The phenology of plants and animals is a powerful indicator of environmental changes. Monitoring phenology therefore is an important tool for quantifying the impacts of climate change impacts on ecosystems. The manual recording of dates at which phenological events happen is an old procedure, as illustrated by the first date of cherry tree blossoming being recorded since the 9th century (Aono & Kazui 2008).
A relatively new alternative tool that allows for the standardized measurement phenological and environmental changes are phenocams (Brown et al. 2016), especially if combined with data on local temperature, precipitation and humidity. Typically, phenocams are mounted on climate towers alongside climatic sensors. Image analysis techniques allow for the quantification of colour patterns and the detection of abrupt changes that represent phenological events.
Camera traps that are placed to capture activity of wildlife also record changes in vegetation, as by-catch, if the camera traps are operated for long periods of time. The idea to actually use these images to determine phenology is relatively new. A recent application with several months of recording is fully manual (Hofmeester et al. 2019). To date, however, no automated image analysis seems to have been applied to camera-trap images.
National Park De Hoge Veluwe has 56-70 permanent camera traps that have been recording wildlife activity in six different major habitat types near-continuously for three-seven years. Moreover, the cameras take a time-lapse photograph every day at noon. This represents a unique phenological dataset.
The challenge is to use image analysis to quantify the phenology of the vegetation in one or more of these habitat types, and link these patterns to the records of a nearby weather station to look for predictors,
You may want to check out various R-packages that have been developed for phenology, such as phenofit (Extract Remote Sensing Vegetation Phenology), bfast (Breaks For Additive Season and Trend), phenocamr (Facilitates ‘PhenoCam’ Data Access and Time Series Post-Processing) and phenopix (Process Digital Images of a Vegetation Cover) (Filippa et al. 2016; Hufkens et al. 2018).
You will be given access to over 100,000 images from all camera trap stations at Hoge Veluwe National Park, collected since July 2013.
Aono, Y., & Kazui, K. (2008). Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century. International Journal of Climatology, 28, 905-914.
Brown, T. B., Hultine, K. R., Steltzer, H., Denny, E. G., Denslow, M. W., Granados, J., … & Sánchez‐Azofeifa, A. (2016). Using phenocams to monitor our changing Earth: toward a global phenocam network. Frontiers in Ecology and the Environment, 14, 84-93.
Filippa, G., Cremonese, E., Migliavacca, M., Galvagno, M., Forkel, M., Wingate, L., … & Richardson, A. D. (2016). Phenopix: AR package for image-based vegetation phenology. Agricultural and Forest Meteorology, 220, 141-150.
Hofmeester, T. R., Young, S., Juthberg, S., Singh, N. J., Widemo, F., Andrén, H., … & Cromsigt, J. P. (2019). Using by‐catch data from wildlife surveys to quantify climatic parameters and the timing of phenology for plants and animals using camera traps. R_emote Sensing in Ecology and Conservation_.
Hufkens, K., Basler, D., Milliman, T., Melaas, E. K., & Richardson, A. D. (2018). An integrated phenology modelling framework in R. Methods in Ecology and Evolution, 9, 1276-1285.