ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015

This dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from...

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Main Authors: ROCHA, A.V., WRIGHT, K.S.
Format: Article in Journal/Newspaper
Language:English
Published: ORNL Distributed Active Archive Center 2019
Subjects:
Online Access:https://dx.doi.org/10.3334/ornldaac/1666
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1666
id ftdatacite:10.3334/ornldaac/1666
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spelling ftdatacite:10.3334/ornldaac/1666 2023-05-15T17:40:10+02:00 ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015 ROCHA, A.V. WRIGHT, K.S. 2019 https://dx.doi.org/10.3334/ornldaac/1666 https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1666 en eng ORNL Distributed Active Archive Center Collection article Data Files 2019 ftdatacite https://doi.org/10.3334/ornldaac/1666 2021-11-05T12:55:41Z This dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from two sources: 1) derived from 1-km, 8-day data from the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD13A2) product, and 2) predicted by the Coupled Carbon and Nitrogen model (CCaN). CCaN is a mass balance carbon and nitrogen model that was driven by 1-km MODIS surface temperature and climate data for the North Slope of Alaska and parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite-based data. Article in Journal/Newspaper north slope Alaska DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language English
description This dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from two sources: 1) derived from 1-km, 8-day data from the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD13A2) product, and 2) predicted by the Coupled Carbon and Nitrogen model (CCaN). CCaN is a mass balance carbon and nitrogen model that was driven by 1-km MODIS surface temperature and climate data for the North Slope of Alaska and parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite-based data.
format Article in Journal/Newspaper
author ROCHA, A.V.
WRIGHT, K.S.
spellingShingle ROCHA, A.V.
WRIGHT, K.S.
ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015
author_facet ROCHA, A.V.
WRIGHT, K.S.
author_sort ROCHA, A.V.
title ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015
title_short ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015
title_full ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015
title_fullStr ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015
title_full_unstemmed ABoVE: MODIS- and CCAN-Derived NDVI and Trends, North Slope of Alaska, 2000-2015
title_sort above: modis- and ccan-derived ndvi and trends, north slope of alaska, 2000-2015
publisher ORNL Distributed Active Archive Center
publishDate 2019
url https://dx.doi.org/10.3334/ornldaac/1666
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1666
genre north slope
Alaska
genre_facet north slope
Alaska
op_doi https://doi.org/10.3334/ornldaac/1666
_version_ 1766141013391310848