Bridging the gap between plot-level and landscape-scale analysis for wildfire risk assessment

Author

Vanessa Leigh Niemczyk, University of MontanaFollow

Year of Award

2025

Document Type

Thesis

Degree Type

Master of Science (MS)

Degree Name

Forestry

Committee Chair

C. Alina Cansler

Commitee Members

Carl Seielstad, Anna Klene, Christopher O'Connor

Keywords

fire, fuels, PODs, remote sensing, TLS, terrestrial laser scanning

Subject Categories

Forest Management | Natural Resources and Conservation | Other Forestry and Forest Sciences

Abstract

Remote sensing technology has advanced greatly over the past couple of decades proving its ability to aid in wildfire risk assessment and improve our understanding of forest structure and fuel inventory across the landscape. While some aerial and satellite sensors perform better than others, they all have a common weakness, their reduced ability to capture understory fuels with high detail. Terrestrial laser scanning is an emerging solution due to its understory perspective. This research leverages the beneficial aspects of both terrestrial laser scanning and various aerial- or satellite-based remote sensing platforms (aerial laser scanning, digital aerial photogrammetry, and Sentinel-2) to capture highly detailed fuel loading and structure across large spatial extents. This study took place in dry-mixed conifer and moist-mixed conifer forests along the East Cascades in Washington State. A robust sample design was used to allow for extrapolation across large areas of the landscape. A multi-stage modeling approach linking field measurements to terrestrial laser scans and then to landscape scale sensors was used to predict common fuel inputs (duff bulk density, woody bulk density, non-woody bulk density, average canopy base height, and canopy bulk density) to fire behavior models. I compared the explanatory power of this approach to a more traditional approach that does not include terrestrial laser scanning. Predictions of woody bulk density and average canopy base height benefited most from the use of terrestrial laser scanning, reducing the root mean square error by 0.1354 and 0.3374, respectively. Duff bulk density, non-woody bulk density, and canopy bulk density showed minimal benefit or slightly reduced model performance with the addition of terrestrial laser scanning. This study demonstrates the potential to increase sampling efficiency with the use of terrestrial laser scanning to better capture fine-scale nuances in forest structure and fuel characterization.

Recommended Citation

Niemczyk, Vanessa Leigh, "Bridging the gap between plot-level and landscape-scale analysis for wildfire risk assessment" (2025). Graduate Student Theses, Dissertations, & Professional Papers. 12497.
https://scholarworks.umt.edu/etd/12497