Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence

Article

Raj, Nawin and Pasfield-Neofitou, Sarah. 2024. "Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence." Remote Sensing. 16 (3), p. 551. https://doi.org/https://doi.org/10.3390/rs16030551
Article Title

Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence

ERA Journal ID201448
Article CategoryArticle
AuthorsRaj, Nawin and Pasfield-Neofitou, Sarah
Journal TitleRemote Sensing
Journal Citation16 (3), p. 551
Article Number3
Number of Pages21
Year2024
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2072-4292
Digital Object Identifier (DOI)https://doi.org/https://doi.org/10.3390/rs16030551
Web Address (URL)https://www.mdpi.com/2072-4292/16/3/551
Abstract

Pacific Island countries are vulnerable to the impacts of climate change, which include the risks of increased ocean temperatures, sea level rise and coastal wetland loss. The destruction of wetlands leads not only to a loss of carbon sequestration but also triggers the release of already sequestered carbon, in turn exacerbating global warming. These climate change effects are interrelated, and small island nations continuously need to develop adaptive and mitigative strategies to deal with them. However, accurate and reliable research is needed to know the extent of the climate change effects with future predictions. Hence, this study develops a new hybrid Convolutional Neural Network (CNN) Multi-Layer Bidirectional Long Short-Term Memory (BiLSTM) deep learning model with Multivariate Variational Mode Decomposition (MVMD) to predict the sea level for study sites in the Solomon Islands and Federated States of Micronesia (FSM). Three other artificial intelligence (AI) models (Random Forest (FR), multilinear regression (MLR) and multi-layer perceptron (MLP) are used to benchmark the CNN-BiLSTM model. In addition to this, remotely sensed satellite Landsat imagery data are also used to assess and predict coastal wetland changes using a Random Forest (RF) classification model in the two small Pacific Island states. The CNN-BiLSTM model was found to provide the most accurate predictions (with a correlation coefficient of >0.99), and similarly a high level of accuracy (>0.98) was achieved using a Random Forest (RF) model to detect wetlands in both study sites. The mean sea levels were found to have risen 6.0 ± 2.1 mm/year in the Solomon Islands and 7.2 ± 2.2 mm/year in the FSM over the past two decades. Coastal wetlands in general were found to have decreased in total area for both study sites. The Solomon Islands recorded a greater decline in coastal wetland between 2009 and 2022.

Keywordsbidirectional long short-term memory (BiLSTM); convolutional neural network (CNN); deep learning (DL); machine learning (ML); mean sea level (MSL); multi-layer perceptron (MLP); multilinear regression (MLR); random forest (RF)
Article Publishing Charge (APC) FundingResearcher
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020410199. Climate change impacts and adaptation not elsewhere classified
370803. Physical oceanography
460207. Modelling and simulation
Byline AffiliationsSchool of Mathematics, Physics and Computing
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