Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence
Article
Article Title | Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence |
---|---|
ERA Journal ID | 201448 |
Article Category | Article |
Authors | Raj, Nawin and Pasfield-Neofitou, Sarah |
Journal Title | Remote Sensing |
Journal Citation | 16 (3), p. 551 |
Article Number | 3 |
Number of Pages | 21 |
Year | 2024 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2072-4292 |
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. |
Keywords | bidirectional 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) Funding | Researcher |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 410199. Climate change impacts and adaptation not elsewhere classified |
370803. Physical oceanography | |
460207. Modelling and simulation | |
Byline Affiliations | School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/z4x35/assessment-and-prediction-of-sea-level-and-coastal-wetland-changes-in-small-islands-using-remote-sensing-and-artificial-intelligence
Download files
37
total views31
total downloads2
views this month2
downloads this month