{"title":"Cover Picture and Issue Information","authors":"","doi":"10.1111/2041-210X.14344","DOIUrl":null,"url":null,"abstract":"<p>This month's cover image features a 72 square meter orthomosaic of a coral reef automatically segmented and classified using RapidBenthos. Developed by Remmers et al., this innovative workflow uses machine learning for feature extraction and analyses of photogrammetric data from underwater orthomosaics of coral reefs. The automated workflow integrates the Segment Anything Model (SAM) and ReefCloud point annotation in a two-stage process: (1) employing a pre-trained, open-source machine learning segmentation model, which removes the need for users to manually generate fine-scale segmented training data, and (2) classifying the resulting segments using the underlying survey images from multiple viewpoints, achieving classification at higher taxonomic levels. This study demonstrates that artificial intelligence tools can automatically and reliably extract data from orthomosaics with an unprecedented level of taxonomic detail and accuracy. While the approach has been developed and tested on images from a shallow coral reef, it has the potential to be applied to any ecosystem monitored via photogrammetry and is scalable for large-area applications. Such advancements enable the sustainable scaling of photogrammetric monitoring techniques, offering a more comprehensive understanding of coral reefs community composition and habitat. In turn, this will inspire new research questions, enhance ecosystem models, and support improved ecosystem management.\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":"16 2","pages":"247-249"},"PeriodicalIF":6.3000,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14344","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Methods in Ecology and Evolution","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/2041-210X.14344","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
This month's cover image features a 72 square meter orthomosaic of a coral reef automatically segmented and classified using RapidBenthos. Developed by Remmers et al., this innovative workflow uses machine learning for feature extraction and analyses of photogrammetric data from underwater orthomosaics of coral reefs. The automated workflow integrates the Segment Anything Model (SAM) and ReefCloud point annotation in a two-stage process: (1) employing a pre-trained, open-source machine learning segmentation model, which removes the need for users to manually generate fine-scale segmented training data, and (2) classifying the resulting segments using the underlying survey images from multiple viewpoints, achieving classification at higher taxonomic levels. This study demonstrates that artificial intelligence tools can automatically and reliably extract data from orthomosaics with an unprecedented level of taxonomic detail and accuracy. While the approach has been developed and tested on images from a shallow coral reef, it has the potential to be applied to any ecosystem monitored via photogrammetry and is scalable for large-area applications. Such advancements enable the sustainable scaling of photogrammetric monitoring techniques, offering a more comprehensive understanding of coral reefs community composition and habitat. In turn, this will inspire new research questions, enhance ecosystem models, and support improved ecosystem management.
期刊介绍:
A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas.
MEE publishes methodological papers in any area of ecology and evolution, including:
-Phylogenetic analysis
-Statistical methods
-Conservation & management
-Theoretical methods
-Practical methods, including lab and field
-This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual.
A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.