Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

Bruce D. Cook, Paul V. Bolstad, Erik Næsset, Ryan S. Anderson, Sebastian Garrigues, Jeffrey T. Morisette, Jaime Nickeson, Kenneth James Davis

Research output: Contribution to journalArticle

34 Citations (Scopus)

Abstract

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Original languageEnglish (US)
Pages (from-to)2366-2379
Number of pages14
JournalRemote Sensing of Environment
Volume113
Issue number11
DOIs
StatePublished - Nov 16 2009

Fingerprint

QuickBird
Wetlands
land cover
primary productivity
net primary production
wetlands
uncertainty
wetland
primary production
MODIS
moderate resolution imaging spectroradiometer
Aqua (satellite)
Terra (satellite)
altimetry
vegetation structure
photosynthetically active radiation
vapor pressure
meteorology
leaf area
plant community

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Geology
  • Computers in Earth Sciences

Cite this

Cook, Bruce D. ; Bolstad, Paul V. ; Næsset, Erik ; Anderson, Ryan S. ; Garrigues, Sebastian ; Morisette, Jeffrey T. ; Nickeson, Jaime ; Davis, Kenneth James. / Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations. In: Remote Sensing of Environment. 2009 ; Vol. 113, No. 11. pp. 2366-2379.
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Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations. / Cook, Bruce D.; Bolstad, Paul V.; Næsset, Erik; Anderson, Ryan S.; Garrigues, Sebastian; Morisette, Jeffrey T.; Nickeson, Jaime; Davis, Kenneth James.

In: Remote Sensing of Environment, Vol. 113, No. 11, 16.11.2009, p. 2366-2379.

Research output: Contribution to journalArticle

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AU - Cook, Bruce D.

AU - Bolstad, Paul V.

AU - Næsset, Erik

AU - Anderson, Ryan S.

AU - Garrigues, Sebastian

AU - Morisette, Jeffrey T.

AU - Nickeson, Jaime

AU - Davis, Kenneth James

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