A deep‐learning framework for enhancing habitat identification based on species composition

Leblanc, César; Bonnet, Pierre; Servajean, Maximilien; Chytrý, Milan; Aćić, Svetlana; Argagnon, Olivier; Bergamini, Ariel; Biurrun, Idoia; Bonari, Gianmaria; Campos, Juan A.; Čarni, Andraž; Ćušterevska, Renata; De Sanctis, Michele; Dengler, Jürgen; Garbolino, Emmanuel; Golub, Valentin; Jandt, Ute; Jansen, Florian; Lebedeva, Maria; Lenoir, Jonathan

doi:10.1111/avsc.12802

A deep‐learning framework for enhancing habitat identification based on species composition

Journal

Applied Vegetation Science

Date Issued

2024-07

Author(s)

Leblanc, César

Bonnet, Pierre

Servajean, Maximilien

Chytrý, Milan

Aćić, Svetlana

Argagnon, Olivier

Bergamini, Ariel

Biurrun, Idoia

Bonari, Gianmaria

Campos, Juan A.

Čarni, Andraž

Ćušterevska, Renata

De Sanctis, Michele

Dengler, Jürgen

Garbolino, Emmanuel

Golub, Valentin

Jandt, Ute

Jansen, Florian

Lebedeva, Maria

Lenoir, Jonathan

Moeslund, Jesper Erenskjold

Pérez‐Haase, Aaron

Pielech, Remigiusz

Šibík, Jozef

Stančić, Zvjezdana

Stanisci, Angela

Swacha, Grzegorz

Uogintas, Domas

Vassilev, Kiril

Wohlgemuth, Thomas

Joly, Alexis

DOI

10.1111/avsc.12802

Abstract

AbstractAimsThe 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.LocationThe framework was designed for use in Europe and adjacent areas (e.g., Anatolia, Caucasus).MethodsWe 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.ConclusionsOur 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.

File(s)

Name

AppliedVegetationScience-2024-Leblanc-Adeeplearningframeworkforenhancinghabitatidentificationbasedon .pdf

Size

4.51 MB

Format

Adobe PDF

Checksum

(MD5):f2748e37005fc1096ffd7aede4440e50