2023
DOI: 10.3897/natureconservation.52.89639
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Evaluating resampled and fused Sentinel-2 data and machine-learning algorithms for mangrove mapping in the northern coast of Qeshm island, Iran

Abstract: Mangrove forests, as an essential component of the coastal zones in tropical and subtropical areas, provide a wide range of goods and ecosystem services that play a vital role in ecology. Mangroves are globally threatened, disappearing, and degraded. Consequently, knowledge on mangroves distribution and change is important for effective conservation and making protection policies. Developing remote sensing data and classification methods have proven to be suitable tools for mapping mangrove forests over a regi… Show more

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Cited by 4 publications
(2 citation statements)
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“…Studies like those by Cao et al in 2018 and Jiang et al in 2021 have successfully employed SVM for classifying mangrove species using UAV hyperspectral imagery, demonstrating the algorithm's precision in distinguishing between species based on subtle spectral differences [36,39]. The comparative effectiveness of SVM against other algorithms, such as Random Forest, further showcases its superior performance in reducing classification errors and enhancing mapping accuracy [40,41].…”
Section: Horizontal Interpretation Using Support Vector Machinementioning
confidence: 90%
“…Studies like those by Cao et al in 2018 and Jiang et al in 2021 have successfully employed SVM for classifying mangrove species using UAV hyperspectral imagery, demonstrating the algorithm's precision in distinguishing between species based on subtle spectral differences [36,39]. The comparative effectiveness of SVM against other algorithms, such as Random Forest, further showcases its superior performance in reducing classification errors and enhancing mapping accuracy [40,41].…”
Section: Horizontal Interpretation Using Support Vector Machinementioning
confidence: 90%
“…The highest accuracies occurred in segmentations with lower similarity thresholds and with higher tree parameters and therefore more complex in RF. Soffianian et al (2023) compared the performance of pixel-and object-based methods in Support Vector Machine (SVM) and Random Forest (RF) algorithms for mapping mangrove ecosystems with Sentinel-2. Pixel-based classifications were strongly influenced by the effect of salt and pepper noise.…”
Section: Discussionmentioning
confidence: 99%