How do deep convolutional SDM trained on satellite images unravel vegetation ecology?

Deneu Benjamin, Joly Alexis, Bonnet Pierre, Servajean Maximilien, Munoz François. 2021. How do deep convolutional SDM trained on satellite images unravel vegetation ecology?. In : Pattern recognition. ICPR International Workshops and Challenges. Virtual event, January 10–15, 2021, Proceedings, Part VI. Del Bimbo Alberto (ed.), Cucchiara Rita (ed.), Sclaroff Stan (ed.), Farinella Giovanni Maria (ed.), Mei Tao (ed.), Bertini Marco (ed.), Escalante Hugo Jair (ed.), Vezzani Roberto (ed.). Cham : Springer, 148-158. (Lecture Notes in Computer Science, 12666) ISBN 978-3-030-68779-3 International Conference on Pattern Recognition (ICPR 2021). 25, Milan, Italie, 10 Janvier 2021/15 Janvier 2021.

Url - jeu de données - Entrepôt autre : https://doi.org/10.48550/arXiv.2004.04192

Note générale : Le congrès s'est tenu en ligne

Résumé : Species distribution models (SDM) assess and predict how species spatial distributions depend on the environment, due to species ecological preferences. These models are used in many different scenarios such as conservation plans or monitoring of invasive species. The choice of a model and of environmental data have strong impact on the model's ability to capture important ecological information. Specifically, state-of-the-art models generally rely on local, punctual environmental information, and do not take into account environmental variation in surrounding landscape. Here we use a convolutional neural network model to analyze and predict species distributions depending on high resolution data including remote sensing images, land cover and altitude. We show that the model unravel the functional response of vegetation to both local and large-scale environmental variation. To demonstrate the ecological significance of the results, we propose an original statistical analysis of t-SNE nonlinear dimension reduction. We illustrate and test the traits-species- environment relationships learned by the model and expressed in t-SNE dimensions.

Mots-clés libres : Species distribution models, Convolutional neural network, Ecological Interpretation, Plant traits, Trait-environment relationships

Auteurs et affiliations

• Deneu Benjamin, CIRAD-BIOS-UMR AMAP (FRA)
• Joly Alexis, INRIA (FRA)
• Bonnet Pierre, CIRAD-BIOS-UMR AMAP (FRA) ORCID: 0000-0002-2828-4389
• Servajean Maximilien, CNRS (FRA)
• Munoz François, CNRS (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/597849/)

Métriques

[ Page générée et mise en cache le 2024-04-06 ]