This session consists of lightning talks highlighting research being performed by members of ASPRS Student Chapters around the country, under the guidance of their faculty advisors.
2:00 PM – 2:10 PM – Lidar Derived Road Profiles to Identify Low Clearance Railroad Crossings
Automatic evaluation of roadway and rail vertical geometry is essential for identifying grade crossings where low ground clearance vehicles are at risk of becoming immobilized. Despite long-standing design guidance, most crossing inventories do not include detailed vertical profile information, and research on low clearance or hump crossings has relied mainly on manual inspection or limited field studies. As a result, many hazardous crossings remain unclassified, and the use of modern high-resolution LiDAR for this purpose has received little systematic attention.
To address this gap, we develop an automated workflow that uses classified LiDAR, vector road and rail data, and dense transect sampling to derive elevation profiles at road and rail intersections. A unified clearance metric based on AASHTO (American Association of State Highway and Transportation Officials) tolerances at the rail, at 2 feet, and at 30 feet provides consistent categorization of crossings as at grade, low clearance, or superelevated without requiring incident data.
Results show that LiDAR derived profiles reliably detect geometric conditions associated with vehicle hang ups, conditions that are not captured in current inventories. This work establishes a scalable foundation for evaluating grade crossing vertical geometry and supports future comparison as automated methods continue to evolve.
Meghan Touat, University of Maryland
2:10 PM – 2:20 PM – Capturing the King Tide Impact: A Case Study on Quantifying Coastal Erosion Using Terrestrial Laser Scanning
As sea levels rise and extreme weather events become more frequent, the ability to rapidly quantify coastal erosion is important for hazard resilience planning. This case study presents a high-resolution change analysis of the bluff and beach systems at Beverly Beach, Oregon, capturing the immediate impacts of the November 2025 King Tide event. Erosion at Beverly Beach is of immediate concern to the stability of adjacent Hwy 101, making these surveys a key factor in vulnerability assessments.
Data was acquired in two discrete epochs: a baseline scan on November 2, 2025, and a post-event scan on November 11, 2025, capturing morphological change from the November 5–7 King Tide. Utilizing the Leica RTC360 terrestrial laser scanner, we captured dense 3D geometry of the unstable bluff face and intertidal zone. The study details the field-to-finish workflow, emphasizing the efficacy of the RTC360 for rapid data collection in dynamic, hazardous coastal environments.
By performing a cloud-to-cloud comparison between the pre and post datasets, we identified discrete areas of bluff and beach erosion and accretion. The results demonstrate how terrestrial scanning can provide a precise dataset to assess structural integrity and predict change rates of the bluff after a high-energy tidal event.”
Sheng Tan and Heather Maran, Oregon State University
2:20 PM – 2:30 PM – Event-Based Spatio-Temporal Graph Convolutional Networks for Rainfall–Runoff Prediction in HUC12 Watersheds
This research develops an event-based Spatio-Temporal Graph Convolutional Network (E-STGCN) designed to improve short-term flood forecasting by combining watershed connectivity with recurrent sequence modeling. The approach represents the river network through a directed topological graph, allowing the model to follow true upstream–downstream flow paths, while an LSTM backbone captures the temporal evolution of storm events. Training the model on individual rainfall–runoff events proved especially effective, enabling it to learn the nonlinear rise and recession characteristics that dominate flood response. Among the tested configurations, the combination of event-based training, a physically informed graph structure, and an MSE loss function delivered the most stable and accurate outcomes, reaching an R2 of 0.9973 across the evaluation basins. The workflow is computationally efficient and supports near-instantaneous inference, demonstrating clear potential for operational use in real-time flood warning and watershed management systems.
Anil Mandal, Florida Atlantic University
2:30 PM – 2:40 PM – Mapping Urban Expansion of Santiago de Queretaro (Mexico) using multi-temporal Landsat imagery
Mexico has experienced steady development since the early 2000s, with some regions growing more rapidly than others, particularly major urban centers such as Mexico City. Urbanization across the country has been driven largely by the expansion of commercial industries and increased trade, contributing to economic growth and improved access to goods and services for the middle class. One city strongly shaped by these dynamics is Santiago de Querétaro, an emerging urban hub for Mexico’s aerospace industry. Querétaro has developed one of the fastest-growing aerospace clusters in the country, and this economic growth has been accompanied by rapid urban expansion.
Since the 1990s, Santiago de Querétaro has experienced increasing migration and consistent population growth. The city is situated within a semi-arid valley, where the surrounding topography creates a bowl-like landscape that has constrained development to lower elevations. As these lower-elevation areas have become increasingly developed, urban expansion has begun to shift upslope into higher-elevation terrain. This study analyzes the spatial patterns of urban growth in Santiago de Querétaro using multispectral satellite and aerial imagery. An unsupervised classification approach, combined with change matrix analysis, is applied to track urban expansion over time and to examine the transition of development from lower to higher elevations.
Rachelle Lavariega, University of Wyoming
2:40 PM – 2:50 PM – Tracking mining and deforestation in SE Ghana using Landsat imagery
Ransford Tege, University of Wyoming
2:50 PM – 3:00 PM – Building a Database of Land Subsidence Targets Under SGMA to Help Evaluate California Central Valley’s Future Sustainability
The San Joaquin Valley in Central California plays an integral role in the United States’ agricultural industry, providing about 25% of food in the U.S. alone. The Central Valley’s agricultural industry is largely dependent on groundwater and has led to severe over-pumping in most parts of the basin, especially southern regions. The consequential passage of California’s Sustainable Groundwater Management Act (SGMA) in 2014 introduced a long-term plan aimed at protecting groundwater resources. SGMA, however, has yet to deter unsustainable management practices from being utilized, these practices having led to excessive groundwater pumping, a principal cause of land subsidence. Without effective management strategies and the regulation of groundwater pumping, subsidence could damage key infrastructure in the valley, including the California Aqueduct. This report will analyze SGMA reports of various groundwater subbasins and include a constructed database of SGMA subsidence monitoring stations that will eventually be used to model projected subsidence levels due to groundwater depletion up to the year 2070. The subbasins analyzed will be Delta-Mendota, Chowchilla, Madera, Tule, Turlock, and Modesto respectively. Subbasins will be ranked in order of highest priority and subsidence severity to lowest. A total of 178 subsidence monitoring sites comprises the constructed subsidence database, and 107 of these sites, more than half, have sustainability targets that use groundwater levels as a proxy for subsidence. This research aims to determine the regions of most severe subsidence and evaluate the effectiveness and sustainability of current SGMA groundwater sustainability plans.
Zoe De Buhr, University of Wyoming
3:00 PM – 3:10 PM – Generating High-Resolution Crop Canopy Height Information Using SkySat Stereopairs
Large-scale phenotyping of crops is pivotal in advancing precision agriculture (PA). It is fundamental to plant breeding that aims to develop improved crop varieties with desirable traits and enhanced nutritional or industrial value. While Uncrewed Aircraft Systems (UAS) have emerged as a popular tool for phenotyping, UAS are limited in area coverage, require consistent calibration and validation, and may suffer from inconsistencies due to variations in flight conditions, sensor performance, and environmental factors. This study addresses these challenges by presenting a farm-scale crop canopy height estimation framework that utilizes high-resolution SkySat tri-stereo satellite imagery, tailored specifically for low-relief agricultural landscapes. The methodology centers on a customized stereophotogrammetric workflow that integrates dual-season image acquisitions and Rational Polynomial Coefficient (RPC) refinement using high-precision Ground Control Points (GCPs). Early-season imagery is used to generate a Digital Terrain Model (DTM), capturing bare-earth conditions, while late-season acquisitions produce Digital Surface Models (DSMs) that reflect the full canopy structure. By differencing these temporally paired surfaces, absolute canopy height models are derived at 1-meter spatial resolution. Initial results over selected farm fields in Driscoll, Texas show the workflow estimates canopy height to 2 m vertical accuracy when compared to UAS lidar derived canopy height. This result demonstrates the feasibility of accurate crop height estimation using commercial satellite imagery and provides a transferable methodology for broader agricultural monitoring applications.
Benjamin Ghansah, Texas A&M University-Corpus Christi
3:10 PM – 3:20 PM – UAS Bathymetric Lidar for River Surveys: An Accuracy Assessment on the Clackamas River, Oregon
UAS-based bathymetric lidar is an emerging technology for acquiring high-resolution bathymetry of rivers in localized areas. It can collect data in areas too shallow or dangerous for boats and potentially provide much higher spatial resolution than conventional bathymetric lidar from crewed aircraft. However, little is known about the accuracy and quality of these systems for river mapping and subsequent hydrodynamic modelling and grain size analysis. To address this, an Oregon State University team collected control and drone survey data on a reach of the Clackamas River in Oregon to conduct an accuracy assessment of a novel topo-bathymetric lidar system, the YellowScan Navigator. The UAS topo-bathymetric data were evaluated against concurrently collected, in-situ GNSS and total station-based control data. Then, the data were compared to other data sets, including multibeam echosounder (MBES) data collected from a small boat, airborne bathymetric lidar data collected from a conventional aircraft, UAS topographic lidar, and UAS photogrammetric data to quantify differences in spatial resolution, accuracy, and completeness. Our initial results suggest that the YellowScan Navigator can collect high-precision, accurate, and dense data suitable for localized river surveying and analysis in conditions supported by the system.
Baird Quinn, Oregon State University
3:20 PM – 3:30 PM – Analysis of Satellite-Based Kd490 Products for Quantifying ICESat-2 Bathymetry Retrievability
The space-based photon-counting laser altimeter (ATLAS) aboard the ICESat-2 provides new capacities for global shallow-water bathymetry. In April 2025, a new global ICESat-2 bathymetric data product, ATL24, was released. However, the depth to which ICESat-2 can reliably detect the seafloor is strongly dependent on water clarity. The basic model used in airborne bathymetric lidar assumes that the maximum depth for the detection of the bottom is an inverse function of the diffuse attenuation coefficient of downwelling irradiance, Kd. It is therefore reasonable to assume that global Kd490 (diffuse attenuation coefficient at 490 nm) datasets should be useful in predicting ICESat-2 bathymetric measurement success and maximum depths. However, with a number of Kd490 data products available, each using different source data and/or algorithms, several questions remain to be answered: 1) do different Kd490 data products differ in their ability to predict successful bathymetric measurement? 2) Does including a rough estimate of seafloor reflectance improve the predictions? 3) Using the best data and techniques, how well can we predict maximum depth retrievals for ICESat-2? In this study, we investigate these questions using ATL24 and three well-known global Kd490 datasets.
Owusuah Osei-Kwakye, Oregon State University
