FilippaCremoneseMigliavaccaEtAl2018

Référence

Filippa, G., Cremonese, E., Migliavacca, M., Galvagno, M., Sonnentag, O., Humphreys, E., Hufkens, K., Ryu, Y., Verfaillie, J., Morra di Cella, U. and Richardson, A.D. (2018) NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types. Agricultural and Forest Meteorology, 249:275-285. (Scopus )

Résumé

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 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 (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. © 2017 Elsevier B.V.

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@ARTICLE { FilippaCremoneseMigliavaccaEtAl2018,
    AUTHOR = { Filippa, G. and Cremonese, E. and Migliavacca, M. and Galvagno, M. and Sonnentag, O. and Humphreys, E. and Hufkens, K. and Ryu, Y. and Verfaillie, J. and Morra di Cella, U. and Richardson, A.D. },
    TITLE = { NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types },
    JOURNAL = { Agricultural and Forest Meteorology },
    YEAR = { 2018 },
    VOLUME = { 249 },
    PAGES = { 275-285 },
    NOTE = { cited By 1 },
    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 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 (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. © 2017 Elsevier B.V. },
    AFFILIATION = { Environmental Protection Agency of Aosta Valley, ARPA Valle d'Aosta, Climate Change Unit, Italy; Max Planck Institute for Biogeochemistry, Department Biogeochemical Integration, Jena, Germany; Département de géographie & Centre d’études nordiques, Université de Montréal, Montréal, QC, Canada; Department of Geography and Environmental Studies, Carleton University, Ottawa, ON, Canada; Northern Arizona University School of Informatics Computing and Cyber System, Flagstaff, AZ, United States; Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, South Korea; Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, United States; Harvard University, Department of Organismic and Evolutionary Biology, Cambridge, MA, United States },
    AUTHOR_KEYWORDS = { Camera NDVI; Color indices; Near-surface remote sensing; PhenoCam; Phenology; Phenopix },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1016/j.agrformet.2017.11.003 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033384296&doi=10.1016%2fj.agrformet.2017.11.003&partnerID=40&md5=d847882d3efaf9fe4a35fcb061d2e7e7 },
}

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