Authors: Andrei Lapenis*, SUNY - Albany, George Robinson, University at Albany, Clare Gaffey, Clark University, Maurizio Mencuccini, Research Centre for Ecology and Forestry Applications, Barcelona, Spain, Alexander Buyantuev, University at Albany, Shiguo Jiang, University at Albany
Topics: Biogeography, Physical Geography, Climatology and Meteorology
Keywords: phenology, remote sensing, radial growth, climate
Session Type: Paper
Start / End Time: 9:55 AM / 11:35 AM
Room: Jackson, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Despite significant advances in remote sensing of forest canopies, carbon allocation to tree stemwood remains difficult to monitor using current methods. Quantification of carbon partitioning among fast cycling (e.g., leaves) and slow cycling (e.g., stem) tree modules is critical for evaluating the role of forests as long-term sinks of atmospheric carbon, and for understanding phenological responses to climate change. Here, we employed precision dendrometers on stems of mature white spruce (Picea glauca) in conjunction with daily near-surface photography of their canopies to investigate correlations between canopy reflectance and radial growth. According to our observations during 2015-2017, the start and the end of radial growth were, respectively, approximately 40 days before and 40 days after the summer solstice. This symmetry around the longest day of the year suggests a photoperiodic control of spruce’s radial growth. The Green Chromatic Coordinate (GCC) index of canopies imaged by the near surface camera reached its seasonal maximum at the same time as the peak of radial growth. Normalized Difference Vegetation Index (NDVI) showed a 1-6 wk delay relative to the radial growth peak. We explain this finding by the seasonal loss of canopy due to the abrasive action of wind. Although in this study we found GCC to be a more suitable proxy of radial growth than the NDVI, additional field experiments in contrasting climatic conditions would be required before we can develop a reliable remote sensing method for the robust detection of radial growth period and partitioning of the Nonstructural Carbon inside trees.