Continuous observations of forest canopy structure using low-cost digital camera traps

Abstract

Assessing forest canopy dynamics is crucial for understanding the response of vegetation to environmental variability and change. While digital repeat photography is gaining increased attention for obtaining field phenology observations, colour indices derived from this method are often affected by leaf colour and actual canopy structure, complicating the physical interpretation of results. In addition, repeated photography requires power, storage capacity and remote data transfer, which are often limited in forest conditions. As an alternative, we tested a simple, cheap and fast solution to derive daily canopy structure observation from digital camera traps (CTs). Formerly deployed for wildlife monitoring, CTs are low-cost digital cameras designed for outdoor conditions and have low battery consumption, enable repeat acquisition, and often feature remote data transfer protocols.The trial was performed in a deciduous oak stand, where continuous images were acquired over a 1-year period using the time-lapse feature of the CT. Daily time series of canopy structure attributes were derived from the collected images using simple and automated procedures. Results were validated against reference manual cover photography measurements. The daily time series of foliage cover and leaf area index were then used to derive phenological transition dates, which were compared against phenological observations obtained from satellite Sentinel-2 data. Results indicated that field and satellite data provided comparable accuracy in determining the start of season (SOS). Larger discrepancies were found in determining the end of season (EOS), which can be attributed to the low number of good quality autumn images available from the satellite data. We concluded that CT is a robust method, which is ideally suited for routine, continuous field monitoring of canopy attributes and phenology. While this method can be used for evaluating remote sensing observations, the combination of CTs with satellite data holds great potential for greater spatiotemporal coverage, from field to landscape scales.