Abstract: This study systematically validates Sentinel-2 MultiSpectral Instrument (MSI) ocean color products against standardized AERONET-OC SeaPRISM measurements at the Banana River site within Florida's Indian River Lagoon (IRL) system. Matchup datasets spanning 2023–2024 were constructed from Sentinel-2 overpasses (±3-hour temporal window) processed through both ACOLITE and RadCOR atmospheric correction algorithms. Complementary environmental observations from the IRL Observation Network (IRLON) provided context on the dynamics of turbidity, wind speed, CDOM, and chlorophyll-a influencing optical variability. Results demonstrate excellent spectral agreement between satellite-derived and in situ remote sensing reflectance (Rrs), particularly at 560 nm, where both processors achieved R² > 0.90 and low bias. Peak Rrs consistently occurred near 560 nm across all matchups, validating the sensors' ability to capture the dominant water-color features in this optically complex environment.
Kalu Okigwe holds a B.S. in Geography and Environmental Management and is currently pursuing an MSGIS at Clark University. Before joining Clark, he worked as a Geospatial Data Scientist and Analyst in waste management, environmental engineering, and urban planning, as well as Deputy... Read More →
The Rhode Island Stone Wall Mapping Project is the first comprehensive inventory of the state's stone walls. Using ArcGIS Pro, the project developed an original methodology to extract linear wall features from the 2022 Rhode Island Statewide LiDAR dataset using relative height filtering of the point cloud. The resulting dataset documents over 5,200 miles of stone walls, revealing historical land-use patterns tied to agriculture, settlement, topography, and geology. With the mapping phase complete, the project has transitioned to research and outreach. This includes a pilot citizen science effort to use mobile data collection and Python to gather field-validated data for future machine learning applications. This presentation offers a case study on the challenges of linear feature extraction from LiDAR, and will discuss the limitations of automated approaches, the role of manual digitization, and practical strategies for operating low-budget, community-driven geospatial projects.
Salt marshes are an important ecosystem that protects shorelines and provides key habitat for many species. Rising sea levels are threatening coastal marshes; however, the marshes have shown resilience by migrating upland. The ability of marshes to migrate depends on the nature of the surrounding landscape. Knowing where salt marshes have potential to migrate can give land stewards the ability to facilitate migration. This study uses the Sea Level Affecting Marshes Model (SLAMM) to simulate future salt marsh migration in 9 different National Parks along the east coast of the US. We use the best available elevation and land cover data for each park to model marsh migration at 1m spatial resolution under various sea level rise scenarios. The models will also account for anthropogenic barriers to help identify where facilitating marsh migration makes sense. This study will provide higher resolution salt marsh migration models than currently exist to guide NPS conservation efforts.
Graduate Research Assistant, The University of Rhode Island
Hello everyone! My name is Atticus Scott. I just completed my first year as a Master's student at the University of Rhode Island, working for Dr. Jason Parent in the URI Environmental Data Center. My research is primarily focused on modeling salt marsh migration along the east coast... Read More →
