, Ruopu

Abstract

Hydrological connectivity is crucial for understanding and managing water resources, ecological processes, and landscape dynamics. High-Resolution Digital Elevation Models (HRDEMs) derived from Light Detection and Ranging (LiDAR) data offer unprecedented detail and accuracy in representing terrain features, making them invaluable for mapping hydrological networks and analyzing landscape connectivity. However, challenges persist in accurately delineating flow networks, identifying flow barriers, and optimizing computational efficiency, particularly in large-scale applications and complex terrain conditions. This dissertation addresses these challenges through a comprehensive exploration of advanced techniques in deep learning, spatial analysis, and parallel computing. A common practice is to breach the elevation of roads near drainage crossing locations to remove flow barriers, which, however, are often unavailable or with variable quality. Thus, developing a reliable drainage crossing dataset is essential to improve the HRDEMs for hydrographic delineation. Deep learning models were developed for classifying images that contain the locations of flow barriers. Based on HRDEMs and aerial orthophotos, different Convolutional Neural Network (CNN) models were trained and compared to assess their effectiveness in image classification in four different watersheds across the U.S. Midwest. The results show that most deep learning models can consistently achieve over 90% accuracies. The CNN model with a batch size of 16, a learning rate of 0.01, an epoch of 100, and the HRDEM as the sole input feature exhibits the best performance with 93% accuracy. The addition of aerial orthophotos and their derived spectral indices is insignificant to or even worsens the model’s accuracy. Transferability assessments across geographic regions show promising potential of best-fit model for broader applications, albeit with varying accuracies influenced by hydrography complexity. Based on identified drainage crossing locations, Drainage Barrier Processing (DBP), such as HRDEM excavation, is employed to remove the flow barriers. However, there's a gap in quantitatively assessing the impact of DBP on HRDEM-derived flowlines, especially at finer scales. HRDEM-derived flowlines generated with different flow direction algorithms were evaluated by developing a framework to measure the effects of flow barrier removal. The results show that the primary factor influencing flowline quality is the presence of flow accumulation artifacts. Quality issues also stem from differences between natural and artificial flow paths, unrealistic flowlines in flat areas, complex canal networks, and ephemeral drainageways. Notably, the improvement achieved by DBP is demonstrated to be more than 6%, showcasing its efficacy in reducing the impact of flow barriers on hydrologic connectivity. To overcome the computational intensity and speed up data processing, the efficiency of parallel computing techniques for GeoAI and hydrological modeling was evaluated. The performance of CPU parallel processing on High-Performance Computing (HPC) systems was compared with serial processing on desktop computers and GPU processing using Graphics Processing Units (GPUs). Results demonstrated substantial performance enhancements with GPU processing, particularly in accelerating computationally intensive tasks such as deep learning-based feature detection and hydrological modeling. However, efficiency trends exhibit nonlinear patterns influenced by factors such as communication overhead, task distribution, and resource contention. In summary, this dissertation presents a GeoAI-Hydro framework that significantly advances the quality of hydrological connectivity modeling. By integrating deep learning for accurate flow barrier identification, employing DBP to enhance flowline quality, and utilizing parallel computing to address computational demands, the framework offers a robust solution for high-quality hydrological network mapping and analysis. It paves the way for contributions to more effective water resource management, ecological conservation, and landscape planning.