A modeling approach for upscaling gross ecosystem production to the landscape scale using remote sensing data

dc.contributor.authorHilker, Thomas
dc.contributor.authorCoops, Nicholas C.
dc.contributor.authorHall, Forrest G.
dc.contributor.authorBlack, T. Andrew
dc.contributor.authorChen, Baozhang
dc.contributor.authorKrishnan, Praveena
dc.contributor.authorWulder, Michael A.
dc.contributor.authorSellers, Piers J.
dc.contributor.authorMiddleton, Elizabeth M.
dc.contributor.authorHuemmrich, Karl
dc.date.accessioned2024-01-29T21:30:47Z
dc.date.available2024-01-29T21:30:47Z
dc.date.issued2008-07-12
dc.description.abstractGross ecosystem production (GEP) can be estimated at the global scale and in a spatially continuous mode using models driven by remote sensing. Multiple studies have demonstrated the capability of high resolution optical remote sensing to accurately measure GEP at the leaf and stand level, but upscaling this relationship using satellite data remains challenging. Canopy structure is one of the complicating factors as it not only alters the strength of a measured signal depending on integrated leaf-angle-distribution and sun-observer geometry, but also drives the photosynthetic output and light-use-efficiency (ɛ) of individual leaves. This study introduces a new approach for upscaling multiangular canopy level reflectance measurements to satellite scales which takes account of canopy structure effects by using Light Detection and Ranging (LiDAR). A tower-based spectro-radiometer was used to observe canopy reflectances over an annual period under different look and solar angles. This information was then used to extract sunlit and shaded spectral end-members corresponding to minimum and maximum values of canopy-ɛ over 8-d intervals using a bidirectional reflectance distribution model. Using three-dimensional information of the canopy structure obtained from LiDAR, the canopy light regime and leaf area was modeled over a 12 km² area and was combined with spectral end-members to derive high resolution maps of GEP. Comparison with eddy covariance data collected at the site shows that the spectrally driven model is able to accurately predict GEP (r² between 0.75 and 0.91, p < 0.05).
dc.description.sponsorshipThe LiDAR data for this project was acquired by Benoit St-Onge, of the University of Quebec at Montreal, as part of an ongoing collaborative project with funds provided by the Government of Canada through NSERC and BIOCAP. We would like to thank Dominic Lessard, Rick Ketler, and Andrew Sauter from UBC Faculty of Land and Food Systems (LFS) for their assistance in technical design, installation, and maintenance of the radiometer platform. This research is partially funded by a DAAD postgraduate scholarship to T. Hilker, an NSERC Discovery Grant to N. Coops, and funds provided to UBC from Fluxnet-Canada, NSERC, and BIOCAP.
dc.description.urihttps://agupubs.onlinelibrary.wiley.com/doi/10.1029/2007JG000666
dc.format.extent15 pages
dc.genrejournal articles
dc.identifier.citationHilker, T., Coops, N. C., Hall, F. G., Black, T. A., Chen, B., Krishnan, P., Wulder, M. A., Sellers, P. J., Middleton, E. M., and Huemmrich, K. F. (2008), A modeling approach for upscaling gross ecosystem production to the landscape scale using remote sensing data, J. Geophys. Res., 113, G03006, doi:10.1029/2007JG000666.
dc.identifier.urihttps://doi.org/10.1029/2007JG000666
dc.identifier.urihttp://hdl.handle.net/11603/31510
dc.language.isoen_US
dc.publisherAGU
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC GESTAR II
dc.relation.ispartofUMBC Geography and Environmental Systems Department
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
dc.rightsPublic Domain Mark 1.0 Universal
dc.rights.urihttps://creativecommons.org/publicdomain/mark/1.0/
dc.titleA modeling approach for upscaling gross ecosystem production to the landscape scale using remote sensing data
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0003-4148-9108

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Journal of Geophysical Research Biogeosciences - 2008 - Hilker - A modeling approach for upscaling gross ecosystem.pdf
Size:
1022.22 KB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description: