Seagrass Percent Cover Research Articles

Habitat area more consistently affects seagrass faunal communities than fragmentation per se

AbstractSeminal ecological theories, island biogeography and the single large or several small (SLOSS) reserve debate, examine whether large contiguous habitats conserve biodiversity better than multiple smaller patches. Today, delineating the ecological effects of habitat area versus configuration in a fragmentation context remains difficult, and often confounds efforts to understand proximate and ultimate drivers of community change in response to habitat alteration. We examined how the major components of fragmentation, habitat division versus area loss, independently influence faunal communities using landscapes constructed from artificial seagrass at scales relevant for juvenile estuarine nekton. We deployed 25 unique, 234‐m2 landscapes designed along orthogonal axes: habitat percent cover (i.e., area) and fragmentation per se (i.e., patchiness) to examine their effects on faunal density, community composition, and probability of bait‐assay consumption. Faunal sampling occurred in both artificial seagrass and interspaced sandflat matrix. We also examined whether larval‐settler density drove faunal density patterns across landscapes. Further, we assessed the relative importance of landscape‐scale parameters versus fine‐scale complexity–canopy height and epiphyte biomass–in determining faunal densities. We most consistently observed increasing epibenthic fish and macroinvertebrate density with increasing seagrass percent cover. Fragmentation per se only negatively affected epibenthic faunal density within the matrix at low seagrass coverage. Bait consumption increased with seagrass cover, suggesting larger habitats are relative foraging hotspots. Alternatively, benthopelagic fish density was unaffected by habitat parameters, reflecting lower seagrass reliance, or increased matrix tolerance. Community compositions did not vary across landscapes, suggesting that abundant species used landscapes indiscriminately. Finally, the relative importance of habitat parameters shifted across faunal guilds and life stages. Landscape percent cover most affected epibenthic faunal density, but not benthopelagic fish density, and neither pattern was related to settler density. Further, only fine‐scale complexity influenced settler densities. Collectively, our results indicate habitat area is a primary, positive driver of faunal densities and generalist consumption, and therefore should be prioritized in seagrass conservation. However, sampling across spatial scales and habitat types revealed nuances in habitat use patterns among faunal guilds and life stages that were not solely area‐dependent, illustrating that a variety of landscape configurations support essential nursery functions.

Mapping Seagrass Percent Cover And Biomass In Nusa Lembongan, Bali, Indonesia

Seagrass meadow is one of the blue-carbon ecosystems capable of absorbing and storing carbon more effectively in the bodies and sediments than terrestrial ecosystems. However, nationwide data on its carbon stock remains elusive due to limitations and challenges in data collection and mapping. Seagrass percent cover and biomass, which were closely related with above-ground carbon stock, can be effectively mapped and monitored using remote sensing techniques. Therefore, this study aimed to compare the accuracy of 4 scenarios as well as assess the performance of random forest and stepwise regression methods, for mapping seagrass percent cover and biomass in Nusa Lembongan, Bali, Indonesia. The scenarios were experimented using only atmospherically corrected images, sunglint, water, as well as sunglint and water column corrected images. Furthermore, WorldView-3 images and in-situ seagrass data were used, with the image corrected by applying the scenarios. Random forest and stepwise regression methods were adopted for mapping and modelling. The optimum mapping scenario and method were chosen based on R2, RMSE, and seagrass spatial distribution. The results show that the atmospherically corrected image produced the best seagrass percent cover and biomass map. Range of R2 using random forest and stepwise regression model was 0.49–0.64 and 0.50–0.58, with RMSE ranging from 18.50% to 21.41% and 19.36% to 20.72%, respectively. Based on R2, RMSE, and seagrass spatial distribution, it was concluded that the random forest model produced better mapping results, specifically for areas with high seagrass percent cover.

Anthropogenic Activities Alter The Seagrass Ecosystem In Southern Philippines

Seagrasses are economically and ecologically important marine habitats. However, anthropogenic activities resulted in their decline globally. In the Philippines, MPAs were established, but most seagrasses need to be acknowledged and directly protected, thus affecting the ecosystem productivity. To prevent this scenario, baseline information that describes the status of seagrass beds is highly needed to help implement sound management practices. The present investigation was carried out to assess the effect of anthropogenic activities on the seagrass ecosystem in 15 municipalities as sampling areas across Southern Philippines. The study used focus group discussions, key informant interviews, and household interviews guided by structured questionnaires. Some 30 to 45 fishers and gleaners were interviewed in every municipality with 476 individuals. A matrix was developed for measuring anthropogenic activities complementary to random sampling of seagrass cover assessment. The anthropogenic activities considered to degrade the seagrass ecosystem and were analyzed in the matrix included tourism, gleaning/fishing, aquaculture, industrial and domestic activities. The result of the study showed that domestic, tourism and gleaning are the prevalent anthropogenic activities affecting seagrasses. The result further showed that higher anthropogenic activities affected lower seagrass percentage cover (R2=0.56). The result indicates that as anthropogenic activity increases, the cover condition of the seagrass ecosystem is averted. This implies that seagrasses should be acknowledged and included in the coastal management plans.

Integrating Sentinel-2 and PlanetScope Image with Drone-based Seagrass Data for Seagrass Percent Cover Mapping

Seagrass field data collection activities to train remote sensing images for seagrass percent cover mapping and assess its accuracy can be laborious, costly, and time-consuming, especially for vast seagrass meadows with high density variations. There is also a potential discrepancy in information between seagrass data collected in the field, which usually covers 0.25m2 or 1m2 ground area, and the spatial resolution of remote sensing image used. PlanetScope at 3m and Sentinel-2 at 10m are the currently frequently remote sensing images used to map seagrass. There is a considerable information gap between seagrass data collected in the field and their spatial resolution. The use of seagrass field data thus involves a generalization process and a set of assumptions to justify its integration with remote sensing image. An alternative is to use the drone-based aerial image (hereafter drone data), which captures seagrass meadows at very high spatial resolution, to interpret seagrass percent cover at a level of precision similar to the remote sensing data used. This research assessed the integration of drone-based seagrass data with PlanetScope and Sentinel-2 images to map seagrass percent cover. Seagrass percent cover was interpreted from drone data for each 9m2 and 100m2 ground size following the PlanetScope and Sentinel-2 grids, respectively. Stepwise, random forest, and support vector regression were employed to develop the seagrass’s percent cover mapping model. The accuracy assessment of the resulting seagrass percent cover map involves the calculation of RMS error and plot 1:1 and its derivative analyses. Our results showed that an unparalleled benefit of using drone data is the possibility to obtain SPC information that matches the spatial resolution of satellite imagery, where techniques such as photo-quadrat and photo-transect cannot match. Drone data is successfully integrated with PlanetScope and Sentinel-2 images to produce a high accuracy SPC map effectively and efficiently. Indeed, there are challenges in using drone data, mainly related to oceanographic and weather conditions, and the difficulties in interpreting SPC at the species level.

Common ecological indicators identify changes in seagrass condition following disturbances in the Gulf of Mexico

Seagrasses are long-lived, clonal plants that can integrate fluctuations in environmental conditions over a range of temporal scales, from days to years, and can act as barometers of coastal change. There are many estimated seagrass traits and ecosystem parameters that have the potential to reflect ecosystem status, linking seagrass condition to natural and anthropogenic drivers of change. We identified five seagrass indicators and seven metrics that are suitable, affordable and frequently measured by 38 monitoring programs across the Gulf of Mexico (GoM). A specific set of ratings and assessment points were formulated for each measurable metric. We determined metric ratings (Acceptable, Concerning, Alarming) and validated assessment points using long-term monitoring data from Texas and Florida, coupled with existing literature and input from a panel of seagrass biologists. We reported scores using a blue-gray-orange (Acceptable-Concerning-Alarming) scale to summarize information in a format accessible to the public, resource managers, stakeholders, and policymakers. Seagrass percent cover, shoot allometry and species composition were sensitive indicators of large-scale climatic disturbances (droughts, hurricanes). Severe drought led to reductions in total seagrass cover and leaf length in Upper Laguna Madre, Texas, and Florida Bay; however, Syringodium filiforme was disproportionally affected in Texas while Thalassia testudinum beds responded strongly to drought impacts in Florida. Hurricanes Harvey (TX) and Irma (FL) also resulted in loss of seagrass cover and diminished leaf length in the Texas Coastal Bend and Florida Keys; both storms largely impacted T. testudinum and to a lesser extent, S. filiforme. Many of the metrics within these affected bays and basins received either a “Concerning” or “Alarming” rating, driven by the impacts of these disturbances. Our proposed indicators serve as a tool to evaluate seagrass condition at the bay or basin scale. Moreover, the indicators, metrics, and assessment points are amenable to large-scale evaluations of ecosystem condition because they are economically feasible. This framework may provide the foundation for a comprehensive assessment of seagrass status and trends across the entire GoM.

Ascidian diversity and abundance in North Carolina seagrass meadows

AbstractAscidians are sessile marine invertebrates found all over the world in a variety of natural and artificial habitats. The objective of this study was to provide the first inventory of ascidian diversity and abundance in North Carolina (NC) seagrass meadows. Eight sites along the NC coast were surveyed in May and June 2021 and at each site, 20 quadrats were deployed. All ascidian species within the quadrats were counted and identified based on morphological characterization and sequencing of the barcoding gene. Seagrass percent cover, biomass, and shoot density were also quantified. Ascidians were found in six sites and four species were recorded: the solitary species Molgula manhattensis, Styela plicata, and Bostrichobranchus sp., and the colonial Didemnum lutarium. Colonial specimens were generally attached to hard substrate, while solitary species were often found attached to seagrass rhizomes and leaves. S. plicata and M. manhattensis haplotype diversity were equivalent to previous descriptions from individuals collected on artificial substrates and other locations. Ascidian abundance was positively correlated with seagrass biomass but not with seagrass percent cover. A fifth species, the solitary Styela canopus, appeared in a site revisited in October 2021. Temperate ascidians are known to have seasonal cycles, thus further research should include monthly surveys to assess ascidian diversity and abundance over time.

Providing a framework for seagrass mapping in United States coastal ecosystems using high spatial resolution satellite imagery

Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no seagrass, and no data. Satellite-derived seagrass coverage was then compared to reference data using either balanced agreement, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the format of the reference data used for comparison. Balanced agreement ranged from 58% to 86%, with better agreement between reference- and satellite-indicated seagrass absence (specificity ranged from 88% to 100%) than between reference- and satellite-indicated seagrass presence (sensitivity ranged from 17% to 73%). Results of the Mann-Whitney U and Kruskal-Wallis tests demonstrated that satellite-indicated seagrass percentage cover had moderate to large correlations with reference-indicated seagrass percentage cover, indicative of moderate to strong agreement between datasets. Satellite classification performed best in areas of dense, continuous seagrass compared to areas of sparse, discontinuous seagrass and provided a suitable spatial representation of seagrass distribution within each study area. This study demonstrates that the same methods can be applied across scenes spanning varying seagrass bioregions, atmospheric conditions, and optical water types, which is a significant step toward developing a consistent, operational approach for mapping seagrass coverage at the national and global scales. Accompanying this manuscript are instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems.

Is All Seagrass Habitat Equal? Seasonal, Spatial, and Interspecific Variation in Productivity Dynamics Within Mediterranean Seagrass Habitat

Seagrass meadows’ ability to capture carbon through sequestering autochthonous carbon via photosynthesis means they could represent a potential nature-based solution to rising carbon emissions. In multispecies seagrass communities, and due to species introduction or predicted range shifts, it is important to know which species deliver different carbon sequestration gains to inform conservation actions. Large benthic chamber experiments (volume = 262L) assessed the seasonal and spatial variation in metabolism dynamics of the endemic and dominant Mediterranean seagrass, P. oceanica whilst small benthic chamber experiments (volume = 7L) compared the dynamics between, P. oceanica the native C. nodosa and non-native H. stipulacea. Within shallow P. oceanica edge habitat lower Net Apparent Productivity (NAP) occurs in autumn (x¯ = 1.3, SD ± 2.95 O2 mmol m-2 d-1) compared to summer (x¯ = 9.9, SD ± 2.75 O2 mmol m-2 d-1 corresponding with periods of light limiting and light saturating conditions, but it remains overall autotrophic annually (2.3 C mol m-2 yr-1). However, spatial heterogeneity exists, the center areas of P. oceanica were more productive (NAP x¯ =19.7, SD± 3.83 O2 mmol m-2 d-1) compared to edge habitat with spatial changes in productivity relating to plant surface area (96%), shoot density (81%), blade length (72%) and seagrass percentage cover (64%). Under comparative conditions in a sparse multispecies area of the meadow the species demonstrated different capacities for carbon fixation. H. stipulacea was carbon positive and P. oceanica fluctuated between positive and negative carbon balance suggesting both can maintain a balance between carbon fixation and carbon utilised for metabolic activity. In contrast the C. nodosa here would be expected to deteriorate as it was utilising carbon more than it was fixing (NAPN2 (x¯ = -0.0012, SD ± 0.0007 O2 mmol cm-2 d-1). This study demonstrates that not all seagrass habitat is equal. If seagrass meadows are to play a part in mitigating CO2 emissions, variability in primary productivity within seagrass meadows needs to be accounted for to produce accurate total fixed carbon estimates, and subsequently autochthonous carbon sequestration estimates. This means seagrass meadow species composition and the condition of these meadows must be better understood.

Consistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows

Seagrass percent cover is a crucial and influential component of the biophysical characteristics of seagrass beds and is a key parameter for monitoring seagrass conditions. Therefore, the availability of seagrass percent cover maps greatly assists in sustainable coastal ecosystem management. This research aimed to assess the consistency of PlanetScope imagery for seagrass percent cover mapping using two study areas, namely Parang Island and Labuan Bajo, Indonesia. Assessing the consistency of the PlanetScope imagery performance in seagrass percent cover mapping helps understand the effects of variations in the image quality on its performance in monitoring changes in seagrass cover. Percent cover maps were derived using object-based image analysis (image segmentation and random forest) and pixel-based random forest algorithm. Accuracy assessment and consistency analysis were conducted on the basis of the following three approaches: overall accuracy consistency, agreement percentage and consistent pixel locations. Results show that PlanetScope images can fairly consistently map seagrass percent cover for a specific area across different dates. However, these images produced different levels of accuracy when used for mapping in seagrass meadows with various characteristics and benthic cover complexities. The mapping accuracy (OA–overall accuracy) and consistency (AP–agreement percentage) in patchy seagrass meadows (Parang Island, mean OA 18.4%–38.6%, AP 44.1%–70.3%) are different from those in continuous seagrass meadows (Labuan Bajo, OA 43.0%–56.2%, and AP 41.8%–55.8%). Moreover, PlanetScope images are consistent when used for mapping seagrasses with low and high percent covers but strive to obtain good consistency for medium percent cover due to the combination of seagrass and non-seagrass in a pixel. Furthermore, images with relatively similar image acquisition conditions (i.e., winds, aerosol optical depth, signal-to-noise ratio, and sunglint intensity) produce better consistency. The OA is related to the image acquisition conditions, whilst the AP is related to variation in these conditions. Nevertheless, PlanetScope is still the best high spatial resolution image that provides daily acquisition and is highly beneficial for various applications in tropical areas with persistent cloud coverage.

Seagrass Diversity and Distribution in Maribojoc Bay, Bohol, Philippines

Seagrasses are major parts of coastal and marine biodiversity. Unfortunately, these aquatic plants and their ecological values are virtually unknown to many Filipinos. This study assessed the seagrasses in Maribojoc Bay, particularly in the coastal areas of the three municipalities, namely Maribojoc, Dauis, Panglao, and the City of Tagbilaran. Ecological assessments were conducted to determine the composition, abundance, distribution, percent cover, diversity, dominance, and evenness of seagrass species. Eight sampling sites were surveyed from October to December 2020. Seven seagrass species were identified, with Thalassia hemprichii as the most abundant (52.79%). There was a significant difference (p>0.05) in seagrass species relative abundance. The Shannon diversity index implies low diversity (H’=1.40) of seagrass species. High dominance (2.98) and low evenness (0.72) were attributed to the high abundance of T. hemprichii in the seagrass beds. The seagrass coverage was characterized by patchy and continuous meadows, with percentage cover ranging from 17.45% (poor) – 60% (good). Maribojoc Bay had a seagrass percentage cover of 38.65%, which can be classified under “fair” conditions. Seagrass community structure implies sparse coverage and low diversity, probably due to the deterioration of once-continuous meadows. However, further studies concerning seagrass communities are recommended in order to implement rehabilitation program or improve current management in Maribojoc Bay.

Assessment of WorldView-2 images for aboveground seagrass carbon stock mapping in patchy and continuous seagrass meadows

ABSTRACT Spatially explicit information on aboveground seagrass carbon stock (AGCseagrass) is required to understand the role of seagrass as a nature-based solution to mitigating and adapting to climate change. Remote sensing provides the most effective and efficient means to map AGCseagrass. This research aimed to assess the accuracy of multispectral images to map AGC in seagrass meadows with different characteristics. Kemujan Island in Indonesia was selected to represent patchy beds with relatively low seagrass percent cover and low water clarity, whereas Labuan Bajo of Indonesia served an example of continuous beds with a relatively high percent cover and water clarity. WorldView-2 is a high-spatial-resolution multispectral image with six visible bands, by which water can be penetrated. Several image processing techniques, including sunglint and water column correction, principal component analysis, and co-occurrence texture analysis, were applied to the atmospherically corrected WorldView-2 images. These datasets were used as inputs in the AGC empirical model using seven regression algorithms, namely, single-band linear regression, band-ratio linear regression, stepwise regression, random forest regression, support vector regression, extreme gradient boosting, and multivariate adaptive regression spline. Seagrass field data collected using photo quadrat technique were used to train the regression model and assess the accuracy of the resulting AGCseagrass maps. The results indicated that WorldView-2 can be used to map AGC in different seagrass meadows with a consistent accuracy. The most accurate AGCseagrass map for Kemujan Island had a root mean square error (RMSE) of 4.11 g C m−2 for the aboveground stock in the range of 0–28.70 g C m−2, and for Labuan Bajo, the RMSE of the map was 9.73 g C m−2, with aboveground stock range of 0.31–64.38 g C m−2. Model cross-validation revealed that the mapping model can be site-specific or robust depending on the characteristics of the field-derived AGCseagrass data used to train the regression algorithm. For example, the model developed for Labuan Bajo seagrasses, which had a higher AGC variance, can be successfully applied to Kemujan Island with its lower AGC variance, but not vice versa. This finding is a key factor in the future development of a robust AGCseagrass mapping model that is applicable to various seagrass meadows as a stepping stone to accelerating the filling of gaps in the global seagrass dataset.

Tides, Topography, and Seagrass Cover Controls on the Spatial Distribution ofPinna nobilison a Coastal Lagoon Tidal Flat

AbstractIn the last two decadesPinna nobilis, the largest bivalve mollusk endemic to the Mediterranean Sea, has recolonized the tidal flats of some coastal lagoons along the Italian Adriatic coast. In this study, we investigate the influence of tides on the spatial distribution and density of aP. nobilispopulation developing on a tidal flat of the Venice lagoon (NE Italy) by exploiting remote sensing technologies. Our results show that there is a threshold topographic elevation (about 0.5 m below mean sea level for the studied tidal flat) above which the number and duration of emersions become limiting factors ofP. nobilisabundance. Above this elevation, the population density decreases sharply. Densely populated areas tend to occur in tidal flat depressions, where the duration and frequency of emersions are low. We find, however, that the population density has large spatial variability in response to other factors, such as the seagrass percentage cover. The density of the shells increases with increasing seagrass percentage cover, and the denseP. nobilispopulation (0.8–2.68 N/m2) grows within theCymodocea nodosameadow that has a high seagrass percentage cover (>85%). However, within the seagrass meadow with the highest percentage cover, the bivalve preferentially colonizes the portion closest to the main channel, a possible source of nutrients. The shift toC. nodosa—dominated meadows in the Venice lagoon occurred during the last two decades has probably facilitated the observed colonization byP. nobilis. Our findings provide management information for protection and/or restoration ofP. nobilisin coastal lagoons.

Using Unoccupied Aerial Vehicles (UAVs) to Map Seagrass Cover from Sentinel-2 Imagery

Seagrass habitats are ecologically valuable and play an important role in sequestering and storing carbon. There is, thus, a need to estimate seagrass percentage cover in diverse environments in support of climate change mitigation, marine spatial planning and coastal zone management. In situ approaches are accurate but time-consuming, expensive and may not represent the larger spatial units collected by satellite imaging. Hence, there is a need for a consistent methodology that uses accurate point-based field surveys to deliver high-quality mapping of percentage seagrass cover at large spatial scales. Here, we develop a three-step approach that combines in situ (quadrats), aerial (unoccupied aerial vehicle—UAV) and satellite data to map percentage seagrass cover at Turneffe Atoll, Belize, the largest atoll in the northern hemisphere. First, the optical bands of four UAV images were used to calculate seagrass cover, in combination with in situ data. The seagrass cover calculated from the UAV was then used to develop training and validation datasets to estimate seagrass cover in Sentinel-2 pixels. Next, non-seagrass areas were identified in the Sentinel-2 data and removed by object-based classification, followed by a pixel-based regression to calculate seagrass percentage cover. Using this approach, percentage seagrass cover was mapped using UAVs (R2 = 0.91 between observed and mapped distributions) and using Sentinel-2 data (R2 = 0.73). This work provides the first openly available and explorable map of seagrass percentage cover across Turneffe Atoll, where we estimate approximately 242 km2 of seagrass above 10% cover is located. We estimate that this approach offers 30 times more data for training satellite data than traditional methods, therefore presenting a substantial reduction in cost-per-point for data. Furthermore, the increase in data helps deliver a high-quality seagrass cover map, suitable for resolving trends of deteriorating, stable or recovering seagrass environments at 10 m2 resolution to underpin evidence-based management and conservation of seagrass.

ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE

Abstract. Remote sensing can make seagrass aboveground carbon stock (AGCseagrass) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGCseagrass in the field to train and assess its remote sensing-based mapping. The aim of this research is to (1) analyze the Percent Cover (PCv)-AGCseagrass relationship in seagrass at the species and community levels to estimate AGCseagrass from PCv and (2) perform AGCseagrass mapping at both levels using WorldView-2 image and assess the accuracy of the resulting map. This research was conducted in Karimunjawa and Kemujan Islands, Indonesia. Support Vector Machine (SVM) classification was used to map seagrass species composition, and stepwise regression was used to model AGCseagrass using deglint, water column corrected, and principle component bands. The results were a rapid AGCseagrass estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGCseagrass map had 58.79% accuracy (SEE = 5.41 g C m−2), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m−2) and EaThCr (70.02%, SEE = 4.32 g C m−2) but decreased for ThCr (55.08%, SEE = 2.55 g C m−2). The results indicate that WorldView-2 image reflectance can accurately map AGCseagrass in the study area in the range of 15–20 g C m−2 for Ea, 10–15 g C m−2 for EaThCr, and 4–8 g C m−2 for ThCr. Based on our model, the AGCseagrass in the study area was estimated at 13.39 t C.

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

In recent decades, seagrass ecosystems are declining globally. Providing spatial information of seagrasses has become growing needs in which data from satellite remote sensing platforms are proven to be useful. While multi-temporal archives of commercial and open access imagery are available, monitoring activity is rarely done frequently, limiting the information of seagrass dynamics particularly in tropical region. Here, we utilized time-series Sentinel-2 images to map seagrass percentage cover at intra-annual basis in two years’ time frame based on empirical method, while also compared maximum likelihood and random forest classifier to map dominant benthic cover type and determined seagrass spatial extent. We found that random forest classifier outperformed maximum likelihood classifier. Furthermore, a pattern of seagrass percentage cover change was present. Our results demonstrate how freely available Sentinel-2 images and limited field data could be useful in monitoring seagrasses at dense time. We anticipate this study to be a starting point for more complicated seagrass change analysis at denser time and link potential change drivers spatially.

Remote sensing for coral reef and seagrass cover mapping to support coastal management of small islands

Coral reefs and seagrass are critical coastal resources due to their role in the ecosystem benefits for the coastal environment in terms of biodiversity, coastal protection, fisheries, and tourism. It is therefore important to preserve and protect these species. Coral and seagrass percent cover mapping is a simple approach to assess coral and seagrass condition. The application of remote sensing of coral and seagrass percent cover mapping is very challenging with respect to performance and accuracy. This research aims to utilize remote sensing data for coral and seagrass percent cover mapping. Linear and machine learning regressions (RF and SVM) were used to develop a coral and seagrass percent cover model from a Sentinel-2 MSI images. The Sentinel-2 MSI images were transformed into deglint, water column (DII), principal component, and mean texture analysis as input bands for the model. The results showed that coral percent cover mapping accuracy is relatively low (RMSE = ±17%) due to various problems, limitations, and an inaccurate model, whereas the results of the seagrass percent cover map had higher accuracy, with RMSE ±11%. The results obtained indicate that the seagrass percent cover map is suitable for use as basic information to support coastal management. However, the coral percent cover map is not an optimal information source due to its low accuracy.

Sentinel-2 remote sensing of Zostera noltei-dominated intertidal seagrass meadows

Accurate habitat mapping methods are urgently required for the monitoring, conservation, and management of blue carbon ecosystems and their associated services. This study focuses on exposed intertidal seagrass meadows, which play a major role in the functioning of nearshore ecosystems. Using Sentinel-2 (S2) data, we demonstrate that satellite remote sensing can be used to map seagrass percent cover (SPC) and leaf biomass (SB), and to characterize its seasonal dynamics. In situ radiometric and biological data were acquired from three intertidal meadows of Zostera noltei along the European Atlantic coast in the summers of 2018 and 2019. This information allowed algorithms to estimate SPC and SB from a vegetation index to be developed and assessed. Importantly, a single SPC algorithm could consistently be used to study Z. noltei-dominated meadows at several sites along the European Atlantic coast. To analyze the seagrass seasonal cycle and to select images corresponding to its maximal development, a two-year S2 dataset was acquired for a French study site in Bourgneuf Bay. The potential of S2 to characterize the Z. noltei seasonal cycle was demonstrated for exposed intertidal meadows. The SPC map that best represented seagrass growth annual maximum was validated using in situ measurements, resulting in a root mean square difference of 14%. The SPC and SB maps displayed a patchy distribution, influenced by emersion time, mudflat topology, and seagrass growth pattern. The ability of S2 to measure the surface area of different classes of seagrass cover was investigated, and surface metrics based on seagrass areas with SPC ≥ 50% and SPC ≥ 80% were computed to estimate the interannual variation in the areal extent of the meadow. Due to the high spatial resolution (pixel size of 10 m), frequent revisit time (≤ 5 days), and long-term objective of the S2 mission, S2-derived seagrass time-series are expected to contribute to current coastal ecosystem management, such as the European Water Framework Directive, but to also guide future adaptation plans to face global change in coastal areas. Finally, recommendations for future intertidal seagrass studies are proposed.

THE RELATIONSHIP BETWEEN SEAGRASS Thalassia hemprichii PERCENTAGE COVER AND THEIR BIOMASS

Seagrass has many functions and values, including as carbon sink. However, to estimate carbon in seagrass, it involves seagrass biomass harvesting which is laborious, costly, and destructive. This study aimed to find out the relationship between seagrass Thalassia hemprichii percent cover with their biomass which will provide alternative method for biomass estimation leading to an efficient, less cost and less destructive method for seagrass carbon stock estimation. Seagrass were sampled in Bonebatang island, South Sulawesi, and estimated their percent cover following SeagrassWatch Method from different seagrass condition and sediment type, as well as harvested for their biomass. In the lab, seagrass biomass was dried. Data was analyzed by using simple regression analysis. Results showed that there is a relatively strong relationship between percent cover and the belowground, aboveground and total biomass (R2 = 0.70; 0,81 and 8,3, respectively). Seagrass percent cover (seagrass health status, i.e. healthy, moderate and poor) also resulted a relatively strong influence on total seagrass biomass (R2>50%). However, apart from segarss percent cover, some other parameters are needed to be taken into consideration, such as seagrass densities and seagrass morphologies

Hurricane Impacts and the Resilience of the Invasive Sea Vine, Halophila stipulacea: a Case Study from Puerto Rico

Category five hurricanes Irma and Maria (September 2017) caused significant damage to shallow seagrass communities across Puerto Rico. The magnitude and spatial extent of hurricane impacts on representative seagrass habitats of Culebra Island were addressed using a combination of random photo-quadrats and before–after hurricanes GIS-based imagery analyses. There was a significant loss of shallow seagrasses across all nine surveyed locations. Most of the documented impacts were associated with sediment bedload (horizontal transport), which resulted in burial and suffocation. There was also localized physical disruption of the seagrass habitat matrix across locations exposed to stronger wave action, creating major scars and exposing below-ground structure to further disintegration by future storm events. Displaced coral rubble also caused seagrass burial. Aerial imagery analyses (2007, 2010, 2017) showed a significant decline in seagrass percent cover. Seagrass decline was positively correlated with wave exposure (p < 0.05). Seagrass cover, density, and changes in benthic community structure were documented across five of the surveyed locations during 2018, and these data were further compared to data collected in 2004 at these same sites. There was a decline in percent seagrass cover and density and a change in benthic community structure favoring habitat homogenization. A remarkable finding was the rapid recovery, expansion, and increased localized dominance of the invasive seagrass, Halophila stipulacea. This was particularly evident in areas impacted by recurrent land-based runoff pulses, anchoring, sediment resuspension due to navigation, trampling or by the accumulation of decaying Sargassum mats. Hurricanes triggered a localized shift in marine vegetation, favoring the invasion of H. stipulacea, with potentially significant consequences on ecosystem resilience and on the ability of native in seagrasses to persist and adapt to projected climate change impacts.

ASSESSMENT OF SEAGRASS PERCENT COVER AND WATER QUALITY USING UAV IMAGES AND FIELD MEASUREMENTS IN BOLINAO, PANGASINAN

Abstract. The sensitivity to changes in water quality inherent to seagrass communities makes them vital for determining the overall health of the coastal ecosystem. Numerous efforts including community-based coastal resource management, conservation and rehabilitation plans are currently undertaken to protect these marine species. In this study, the relationship of water quality parameters, specifically chlorophyll-a (chl-a) and turbidity, with seagrass percent cover is assessed quantitatively. Support Vector Machine, a pixel-based image classification method, is applied to determine seagrass and non-seagrass areas from the orthomosaic which yielded a 91.0369% accuracy. In-situ measurements of chl-a and turbidity are acquired using an infinity-CLW water quality sensor. Geostatistical techniques are utilized in this study to determine accurate surfaces for chl-a and turbidity. In two hundred interpolation tests for both chl-a and turbidity, Simple Kriging (Gaussian-model type and Smooth- neighborhood type) performs best with Mean Prediction equal to −0.1371 FTU and 0.0061 μg/L, Root Mean Square Standardized error equal to −0.0688 FTU and −0.0048 μg/L, RMS error of 8.7699 FTU and 1.8006 μg/L and Average Standard Error equal to 10.8360 FTU and 1.6726 μg/L. Zones are determined using fishnet tool and Moran’s I to calculate for the seagrass percent cover. Ordinary Least Squares (OLS) is used as a regression analysis to quantify the relationship of seagrass percent cover and water quality parameters. The regression analysis result indicates that turbidity has an inverse relationship while chlorophyll-a has a direct relationship with seagrass percent cover.