GIS in Action 2025

We developed and tested a method for mapping shade cast on water channels by riparian vegetation using imagery sources that are affordable and regularly available across Oregon. We validated the optical imagery-based results against lidar-based shade estimates that, while more accurate, are not viable for ongoing statewide monitoring due to their expense. This work provides insights toward determining a viable strategy for statewide monitoring of riparian vegetation condition, which could in turn support a data-driven prioritization and assessment framework to increase the efficiency, effectiveness and accountability of riparian restoration efforts.

We created a model to predict shade from 1-foot Oregon Statewide Imagery Program (OSIP) imagery and 10-meter Sentinel-2 satellite imagery, trained from shade estimated using solar-path modeling applied to lidar data collected over three disparate study areas. The model explains nearly 77% of the variation in lidar-derived shade across the study areas. NAIP-based models significantly outperformed Sentinel-2 models; we found that multi-scale image textural information derived from NAIP was important in creating accurate shade estimates. Maps of shade from the optical-based model were created over the entire Johnson Creek watershed in metropolitan Portland; visual inspection of the results shows a very high correspondence to photo-interpreted NAIP imagery, including accurate response to subtle and fine-scale variation in conditions.

What do fish, ecological restoration, and drones have in common? They all play a vital role in the Stage 0 restoration of Wasson Valley!
Stage 0 restoration is an innovative approach aimed at returning landscapes to their initial state, allowing nature to restore itself. Using drones to monitor these areas allows for landscape level analysis of the imagery collected, enhancing our ability to track and support the recovery process. This method of monitoring could reduce the amount of field work for future restoration sites. Scientific monitoring of habitat restoration has generally occurred on-the-ground with time-intensive efforts. Monitoring by UAS will allow for landscape-wide monitoring using multispectral, LiDAR and thermal sensors to track environmental changes resulting from the restoration, such as vegetation growth rate, sediment movement, elevation changes, and surface temperature of soil and water.

High-resolution aerial imagery is a game-changer for GIS professionals, providing unparalleled accuracy and detail that supports critical decision-making across multiple sectors. This presentation will explore how advanced aerial imagery enhances GIS data integrity for public safety agencies, urban and regional planning departments, utility providers, and land use managers.

By integrating high-resolution imagery with GIS workflows, professionals can improve situational awareness, conduct precise asset mapping, and streamline operations such as emergency response planning, zoning assessments, and infrastructure management. Case studies will demonstrate how agencies leverage updated imagery to reduce errors, enhance predictive modeling, and improve resource allocation.

Attendees will gain insights into the latest advancements in aerial imaging technology, best practices for incorporating high-resolution datasets, and the future of remote sensing applications in GIS. Whether optimizing emergency response routes, refining parcel data, or enhancing vegetation management, high-resolution aerial imagery is a powerful tool for maximizing the accuracy and effectiveness of GIS-driven solutions.

Debris flows are natural hazards that can damage ecosystems and infrastructure, especially in mountainous areas after wildfires. Debris flow mapping and inventory development provide the groundwork for understanding the frequency, spatial distribution, and key influencing parameters that help identify high-risk areas. This study evaluates parameters associated with post-fire debris flow mapping by analyzing lidar differencing, satellite imagery, and machine learning (ML) applications. The study area includes seven watersheds in the Columbia River Gorge, Oregon, USA. The combination of wildfires in 2017, followed by an intense rainstorm in 2021, led to multiple debris flow events in the area. Lidar and satellite imagery datasets from 2018 and 2021 are analyzed using machine learning algorithms: Random Forest, XGBoost, SVM, and logistic regression. In implementing these models, we focus on feature selection optimization and handling class imbalance. Random Forest and XGBoost performed best, achieving an F1 score of 85% to 90% in mapping debris flow locations. Spatial visualization of the results validated the models against historical data. Adding soil burn severity to the analysis highlights its influence on sediment erosion and vegetation patterns. The study showed that the watersheds with higher burn severity areas experienced more significant sediment erosion.