A deep-learning framework for enhancing habitat identification based on species composition
2024
Leblanc, César | Bonnet, Pierre | Servajean, Maximilien | Chytrý, Milan | Aćić, Svetlana | Argagnon, Olivier | Bergamini, Ariel | Biurrun, Idoia | Bonari, Gianmaria | Campos, Juan Antonio | Ćušterevska, Renata | Čarni, Andraž | de Sanctis, Michele | Dengler, Juergen | Garbolino, Emmanuel | Golub, Valentin | Jandt, Ute | Jansen, Florian | Lebedeva, Maria | Lenoir, Jonathan, Roger Michel Henri | Moeslund, Jesper Erenskjold | Pérez-Haase, Aaron | Pielech, Remigiusz | Šibík, Jozef | Stančić, Zvjezdana | Stanisci, Angela | Swacha, Grzegorz | Uogintas, Domas | Vassilev, Kiril | Wohlgemuth, Thomas | Joly, Alexis | Scientific Data Management (ZENITH) ; Centre Inria d'Université Côte d'Azur (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) ; Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM) | Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [Occitanie])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM) | Département Systèmes Biologiques (Cirad-BIOS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | ADVanced Analytics for data SciencE (LIRMM | ADVANSE) ; Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) ; Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM) | Université Paul-Valéry - Montpellier 3 (UPVM) | Department of Botany and Zoology [Brno] (SCI / MUNI) ; Faculty of Science [Brno] (SCI / MUNI) ; Masaryk University [Brno] = Masarykova univerzita [Brno] = Université Masaryk [Brno] (MU / MUNI)-Masaryk University [Brno] = Masarykova univerzita [Brno] = Université Masaryk [Brno] (MU / MUNI) | Faculty of Agriculture [Belgrade] ; University of Belgrade [Belgrade] | Conservatoire Botanique National Méditerranéen de Porquerolles | Swiss Federal Institute for Forest, Snow and Landscape Research WSL | Department of Plant Biology and Ecology (Bilbao, Spain) ; Universidad del País Vasco [Espainia] / Euskal Herriko Unibertsitatea [España] = University of the Basque Country [Spain] = Université du pays basque [Espagne] (UPV / EHU) | Università degli Studi di Siena = University of Siena (UNISI) | Ss. Cyril and Methodius University in Skopje (UKIM) | ZRC SAZU | University of Nova Gorica | Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome] (UNIROMA) | Zürcher Hochschule für Angewandte Wissenschaften = Zurich University of Applied Sciences (ZHAW) | Universität Bayreuth [Deutschland] = University of Bayreuth [Germany] = Université de Bayreuth [Allemagne] | Institut Supérieur d'Ingénierie et de Gestion de l'Environnement (ISIGE) ; Mines Paris - PSL (École nationale supérieure des mines de Paris) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL) | German Centre for Integrative Biodiversity Research (iDiv) | University of Rostock = Universität Rostock | Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 UPJV (EDYSAN) ; Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS) | Department of Ecoscience [Aarhus] ; Aarhus University [Aarhus] | Institut de Recerca de la Biodiversitat - Biodiversity Research Institute [Barcelona, Spain] (IRBio UB) ; Universitat de Barcelona (UB) | Institute of Botany [Kraków] ; Uniwersytet Jagielloński w Krakowie = Jagiellonian University = Université Jagellon de Cracovie (UJ) | Slovak Academy of Sciences (SAS) | University of Zagreb | Università degli Studi del Molise = University of Molise (UNIMOL) | Uniwersytet Wroclawski = University of Wroclaw | Nature Research Centre [Vilnius] | Institute of Biodiversity and Ecosystem Research [Sofia, Bulgaria] (IBER) | European Project: 101060693,HORIZON.2.6 - Food, Bioeconomy Natural Resources, Agriculture and Environment / HORIZON.2.6.1 - Environmental Observation ,GUARDEN(2022) | European Project: 101060639,MAMBO
International audience
اظهر المزيد [+] اقل [-]إنجليزي. Aims The accurate classification of habitats is essential for effective biodiversity conservation. The goal of this study was to harness the potential of deep learning to advance habitat identification in Europe. We aimed to develop and evaluate models capable of assigning vegetation-plot records to the habitats of the European Nature Information System (EUNIS), a widely used reference framework for European habitat types.Location The framework was designed for use in Europe and adjacent areas (e.g., Anatolia, Caucasus).Methods We leveraged deep-learning techniques, such as transformers (i.e., models with attention components able to learn contextual relations between categorical and numerical features) that we trained using spatial k-fold cross-validation (CV) on vegetation plots sourced from the European Vegetation Archive (EVA), to show that they have great potential for classifying vegetation-plot records. We tested different network architectures, feature encodings, hyperparameter tuning and noise addition strategies to identify the optimal model. We used an independent test set from the National Plant Monitoring Scheme (NPMS) to evaluate its performance and compare its results against the traditional expert systems.ResultsExploration of the use of deep learning applied to species composition and plot-location criteria for habitat classification led to the development of a framework containing a wide range of models. Our selected algorithm, applied to European habitat types, significantly improved habitat classification accuracy, achieving a more than twofold improvement compared to the previous state-of-the-art (SOTA) method on an external data set, clearly outperforming expert systems. The framework is shared and maintained through a GitHub repository.Conclusions Our results demonstrate the potential benefits of the adoption of deep learning for improving the accuracy of vegetation classification. They highlight the importance of incorporating advanced technologies into habitat monitoring. These algorithms have shown to be better suited for habitat type prediction than expert systems. They push the accuracy score on a database containing hundreds of thousands of standardized presence/absence European surveys to 88.74%, as assessed by expert judgment. Finally, our results showcase that species dominance is a strong marker of ecosystems and that the exact cover abundance of the flora is not required to train neural networks with predictive performances. The framework we developed can be used by researchers and practitioners to accurately classify habitats.
اظهر المزيد [+] اقل [-]الكلمات المفتاحية الخاصة بالمكنز الزراعي (أجروفوك)
المعلومات البيبليوغرافية
تم تزويد هذا السجل من قبل Institut national de la recherche agronomique