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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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Global monthly percentage of vegetation cover (MODIS FCover MODV1B product: Eurasia, Africa, Oceania)

Authors: Filipponi, Federico; Valentini, Emiliana; Nguyen Xuan, Alessandra; Guerra, Carlos António; Wolf, Florian; Andrzejak, Martin; Taramelli, Andrea;

Global monthly percentage of vegetation cover (MODIS FCover MODV1B product: Eurasia, Africa, Oceania)

Abstract

Monthly Global FCover product generated from MODIS data. Dataset represent monthly gap-filled FCover estimates the period 2000-2015 over Eurasia, Africa and Oceania. FCover was estimated using linear spectral mixture analysis and interpolated using empirical orthogonal functions algorithm to take advantage of all non-missing available pixels in both the spatial and temporal dimensions to gap-fill missing satellite observations. The global product of vegetation cover (as percentage of cover) based on MODIS images with monthly variation can be used as a critical support for several indicators related to ecologically based modelling.

{"references": ["Filipponi, F., Valentini, E., Nguyen Xuan, A., Guerra, C., Wolf, F., Andrzejak, M., & Taramelli, A. (2018). Global MODIS Fraction of Green Vegetation Cover for Monitoring Abrupt and Gradual Vegetation Changes. Remote Sensing, 10(4), 653."]}

The H2020 Ecopotential project GA 641762 "Improving future ecosystem benefits through earth observations" is gratefully acknowledged.

Keywords

MODIS, global product, linear spectral mixture analysis, breakpoint detection, trend analysis, fraction of green vegetation cover

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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