Machine Learning and remote sensing applications to shoreline dynamics
type: CreativeWork
description: Coastal communities and land covers are vulnerable receptors of erosion, flooding, or both in combination. The accurate, automated, and wide-scale determination of shoreline position, and its migration at the engineering scale (10-1 – 102 km), is imperative for future coastal risk adaptation and management. The recent increase in the acquisition and availability of Big Datasets, including multispectral remote sensing imagery, is providing new opportunities to monitor engineering scale rates of shoreline change and other constituents of coastal risk, including changes to human coastal population densities. This increase in data availability comes with novel challenges to devise and utilise methods to store, process, analyse and extract information from these Big Datasets. This thesis assesses the suitability of different Big Data approaches, namely Machine Learning (ML) and non-ML based tools, for the automated extraction of the coastal vegetation edge in remote sensing imagery. Compared to the instantaneous waterline, few vegetation edge methods have been developed and analysis of the coastal zone processes that can be detected using the shoreline proxy remain understudied. This thesis initially investigates whether non-ML methods are suitable for the extraction of the coastal vegetation edge from multispectral remote sensing imagery. A novel non-ML tool is introduced and applied, CoasTool, which considers the proximity of the instantaneous water line during vegetation edge extraction. CoasTool performance is compared to the outputs derived from well-established threshold contouring techniques and kernel-based methods as well as one form of ML, Support Vector Machines (SVM). Limitations in the performance of these tools, particularly along shorelines with discontinuous or graded vegetation boundaries, provide justification for the application of a separate form of ML, convolutional neural network (CNN), to this task. A novel CNN, VEdge_Detector, is trained and appli…
abstract: Coastal communities and land covers are vulnerable receptors of erosion, flooding, or both in combination. The accurate, automated, and wide-scale determination of shoreline position, and its migration at the engineering scale (10-1 – 102 km), is imperative for future coastal risk adaptation and management. The recent increase in the acquisition and availability of Big Datasets, including multispectral remote sensing imagery, is providing new opportunities to monitor engineering scale rates of shoreline change and other constituents of coastal risk, including changes to human coastal population densities. This increase in data availability comes with novel challenges to devise and utilise methods to store, process, analyse and extract information from these Big Datasets. This thesis assesses the suitability of different Big Data approaches, namely Machine Learning (ML) and non-ML based tools, for the automated extraction of the coastal vegetation edge in remote sensing imagery. Compared to the instantaneous waterline, few vegetation edge methods have been developed and analysis of the coastal zone processes that can be detected using the shoreline proxy remain understudied. This thesis initially investigates whether non-ML methods are suitable for the extraction of the coastal vegetation edge from multispectral remote sensing imagery. A novel non-ML tool is introduced and applied, CoasTool, which considers the proximity of the instantaneous water line during vegetation edge extraction. CoasTool performance is compared to the outputs derived from well-established threshold contouring techniques and kernel-based methods as well as one form of ML, Support Vector Machines (SVM). Limitations in the performance of these tools, particularly along shorelines with discontinuous or graded vegetation boundaries, provide justification for the application of a separate form of ML, convolutional neural network (CNN), to this task. A novel CNN, VEdge_Detector, is trained and appli…
author: Rogers, Martin
language: eng
keywords: Machine learning, Remote sensing, Shoreline change, Coastal vegetation, Convolutional Neural Networks
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2020-11-01
date modified: 2026-07-22T22:24:20Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.84999