Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USA; Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA.
Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USA; Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305, USA; Present address: Department of Organismic and Evolutionary Biology, Harvard University, 22 Divinity Ave, Cambridge, MA 02138, USA. Electronic address: https://twitter.com/andrewbdavies.
Trends Ecol Evol. 2019 Aug;34(8):734-745. doi: 10.1016/j.tree.2019.03.006. Epub 2019 May 8.
Using remotely sensed imagery to identify biophysical components across landscapes is an important avenue of investigation for ecologists studying ecosystem dynamics. With high-resolution remotely sensed imagery, algorithmic utilization of image context is crucial for accurate identification of biophysical components at large scales. In recent years, convolutional neural networks (CNNs) have become ubiquitous in image processing, and are rapidly becoming more common in ecology. Because the quantity of high-resolution remotely sensed imagery continues to rise, CNNs are increasingly essential tools for large-scale ecosystem analysis. We discuss here the conceptual advantages of CNNs, demonstrate how they can be used by ecologists through distinct examples of their application, and provide a walkthrough of how to use them for ecological applications.
利用遥感图像来识别景观中的生物物理成分，是研究生态系统动态的生态学家的一个重要研究途径。利用高分辨率遥感图像，算法利用图像上下文对于在大尺度上准确识别生物物理成分至关重要。近年来，卷积神经网络（CNN）在图像处理中无处不在，并且在生态学中越来越普遍。由于高分辨率遥感图像的数量持续增加，CNN 成为大规模生态系统分析的重要工具。在这里，我们讨论了 CNN 的概念优势，通过它们在应用中的具体示例展示了如何被生态学家使用，并提供了如何将它们用于生态应用的操作指南。
