Date of Award

Spring 6-12-2026

Document Type

Masters Capstone Project

Degree Name

M.S. in Geographic Information Science

Organizational Unit

College of Natural Science and Mathematics, Geography and the Environment

First Advisor

Steven Hick

Second Advisor

Matt Cross

Copyright Statement / License for Reuse

All Rights Reserved
All Rights Reserved.

Keywords

Terrain, Remote sensing, Snow water equivalent, Machine learning, Random forest regression

Abstract

Terrain variables are widely incorporated into machine learning Snow Water Equivalent (SWE) models but rarely evaluated for their independent contribution relative to spectral predictors. Using a four-tier stepwise Random Forest framework with Harmonized Landsat Sentinel-2 imagery and Airborne Snow Observatory LiDAR ground truth, this study isolates the contribution of elevation, slope, northness, and eastness across Peak and Ablation snowpack regimes in the East Taylor River Watershed, Colorado. During peak snowpack, adding terrain improved R² by 0.214, with elevation alone accounting for 42.8% of model importance. During ablation, full-dataset terrain gains were modest, increasing R² by only 0.036. However, when the ablation dataset was restricted to pixels with observed SWE exceeding 10 mm, this snow-only sensitivity analysis revealed that terrain provides 2.25 times greater proportional improvement in SWE magnitude estimation than the full-dataset metrics indicate.

Copyright Date

6-6-2026

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Brant Giovannetti

Provenance

Received from Author

File Format

application/pdf

Language

English (eng)

Extent

62 pgs

File Size

2.5 MB

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