DOI: 10.17603/ds2-8hz4-zb88
title: Cle Elum Snow Pack Study, in Cle Elum Ridge Snow-On Lidar for Forest Management

description: This mission investigated how forest structure and snowpack distribution respond to forest-thinning treatments designed to increase resilience to future wildfire disturbance. Forests across the Western United States are being actively managed to reduce wildfire risk through selective thinning and fuel reduction treatments, yet these actions can also alter snow accumulation and melt processes that influence downstream water resources and flood potential. This mission collected high-resolution airborne lidar data over experimental forest-treatment units to quantify changes in canopy structure and ground surface conditions following thinning. The resulting dataset enables analysis of how forest disturbance influences snow storage and melt dynamics in complex terrain.

These data can be reused to support model validation, develop methods to couple forest and snow process models, and assess the co-benefits of fuel treatment for both wildfire mitigation and water resource management. The dataset provides a valuable baseline for future multi-temporal analyses of vegetation recovery, canopy closure, and snowpack evolution as treated forests continue to mature.
description: This project provides a high-resolution drone-based lidar survey of a forest-snow system undergoing fuel-reduction treatments designed to reduce wildfire severity and enhance watershed resilience in a mountainous environment. The study area is a mixed-conifer forest on Cle Elum Ridge, Washington, where forest-thinning and understory treatments were implemented as part of ongoing wildfire-hazard mitigation and ecosystem-restoration efforts. The dataset supports research on how structural changes in forest canopies—intended to reduce wildfire risk—affect snow accumulation and melt processes that influence downstream water supply, flood risk, and landscape hydrology.

The dataset arises from a campaign conducted by the Natural Hazards Reconnaissance (RAPID) Facility on 6 March 2023, using a Freefly Alta X drone equipped with a Phoenix MiniRanger lidar sensor and Leica GS18T GNSS receiver. The survey targeted both treated and untreated stands across north- and south-facing slopes of Cle Elum Ridge. The point-cloud data were collected at an average density of approximately 100 points m⁻², providing detailed three-dimensional information on vegetation structure, snow-on surface topography, and terrain. The data include raw point clouds and processed, quality-controlled LAS files produced by the RAPID Facility.

This 2023 RAPID dataset complements an earlier snow-on airborne lidar dataset collected on 1 April 2021 by the National Center for Airborne Laser Mapping (NCALM). Together, these two datasets provide a pre- and post-treatment comparison of forest and snowpack conditions on Cle Elum Ridge, enabling assessment of how fuel-reduction treatments influence forest structure, snow storage, and related hydrologic processes.

Users can reuse this dataset for multiple applications: validating snow depth and snow distribution models, quantifying snow-forest interactions, and supporting hydrologic, forest, and hazard management decision-making. The data are particularly valuable because they connect wildfire hazard mitigation with snowpack and hydrologic response in an experimental forest representative of low-elevation seasonal snow watersheds.

Overall, this dataset supports research across hydrology, snow science, forest ecology, remote sensing, and natural-hazard engineering. Land managers, modelers, researchers, and students investigating forest and water interactions, wildfire resilience, and mountain snow processes will find the data especially useful. By publishing these data through the NSF NHERI DesignSafe platform, we aim to promote transparency, reproducibility, and future studies that extend beyond the scope of the original project.