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 Publications
2021
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Online Access: | https://hdl.handle.net/10037/22078 https://doi.org/10.5194/essd-13-3593-2021 |
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ftunivtroemsoe:oai:munin.uit.no:10037/22078 2023-05-15T13:05:47+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. 2021-07-29 https://hdl.handle.net/10037/22078 https://doi.org/10.5194/essd-13-3593-2021 eng eng Copernicus Publications Earth System Science Data Norges forskningsråd: 274711 Norges forskningsråd: 269927 Vetenskapsrådet: 2017-05268 Norges forskningsråd: 230970 Norges forskningsråd: 287402 info:eu-repo/grantAgreement/RCN/FRINATEK/274711/Norway/Winter-proofing land surface models - quantifying the critical role of cold season processes in vegetation-permafrost feedbacks// info:eu-repo/grantAgreement/RCN/FORINFRA/269927/Norway/Svalbard Integrated Arctic Earth Observing System - Infrastructure development of the Norwegian node (SIOS InfraNOR) - revised// info:eu-repo/grantAgreement/RCN/FRIMEDBIO/230970/Norway/The effect of snow depth and snow melt timing on arctic terrestrial ecosystems// info:eu-repo/grantAgreement/RCN/POLARPROG/287402/Norway/The vanishing white: management of stressors causing reduction of pale vegetation surfaces in the Arctic and the Qinghai-Tibetan Plateau// https://essd.copernicus.org/articles/13/3593/2021/ Parmentier F .J. W., Nilsen L, Tømmervik H, Cooper E.J. A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales. Earth System Science Data. 2021;13:3593-3606 FRIDAID 1923675 doi:10.5194/essd-13-3593-2021 1866-3508 1866-3516 https://hdl.handle.net/10037/22078 openAccess Copyright 2021 The Author(s) VDP::Mathematics and natural science: 400::Zoology and botany: 480 VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480 Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2021 ftunivtroemsoe https://doi.org/10.5194/essd-13-3593-2021 2021-08-18T22:53:41Z 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 Arctic Svalbard University of Tromsø: Munin Open Research Archive Adventdalen ENVELOPE(16.264,16.264,78.181,78.181) Arctic Svalbard Svalbard Archipelago Earth System Science Data 13 7 3593 3606 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Mathematics and natural science: 400::Zoology and botany: 480 VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480 |
spellingShingle |
VDP::Mathematics and natural science: 400::Zoology and botany: 480 VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480 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 |
VDP::Mathematics and natural science: 400::Zoology and botany: 480 VDP::Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480 |
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 |
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 Publications |
publishDate |
2021 |
url |
https://hdl.handle.net/10037/22078 https://doi.org/10.5194/essd-13-3593-2021 |
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 Arctic Svalbard |
genre_facet |
Adventdalen Arctic Arctic Svalbard |
op_relation |
Earth System Science Data Norges forskningsråd: 274711 Norges forskningsråd: 269927 Vetenskapsrådet: 2017-05268 Norges forskningsråd: 230970 Norges forskningsråd: 287402 info:eu-repo/grantAgreement/RCN/FRINATEK/274711/Norway/Winter-proofing land surface models - quantifying the critical role of cold season processes in vegetation-permafrost feedbacks// info:eu-repo/grantAgreement/RCN/FORINFRA/269927/Norway/Svalbard Integrated Arctic Earth Observing System - Infrastructure development of the Norwegian node (SIOS InfraNOR) - revised// info:eu-repo/grantAgreement/RCN/FRIMEDBIO/230970/Norway/The effect of snow depth and snow melt timing on arctic terrestrial ecosystems// info:eu-repo/grantAgreement/RCN/POLARPROG/287402/Norway/The vanishing white: management of stressors causing reduction of pale vegetation surfaces in the Arctic and the Qinghai-Tibetan Plateau// https://essd.copernicus.org/articles/13/3593/2021/ Parmentier F .J. W., Nilsen L, Tømmervik H, Cooper E.J. A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales. Earth System Science Data. 2021;13:3593-3606 FRIDAID 1923675 doi:10.5194/essd-13-3593-2021 1866-3508 1866-3516 https://hdl.handle.net/10037/22078 |
op_rights |
openAccess Copyright 2021 The Author(s) |
op_doi |
https://doi.org/10.5194/essd-13-3593-2021 |
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Earth System Science Data |
container_volume |
13 |
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7 |
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3593 |
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3606 |
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