![]() We focus on two Arctic species, the mountain avens Dryas octopetala and Dryas integrifolia in 20 image series from four sites. We demonstrate the feasibility of collecting flower phenology data using automatic time-lapse cameras and show that the temporal resolution of the results surpasses what can be collected by traditional observation methods. The method consists of image-based monitoring of field plots using near-surface time-lapse cameras and subsequent automated detection and counting of flowers in the images using a convolutional neural network. We present a method for automated monitoring of flowering phenology of specific plant species at very high temporal resolution through full growing seasons and across geographical regions. Data deficiency is especially pronounced in the Arctic where the warming is particularly severe. This is partly due to a lack of data, which are typically collected by direct observations, and thus very time-consuming to obtain. However, the species level responses to warming are complex and the underlying mechanisms are difficult to disentangle. The advancement of spring is a widespread biological response to climate change observed across taxa and biomes. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |