English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
/persons/resource/persons62486

Migliavacca,  Mirco
Biosphere-Atmosphere Interactions and Experimentation, Dr. M. Migliavacca, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
Citation

Filippa, G., Cremonese, E., Migliavacca, M., Galvagno, M., Sonnentag, O., Humphreys, E., et al. (2018). NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types. Agricultural and Forest Meteorology, 249, 275-285. doi:10.1016/j.agrformet.2017.11.003.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002E-2308-0
Abstract
Time series of vegetation indices (e.g. normalized difference vegetation index [NDVI]) and color indices (e.g. green chromatic coordinate [GCC]) 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 nearsurface 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 (NDVIC) at 17 sites (for a total of 74 year-sites) encompassing six plant functional types (PFT) from the PhenoCam network. The seasonality of NDVIC 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 NDVIC values and recommend the use of site-specific NDVI from MODIS in order to scale NDVIC. We also compared GCC extracted from red-green-blue images to NDVIC and found PFT-dependent systematic differences in their seasonalities. During senescence, NDVIC lags behind GCC in deciduous broad-leaf forests and grasslands, suggesting that GCC is more sensitive to changes in leaf color and NDVIC is more sensitive to changes in leaf area. In evergreen forests, NDVIC peaks later than GCC in spring, probably tracking the processes of shoot elongation and new needle formation. Both GCC and NDVIC can be used as validation tools for the MODIS Land Cover Dynamics Product (MCD12Q2) for deciduous broad-leaf spring phenology, whereas NDVIC is more comparable than GCC 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 NDVIC is in excellent agreement with NDVI obtained from spectral measurements, and that NDVIC and GCC can complement each other in describing ecosystem phenology. Additionally, NDVIC allows the detection of structural changes in the canopy that cannot be detected by visible-wavelength imagery.