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How Do Deep Convolutional SDM Trained on Satellite Images Unravel Vegetation Ecology ?

Benjamin Deneu 1, 2 Alexis Joly 2 Pierre Bonnet Maximilien Servajean 3 François Munoz 4
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
3 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : 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.
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https://hal.inrae.fr/hal-03167637
Contributor : Yannick Brohard <>
Submitted on : Friday, March 12, 2021 - 3:31:20 PM
Last modification on : Wednesday, May 26, 2021 - 4:54:39 PM

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Benjamin Deneu, Alexis Joly, Pierre Bonnet, Maximilien Servajean, François Munoz. How Do Deep Convolutional SDM Trained on Satellite Images Unravel Vegetation Ecology ?. ICPR 2020 - International Conference on Pattern Recognition, Alberto Del Bimbo; Rita Cucchiara; Stan Sclaroff; Giovanni Maria Farinella; Tao Mei; Marco Bertini; Hugo Jair Escalante; Roberto Vezzani, Jan 2021, Milan / Virtual, Italy. pp.148-158, ⟨10.1007/978-3-030-68780-9_15⟩. ⟨hal-03167637⟩

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