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Assessing uncertainties related to the use of satellite remote sensing indices to estimate Gross Primary Production

  • Author / Creator
    Hernández Mora, Ronny A.
  • Methods to quantify Gross Primary Production (GPP) are classified into two categories: Eddy
    Covariance techniques (EC) and satellite data-driven. EC techniques can measure carbon
    fluxes directly, albeit with spatial constraints. Satellite data-driven methods are promising
    because they overcome spatial constraints associated with EC techniques. However, satellite-
    driven products have potentially greater uncertainty than EC methods for GPP estimation
    such as mixed pixels, cloud cover, and the ability of the sensor to retrieve vegetation under sat-
    uration conditions in high biomass environments. Therefore, an effort to analyze and quantify
    the uncertainty of GPP products derived from satellite platforms is needed. This study quan-
    tifies the uncertainty of commonly used satellite vegetation indices such as Normalized Dif-
    ference Vegetation Index (NDVI), Enhance Vegetation Index (EVI), Chlorophyll/Carotenoid
    Index (CCI), and Near-Infrared Reflectance Index (NIRv) for GPP estimation compared with
    direct methods such as EC measurements. We conduct this study on three different sites: the
    University of Michigan Biological Station (USA), the Borden Forest Research Station flux-site
    (Canada), and Bartlett Experimental Forest (USA) using traditional regression methods and
    ML approaches.

  • Subjects / Keywords
  • Graduation date
    Spring 2024
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-f8dw-0t12
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.