2026-09-18T18:29:21Zhttps://api.mountainscholar.org/server/oai/request

oai:mountainscholar.org:10217/2417802026-09-03T17:21:11Zcom_10217_100532com_10217_100000com_10217_100411com_10217_100303col_10217_182111col_10217_100415

Taulbee, Luke, author Chen, Haonan, advisor Simske, Steve, committee member Venkatachalem, Chandrasekar, committee member 2025-09-01T10:42:08Z 2026-08-25 2025 Taulbee_colostate_0053N_19116.pdf https://hdl.handle.net/10217/241780 https://doi.org/10.25675/3.02100 This thesis aims to address the challenge of accurate wildfire detection using satellite imagery. Despite the availability of various satellite-based fire products, real-time detection of fire perimeters remain difficult due to limitations in the spatio-temporal resolution of current satellite imagery. For example, the Geostationary Operational Environmental Satellites (GOES-R) series containing the Advanced Baseline Imager (ABI) offers high temporal resolution for frequent observations but suffers from low spatial resolution. In contrast, low Earth orbit (LEO) satellites like Suomi-NPP, NOAA-20, and NOAA-21 with the Visible Infrared Imaging Radiometer Suite (VIIRS) imager provide high spatial resolution but with limited temporal coverage. To overcome these limitations, this research proposes a deep learning framework for wildfire detection that leverages GOES ABI observations, which are downscaled to a spatial resolution of 375 meters using a Generative Adversarial Network (GAN). High-resolution VIIRS images are used as ground truth labels during the training phase. Experimental results demonstrate that the proposed framework successfully enhances the spatial resolution of GOES ABI data while preserving its high temporal frequency, allowing more precise and timely wildfire detection. born digital masters theses English eng Colorado State University. Libraries 2020- Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. generative adversarial network satellite imagery wildfire detection GOES-R series deep learning VIIRS Deep learning for downscaling GOES-18 measurements for wildfire detection Text 2026-08-25 2026-08-25 This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). Master of Science (M.S.) Masters Electrical and Computer Engineering Colorado State University open.access