This work presents system concepts, integration efforts and results of the incorporation of recent advances in geospatial technologies, including augmented reality, virtual reality and unmanned aerial systems (UAS), into teaching and learning in the geosciences. Descriptions include the exploration of multiple technological alternatives and introduce system design and integration to enhance and innovate instructional materials in classrooms. The 3D Immersion and Geovisualization (3DIG) system, implemented at the Center for Geospatial Research at the University of Georgia incorporates augmented/customized commercial-off-the-shelf solutions for data acquisition, visualization and human-machine interaction. Through the immersive capabilities of 3DIG, students can be involved in a full data acquisition-processing-analysis workflow. Data streams are used for system integration, with emphasis to model generation/manipulation and remote sensing applications, including multispectral data acquisition/analyses, structure-from-motion based point-cloud/model generation, DEM and texture extraction, and orthomosaics. Resulting products are used with virtual and mixed reality holographic devices, a Geographic Information System (GIS) and with game engines (Unreal Engine and Unity) to create realistic multi-scale multi-theme 3D reconstructions of planet Earth, landscapes and/or objects. Among other system components, an augmented reality digital sandbox equipped with two depth cameras supports experiential learning and experimentation involving scaled down replicas of landscapes or user-defined topographies. The system allows for fast representation of landscape changes (near-real time response), which simulates fluid flow over modified terrain, as well as quantitative analyses, modeling and what-if scenarios through the integration with a GIS. The 3DIG system has been incorporated into classwork and results have been evaluated. This work introduces the interconnected and complementary technologies of 3DIG; presents lessons learned during system design; introduces system implementation and evolution (including the recent integration of new components); describes system use for hands-on and immersive experiential learning; and discusses system evaluation.

Challenges in remote sensing, including remote sensing of vegetation, include the spectral characterization of objects over space and time. One key aspect for this characterization involves the geometry of data acquisition and positional relationships between light source, the target and the remote sensor. Several configurations of goniometers have been used to acquire spectral data as a function of this geometry and this strategy has been particularly efficient when applied to the study of short canopies (e.g., grasses). Tall canopies present logistical challenges when conducting these analyses, which can be resolved by replacing physical structures (rails) with flying systems capable to conform to different canopy geometries and data acquisition requirements. This work (the Droniometer Experiment) investigates anisotropies of a forest using radiometrically calibrated images from a multispectral camera (MicaSense RedEdge) mounted on a rotary-wing unmanned aerial system programmed to follow a planned flight that simulates data acquisition by a goniometer assembled over tall canopy. The experiment used multiple planned flights, conducted to represent changes in illumination, considering sun azimuth and elevation (multiple flights per day and over the course of months). Multi-angle data acquisition was addressed by controlling aircraft position and camera pitch at regular intervals. This work presents the integration of the droniometer system, including platform and camera requirements and control, data acquisition and processing, and analyses of results for target/vegetation characterization and to support information extraction and multi-angle remote sensing. A radiative transfer model, the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) was used for comparative analysis and to further describe anisotropies in spectral responses of tall canopies.

This work reports on the design and implementation of advanced geospatial simulations using an Agent-Based Model (ABM) integrated with an augmented reality solution for interactive and immersive modeling exploration. The multi-scenario modeling framework allows for emergent phenomena and provides flexible representation of biological and physical environmental factors associated with natural and man-made systems. Augmented reality is provided by a sandbox running Tangible Landscape, based on a customization of GRASS GIS. An integrated Microsoft Kinect sensor mounted over the sandbox captures real-time topography produced by physical interactions with sand and resulting digital elevation models are ingested into the Recursive Porous Agent Simulation Toolkit (Repast) as landscape definition input. We illustrate the implementation by presenting a model system that includes a classic predator-prey relationship over a grassland habitat where sheep and wolves coexist as agents. Food sources for sheep are scattered over the landscape and are consumed as agents forage. Wolves control sheep population by actively searching for sheep and chasing individuals when their presence is detected. We simulate natural conditions by defining that the presence and movement of agents over the landscape is controlled by elevation provided by the sandbox. For instance, the presence of agents and resources can be limited to specific elevation ranges and slope is used to incorporate movement cost (energy loss) while individual agents travel over the landscape. Ecological conditions are further simulated by the consumption and regrowth of food resources. Users interact with the sandbox and the modeling effort by manually moving sand and altering landforms. This effort brings together multiple technologies and data manipulation/visualization strategies and allows for feature-rich experimentation by supporting multiple co-located and georeferenced layers (e.g., land use/land cover, soil, hydrography).