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Keith Summey North Charleston Library
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Phone: (843) 744-2489
Folly Beach Library
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Phone: (843) 588-2001
Main Library
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West Ashley Library
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Wando Mount Pleasant Library
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Miss Jane's Building (Edisto Library Temporary Location)
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St. Paul's/Hollywood Library
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Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations
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- Author(s): Cook, Bruce D.1 ; Bolstad, Paul V.1; Næsset, Erik2; Anderson, Ryan S.3; Garrigues, Sebastian4; Morisette, Jeffrey T.5; Nickeson, Jaime6; Davis, Kenneth J.7
- Source:
Remote Sensing of Environment. Nov2009, Vol. 113 Issue 11, p2366-2379. 14p.- Subject Terms:
- Source:
- Additional Information
- Abstract: 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. [Copyright &y& Elsevier]
- Abstract: Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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