Editorial: Lidar and ocean color remote sensing for marine ecology
Notice bibliographique
Résumé
The advent of the Coastal Zone Color Scanner (CZCS) in 1978 heralded a transformative era in ocean color remote sensing, paving the way for a deeper understanding of upper-ocean biogeochemistry. Over the past decades, the field has evolved significantly, with the recent inclusion of light detection and ranging (lidar) technology offering unprecedented insights into the marine environment. This Research Topic aims to encapsulate the collective knowledge and advancements presented in the Research Topic, highlighting the innovative applications of lidar and ocean color remote sensing in marine ecology. It is our intent to provide a comprehensive overview that not only summarizes the articles but also contextualizes their contributions within the broader scope of marine and atmospheric research. Four papers have been published, featuring contributions from a wide array of academic and industrial entities spanning 15 organizations, including the University of Iowa, Science Systems and Applications, Inc., NASA Goddard Space Flight Center, Université Laval (Canada), ArcticNet, QuébecOcéan, Département de biologie, University of Toronto Scarborough, Département de Physique, BeamSea Associates, Ministry of Natural Resources, South China Sea Institute of Oceanology (CAS), Nanchang Hangkong University, Université de Lille.Within the scope of this Research Topic, significant advancements have been presented by esteemed researchers. McGill et al., demonstrates the utility of machine learning algorithms for real-time detection of cloud and aerosol layers using airborne lidar data. This advancement is pivotal for improving the temporal resolution of atmospheric data, which is crucial for weather prediction and climate modeling.This advancement in atmospheric data acquisition, particularly those related to cloud and aerosol layers, is critical for marine ecology as it enhances our understanding of the interactions between the atmosphere and the marine environment, which are essential for modeling and predicting changes in marine ecosystems.Palm et al., presents a study on the estimation of planetary boundary layer height from ICESat-2 and CATS backscatter measurements. Utilizing both traditional techniques and machine learning, the insights gained from this study on atmospheric boundary layer structure are integral to understanding the air-sea interactions that influence marine ecosystems, thereby providing a foundation for more accurate ecological assessments and predictionsthe research provides valuable insights into the structure and variability of the atmospheric boundary layer, which has implications for air quality and weather forecasting.Huot et al., explores the application of machine learning for underwater laser detection and differentiation between macroalgae and coral. Their work highlights the potential of multispectral laser imaging for enhancing the detection and classification of these essential marine organisms, contributing to the monitoring of marine habitats and the assessment of climate change impacts.Vadakke Chanat and Jamet propose a validation protocol for space-borne lidar measurements of the particulate back-scattering coefficient in the ocean. Their research is instrumental in ensuring the accuracy and reliability of space-borne lidar data, which is vital for ocean color remote sensing and the study of marine ecosystems.In conclusion, the Research Topic "Lidar and Ocean Color Remote Sensing for Marine Ecology" showcases the innovative applications of remote sensing technologies including lidar and passive ocean color remote sensing technologies in understanding complex marine environments. The articles presented in this collection not only reflect the current state-of-the-art in this field but also point toward future directions for research and application, emphasizing the importance of interdisciplinary approaches in advancing marine ecological studies.1 Conflict of InterestThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.2 Author ContributionsPeng Chen: Writing – original draft; Funding acquisition. Panagiotis Kokkalis: Writing – review & editing. Yudi Zhou: Writing – review & editing. Iwona S. Stachlewska: Writing – review & editing.3 FundingNational Natural Science Foundation (42322606; 42276180; 61991453), National Key Research and Development Program of China (2022YFB3901703; 2022YFB3902603), Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (GML2021GD0809), Donghai Laboratory Preresearch project (DH2022ZY0003), and Key R&D Program of Shandong Province, China(2023ZLYS01).4 AcknowledgmentsWe thank the reviewers for their suggestions, which significantly improved the presentation of the paper.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,005 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,008 | 0,007 |
| Science ouverte | 0,005 | 0,002 |
| Intégrité de la recherche | 0,016 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,020 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».