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.

Measuring the Oregon landscape: An update from the Oregon Lidar Consortium on data collection, analysis, and distribution
The landscape of Oregon is varied and dynamic, the product of past and ongoing earth system processes. High resolution topographic data characterize earth surface morphology, vegetation characteristics, and the built environment. The Oregon Lidar Consortium (OLC), formed in 2007, is mandated to collect high quality, dense (≥ 8 points/sq m) lidar data across the state and to make these data available to the public. By late 2024 78% of the state had publicly available lidar coverage, while the remainder of the area has been collected and is being processed. Additionally, many areas throughout the State of Oregon have now been covered by one or more repeat lidar datasets, allowing detailed measurements of landscape changes including those driven by coastal, landslide, riverine, vegetation, fire, and urban processes. Opportunities for future collection include targeting known topographic changes as well as to maintain recent observations over wide areas. Another important direction is topobathymetric lidar collection for areas of shallow water including rivers and estuaries to better resolve flood and tsunami hazards and aquatic habitat. This presentation will review the status of lidar acquisition and data availability across the state, example applications to resolve diverse hazards, change detection, and best practices for efficient access to OLC data.

Interferometric Synthetic Aperture Radar (InSAR) provides a valuable means of assessing ground and structural changes using satellite-based radar imagery. This presentation walks through the workflow of acquiring, processing, and preparing coherence data for use in a building damage assessment study.

We begin with data acquisition using the Alaska Satellite Facility’s Vertex tool, selecting and downloading InSAR coherence products. Next, we outline the use of ArcPy to automate key preprocessing steps: applying masks, clipping coherence rasters to building footprints, and structuring the dataset for statistical analysis. Challenges in handling large datasets, dealing with null values, and ensuring spatial alignment are discussed.

With processed coherence values linked to individual building footprints, we then explore initial statistical methods for assessing damage patterns. The goal is to establish a foundation for classification, setting the stage for more advanced spatial and statistical techniques. This workflow provides a replicable approach for integrating InSAR data into disaster impact studies.

A walk through of using open source libraries in ArcGIS Pro and QGIS to 3D print topographic maps from publicly available raster data.

The Marbled Murrelet is an endangered seabird that relies on forests for nesting. Traditionally, a species survey crew would be sent out to see if the species is present. However, that is labor and time intensive. This project explores using high resolution LiDAR data to determine the possibility of Marbled Murrelet habitat in the area. Using characteristics derived from the LiDAR data such as canopy height, density, etc. we can make calculate the probability of habitat in the area and confirm in person. LiDAR technology can be used to speed up the environmental analysis process in what is presently a costly and timely endeavor.

This study explores land cover classification methods using high-resolution UAV imagery collected at Lakeside Farm, Oregon in July 2024, with a focus on mapping land cover types. The goal is to assess the effectiveness of multiple supervised classification techniques: K-Nearest Neighbor, Maximum Likelihood, Random Trees, and Support Vector Machine. Object-based and pixel-based classification methods were applied to RGB imagery collected using the Parrot ANAFI drone. The study area provided a unique array of land cover types, including varied vegetation, unvegetated areas, water, and wetlands, which help provide insight as to the advantages and disadvantages of the different classification methods when dealing with each land cover type. This research provides insights into the optimal conditions and methods for UAV data integration in agricultural land cover mapping, contributing to enhanced precision agriculture practices.