Theses and Dissertations

ORCID

https://orcid.org/0000-0002-6627-5635

Advisor

Zope, Anup

Committee Member

Evans, Kristine

Committee Member

Samiappan, Sathishkumar

Date of Degree

12-12-2025

Original embargo terms

Visible MSU Only 6 months

Document Type

Graduate Thesis - Campus Access Only

Major

Computational Engineering

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Computational Engineering Program

Abstract

Analyzing large-scale, high-resolution satellite imagery is a computationally intensive task requiring time and computing resources. This can be accelerated using cloud computing platforms such as Google Earth Engine (GEE) where computational and storage requirements can be scaled based on demand. However, cloud-based platforms for processing high-resolution imagery remain underutilized in environmental applications such as agriculture, and forest health. This thesis explored the application of GEE to two geospatial problems in agricultural conservation and disease mapping in forestry: 1) Extraction of agricultural field boundaries from Sentinel-2 satellite imagery, for use in conservation, precision agriculture, land management, and organization, etc., and 2) Mapping and evaluation of the phenology of Brown Spot Needle Blight (BSNB) (Mycosphaerella dearnessii) disease in Loblolly Pines (Pinus taeda) in Mississippi using Sentinel-2 imagery from 2019-2024, which can be used to support management actions for forest health. Computed agricultural field boundaries were compared with digitized polygons created by a subject matter expert, and the results showed high delineation accuracy ( 84This study aimed to speed up the analysis of large satellite images relevant to detecting and monitoring Lecanosticta acicola. It did this by developing efficient workflows within the Google Earth Engine (GEE) platform. By utilizing GEE’s cloud-based processing and extensive collection of multispectral and temporal satellite data, the study demonstrated that high-resolution geospatial analyses can be conducted quickly and at a large scale. This greatly lowered local computing needs. Combining satellite images with GEE enabled detailed mapping, improved the accuracy of mapping disease vectors, and made it possible to automate important analytical tasks. These improvements facilitate faster and more informed decision-making in managing agriculture and forestry, particularly in areas with limited resources or sensitive ecosystems.

Sponsorship (Optional)

CRP Menu Tool by USDA, John Riggins for Loblloly Pine research

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