[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124034-en":3,"doc-seo-124034-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124034,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Monitoring seagrass meadows in Maputo Bay using integrated remote sensing techniques and machine learning","Seagrass meadows are critical, highly productive coastal ecosystems, and consistent monitoring is necessary to support management and conservation. This study integrates Sentinel-2 satellite imagery with UAV (unmanned aerial vehicle) surveys and applies machine learning to classify seagrass in Maputo Bay, southern Mozambique. Sentinel-2 quantifies seagrass extent and change, while UAV data resolve species-level patterns and aboveground biomass. Across tested algorithms, high producer accuracy and Kappa performance were achieved, revealing a 33.4% seagrass decline from 1991 to 2023 and guiding restoration.","Regional Studies in Marine Science 79 (2024) 103816  \nContents lists available at ScienceDirect  \nRegional Studies in Marine Science  \njournal [homepage: www.elsevier.com/locate/rsma](homepage: www.elsevier.com/locate/rsma)  \n| Monitoring seagrass meadows in Maputo Bay using integrated remote sensing techniques and machine learning\u003Cbr>M. Amone-Mabutoa,b, S. Bandeiraa, J. Hollander c, D. Hume d, J. Campiraa, JB Adams b,*\u003Cbr>a Department of Biological Sciences, Eduardo Mondlane University, Maputo, Mozambique\u003Cbr>b Department of Botany, Institute for Coastal and Marine Research, Nelson Mandela University, PO Box 77000, Gqeberha, South Africa c World Maritime University, Ocean Sustainability, Governance & Management Unit, Malm¨o, Sweden\u003Cbr>d Department of Aquatic Resources, Swedish University of Agricultural Sciences, Sweden |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Seagrass cover Extent change Aboveground biomass UAV systems Southern Mozambique |  | Seagrass meadows are one of the most productive and valuable ecosystems on the planet. Monitoring seagrass meadows is essential to understand how these habitats change, and to develop better management and conservation practices. This study integrated satellite imagery from Sentinel-2 and Unmanned Aerial Vehicles (UAV) using machine learning to provide a consistent classification approach for monitoring seagrass in Maputo Bay, southern Mozambique. Sentinel-2 imagery was used to map seagrass extent and changes in Maputo Bay. The UAV systems were used to map seagrass at species level and biomass. All three algorithms tested in the ArcGIS environment could detect seagrass with high producer accuracy and Kappa coefficient. The area of seagrass in Maputo Bay decreased by 33.4 % between 1991 and 2023, with a decreasing trend of 0.48 km2/yr. A zonation pattern was observed for Oceana serrulata and Zostera capensis from the UAV imagery. The small and narrow leaved species (Z. capensis) occurred in the intertidal zone replaced by the broadleaved species (O. serrulata) in the subtidal. The total average aboveground biomass was 33.2 kg dry weight for the mapped area. The results of this study will guide implementation of combined satellite and UAV imagery with machine learning techniques for seagrass monitoring and restoration in Mozambique. |\n\n1. Introduction  \nSeagrass ecosystems provide a range of provisioning, regulating and cultural ecosystem services (Nordlund and Gullstro¨m, 2013, Amone-Mabuto et al., 2023) that contribute to human welfare and other goods and services (Findlay et al., 2011). The Mozambique coastline stretching over 2700 km, hosts one of the highest diversities of seagrass of the Western Indian Ocean region (Green and Short, 2003; Gullstro¨met al., 2021). They are economically critical for coastal communities where fish, clams and crustaceans are collected, with 90 % of the national Gross Domestic Product (GDP) coming from artisanal fisheries (Poursanidis et al., 2021). However, they undergo changes in biomass and productivity continuously. The impact of natural disasters such as cyclones, floods (Amone-Mabuto et al., 2017; Bandeira et al., 2021), and the effects of higher sea-surface temperatures as well as sea level fluctuations (Solana et al., 2020; Asante et al., 2023) on seagrass ecosystems have been documented in the Maputo and Inhambane bays. Furthermore, destructive fishing practices including both semi-industrial  \nshrimp trawlers and artisanal beach seine netting are damaging seagrass habitats (Gullstro¨m et al., 2021). For example, in the Bazaruto Archipelago, despite being incorporated in an established Marine Protected Area (with both permanent and seasonal closures), seagrass meadows are heavily fished using beach-seine netting (D’Agata, 2016; Gullstro¨m et al., 2021). In the northwest region of Maputo Bay, large areas of Zostera capensis have disappeared where a previous seagrass cover of 60 % in 1991 decreased ","cbCaioKX4rf94AS2","https://ap.wps.com/l/cbCaioKX4rf94AS2","pdf",7192414,1,11,"English","en",105,"# Introduction\n## Study context and ecological importance\n## Drivers of seagrass change and need for mapping\n## Role of cover and AGB as health indicators\n# Methods\n## In situ mapping approaches and limitations\n## Remote sensing approach and data integration\n## Sentinel-2 and UAV data with machine learning","[{\"question\":\"What data sources and modeling approach are used to monitor seagrass in Maputo Bay?\",\"answer\":\"The study integrates Sentinel-2 satellite imagery with UAV (unmanned aerial vehicle) systems and applies machine learning to produce consistent seagrass classifications.\"},{\"question\":\"How do Sentinel-2 and UAV contributions differ in the workflow?\",\"answer\":\"Sentinel-2 is used to map seagrass extent and changes, while UAV systems support species-level mapping and aboveground biomass estimates.\"},{\"question\":\"What major long-term change in seagrass extent was reported between 1991 and 2023?\",\"answer\":\"Seagrass area in Maputo Bay decreased by 33.4% from 1991 to 2023, with a decreasing trend of 0.48 km² per year.\"}]","Monitoring seagrass meadows in Maputo Bay using integrated remote sensing techniques and machine learning | 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data sources and modeling approach are used to monitor seagrass in Maputo Bay?","Question",{"text":75,"@type":76},"The study integrates Sentinel-2 satellite imagery with UAV (unmanned aerial vehicle) systems and applies machine learning to produce consistent seagrass classifications.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do Sentinel-2 and UAV contributions differ in the workflow?",{"text":80,"@type":76},"Sentinel-2 is used to map seagrass extent and changes, while UAV systems support species-level mapping and aboveground biomass estimates.",{"name":82,"@type":73,"acceptedAnswer":83},"What major long-term change in seagrass extent was reported between 1991 and 2023?",{"text":84,"@type":76},"Seagrass area in Maputo Bay decreased by 33.4% from 1991 to 2023, with a decreasing trend of 0.48 km² per 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