Dwindling seagrasses: A multi-temporal analysis on Google Earth Engine

Sebastian, Twinkle; Sreenath, K R; Sreeram, Miriam Paul; Ranith, R.

doi:10.1016/j.ecoinf.2022.101964

Cited by 6 publications

(7 citation statements)

References 56 publications

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“…To show the relevance of data analytics in sustainable sheries management, we breakdown each data analytics technique to show which shing ecosystem/zone or value chain (Table 2) it can be applied to among others coastal coral reef and offshore zones [47], [48], high seas [49], and in marine [50] and freshwater species zones [51], [52]; so as to mitigate the myriad sheries management challenges [3] such as marine pollution [53], sh supply chains [54], species stock assessments [55], IUU [56], and sea grass assessments [57] among others.…”

Section: Relevance Of Data Analyticsmentioning

confidence: 99%

“…Coastal zones are a rich habitat for species and ecosystems [47], [61] such as mangroves [88], estuaries [59] sea grasses [57], and coral reefs [48], [87] among others which not only provide sh resources but are crucial management zones [3]. However, the increase in sh effort coupled with management gaps and IUU shing [3], [46], [75] are threatening the sustainability of such ecosystems such as migratory species [87].…”

Section: Fresh Water Ecosystemsmentioning

confidence: 99%

“…Recent studies in the review revealed that data analytics methods and techniques are relevant in coastal zones as they aid benthic species mapping using the [77], determination of salinity and surface temperatures in estuaries using the downscaling method, statistical and hybrid dynamical-statistical methods [59], segmentation of shing vessel trajectories in coral reef zones using Global Positioning Systems (GPS) [48], determination of coastal sh vulnerability zones in the Indian Ocean using the mKRISHI Fisheries Analytic system and App (Singh, 2016), analysis of sea grass losses in near shore ecosystems, Island zones and the Arabian sea using Google Earth Engine (GEE) and regression plots [57], detection and quanti cation of marine litter in mangrove zones using the Non-Parametric Kruskal-Wallis test and PERMANOVA [67], identi cation of changes in micro plastics pollution in coastal sh species in estuaries using multivariate techniques such as PCO analysis [69] and microplastic distribution in coastal marshlands using ArcGIS and Google Earth [83], radiological evaluation of the risk of species habituating along and in beaches such as crabs along the Gulf of Guinea using Pearson correlation, cluster analysis and Principle Component Analysis (PCA) [74], long-term understanding of species shocks and uctuations due to environmental change using machine learning, computer vision techniques, Python, and Annotators [80], and obtaining insights into the spatial dynamics of sh anglers behaviors to sh populations especially along the US coast using visualization With the aid of these techniques, it was possible to assess the signi cance of spatial variation differences in litter abundance among mangrove stands based on habitat characteristics, concentration differences between rural, periurban, and megacity regions, and variation in litter abundance between mangrove oors and canopies based on location. The paper also discusses the techniques for data analysis used to evaluate the homogeneity and normality of variances.…”

Section: Fresh Water Ecosystemsmentioning

confidence: 99%

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Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

Isaac,

Baker,

Bhavani

et al. 2023

Preprint

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The value of fish resources to socioeconomic development is immense. However, there is growing evidence that continuous fishing-both at sea and in freshwater sources is threatening the availability of fish resources evidenced by the dwindling fisheries resources. Cognizant of this, several fisheries institutions, governments, and local users have come up with several strategies to promote sustainable fishing. Unfortunately, most of the initiatives have not fully managed to repulse unsustainable fishing practices. Owing to this we use a systematic literature review to argue and present a case for governments and fishery management organizations to invest in data analytics as a new pathway that can give a comprehensive, near real-time view of both ocean resources and coastal fishing activities in light of the declining fish stocks and escalating environmental problems. The review documented that by using data analytics, governments, and fisheries management organizations/individuals may respond more swiftly to external forces like climate change and implement new policies and regulations thus promoting sustainable fisheries management.

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Section: Relevance Of Data Analyticsmentioning

confidence: 99%

Section: Fresh Water Ecosystemsmentioning

confidence: 99%

Section: Fresh Water Ecosystemsmentioning

confidence: 99%

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Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

Isaac,

Baker,

Bhavani

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Coastal zones are a rich habitat for species and ecosystems (Wiltshire et al, 2018;Noelia et al, 2021) such as mangroves (Santoso et al, 2021), estuaries (Drenkard et al, 2021), sea grasses (Sebastian et al, 2023), and coral reefs (Behivoke et al, 2021;C.A. Bradley and Brown, 2021), among others, which not only provide fish resources but are crucial management zones (FAO, 2022).…”

Section: Relevance In Coastal and Near-shore Zones Fisheries Managementmentioning

confidence: 99%

Relevance of Data Analytics in Sustainable Fisheries Management: An Evidence-Based Study

Lukambagire,

Matovu,

Rao R.

et al. 2023

ASP.FC

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Aim of the study: The value of fish resources to socioeconomic development is immense. However, there is growing evidence that loss of quality in the marine environments e.g. marine waters at sea and coastal freshwater zones is threatening the availability of livelihood resources e.g. fish resources evidenced by the dwindling fisheries resources that promote sustainable livelihood. Cognizant of this, several fisheries institutions, governments, and local users have come up with several strategies to boost marine water environment management and promote sustainable fishing. Unfortunately, most of the initiatives have not fully managed to repulse unsustainable practices e.g. marine water pollution that leads to loss of wild catches and leads to unsustainable fishing practices. Material and methods: Owing to this we use a mixed research methods approach involving a systematic literature review and field survey in Alappad to argue and present a case for governments and fishery management organizations to invest in data analytics as a new pathway that can give a comprehensive, near real-time view of both ocean resources and coastal fishing activities in light of the declining fish stocks and escalating environmental problems e.g. marine water quality loss e.g. due to pollution. Results and conclusions: Findings documented that by using data analytics, governments, and fisheries management organizations/individuals may respond more swiftly to external forces like climate change, and marine water quality loss and implement new policies and regulations thus promoting sustainable fisheries management. Local perspectives of coastal communities could also guide the best data analytics tool or method to be used.

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“…By including segmentation, texture features, as well as a Principal Component Analysis in conjunction with the variable selection and dataset normalisation, Blume et al [21] were able to increase the overall accuracy to 71.0% and 76.6%, depending on whether the minimum or maximum seagrass extent of the Bahamas was considered. Sebastian et al [25] mapped the seagrasses in Kalpeni Lagoon, India, over ten epochs between 2003 and 2020, achieving an R 2 value of 0.97 when validated with field survey data.…”

Section: Introductionmentioning

confidence: 99%

Mapping the National Seagrass Extent in Seychelles Using PlanetScope NICFI Data

Lee,

Martin,

Traganos

et al. 2023

Remote Sensing

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Seagrasses provide ecosystem services worth USD 2.28 trillion annually. However, their direct threats and our incomplete knowledge hamper our capabilities to protect and manage them. This study aims to evaluate if the NICFI Satellite Data Program basemaps could map Seychelles’ extensive seagrass meadows, directly supporting the country’s ambitions to protect this ecosystem. The Seychelles archipelago was divided into three geographical regions. Half-yearly basemaps from 2015 to 2020 were combined using an interval mean of the 10th percentile and median before land and deep water masking. Additional features were produced using the Depth Invariant Index, Normalised Differences, and segmentation. With 80% of the reference data, an initial Random Forest followed by a variable importance analysis was performed. Only the top ten contributing features were retained for a second classification, which was validated with the remaining 20%. The best overall accuracies across the three regions ranged between 69.7% and 75.7%. The biggest challenges for the NICFI basemaps are its four-band spectral resolution and uncertainties owing to sampling bias. As part of a nationwide seagrass extent and blue carbon mapping project, the estimates herein will be combined with ancillary satellite data and contribute to a full national estimate in a near-future report. However, the numbers reported showcase the broader potential for using NICFI basemaps for seagrass mapping at scale.

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Dwindling seagrasses: A multi-temporal analysis on Google Earth Engine

Cited by 6 publications

References 56 publications

Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

Relevance of Data Analytics in Fisheries: Unveiling Insights for Sustainable Management - A Systematic Literature Review

Relevance of Data Analytics in Sustainable Fisheries Management: An Evidence-Based Study

Mapping the National Seagrass Extent in Seychelles Using PlanetScope NICFI Data

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