Characterizing Derawan seagrass cover change with time-series Sentinel-2 images

Fauzan, Muhammad Afif; Wicaksono, Pramaditya; HARTONO, Hartono

doi:10.1016/j.rsma.2021.102048

Cited by 12 publications

(12 citation statements)

References 24 publications

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“…Our temporal analysis spanning the years 2003, 2011, and 2021, revealed significant shifts in the coral reef habitat types and densities [ 23 , 30 , 62 ]. These changes, shaped by both natural phenomena and human activities, highlight the dynamic and ever-changing nature of marine ecosystems [ 31 , 32 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

Assessing Derawan Island’s Coral Reefs over Two Decades: A Machine Learning Classification Perspective

Manessa,

Ummam,

Efriana

et al. 2024

Sensors

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This study aims to understand the dynamic changes in the coral reef habitats of Derawan Island over two decades (2003, 2011, and 2021) using advanced machine learning classification techniques. The motivation stems from the urgent need for accurate, detailed environmental monitoring to inform conservation strategies, particularly in ecologically sensitive areas like coral reefs. We employed non-parametric machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), to assess spatial and temporal changes in coral habitats. Our analysis utilized high-resolution data from Landsat 9, Landsat 7, Sentinel-2, and Multispectral Aerial Photos. The RF algorithm proved to be the most accurate, achieving an accuracy of 71.43% with Landsat 9, 73.68% with Sentinel-2, and 78.28% with Multispectral Aerial Photos. Our findings indicate that the classification accuracy is significantly influenced by the geographic resolution and the quality of the field and satellite/aerial image data. Over the two decades, there was a notable decrease in the coral reef area from 2003 to 2011, with a reduction to 16 hectares, followed by a slight increase in area but with more heterogeneous densities between 2011 and 2021. The study underscores the dynamic nature of coral reef habitats and the efficacy of machine learning in environmental monitoring. The insights gained highlight the importance of advanced analytical methods in guiding conservation efforts and understanding ecological changes over time.

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Section: Discussionmentioning

confidence: 99%

Assessing Derawan Island’s Coral Reefs over Two Decades: A Machine Learning Classification Perspective

Manessa,

Ummam,

Efriana

et al. 2024

Sensors

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“…Several scientific articles have published maps of seagrass cover obtained using satellite images (e.g. Roelfsema et al, 2009;Baumstark et al, 2013;Hossain et al, 2015;Coffer et al, 2020;Fauzan et al, 2021). In marine ecosystems, the use of PlanetScope satellite imagens has become a powerful tool for obtaining information about landscape characteristics and spatio temporal changes (Kim et al, 2015;Purnamasari et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Mapping the structure of mixed seagrass meadows in the Mexican Caribbean

et al. 2022

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The physical and ecological importance of seagrass meadows in coastal processes is widely recognized, and the development of tools facilitating characterization of their structure and distribution is important for improving our understanding of these processes. Mixed (multi-specific) meadows in a Mexican Caribbean reef lagoon were mapped employing a multiparameter approach, using PlanetScope remote sensing images, and supervised classification based on parameters related to the structure of the seagrasses meadows, including the cover percentages of seagrass/algae/sediment, algae thalli and seagrass shoot densities, canopy heights and estimated leaf area index (LAI). The cover, seagrass and algae densities, and seagrass canopy heights were obtained using ground truth sampling, while the LAI was estimated using data obtained from long-term monitoring programs. The maps do not show the differentiation of seagrass species, but ground truthing contemplated characterization of the density of Thalassia testudinum, Syringodium filiforme and Halodule wrightii and their respective LAIs. S. filiforme was the dominant species in terms of shoot density, and T. testudinum was dominant in terms of LAI. In the multiparameter-based map four classes were defined, based on the cover and structural characteristics, and its overall accuracy was very high (~90%). Maps based on sediment cover and LAI alone also had 4 classes, but they were less accurate than the multiparameter-based map (~70% and ~80%, respectively). The multiparameter-based seagrass map provided spatially-explicit data on the abundance and structure of seagrasses, useful for future monitoring of the changes in the meadows, and also for studies of that require data of large-scale meadow structure, such as inventories of associated biota, blue carbon storage, or modelling of the local hydrodynamics.

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“…Field research was conducted between October 8 and 12, 2022, utilizing the Photo Quadrat method proposed by Roelfsema and Phinn (2008) 16 along with Fauzan et al (2021) approach 17 . Photo collection involved capturing images every 2-3 meters using an underwater camera and a GPS (Global Positioning System) device in tracing mode by snorkeling.…”

Section: Research Methods 21 Research Locationmentioning

confidence: 99%

Assessment of seagrass coverage on Panggang Island and Karang Congkak Island: a comparison between pre and post COVID-19 pandemic using Planetscope imagery

Gustilasessio,

Wicaksono,

Djumanto

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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Seagrass beds are ecosystems that are sensitive to anthropogenic pressures. The restrictions imposed on human activities during the Covid-19 pandemic provide a unique opportunity for coastal ecosystems to recover. This situation presents a chance to monitor seagrass ecosystems on Panggang Island and Karang Congkak Island, aiming to observe any changes in the seagrass area. The method employed to ascertain the community structure involved photo transects, which were then processed using a machine learning supervised classification approach utilizing the random forest algorithm. The classification process categorized the area into five classes: seagrass, macroalgae, coral, bare substrate, and dead coral algae. Confusion Matrix Test was chosen to assess mapping accuracy, setting an accuracy threshold of ≥ 60%. The findings reveal notable alterations in seagrass areas across both islands. Panggang Island exhibited a considerable increase in seagrass coverage by 21,05 hectares. Conversely, Karang Congkak Island experienced a decrease of 2,88 hectares in seagrass coverage. These observed changes are likely not influenced by the anthropause phenomenon but rather by natural occurrences between 2019 and 2022, including the La Niña Triple Dip phenomenon and variations in sampling time during the transition season. The Overall mapping accuracy (OA) results for

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Characterizing Derawan seagrass cover change with time-series Sentinel-2 images

Cited by 12 publications

References 24 publications

Assessing Derawan Island’s Coral Reefs over Two Decades: A Machine Learning Classification Perspective

Assessing Derawan Island’s Coral Reefs over Two Decades: A Machine Learning Classification Perspective

Mapping the structure of mixed seagrass meadows in the Mexican Caribbean

Assessment of seagrass coverage on Panggang Island and Karang Congkak Island: a comparison between pre and post COVID-19 pandemic using Planetscope imagery

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