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Dataset . 2020
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ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
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Live Fuel Moisture Content Dataset

Authors: Chang Wei Tan; Christoph Bergmeir; Francois Petitjean; Geoffrey I Webb;

Live Fuel Moisture Content Dataset

Abstract

This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/ The goal of this dataset is to predict the moisture content in vegetation. The moisture is the ratio between the weight of water in vegetation and the weight of the dry part of vegetation. This is known as the live fuel moisture content (LFMC) and is an important variable as the risk of fire increases very rapidly as soon as the LFMC goes below 80%.This data is paired with one year of daily reflectance data at 7 spectral bands (459 nm to 2155 nm) before the LFMC sampling date from the Moderate Resolution Imaging Spectrometer (MODIS) satellite. This dataset contains 5003 time series obtained from researchers at Monash University with 7 dimensions, each corresponding to one spectral band. Data Donor Liujun Zhu, liujun.zhu@monash.edu

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Keywords

regression, time series

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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
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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