A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales
Near-surface remote sensing techniques are essential monitoring tools to provide spatial and temporal resolutions beyond the capabilities of orbital methods. This high level of detail is especially helpful to monitor specific plant communities and to accurately time the phenological stages of vegeta...
Published in: | Earth System Science Data |
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Format: | Article in Journal/Newspaper |
Language: | English |
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Copernicus Publications
2021
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Online Access: | https://doi.org/10.5194/essd-13-3593-2021 https://essd.copernicus.org/articles/13/3593/2021/essd-13-3593-2021.pdf https://doaj.org/article/2f8968894b7a41b3be5cf68bbb86705e |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:2f8968894b7a41b3be5cf68bbb86705e 2023-05-15T13:05:46+02:00 A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales F.-J. W. Parmentier L. Nilsen H. Tømmervik E. J. Cooper 2021-07-01 https://doi.org/10.5194/essd-13-3593-2021 https://essd.copernicus.org/articles/13/3593/2021/essd-13-3593-2021.pdf https://doaj.org/article/2f8968894b7a41b3be5cf68bbb86705e en eng Copernicus Publications doi:10.5194/essd-13-3593-2021 1866-3508 1866-3516 https://essd.copernicus.org/articles/13/3593/2021/essd-13-3593-2021.pdf https://doaj.org/article/2f8968894b7a41b3be5cf68bbb86705e undefined Earth System Science Data, Vol 13, Pp 3593-3606 (2021) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2021 fttriple https://doi.org/10.5194/essd-13-3593-2021 2023-01-22T18:10:42Z Near-surface remote sensing techniques are essential monitoring tools to provide spatial and temporal resolutions beyond the capabilities of orbital methods. This high level of detail is especially helpful to monitor specific plant communities and to accurately time the phenological stages of vegetation – which satellites can miss by days or weeks in frequently clouded areas such as the Arctic. In this paper, we describe a measurement network that is distributed across varying plant communities in the high Arctic valley of Adventdalen on the Svalbard archipelago with the aim of monitoring vegetation phenology. The network consists of 10 racks equipped with sensors that measure NDVI (normalized difference vegetation index), soil temperature, and moisture as well as time-lapse RGB cameras (i.e. phenocams). Three additional time-lapse cameras are placed on nearby mountains to provide an overview of the valley. We derived the vegetation index GCC (green chromatic channel) from these RGB photos, which has similar applications as NDVI but at a fraction of the cost of NDVI imaging sensors. To create a robust time series for GCC, each set of photos was adjusted for unwanted movement of the camera with a stabilizing algorithm that enhances the spatial precision of these measurements. This code is available at https://doi.org/10.5281/zenodo.4554937 (Parmentier, 2021) and can be applied to time series obtained with other time-lapse cameras. This paper presents an overview of the data collection and processing and an overview of the dataset that is available at https://doi.org/10.21343/kbpq-xb91 (Nilsen et al., 2021). In addition, we provide some examples of how these data can be used to monitor different vegetation communities in the landscape. Article in Journal/Newspaper Adventdalen Arctic Svalbard Unknown Adventdalen ENVELOPE(16.264,16.264,78.181,78.181) Arctic Svalbard Svalbard Archipelago Earth System Science Data 13 7 3593 3606 |
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English |
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geo envir |
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geo envir F.-J. W. Parmentier L. Nilsen H. Tømmervik E. J. Cooper A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales |
topic_facet |
geo envir |
description |
Near-surface remote sensing techniques are essential monitoring tools to provide spatial and temporal resolutions beyond the capabilities of orbital methods. This high level of detail is especially helpful to monitor specific plant communities and to accurately time the phenological stages of vegetation – which satellites can miss by days or weeks in frequently clouded areas such as the Arctic. In this paper, we describe a measurement network that is distributed across varying plant communities in the high Arctic valley of Adventdalen on the Svalbard archipelago with the aim of monitoring vegetation phenology. The network consists of 10 racks equipped with sensors that measure NDVI (normalized difference vegetation index), soil temperature, and moisture as well as time-lapse RGB cameras (i.e. phenocams). Three additional time-lapse cameras are placed on nearby mountains to provide an overview of the valley. We derived the vegetation index GCC (green chromatic channel) from these RGB photos, which has similar applications as NDVI but at a fraction of the cost of NDVI imaging sensors. To create a robust time series for GCC, each set of photos was adjusted for unwanted movement of the camera with a stabilizing algorithm that enhances the spatial precision of these measurements. This code is available at https://doi.org/10.5281/zenodo.4554937 (Parmentier, 2021) and can be applied to time series obtained with other time-lapse cameras. This paper presents an overview of the data collection and processing and an overview of the dataset that is available at https://doi.org/10.21343/kbpq-xb91 (Nilsen et al., 2021). In addition, we provide some examples of how these data can be used to monitor different vegetation communities in the landscape. |
format |
Article in Journal/Newspaper |
author |
F.-J. W. Parmentier L. Nilsen H. Tømmervik E. J. Cooper |
author_facet |
F.-J. W. Parmentier L. Nilsen H. Tømmervik E. J. Cooper |
author_sort |
F.-J. W. Parmentier |
title |
A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales |
title_short |
A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales |
title_full |
A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales |
title_fullStr |
A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales |
title_full_unstemmed |
A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales |
title_sort |
distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales |
publisher |
Copernicus Publications |
publishDate |
2021 |
url |
https://doi.org/10.5194/essd-13-3593-2021 https://essd.copernicus.org/articles/13/3593/2021/essd-13-3593-2021.pdf https://doaj.org/article/2f8968894b7a41b3be5cf68bbb86705e |
long_lat |
ENVELOPE(16.264,16.264,78.181,78.181) |
geographic |
Adventdalen Arctic Svalbard Svalbard Archipelago |
geographic_facet |
Adventdalen Arctic Svalbard Svalbard Archipelago |
genre |
Adventdalen Arctic Svalbard |
genre_facet |
Adventdalen Arctic Svalbard |
op_source |
Earth System Science Data, Vol 13, Pp 3593-3606 (2021) |
op_relation |
doi:10.5194/essd-13-3593-2021 1866-3508 1866-3516 https://essd.copernicus.org/articles/13/3593/2021/essd-13-3593-2021.pdf https://doaj.org/article/2f8968894b7a41b3be5cf68bbb86705e |
op_rights |
undefined |
op_doi |
https://doi.org/10.5194/essd-13-3593-2021 |
container_title |
Earth System Science Data |
container_volume |
13 |
container_issue |
7 |
container_start_page |
3593 |
op_container_end_page |
3606 |
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1766393378574958592 |