TY - JOUR
T1 - Carnegie Airborne Observatory-2
T2 - Increasing science data dimensionality via high-fidelity multi-sensor fusion
AU - Asner, Gregory P.
AU - Knapp, David E.
AU - Boardman, Joseph
AU - Green, Robert O.
AU - Kennedy-Bowdoin, Ty
AU - Eastwood, Michael
AU - Martin, Roberta E.
AU - Anderson, Christopher
AU - Field, Christopher B.
N1 - Funding Information:
The Carnegie Airborne Observatory is supported by the Gordon and Betty Moore Foundation , the John D. and Catherine T. MacArthur Foundation , the Grantham Foundation for the Protection of the Environment , the W.M. Keck Foundation , and William Hearst III .
PY - 2012/9
Y1 - 2012/9
N2 - The Carnegie Airborne Observatory (CAO) was developed to address a need for macroscale measurements that reveal the structural, functional and organismic composition of Earth's ecosystems. In 2011, we completed and launched the CAO-2 next generation Airborne Taxonomic Mapping Systems (AToMS), which includes a high-fidelity visible-to-shortwave infrared (VSWIR) imaging spectrometer (380-2510. nm), dual-laser waveform light detection and ranging (LiDAR) scanner, and high spatial resolution visible-to-near infrared (VNIR) imaging spectrometer (365-1052. nm). Here, we describe how multiple data streams from these sensors can be fused using hardware and software co-alignment and processing techniques. With these data streams, we quantitatively demonstrate that precision data fusion greatly increases the dimensionality of the ecological information derived from remote sensing. We compare the data dimensionality of two contrasting scenes - a built environment at Stanford University and a lowland tropical forest in Amazonia. Principal components analysis revealed 336 dimensions (degrees of freedom) in the Stanford case, and 218 dimensions in the Amazon. The Amazon case presents what could be the highest level of remotely sensed data dimensionality ever reported for a forested ecosystem. Simulated misalignment of data streams reduced the effective information content by up to 48%, highlighting the critical role of achieving high precision when undertaking multi-sensor fusion. The instrumentation and methods described here are a pathfinder for future airborne applications undertaken by the National Ecological Observatory Network (NEON) and other organizations.
AB - The Carnegie Airborne Observatory (CAO) was developed to address a need for macroscale measurements that reveal the structural, functional and organismic composition of Earth's ecosystems. In 2011, we completed and launched the CAO-2 next generation Airborne Taxonomic Mapping Systems (AToMS), which includes a high-fidelity visible-to-shortwave infrared (VSWIR) imaging spectrometer (380-2510. nm), dual-laser waveform light detection and ranging (LiDAR) scanner, and high spatial resolution visible-to-near infrared (VNIR) imaging spectrometer (365-1052. nm). Here, we describe how multiple data streams from these sensors can be fused using hardware and software co-alignment and processing techniques. With these data streams, we quantitatively demonstrate that precision data fusion greatly increases the dimensionality of the ecological information derived from remote sensing. We compare the data dimensionality of two contrasting scenes - a built environment at Stanford University and a lowland tropical forest in Amazonia. Principal components analysis revealed 336 dimensions (degrees of freedom) in the Stanford case, and 218 dimensions in the Amazon. The Amazon case presents what could be the highest level of remotely sensed data dimensionality ever reported for a forested ecosystem. Simulated misalignment of data streams reduced the effective information content by up to 48%, highlighting the critical role of achieving high precision when undertaking multi-sensor fusion. The instrumentation and methods described here are a pathfinder for future airborne applications undertaken by the National Ecological Observatory Network (NEON) and other organizations.
KW - Airborne remote sensing
KW - CAO
KW - Data fusion
KW - Hyperspectral
KW - Imaging spectroscopy
KW - LiDAR
KW - Macroscale ecology
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U2 - 10.1016/j.rse.2012.06.012
DO - 10.1016/j.rse.2012.06.012
M3 - Article
AN - SCOPUS:84863214543
SN - 0034-4257
VL - 124
SP - 454
EP - 465
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
ER -