Abstract
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 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.
Highlights
Remote sensing techniques from orbital and suborbital platforms have vastly improved our understanding of the world’s biomes, especially in hard-to-reach regions such as the Arctic
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
Previous analysis of the data collected in 2015 showed a high correlation between NDVI and several greenness indices derived from the RGB cameras, i.e. GCC, 2G_RBi, and green– red vegetation index (GRVI) (Anderson et al, 2016)
Summary
Remote sensing techniques from orbital and suborbital platforms have vastly improved our understanding of the world’s biomes, especially in hard-to-reach regions such as the Arctic. Recent studies have shown that it is possible to calculate vegetation indices with similar applications as NDVI, such as GCC (green chromatic coordinate or green chromatic channel), from photos taken with ordinary RGB cameras, commonly known as a phenocam (Anderson et al, 2016; Gillespie et al, 1987; Sonnentag et al, 2012; Westergaard-Nielsen et al, 2017) This makes it possible to deploy a large number of cameras for the fraction of the budget needed to acquire specialized NDVI imaging sensors. These racks were complemented with measurements of NDVI, soil temperature and moisture, and thermal infrared (TIR) In addition to these near-surface set-ups, landscape cameras were installed on top of nearby mountains to provide an overview of the valley and to calculate greenness indices at a landscape scale.
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