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). A novel non-ML tool is introduced and applied, CoasTool, which considers the proximity of the instantaneous water line during vegetation edge extraction. This thesis initially investigates whether non-ML methods are suitable for the extraction of the coastal vegetation edge from multispectral 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 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. 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. 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.
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. All rights reserved.Ĭoastal communities and land covers are vulnerable receptors of erosion, flooding, or both in combination. Our focus is specific to ecological applications of Landsat data, yet many of the concepts and recommendations discussed are also appropriate for other disciplines and remote sensing platforms. We recommend a parsimonious approach to Landsat preprocessing that avoids unnecessary steps and recommend approaches and data products that are well-tested, easily-available, and sufficiently-documented. We then synthesize this information by presenting workflows and a decision tree for determining the appropriate level of imagery preprocessing given an ecological research question, while emphasizing the need to tailor each workflow to the study site and question at hand. Preprocessing steps commonly applied to Landsat data are differentiated and explained, including georeferencing and co-registration, conversion to radiance, solar correction, atmospheric correction, topographic correction, and relative correction. We address these problems by providing a concise overview of the Landsat missions and sensors and by clarifying frequently conflated terms and methods. These issues can, at best, make determining the correct preprocessing workflow a difficult and time-consuming task and, at worst, lead to erroneous results. However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations. These data often require preprocessing prior to analysis to account for sensor, solar, atmospheric, and topographic effects. Landsat data are increasingly used for ecological monitoring and research.
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