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  • SBC LTER: Time series of quarterly NetCDF files of kelp biomass in the canopy from Landsat 5, 7 and 8, since 1984 (ongoing)
  • Santa Barbara Coastal LTER
    Bell, Tom W
    Cavanaugh, Kyle C
    Siegel, David A
  • 2022-01-10
  • Santa Barbara Coastal LTER, T. Bell, K. Cavanaugh, and D. Siegel. 2022. SBC LTER: Time series of quarterly NetCDF files of kelp biomass in the canopy from Landsat 5, 7 and 8, since 1984 (ongoing) ver 17. Environmental Data Initiative. https://doi.org/DOI_PLACE_HOLDER (Accessed 2024-12-27).

  • This data file represents a time series of canopy area of giant kelp, Macrocystis pyrifera, and bull kelp, Nereocystis luetkeana, and canopy biomass of giant kelp derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI) satellite imagery, along with relevant metadata. The kelp canopy is composed of the portions of fronds and stipes floating on the surface of the water. Canopy area (m2) data are given for individual 30 x 30 meter pixels for all coastal areas of Baja California, Mexico, California, and Oregon, including offshore islands. Biomass data (wet weight, kg) are given for individual 30 x 30 meter pixels in the coastal areas extending from near Ano Nuevo, CA through the southern range limit in Baja California (including offshore islands), representing the range where giant kelp is the dominant canopy forming species.

    Data were derived from the three Landsat sensors listed above. Observations are made on a 16 day repeat cycle, for each instrument, but the temporal coverage is irregular because of cloud cover, instrument failure, and the mission length of each sensor (TM: 1984 – 2011, ETM+: 1999 – present, OLI: 2013 – present). Estimates of canopy area are derived from the fractional cover of kelp canopy determined from satellite surface reflectance. Estimates of kelp canopy biomass are derived from the relationship between giant kelp fractional cover determined from satellite surface reflectance and empirical measurements of giant kelp canopy biomass in long-term SBC LTER study plots obtained using SCUBA. The different Landsat sensors were calibrated to each other using simulated Landsat data derived from hyperspectral imagery. Missing data due to the ETM+ scan line corrector error were filled using a synchrony-based gap filling method.

    Data are organized into a single NetCDF file and contain the quarterly area and biomass means for each Landsat pixel across the three sensors. Relevant metadata such as number of Landsat estimates from which the mean was derived, the number of estimates from each sensor, standard error for each quarterly estimate, spatial coordinates, and date are all included in the file. For assistance with the data, please contact sbclter@msi.ucsb.edu.

  • N: 45.9353      S: 26.702      E: -113.54      W: -124.79
  • This data package is released under the Creative Commons License Attribution 4.0 International (CC BY 4.0, see https://creativecommons.org/licenses/by/4.0/). This license states that consumers ("Data Users" herein) may distribute, adapt, reuse, remix, and build upon this work, as long as they give appropriate credit, provide a link to the license, and indicate if changes were made. If redistributed, a Data User may not apply additional restrictions or technological measures that prevent access. The Data User has an ethical obligation to cite the data source appropriately in any publication or product that results from its use, and notify the data contact or creator. Communication, collaboration, or co-authorship (as appropriate) with the creators of this data package is encouraged to prevent duplicate research or publication. The Data User is urged to contact the authors of these data if any questions about methodology or results occur. The Data User should realize that these data may be actively used by others for ongoing research and that coordination may be necessary to prevent duplication or inappropriate use. The Data User should realize that misinterpretation may occur if data are used outside of the context of the original study. The Data User should be aware that data are updated periodically and it is the responsibility of the Data User to check for new versions of the data. While substantial efforts are made to ensure the accuracy of data and associated documentation, complete accuracy of data sets cannot be guaranteed. This data package (with its components) is made available “as is” and with no warranty of accuracy or fitness for use. The creators of this data package and the repository where these data were obtained shall not be liable for any damages resulting from misinterpretation, use or misuse of the data package or its components.
  • DOI PLACE HOLDER

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