GIS in Action 2026

The removal of the J.C. Boyle Dam in 2024 marked a transformative moment in the ecological restoration of the Klamath River Basin. This study presents a multi-sensor UAV-based assessment of post-dam removal landscape dynamics using high-resolution RGB, multispectral, and LiDAR datasets acquired in July and November 2024.
A comprehensive land cover classification was conducted to evaluate vegetation recolonization, exposed sediment distribution, and hydrologic surface changes. Multispectral indices, including NDVI, NDRE, and SAVI, were used to quantify vegetation recovery and stress patterns across newly exposed riparian zones. Additionally, LiDAR-derived digital terrain models enabled a DEM of Difference (DoD) analysis to identify erosion and deposition patterns, channel reconfiguration, and microtopographic variability.
Preliminary results indicate significant sediment redistribution and early-stage riparian vegetation establishment within four months of dam removal. The integration of UAV photogrammetry and LiDAR data provides a scalable framework for monitoring rapid geomorphic and ecological transitions in post-dam landscapes.
This study demonstrates the value of high-resolution UAV-based monitoring for adaptive river restoration management and long-term ecosystem recovery assessment.

Ground control points (GCPs) are highly useful in UAS photogrammetry and Lidar collection for a variety of infrastructure mapping applications. It is important that GCPs reflect the true elevation at the point location where they are registered. One common problem with registering a GCP is that there is distortion in the observed elevation related to the geoid separation height. An application was developed for orthometric correction of ground control points using a Geoid API provided by the National Oceanic and Atmospheric Administration (NOAA) which outputs elevations that are more accurate and more useful for drone mapping. This application was built into a Python toolbox that was made accessible to the Massachusetts Dept. of Transportation (MassDOT) for future drone missions.

Imagery is no longer just a snapshot in time. It is becoming an essential infrastructure for how organizations plan, operate, and make decisions. Building a Resilient Imagery Program explores how organizations can move beyond one-time image capture toward a
planned, repeatable, and shared imagery program that supports standardized workflows, and broader organizational use. This session will examine the progression from ad hoc imagery collection to a resilient program model, highlighting how deliberate and repeatable
design choices help sustain long-term value while reducing risk. Attendees will gain insight into how imagery programs can be structured to support continuity, improve access to historical and operational context, and better serve planning, operations, and
decision-making across an organization. Ideal for GIS professionals, program managers, and organizational leaders, this session offers a practical framework for building imagery programs that endure.

The aim of Elevation-Derived Hydrography (EDH) via the USGS 3D Elevation Program (3DEP) is to satisfy the pressing need for better integration between high resolution elevation data and hydrography data. This data has a myriad of applications from infrastructure asset management to conservation, supporting many varieties of analytical and cartographic applications.

Real-world lidar datasets rarely behave as cleanly as theory suggests. Multi-time-around artifacts, shoreline ringing, mission-level inconsistencies, and environmental effects such as steep terrain or water interfaces can introduce systematic noise that standard workflows struggle to resolve.
This presentation examines common lidar artifacts encountered in coastal, tropical, and complex terrain environments and discusses practical strategies for diagnosing root causes and applying targeted cleanup methods. Emphasis is placed on distinguishing systematic artifact patterns from random noise and adapting classification parameters to existing spatial behavior. Then implementing repeatable mitigation strategies and structuring workflows to improve coherence without relying solely on reflying data.
Attendees will gain insight into how critical analysis, structured automation, and adaptive classification approaches can improve data quality while preserving efficiency in production environments.

This lightning talk introduces the Eskey System, a conceptual map that imagines how future explorers might navigate the lunar surface. Using GIS terrain analysis and lunar elevation data, low-slope routes were modeled between potential landing zones and key destinations such as craters and maria. These routes represent the easiest travel paths for rovers or astronauts, forming a network of potential lunar “roads.”

The system builds on the lunar quadrangle framework already used in planetary mapping. Quadrangles act like regional boundaries, while astronomical chart numbers function similarly to postal codes. Within this structure, routes are named to honor astronauts, missions, and themes from the history of space exploration.

The result is both an analytical and imaginative map—one that translates complex terrain data into familiar navigation concepts. By applying terrestrial wayfinding ideas to an extraterrestrial landscape, the project explores how GIS and cartography could help humans organize and navigate the Moon.

This presentation discusses methods for integration of tide-coordinated data sources to produce seamless surface of Jetty Structures.

Topographic-bathymetric lidar is a critical tool for coastal resilience, infrastructure planning, and nautical charting. Woolpert, Inc. collects topo-bathymetric lidar using Leica’s HawkEye5 system mounted on a fixed-wing aircraft, enabling efficient, high-resolution mapping of coasts, rivers, and lakes. The system simultaneously collects topographic and bathymetric data, allowing continuous coverage across the littoral zone.
Compared to traditional vessel based acoustic surveys, airborne topo-bathymetric lidar excels in nearshore and shallow regions (~0–20 m) where vessel operations can be unsafe or inefficient. Clients include the National Oceanographic and Atmospheric Administration (NOAA), the National Geospatial-Intelligence Agency (NGA), and the Florida Department of Environmental Protection (FDEP). These projects support applications such as change analysis, disaster response, navigational charting, and deeper water applications (up to 50m) such as benthic habitat mapping, underwater obstacle detection.
Collection and processing challenges, including weather, water clarity, turbidity, and vegetation, require careful planning and adaptive workflows. GIS plays a central role in the workflow, supporting flight planning, coverage assessment, quality control, result validation, and product creation. Examples of final deliverables include seamless topo-bathymetric digital elevation models (DEM), web maps, and derived feature layers to support informed decision making.

LiDAR-based forest inventory has advanced over the past decade, with studies reporting cm-level DBH accuracy and sub-meter height estimates. However, most of this work was done in urban forests, plantations, or younger forests with simple structure. In Pacific Northwest natural forests, undergrowth, tall trees, and closed canopy limit high-precision GNSS, making spatially explicit inventory difficult. We tried to register Handheld Laser Scans (HLS) with RTK-based Aerial Laser Scanning (ALS) as a reference. ICP registration failed, likely due to density differences and lack of common features. Therefore, alternatively, we used a two-step process: rough alignment with 'Register-Clouds' and manual refinement using canopy gaps and crown patterns. Despite good crown matching quite well, stem positions from the two scans were far apart, and stem sizes differed from under the canopy. These uncertainties propagate through DBH, height, volume, and biomass estimates. Quantifying their impact on operational forest inventory is essential, and developing matching algorithms robust to complex environments and dense tree clusters is encouraged