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Lookup NU author(s): Dr Paula Lightfoot, Dr Catherine Scott, Professor Clare Fitzsimmons
This is the authors' accepted manuscript of an article that has been published in its final definitive form by John Wiley and Sons Ltd, 2020.
For re-use rights please refer to the publisher's terms and conditions.
© 2020 John Wiley & Sons, Ltd. Intertidal habitat maps are needed at both fine and coarse scales to monitor change and inform conservation and management, but current methods of field survey and expert interpretation of aerial imagery can be time-consuming and subjective. Object-based image analysis (OBIA) of remote sensing data is increasingly employed for producing habitat or land cover maps. Users create automated workflows to segment imagery, creating ecologically meaningful objects, which are then classified based on their spectral or geometric properties, relationships to other objects and contextual data. This study evaluates the change-detection capability of OBIA in the intertidal environment by developing and comparing two OBIA methods for quantifying change in extent and distribution of habitats from freely available multi-temporal aerial imagery and LiDAR data. Despite considerable variability in the data, pre- and post-classification change detection methods had sufficient accuracy (mean overall accuracy from 70.5 to 82.6%) to monitor deviation from a background level of natural environmental fluctuation. This insight into spatial and temporal patterns of natural cyclical change and their detectability by OBIA could inform use of remote sensing for regular, rapid coastal assessment, providing an alert system to direct survey resources to areas of ecologically relevant change.
Author(s): Lightfoot P, Scott C, Fitzsimmons C
Publication type: Article
Publication status: Published
Journal: Aquatic Conservation: Marine and Freshwater Ecosystems
Year: 2020
Volume: 30
Issue: 3
Pages: 514-531
Print publication date: 01/03/2020
Online publication date: 12/02/2020
Acceptance date: 20/11/2019
Date deposited: 11/06/2020
ISSN (print): 1052-7613
ISSN (electronic): 1099-0755
Publisher: John Wiley and Sons Ltd
URL: https://doi.org/10.1002/aqc.3277
DOI: 10.1002/aqc.3277
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