Estopinan Joaquim, Servajean Maximilien, Bonnet Pierre, Munoz François, Joly Alexis
INRIA, Montpellier, France.
LIRMM, Univ Montpellier, CNRS, Montpellier, France.
Front Plant Sci. 2022 Apr 22;13:839327. doi: 10.3389/fpls.2022.839327. eCollection 2022.
Species distribution models (SDMs) are widely used numerical tools that rely on correlations between geolocated presences (and possibly absences) and environmental predictors to model the ecological preferences of species. Recently, SDMs exploiting deep learning and remote sensing images have emerged and have demonstrated high predictive performance. In particular, it has been shown that one of the key advantages of these models (called deep-SDMs) is their ability to capture the spatial structure of the landscape, unlike prior models. In this paper, we examine whether the temporal dimension of remote sensing images can also be exploited by deep-SDMs. Indeed, satellites such as Sentinel-2 are now providing data with a high temporal revisit, and it is likely that the resulting time-series of images contain relevant information about the seasonal variations of the environment and vegetation. To confirm this hypothesis, we built a substantial and original dataset (called ) aimed at modeling the distribution of orchids on a global scale based on Sentinel-2 image time series. It includes around 1 million occurrences of orchids worldwide, each being paired with a 12-month-long time series of high-resolution images (640 x 640 m RGB+IR patches centered on the geolocated observations). This ambitious dataset enabled us to train several deep-SDMs based on convolutional neural networks (CNNs) whose input was extended to include the temporal dimension. To quantify the contribution of the temporal dimension, we designed a novel interpretability methodology based on temporal permutation tests, temporal sampling, and temporal averaging. We show that the predictive performance of the model is greatly increased by the seasonality information contained in the temporal series. In particular, occurrence-poor species and diversity-rich regions are the ones that benefit the most from this improvement, revealing the importance of habitat's temporal dynamics to characterize species distribution.
物种分布模型（SDMs）是广泛使用的数值工具，它依赖于地理位置上的物种出现情况（可能还包括未出现情况）与环境预测因子之间的相关性，来模拟物种的生态偏好。最近，利用深度学习和遥感图像的物种分布模型已经出现，并展现出了很高的预测性能。特别是，与先前的模型不同，这些模型（称为深度物种分布模型）的一个关键优势在于它们能够捕捉景观的空间结构。在本文中，我们研究了深度物种分布模型是否也能利用遥感图像的时间维度。事实上，像哨兵 -2 这样的卫星现在能够提供高时间重访频率的数据，并且由此产生的图像时间序列很可能包含有关环境和植被季节变化的相关信息。为了证实这一假设，我们构建了一个规模庞大且原创的数据集（称为 ），旨在基于哨兵 -2 图像时间序列在全球范围内模拟兰花的分布。它包括全球范围内约 100 万个兰花出现记录，每个记录都与一个长达 12 个月的高分辨率图像时间序列（以地理位置观测为中心的 640 x 640 米 RGB + IR 斑块）配对。这个雄心勃勃的数据集使我们能够训练几个基于卷积神经网络（CNN）的深度物种分布模型，其输入被扩展以纳入时间维度。为了量化时间维度的贡献，我们设计了一种基于时间排列检验、时间采样和时间平均的新型可解释性方法。我们表明，时间序列中包含的季节性信息极大地提高了模型的预测性能。特别是，出现记录较少的物种和多样性丰富的地区从这种改进中受益最大，这揭示了栖息地时间动态对于表征物种分布的重要性。
