Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

J. Almeida, J. A. D. Santos, Bruna Alberton, R. Torres, L. Morellato
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引用次数: 19

Abstract

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground.
远程物候学:应用机器学习来检测塞拉多稀树草原的物候模式
植物物候学在全球变化研究中越来越重要,促进了物候观测新技术的发展。数码相机已经成功地用作多通道成像传感器,提供植物叶片颜色变化信息(RGB通道)或叶片物候变化的测量。我们通过每天拍摄数字图像来监测塞拉多稀树草原植被的叶子变化模式。我们从数字图像中提取RGB通道,并与物候变化相关联。我们的第一个目标是:(1)测试颜色变化信息是否能够表征一组物种的物候模式;(2)测试是否可以使用数字图像自动识别来自同一功能群的个体。在本文中,我们提出了一种机器学习方法来检测数字图像中的物候模式。初步结果表明:(1)极端时段(上午和下午)是识别植物种类的最佳时段;(2)不同植物种类对颜色变化信息表现出不同的行为。在此基础上,我们建议利用数字图像识别同一功能群的个体,并引入一种新的工具来帮助物候学专家在实地进行物种识别和定位。
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