Wetlands are invaluable ecosystems, offering essential services such as carbon sequestration, water purification, flood control and habitat for countless aquatic species. However, these critical environments are under increasing threat from factors like industrialization and agricultural expansion. In this research, we focused on small-sized wetlands, typically less than 10 acres in size, due to their unique ecological roles and groundwater recharge contributions. To effectively protect and manage these wetlands, precise mapping and monitoring are essential. To achieve this, we exploited the capabilities of Sentinel-2 imagery and employ a range of machine learning algorithms, including Random Forest (RF), Classification and Regression Tree (CART), Gradient Tree Boost (GTB), Naive Bayes (NB), k-nearest neighbors (KNN) and Support Vector Machine (SVM). Our evaluation used variables, such as spectral bands, indices and image texture. We also utilized Google Earth Engine (GEE) for streamlined data processing and visualization. We found that Random Forest (RF) and Gradient Tree Boost (GTB) outperformed other classifiers according to the performance evaluation. The Normalized Difference Water Index (NDWI) came out to be one of the important predictors in mapping wetlands. By exploring the synergistic potential of these algorithms, we aim to address existing gaps and develop an optimized approach for accurate small-sized wetland mapping. Our findings will be useful in understanding the value of small wetlands and their conservation in the face of environmental challenges. They will also lay the framework for future wetland research and practical uses.
Citation: Eric Ariel L. Salas, Sakthi Subburayalu Kumaran, Robert Bennett, Leeoria P. Willis, Kayla Mitchell. Machine Learning-Based Classification of Small-Sized Wetlands Using Sentinel-2 Images[J]. AIMS Geosciences, 2024, 10(1): 62-79. doi: 10.3934/geosci.2024005
Wetlands are invaluable ecosystems, offering essential services such as carbon sequestration, water purification, flood control and habitat for countless aquatic species. However, these critical environments are under increasing threat from factors like industrialization and agricultural expansion. In this research, we focused on small-sized wetlands, typically less than 10 acres in size, due to their unique ecological roles and groundwater recharge contributions. To effectively protect and manage these wetlands, precise mapping and monitoring are essential. To achieve this, we exploited the capabilities of Sentinel-2 imagery and employ a range of machine learning algorithms, including Random Forest (RF), Classification and Regression Tree (CART), Gradient Tree Boost (GTB), Naive Bayes (NB), k-nearest neighbors (KNN) and Support Vector Machine (SVM). Our evaluation used variables, such as spectral bands, indices and image texture. We also utilized Google Earth Engine (GEE) for streamlined data processing and visualization. We found that Random Forest (RF) and Gradient Tree Boost (GTB) outperformed other classifiers according to the performance evaluation. The Normalized Difference Water Index (NDWI) came out to be one of the important predictors in mapping wetlands. By exploring the synergistic potential of these algorithms, we aim to address existing gaps and develop an optimized approach for accurate small-sized wetland mapping. Our findings will be useful in understanding the value of small wetlands and their conservation in the face of environmental challenges. They will also lay the framework for future wetland research and practical uses.
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Figures(2) / Tables(3)
Eric Ariel L. Salas, Sakthi Subburayalu Kumaran, Robert Bennett, Leeoria P. Willis, Kayla Mitchell. Machine Learning-Based Classification of Small-Sized Wetlands Using Sentinel-2 Images[J]. AIMS Geosciences, 2024, 10(1): 62-79. doi: 10.3934/geosci.2024005
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