A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity
Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity...
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ftdatacite:10.5446/39374 2023-05-15T14:28:55+02:00 A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity Karlsen, Stein Rune Anderson, Helen B Wal, René Van Der Hansen, Brage Bremset 2018 https://dx.doi.org/10.5446/39374 https://av.tib.eu/media/39374 en eng Institute of Physics (IOP) Physics Audiovisual Video Abstract article MediaObject 2018 ftdatacite https://doi.org/10.5446/39374 2021-11-05T12:55:41Z Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity from being estimated over large areas. We used a field-based time-series (2000−2009) of live aboveground vascular plant biomass data and a recently processed cloud-free MODIS-Normalised Difference Vegetation Index (NDVI) data set (2000−2014) to estimate, on a pixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring to the average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensed productivity measure was then compared, at two different spatial scales, with the peak plant biomass field data. At both the local scale, surrounding the field data site, and the larger regional scale, our NDVI measure was found to predict plant biomass (adjusted R 2 = 0.51 and 0.44, respectively). The commonly used 'maximum NDVI' plant productivity index showed no relationship with plant biomass, likely due to some years having very few cloud-free images available during the peak plant growing season. Thus, we propose this new summed NDVI from onset of spring to time of peak NDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climatic conditions restrict the availability of cloud-free images. Article in Journal/Newspaper Arctic Archipelago Arctic Svalbard DataCite Metadata Store (German National Library of Science and Technology) Arctic Svalbard |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Physics |
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Physics Karlsen, Stein Rune Anderson, Helen B Wal, René Van Der Hansen, Brage Bremset A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity |
topic_facet |
Physics |
description |
Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity from being estimated over large areas. We used a field-based time-series (2000−2009) of live aboveground vascular plant biomass data and a recently processed cloud-free MODIS-Normalised Difference Vegetation Index (NDVI) data set (2000−2014) to estimate, on a pixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring to the average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensed productivity measure was then compared, at two different spatial scales, with the peak plant biomass field data. At both the local scale, surrounding the field data site, and the larger regional scale, our NDVI measure was found to predict plant biomass (adjusted R 2 = 0.51 and 0.44, respectively). The commonly used 'maximum NDVI' plant productivity index showed no relationship with plant biomass, likely due to some years having very few cloud-free images available during the peak plant growing season. Thus, we propose this new summed NDVI from onset of spring to time of peak NDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climatic conditions restrict the availability of cloud-free images. |
format |
Article in Journal/Newspaper |
author |
Karlsen, Stein Rune Anderson, Helen B Wal, René Van Der Hansen, Brage Bremset |
author_facet |
Karlsen, Stein Rune Anderson, Helen B Wal, René Van Der Hansen, Brage Bremset |
author_sort |
Karlsen, Stein Rune |
title |
A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity |
title_short |
A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity |
title_full |
A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity |
title_fullStr |
A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity |
title_full_unstemmed |
A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity |
title_sort |
new ndvi measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity |
publisher |
Institute of Physics (IOP) |
publishDate |
2018 |
url |
https://dx.doi.org/10.5446/39374 https://av.tib.eu/media/39374 |
geographic |
Arctic Svalbard |
geographic_facet |
Arctic Svalbard |
genre |
Arctic Archipelago Arctic Svalbard |
genre_facet |
Arctic Archipelago Arctic Svalbard |
op_doi |
https://doi.org/10.5446/39374 |
_version_ |
1766303043825958912 |