Dissertation/Thesis Abstract

Assessing Palm Decline in Florida by Using Advanced Remote Sensing with Machine Learning Technologies and Algorithms
by Hanni, Christopher B., M.A., University of South Florida, 2019, 74; 13811380
Abstract (Summary)

Native palms, such as the Sabal palmetto, play an important role in maintaining the ecological balance in Florida. As a side-effect of modern globalization, new phytopathogens like Texas Phoenix Palm Decline have been introduced into forest systems that threaten native palms. This presents new challenges for forestry managers and geographers. Advances in remote sensing has assisted the practice of forestry by providing spatial metrics regarding the type, quantity, location, and the state of heath for trees for many years. This study provides spatial details regarding the general palm decline in Florida by taking advantage of the new developments in deep learning constructs coupled with high resolution WorldView-2 multispectral/temporal satellite imagery and LiDAR point cloud data. A novel approach using TensorFlow deep learning classification, multiband spatial statistics and indices, data reduction, and step-wise refinement masking yielded a significant improvement over Random Forest classification in a comparison analysis. The results from the TensorFlow deep learning were then used to develop an Empirical Bayesian Kriging continuous raster as an informative map regarding palm decline zones using Normalized Difference Vegetation Index Change. The significance from this research showed a large portion of the study area exhibiting palm decline and provides a new methodology for deploying TensorFlow learning for multispectral satellite imagery.

Indexing (document details)
Advisor: Pu, Ruiliang
Commitee: Bahder, Brian, Downs, Joni
School: University of South Florida
Department: Geography
School Location: United States -- Florida
Source: MAI 58/06M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Geography, Geographic information science
Keywords: Change detection, Empirical Bayesian Kriging, GLCM, Random forest, TensorFlow, WorldView-2
Publication Number: 13811380
ISBN: 978-1-392-22859-3
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