NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types.

Time series of vegetation indices (e.g. normalized difference vegetation index [NDVI]) and color indices (e.g. green chromatic coordinate [ G CC ]) based on radiometric measurements are now available at different spatial and temporal scales ranging from weekly satellite observations to sub-hourly in situ measurements by means of near-surface remote sensing (e.g. spectral sensors or digital cameras). In situ measurements are essential for providing validation data for satellite-derived vegetation indices. In this study we used a recently developed method to calculate NDVI from near-infrared (NIR) enabled digital cameras (NDVI C ) at 17 sites (for a total of 74 year-sites) encompassing six plant functional types (PFT) from the PhenoCam network. The seasonality of NDVI C was comparable to both NDVI measured by ground spectral sensors and by the moderate resolution imaging spectroradiometer (MODIS). We calculated site- and PFT-specific scaling factors to correct NDVI C values and recommend the use of site-specific NDVI from MODIS in order to scale NDVI C . We also compared G CC extracted from red-green-blue images to NDVI C and found PFT-dependent systematic differences in their seasonalities. During senescence, NDVI C lags behind G CC in deciduous broad-leaf forests and grasslands, suggesting that G CC is more sensitive to changes in leaf color and NDVI C is more sensitive to changes in leaf area. In evergreen forests, NDVI C peaks later than G CC in spring, probably tracking the processes of shoot elongation and new needle formation. Both G CC and NDVI C can be used as validation tools for the MODIS Land Cover Dynamics Product (MCD12Q2) for deciduous broad-leaf spring phenology, whereas NDVI C is more comparable than G CC with autumn phenology derived from MODIS. For evergreen forests, we found a poor relationship between MCD12Q2 and camera-derived phenology, highlighting the need for more work to better characterize the seasonality of both canopy structure and leaf biochemistry in those ecosystems. Our results demonstrate that NDVI C is in excellent agreement with NDVI obtained from spectral measurements, and that NDVI C and G CC can complement each other in describing ecosystem phenology. Additionally, NDVI C allows the detection of structural changes in the canopy that cannot be detected by visible-wavelength imagery. [ABSTRACT FROM AUTHOR]