Camera derived vegetation greenness index as proxy for gross primary production in a low Arctic wetland area |
| |
| Institution: | 1. Departamento de Biología Vegetal II, Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Spain;2. Biological Sciences, Waikato University, Hamilton, New Zealand;3. Plant Ecology and Systematics, Biology, University of Kaiserslautern, Kaiserslautern, Germany;4. Multiphase Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany;1. Center for Permafrost (CENPERM), Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, DK-1350 Copenhagen K, Denmark;2. Danish Climate Centre, Danish Meteorological Institute, Lyngbyvej 100, DK-2100 Copenhagen, Denmark |
| |
| Abstract: | The Arctic is experiencing disproportionate warming relative to the global average, and the Arctic ecosystems are as a result undergoing considerable changes. Continued monitoring of ecosystem productivity and phenology across temporal and spatial scales is a central part of assessing the magnitude of these changes. This study investigates the ability to use automatic digital camera images (DCIs) as proxy data for gross primary production (GPP) in a complex low Arctic wetland site. Vegetation greenness computed from DCIs was found to correlate significantly (R2 = 0.62, p < 0.001) with a normalized difference vegetation index (NDVI) product derived from the WorldView-2 satellite. An object-based classification based on a bi-temporal image composite was used to classify the study area into heath, copse, fen, and bedrock. Temporal evolution of vegetation greenness was evaluated and modeled with double sigmoid functions for each plant community. GPP at light saturation modeled from eddy covariance (EC) flux measurements were found to correlate significantly with vegetation greenness for all plant communities in the studied year (i.e., 2010), and the highest correlation was found between modeled fen greenness and GPP (R2 = 0.85, p < 0.001). Finally, greenness computed within modeled EC footprints were used to evaluate the influence of individual plant communities on the flux measurements. The study concludes that digital cameras may be used as a cost-effective proxy for potential GPP in remote Arctic regions. |
| |
| Keywords: | Low Arctic Digital camera Vegetation index Carbon Eddy covariance Gross primary production |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
