TY - JOUR
T1 - Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations
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 J.
N1 - Funding Information:
This research was funded in part by the National Institute for Climatic Change Research (NICCR) and Terrestrial Carbon Processes (TCP) programs of the US Department of Energy (DoE); US National Aeronautics and Space Administration (NASA) in support of the North American Carbon Program (NACP) and Mid-Continent Intensive (MCI) campaign; US National Science Foundation (NSF); and University of Minnesota Initiative for Renewable Energy and the Environment (IREE). Any opinions, findings, and conclusions or recommendations herein are those of the authors and do not necessarily reflect the view of DoE, NASA, NSF, or IREE. The authors wish to thank Tom Steele, Gary Kellner, and Karla Ortman at the Kemp Natural Resources Station, University of Wisconsin, who provided technical support and accommodations throughout this project; and to Tim Brass, Steve Burns, and Andy Rasmussen, who demonstrated tremendous attention to detail while collecting large quantities of field data. From Bruce, a special thanks and appreciation goes to Wu Yang for her constant support and encouragement while writing this manuscript.
PY - 2009/11/16
Y1 - 2009/11/16
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=70349164857&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2009.06.017
DO - 10.1016/j.rse.2009.06.017
M3 - Article
AN - SCOPUS:70349164857
SN - 0034-4257
VL - 113
SP - 2366
EP - 2379
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
IS - 11
ER -
