TY - JOUR
T1 - A robust signal preprocessing chain for small-footprint waveform LiDAR
AU - Wu, Jiaying
AU - Van Aardt, J. A.N.
AU - McGlinchy, Joseph
AU - Asner, Gregory P.
N1 - Funding Information:
Manuscript received February 18, 2011; revised June 29, 2011 and October 17, 2011; accepted November 19, 2011. Date of publication January 4, 2012; date of current version July 18, 2012. This work was supported by Ph.D. research funding provided by the Rochester Institute of Technology.
Funding Information:
The authors are grateful for Ph.D. student funding provided by the Rochester Institute of Technology (RIT). The authors would like to thank T. Kennedy-Bowdoin, D. Knapp, and A. Balaji at the Carnegie Institution for Science for providing the airborne waveform LiDAR data and technical support, and Dr. K. Wessels, Dr. R. Mathieu (Council for Scientific and Industrial Research), and Dr. B. Erasmus (University of the Witwatersrand) for their field data support. The authors would also like to acknowledge the inputs from Dr. M. Long (RIT) during discussions on statistical analysis. The airborne campaign was funded by the Andrew Mellon Foundation. The CAO is made possible by the W. M. Keck Foundation and William Hearst III.
PY - 2012
Y1 - 2012
N2 - The extraction of structural object metrics from a next-generation remote sensing modality, namely waveform Light Detection and Ranging (LiDAR), has garnered increasing interest from the remote sensing research community. However, the raw incoming (received) LiDAR waveform typically exhibits a stretched, misaligned, and relatively distorted character. In other words, the LiDAR signal is smeared and the effective temporal (vertical) resolution decreases, which is attributed to a fixed time span allocated for detection, the sensor's variable outgoing pulse signal, off-nadir scanning, the receiver impulse response impacts, and system noise. Theoretically, such a loss of resolution and increased data ambiguity can be remediated by using proven signal preprocessing approaches. In this paper, we present a robust signal preprocessing chain for waveform LiDAR calibration, which includes noise reduction, deconvolution, waveform registration, and angular rectification. This preprocessing chain was initially validated using simulated waveform data, which were derived via the digital imaging and remote sensing image generation modeling environment. We also verified the approach using real small-footprint waveform LiDAR data collected by the Carnegie Airborne Observatory in a savanna region of South Africa and specifically in terms of modeling woody biomass in this region. Metrics, including the spectral angle for cross-section recovery assessment and goodness-of-fit $(R^{2})$ statistics, along with the root-mean-squared error for woody biomass estimation, were used to provide a comprehensive quantitative evaluation of the performance of this preprocessing chain. Results showed that our approach significantly increased our ability to recover the temporal signal resolution, improved geometric rectification of raw waveform LiDAR, and resulted in improved waveform-based woody biomass estimation. This preprocessing chain has the potential to be applied across the board for high fidelity processing of small-footprint waveform LiDAR data, thereby facilitating the extraction of valid and useful structural metrics from ground objects.
AB - The extraction of structural object metrics from a next-generation remote sensing modality, namely waveform Light Detection and Ranging (LiDAR), has garnered increasing interest from the remote sensing research community. However, the raw incoming (received) LiDAR waveform typically exhibits a stretched, misaligned, and relatively distorted character. In other words, the LiDAR signal is smeared and the effective temporal (vertical) resolution decreases, which is attributed to a fixed time span allocated for detection, the sensor's variable outgoing pulse signal, off-nadir scanning, the receiver impulse response impacts, and system noise. Theoretically, such a loss of resolution and increased data ambiguity can be remediated by using proven signal preprocessing approaches. In this paper, we present a robust signal preprocessing chain for waveform LiDAR calibration, which includes noise reduction, deconvolution, waveform registration, and angular rectification. This preprocessing chain was initially validated using simulated waveform data, which were derived via the digital imaging and remote sensing image generation modeling environment. We also verified the approach using real small-footprint waveform LiDAR data collected by the Carnegie Airborne Observatory in a savanna region of South Africa and specifically in terms of modeling woody biomass in this region. Metrics, including the spectral angle for cross-section recovery assessment and goodness-of-fit $(R^{2})$ statistics, along with the root-mean-squared error for woody biomass estimation, were used to provide a comprehensive quantitative evaluation of the performance of this preprocessing chain. Results showed that our approach significantly increased our ability to recover the temporal signal resolution, improved geometric rectification of raw waveform LiDAR, and resulted in improved waveform-based woody biomass estimation. This preprocessing chain has the potential to be applied across the board for high fidelity processing of small-footprint waveform LiDAR data, thereby facilitating the extraction of valid and useful structural metrics from ground objects.
KW - Digital imaging and remote sensing image generation (DIRSIG)
KW - light detection and ranging (LiDAR)
KW - signal preprocessing chain
KW - structural metrics
KW - waveform
UR - http://www.scopus.com/inward/record.url?scp=84864283876&partnerID=8YFLogxK
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U2 - 10.1109/TGRS.2011.2178420
DO - 10.1109/TGRS.2011.2178420
M3 - Article
AN - SCOPUS:84864283876
SN - 0196-2892
VL - 50
SP - 3242
EP - 3255
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 8
M1 - 6122503
ER -