Phenopix: A R package for image-based vegetation phenology

被引:160
作者
Filippa, Gianluca [1 ]
Cremonese, Edoardo [1 ]
Migliavacca, Mirco [2 ]
Galvagno, Marta [1 ]
Forkel, Matthias [2 ]
Wingate, Lisa [3 ]
Tomelleri, Enrico [4 ]
di Cella, Umberto Morra [1 ]
Richardson, Andrew D. [5 ]
机构
[1] Environm Protect Agcy Aosta Valley, ARPA Valle dAosta, Climate Change Unit, Aosta, Italy
[2] Max Planck Inst Biogeochem, Dept Biogeochem Integrat, D-07745 Jena, Germany
[3] INRA, UMR ISPA, Bordeaux, France
[4] Inst Appl Remote Sensing, EURAC, Bolzano, Italy
[5] Harvard Univ, Dept Organism & Evolutionary Biol, Cambridge, MA 02138 USA
基金
美国国家科学基金会;
关键词
Image analysis; Community ecology; Pixel-based analysis; Phenology; DECIDUOUS FOREST; NEAR-SURFACE; COLOR; DYNAMICS; SNOWMELT;
D O I
10.1016/j.agrformet.2016.01.006
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
In this paper we extensively describe new software available as a R package that allows for the extraction of phenological information from time-lapse digital photography of vegetation cover. The phenopix R package includes all steps in data processing. It enables the user to: draw a region of interest (ROI) on an image; extract red green and blue digital numbers (DN) from a seasonal series of images; depict greenness index trajectories; fit a curve to the seasonal trajectories; extract relevant phenological thresholds (phenophases); extract phenophase uncertainties. The software capabilities are illustrated by analyzing one year of data from a selection of seven sites belonging to the PhenoCam network (http://phenocam.sr.unh.edu/), including an unmanaged subalpine grassland, a tropical grassland, a deciduous needle-leaf forest, three deciduous broad-leaf temperate forests and an evergreen needle-leaf forest. One of the novelties introduced by the package is the spatially explicit, pixel-based analysis, which potentially allows to extract within-ecosystem or within-individual variability of phenology. We examine the relationship between phenophases extracted by the traditional ROI-averaged and the novel pixel-based approaches, and further illustrate potential applications of pixel based image analysis available in the phenopix R package. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:141 / 150
页数:10
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