Conference Paper

Connecting the Dots between Laser Waveforms and Herbaceous Biomass for Assessment of Land Degradation using Small-footprint Waveform LiDAR Data.

Chester F. Carlson Center for Imaging Sci., Rochester Inst. of Technol., Rochester, NY, USA
DOI: 10.1109/IGARSS.2009.5418078 Conference: IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2009, July 12-17, 2009, University of Cape Town, Cape Town, South Africa, Proceedings
Source: DBLP


Measurement and management of vegetation biomass accumulation in ecosystems typically involves extensive field data collection, which can be expensive and time consuming, while leaving the user with relatively crude inputs to intricate biomass models. Light detection and ranging (LiDAR) remote sensing, which provides extensive height measurements of terrain and vegetation, has become an effective alternative to characterize vegetation structure. In this paper, we report on ongoing efforts at developing signal processing approaches to model herbaceous biomass using a new generation of airborne laser scanners, namely full-waveform LiDAR systems. Structural and statistic-based feature metrics are directly derived from LiDAR waveforms at the pixel level and related to plot-level field data. Initial results reveal a definite correlation between the LiDAR waveform and herbaceous biomass. Ongoing research focuses on the links between fractional cover estimated from imaging spectroscopy and woody biomass.

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    • "It is possible that ''first peak'' information, higher than 2 m, was removed in this process. Another difference in this study is that a generic Gaussian modeling algorithm was used to perform composite waveform modeling for waveforms with two peaks or fewer, compared with the expectation maximization algorithm that was used by Wu et al. (2009b). However, the generic Gaussian modeling algorithm performed reasonably well considering the 5% error rate. "
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    ABSTRACT: The mapping of tree species, in general and specifically in diverse savanna environments, is of great interest to ecologists and natural resource managers. This study focused on the fusion of imaging spectroscopy and small footprint waveform light detection and ranging (wLiDAR) data to improve per species structural parameter estimation towards classification and herbaceous biomass modeling. The species classification approach was based on stepwise discriminant analysis (SDA) and used feature metrics from hyperspectral imagery (HSI) combined with wLiDAR data. It was found that fusing data with the SDA did not improve classification significantly, especially compared with the HSI classification results. As for herbaceous biomass modeling, the statistical approach used for the fusion of wLiDAR and HSI was forward selection regression modeling, which selects significant independent metrics and models those to measured biomass. The results indicated that fine scale wLiDAR may not be able to provide accurate measurement of herbaceous biomass, although other factors could have contributed to the relatively poor results, such as the senescent state of grass, the narrow biomass range that was measured, and the low biomass values, i.e., the limited laser-target interactions. We concluded that although fusion did not result in significant improvements over single modality approaches in these two use cases, there is a need for further investigation during the peak growing season.
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    • "The " x-axis " for each waveform corresponds to the time bins, which can be converted to the height above ground, starting from 24.995 m to −8.605 m at increments of 0.15 m. This range was arbitrarily set based on the size of the tree and considering temporally delayed returns due to the multiple scattering of photons [33]. The negative value was used so that multiple scatters could also be included in the simulated waveform, e.g., the minor peak after ground response. "
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    ABSTRACT: 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 (R2) 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 h- gh fidelity processing of small-footprint waveform LiDAR data, thereby facilitating the extraction of valid and useful structural metrics from ground objects.
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