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Large-scale, high-quality phenological observations of trees are key to a better understanding of the environmental factors that control phenological processes, as well as their responses to a changing climate. Over the last decades, phenocams i. Phenocams combine the advantages of automated, real-time data acquisition and a high resolution that allows for the monitoring of individual organisms.
Here, we focus on tree species in Switzerland and lay the foundation for a country-scale phenocam network.
In comparison to the global spatial coverage of satellite data, phenocam coverage is bound by the local implantation of cameras. To mitigate this limitation, we integrated a diversity of sources into our data pipeline: weather cameras, private cameras e.
Combining those sources, we identified over potential sites across the Swiss territory with cameras installed by the same industrial provider. In our first iteration, we focused on 27 of those sites, prioritizing based on the amount of clearly visible trees.
We collected the image time series for each location with up to 12 years of site-level history. Due to the diversity of image sources the temporal resolution varied between 1 and images per day. For each of the sites, we annotated the polygon delineating the boundaries of each tree, or group of trees in image pixel coordinates. Next, we identified the species of each tree via on-site visual inspection.