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

• Published in: Earth System Science Data
• Main Authors: Parmentier, Frans-Jan W., Nilsen, Lennart, Tømmervik, Hans, Cooper, Elisabeth J.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus GmbH 2021
• Subjects: Adventdalen; Arctic; Svalbard; Svalbard Archipelago
• Online Access: http://hdl.handle.net/10852/86796; http://urn.nb.no/URN:NBN:no-89437; https://doi.org/10.5194/essd-13-3593-2021

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.