Preprints
https://doi.org/10.5194/essd-2021-56
https://doi.org/10.5194/essd-2021-56

  12 Mar 2021

12 Mar 2021

Review status: this preprint is currently under review for the journal ESSD.

A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales

Frans-Jan W. Parmentier1,2,3, Lennart Nilsen3, Hans Tømmervik4, and Elisabeth J. Cooper3 Frans-Jan W. Parmentier et al.
  • 1Center for Biogeochemistry in the Anthropocene, Department of Geosciences, University of Oslo, Oslo, 0315, Norway
  • 2Department of Physical Geography and Ecosystem Science, Lund University, Lund, 223 62, Sweden
  • 3Department of Arctic and Marine Biology, UiT–The Arctic University of Norway, Tromsø, 9037, Norway
  • 4Norwegian Institute of Nature Research (NINA), FRAM—High North Centre for Climate and the Environment, Tromsø, 9296, Norway

Abstract. 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 to monitor vegetation phenology. The network consists of ten racks equipped with sensors that measure NDVI (Normalized Difference Vegetation Index), soil temperature and moisture, as well as time-lapse RGB cameras. Three additional time-lapse cameras are placed on nearby mountain tops to provide an overview of the valley. The vegetation index GCC (Green Chromatic Channel) was derived 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 timeseries 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 which is available at https://doi.org/10.21343/kbpq-xb91 (Nilsen et al. 2021). In addition, we provide some examples of how this data can be used to monitoring different vegetation communities in the landscape.

Frans-Jan W. Parmentier et al.

Status: open (until 14 May 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-56', Anonymous Referee #1, 06 Apr 2021 reply

Frans-Jan W. Parmentier et al.

Data sets

Near-surface vegetation monitoring in Adventdalen, Svalbard (Rack #1-#10, 2015-2018) Nilsen, L., Parmentier, F. J. W., Tømmervik, H., and Cooper, E. J. https://doi.org/10.21343/kbpq-xb91

Model code and software

frans-jan/stable-cam v1.0 (Version v1.0) Parmentier, F. J. W. https://doi.org/10.5281/zenodo.4554938

Frans-Jan W. Parmentier et al.

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Short summary
Satellites provide a global overview of Earth's ecosystems, but they have coarse resolutions and low revisit times. Small scale vegetation patterns and sudden shifts in plant growth can therefore be missed. In this paper, we show how to fill these gaps with vegetation indices obtained with ordinary time-lapse cameras – deployed across a valley on Svalbard. We show how to adjust for unwanted camera movement, and that vegetation indices from ordinary cameras compare well to those from satellites.