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Conference Papers Year : 2021

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

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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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Dates and versions

hal-03167637 , version 1 (12-03-2021)

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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 - 25th 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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