OOS 4-5
Creating an international phenocam network: Envisioning the future of near-surface remote sensing
Understanding global environmental change is a grand challenge of the 21st century. Satellite remote sensing informs our understanding of broad ecosystem processes but quantifying multiscale ecological processes at high spatial resolution is still extremely difficult. Digital timelapse cameras (phenocams) and other new near-surface remote sensing tools like UAVs and sensor networks can bridge the gap between satellite and field-level observations.
Phenocams are being installed at research sites and national observatory networks such as NEON in the US, TERN in Australia and Fluxnet. However, generating standardized phenocam data that can match the scale and quality of satellite data is still challenging. NEON, as a 30-year continental-scale project, is uniquely suited to contribute to the maturation of this field. NEON can promote standardization of research parctices in the US and lead in establishing standards for other international observatory networks. Establishment of global standards will promote widespread deployment of cameras and other sensors and yield global data sets that are of considerable utility to scientists, resource managers and educators.
Results/Conclusions
Based on the results of international workshops in the US (Neon phenocam workshop, Oct 2013) and Australia (TERN/ACEAS phenocam workshop, March 2014) and a review of recent phenocam literature, we will provide current recommendations for phenocam hardware selections, data management and other considerations for deploying phenocams and related NSRS technologies. We will describe how phenocams work, what science questions can be answered with phenocam data, review existing phenocam networks globally, and examine what data products are currently available. We also propose an outline for how phenocam datasets can be better managed to improve data quality, standardization and availability. We will also provide an overview of current and emerging NSRS technologies and provide a vision for how NSRS can integrate with more traditional data sources at NEON and more widely.