Image courtesy of the National Academy of Sciences Ocean Studies Board
GIS&T has traditionally provided effective technological solutions to the integration, visualization, and analysis of heterogeneous, georeferenced data on land. In recent years, our ability to measure change in the ocean is increasing, not only because of improved measuring devices and scientific techniques, but also because new GIS&T is aiding us in better understanding this dynamic environment. The domain has progressed from applications that merely collect and display data to complex simulation, modeling, and the development of new research methods and concepts.
LiDAR point cloud analysis refers to the techniques and methods used to explore LiDAR point cloud data in order to extract and visualize specific information about target features, such as land surface topography, lake bathymetry, vegetation canopy height, etc. In general, the procedures involved include noise filtering, point classification, feature extraction, quantification, and 3D reconstruction. The sequence of the procedures and the method used in each procedure can vary based on the nature of the LiDAR point cloud data, the application scenario, and the required accuracy. Recent advancements in technology, particularly drone technology, have made LiDAR data collection easier and more cost-effective. With the widespread availability of LiDAR data, point cloud analysis is facilitated by various software tools and is applied in many different domains, including urban planning, forestry inventory, biomass mapping, topography visualization, and environmental monitoring, etc.
Volumes in Geographic Information Science (GIScience) represent three-dimensional (3D) spatial phenomena that are often dynamic and have hard-to-determine boundaries. Studying their temporal evolution and dynamics is crucial for understanding and managing complex spatial systems such as atmospheric layers, aquatic systems, and subsurface structures. This chapter introduces a comprehensive framework for analyzing 3D volumes, including voxel-based models, Triangulated Irregular Networks (TIN), and boundary representation techniques. Additionally, it explores space-time volume representation, integrating temporal dynamics into 3D models to capture the evolution of phenomena over time through a graph-based spatiotemporal data framework. Practical applications in climate change detection and disaster management are highlighted, showcasing the potential of this framework to enhance predictive accuracy and support strategic decision-making.