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...
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Copernicus GmbH
2021
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Online Access: | https://lup.lub.lu.se/record/fcaaa669-67f1-4d3d-b2b9-8e22d1240707 https://doi.org/10.5194/essd-13-3593-2021 |
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ftulundlup:oai:lup.lub.lu.se:fcaaa669-67f1-4d3d-b2b9-8e22d1240707 2023-05-15T13:05:45+02:00 A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales Parmentier, Frans Jan W. Nilsen, Lennart Tømmervik, Hans Cooper, Elisabeth J. 2021 https://lup.lub.lu.se/record/fcaaa669-67f1-4d3d-b2b9-8e22d1240707 https://doi.org/10.5194/essd-13-3593-2021 eng eng Copernicus GmbH https://lup.lub.lu.se/record/fcaaa669-67f1-4d3d-b2b9-8e22d1240707 http://dx.doi.org/10.5194/essd-13-3593-2021 scopus:85111756825 Earth System Science Data; 13(7), pp 3593-3606 (2021) ISSN: 1866-3508 Physical Geography contributiontojournal/article info:eu-repo/semantics/article text 2021 ftulundlup https://doi.org/10.5194/essd-13-3593-2021 2023-02-01T23:38:53Z 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 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 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 Lund University Publications (LUP) Arctic Svalbard Svalbard Archipelago Adventdalen ENVELOPE(16.264,16.264,78.181,78.181) Earth System Science Data 13 7 3593 3606 |
institution |
Open Polar |
collection |
Lund University Publications (LUP) |
op_collection_id |
ftulundlup |
language |
English |
topic |
Physical Geography |
spellingShingle |
Physical Geography Parmentier, Frans Jan W. Nilsen, Lennart Tømmervik, Hans Cooper, Elisabeth J. A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales |
topic_facet |
Physical Geography |
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 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 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 |
Parmentier, Frans Jan W. Nilsen, Lennart Tømmervik, Hans Cooper, Elisabeth J. |
author_facet |
Parmentier, Frans Jan W. Nilsen, Lennart Tømmervik, Hans Cooper, Elisabeth J. |
author_sort |
Parmentier, Frans Jan W. |
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 GmbH |
publishDate |
2021 |
url |
https://lup.lub.lu.se/record/fcaaa669-67f1-4d3d-b2b9-8e22d1240707 https://doi.org/10.5194/essd-13-3593-2021 |
long_lat |
ENVELOPE(16.264,16.264,78.181,78.181) |
geographic |
Arctic Svalbard Svalbard Archipelago Adventdalen |
geographic_facet |
Arctic Svalbard Svalbard Archipelago Adventdalen |
genre |
Adventdalen Arctic Svalbard |
genre_facet |
Adventdalen Arctic Svalbard |
op_source |
Earth System Science Data; 13(7), pp 3593-3606 (2021) ISSN: 1866-3508 |
op_relation |
https://lup.lub.lu.se/record/fcaaa669-67f1-4d3d-b2b9-8e22d1240707 http://dx.doi.org/10.5194/essd-13-3593-2021 scopus:85111756825 |
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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1766393084219752448 |