Seagrass cover expansion off Caja de Muertos Island, Puerto Rico, as determined by long-term analysis of historical aerial and satellite images (1950–2014)

León-Pérez, Mariana C.; Armstrong, Roy A.; Hernández, William J.; Aguilar‐Perera, Alfonso; Thompson-Grim, Jill

doi:10.1016/j.ecolind.2020.106561

Cited by 9 publications

(13 citation statements)

References 39 publications

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“…This is a very important point for future research in the area: the more information that is recorded about the presence of macroalgal blooms, the better this type of automatic detection technique can be tuned, helping create a database of conditions throughout the year that will help future predictions be less reliant on in situ observation, thus reducing management costs. Sentinel-2 satellites can certainly record the reflectance spectra that contain the scattering, absorption, and fluorescence signatures of algal blooms near the ocean surface [12][13][14][15][16]31], providing a 5-day revisit at the Equator (improved for higher latitudes) of global coastal zones.…”

Section: Prediction Of Future Macroalgal Bloomsmentioning

confidence: 99%

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Machine Learning for Detection of Macroalgal Blooms in the Mar Menor Coastal Lagoon Using Sentinel-2

et al. 2023

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The Mar Menor coastal lagoon in southeastern Spain has experienced a decline in water quality due to increased nutrient input, leading to the eutrophication of the lagoon and the occurrence of microalgal and macroalgal blooms. This study analyzes the macroalgal bloom that occurred in the lagoon during the spring-summer of 2022. A set of machine learning techniques are applied to Sentinel-2 satellite imagery in order to obtain indicators of the presence of macroalgae in specific locations within the lagoon. This is supported by in situ observations of the blooming process in different areas of the Mar Menor. Our methodology successfully identifies the macroalgal bloom locations (accuracies above 98%, and Matthew’s Correlation Coefficients above 78% in all cases), and provides a probabilistic approach to understand the likelihood of occurrence of this event in given pixels. The analysis also identifies the key parameters contributing to the classification of pixels as algae, which could be used to develop future algorithms for detecting macroalgal blooms. This information can be used by environmental managers to implement early warning and mitigation strategies to prevent water quality deterioration in the lagoon. The usefulness of satellite observations for ecological and crisis management at local and regional scales is also highlighted.

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Section: Prediction Of Future Macroalgal Bloomsmentioning

confidence: 99%

“…Several remote sensing approaches were recently carried out to monitor the algal bloom distribution over coastal regions [12][13][14][15][16]. In addition, remote sensing approaches have been developed during recent decades to monitor the water quality in inland and coastal waters [17,18].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Detection of Macroalgal Blooms in the Mar Menor Coastal Lagoon Using Sentinel-2

et al. 2023

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“…In contrast to the long-term stability of the seagrass populations in St. Joseph Bay, studies of the long-term worldwide temporal dynamics of seagrass have documented declines and losses due to direct anthropogenic disturbances (Tomasko et al 2005;Thorhaug et al 2017), hurricane-related impacts (Cho et al 2017;Challener et al 2019;León-Pérez et al 2020), grazing by sea urchins and green turtles (Challener et al 2019;León-Pérez et al 2020;Heck 2020 2021), and climate change (Orth et al 2006;Lyons et al 2012;Short et al 2016) in other locations. While seagrass extent was assessed as stable in St. Joseph Bay, propeller scarring remains a chronic problem, especially in the southern portion of the Bay (Yarbro and Carlson 2016).…”

Section: Temporal Assessment Of Seagrass Extent Lai and Bgcmentioning

confidence: 99%

“…Hurricanes can have direct, physical impacts on seagrasses, causing sediment deposition, defoliation and/or uprooting (van Tussenbroek et al 2008), as well as longer term impacts such as light limitation from persistent water column turbidity resulting from heavy rainfall and runoff (Ridler et al 2006;Steward et al 2006;Tomasko et al 2020). Alternating patterns of seagrass decline and growth have also been observed in response to mechanical hurricane damage and recolonization, possibly from storminduced dispersal seeds and vegetative propagules, as well as reduced herbivore pressure (Creed et al 2003;León-Pérez et al 2020). However, even a direct hit by a Category 5 hurricane does not always result in significant damage to healthy seagrass meadows (Dierssen et al 2003).…”

Section: Temporal Assessment Of Seagrass Extent Lai and Bgcmentioning

confidence: 99%

Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis

Lebrasse

Schaeffer

Coffer

et al. 2022

Estuaries and Coasts

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Seagrasses are globally recognized for their contribution to blue carbon sequestration. However, accurate quantification of their carbon storage capacity remains uncertain due, in part, to an incomplete inventory of global seagrass extent and assessment of its temporal variability. Furthermore, seagrasses are undergoing significant decline globally, which highlights the urgent need to develop change detection techniques applicable to both the scale of loss and the spatial complexity of coastal environments. This study applied a deep learning algorithm to a 30-year time series of Landsat 5 through 8 imagery to quantify seagrass extent, leaf area index (LAI), and belowground organic carbon (BGC) in St. Joseph Bay, Florida, between 1990 and 2020. Consistent with previous field-based observations regarding stability of seagrass extent throughout St. Joseph Bay, there was no temporal trend in seagrass extent (23 ± 3 km2, τ = 0.09, p = 0.59, n = 31), LAI (1.6 ± 0.2, τ = -0.13, p = 0.42, n = 31), or BGC (165 ± 19 g C m−2, τ = - 0.01, p = 0.1, n = 31) over the 30-year study period. There were, however, six brief declines in seagrass extent between the years 2004 and 2019 following tropical cyclones, from which seagrasses recovered rapidly. Fine-scale interannual variability in seagrass extent, LAI, and BGC was unrelated to sea surface temperature or to climate variability associated with the El Niño-Southern Oscillation or the North Atlantic Oscillation. Although our temporal assessment showed that seagrass and its belowground carbon were stable in St. Joseph Bay from 1990 to 2020, forecasts suggest that environmental and climate pressures are ongoing, which highlights the importance of the method and time series presented here as a valuable tool to quantify decadal-scale variability in seagrass dynamics. Perhaps more importantly, our results can serve as a baseline against which we can monitor future change in seagrass communities and their blue carbon.

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“…Puerto Rico central mountain chain can create a shade effect, that may have provided additional protection from the storm surge at out sites [17].…”

Section: Changes In Benthic Compositionmentioning

confidence: 99%

High-Resolution Satellite Imagery to AssessSargassumInundation Impacts to Coastal Areas

Hernández

Morell

Armstrong

2020

Preprint

Self Cite

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A change detection analysis utilizing Very High-resolution (VHR) satellite imagery was performed to evaluate the changes in benthic composition and coastal vegetation in La Parguera, southwestern Puerto Rico, attributable to the increased influx of pelagic Sargassum spp and its accumulations in cays, bays, inlets and near-shore environments. Satellite imagery was co-registered, corrected for atmospheric effects, and masked for water and land. A Normalized Difference Vegetation Index (NDVI) and an unsupervised classification scheme were applied to the imagery to evaluate the changes in coastal vegetation and benthic composition. These products were used to calculate the differences from 2010 baseline imagery, to potential hurricane impacts (2018 image), and potential Sargassum impacts (2020 image). Results show a negative trend in Normalized Difference Vegetation Index (NDVI) from 2010 to 2020 for the total pixel area of 24%, or 546,446 m 2 . These changes were also observed in true color images from 2010 to 2020. Changes in the NDVI negative values from 2018 to 2020 were higher, especially for the Isla Cueva site (97%) and were consistent with the field observations and drone surveys conducted since 2018 in the area. The major changes from 2018 and 2020 occurred mainly in unconsolidated sediments ( e.g. sand, mud) and submerged aquatic vegetation ( e.g. seagrass, algae), which can have similar spectra limiting the differentiation from multi-spectral imagery. Areas prone to Sargassum accumulation were identified using a combination of 2018 and 2020 true color VHR imagery and drone observations. This approach provides a quantifiable method to evaluate Sargassum impacts to the coastal vegetation and benthic composition using change detection of VHR images, and to separate these effects from other extreme events.

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Seagrass cover expansion off Caja de Muertos Island, Puerto Rico, as determined by long-term analysis of historical aerial and satellite images (1950–2014)

Cited by 9 publications

References 39 publications

Machine Learning for Detection of Macroalgal Blooms in the Mar Menor Coastal Lagoon Using Sentinel-2

Machine Learning for Detection of Macroalgal Blooms in the Mar Menor Coastal Lagoon Using Sentinel-2

Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis

High-Resolution Satellite Imagery to AssessSargassumInundation Impacts to Coastal Areas

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