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Phenology and vegetation change measurements from true colour digital photography in high Arctic tundra
Author(s) -
Alison Beamish,
Wiebe Nijland,
Marc Edwards,
Nicholas C. Coops,
Greg H. R. Henry
Publication year - 2016
Publication title -
arctic science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 4
ISSN - 2368-7460
DOI - 10.1139/as-2014-0003
Subject(s) - tundra , phenology , environmental science , normalized difference vegetation index , growing season , vegetation (pathology) , biomass (ecology) , arctic , physical geography , climate change , remote sensing , ecology , geography , biology , medicine , pathology
Manual collection of accurate phenology data is time-consuming and expensive. In this study, we investigate whether repeat colour digital photography can be used (1) to identify phenological patterns, (2) to identify differences in vegetation due to experimental warming and site moisture conditions, and (3) as a proxy for biomass. Pixel values (RGB) were extracted from images taken of permanent plots in long-term warming experiments in three tundra communities at a high Arctic site during one growing season. The Greenness Excess Index (GEI) was calculated from image data at the plot scale (1 × 1 m) as well as for two species, Dryas integrifolia and Salix arctica. GEI values were then compared to corresponding field-based phenology observations. GEI and Normalized Difference Vegetation Index (NDVI) values from a paired set of true colour and infrared images were compared with biomass data. The GEI values followed seasonal phenology at the plot and species scale and correlated well with standardized observations. GEI correlated well with biomass and was able to detect quantitative differences between warmed and control plots and the differences between communities due to site-specific moisture conditions. We conclude that true colour images can be used effectively to monitor phenology and biomass in high Arctic tundra. The simplicity and affordability of the photographic method represents an opportunity to expand observations in tundra ecosystems.

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