globalchange  > 过去全球变化的重建
DOI: 10.1371/journal.pone.0171918
论文题名:
Automated processing of webcam images for phenological classification
作者: Ludwig Bothmann; Annette Menzel; Bjoern H. Menze; Christian Schunk; Göran Kauermann
刊名: PLOS ONE
ISSN: 1932-6203
出版年: 2017
发表日期: 2017-2-24
卷: 12, 期:2
语种: 英语
英文关键词: Trees ; Cameras ; Conifers ; Seasons ; Grasslands ; Image analysis ; Imaging techniques ; Database and informatics methods
英文摘要: Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels’ time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the statistical software package R and publicly available in the R package phenofun. Executable example code is provided as supplementary material.
URL: http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0171918&type=printable
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/25808
Appears in Collections:过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响

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作者单位: Institut für Statistik, Ludwig-Maximilians-Universität München, Munich, Germany;Ökoklimatologie, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Technische Universität München, Freising, Germany;Institute for Advanced Study, Technische Universität München, Garching, Germany;Institute for Advanced Study, Technische Universität München, Garching, Germany;Department of Informatics, Technische Universität München, Munich, Germany;Ökoklimatologie, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Technische Universität München, Freising, Germany;Institut für Statistik, Ludwig-Maximilians-Universität München, Munich, Germany

Recommended Citation:
Ludwig Bothmann,Annette Menzel,Bjoern H. Menze,et al. Automated processing of webcam images for phenological classification[J]. PLOS ONE,2017-01-01,12(2)
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