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.
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.
