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

ABSTRACT 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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    ABSTRACT: A raw incoming (received) Light Detection And Ranging (LiDAR) waveform typically exhibits a stretched and relatively featureless character, e.g., the LiDAR signal is smeared and the effective spatial resolution decreases. This is attributed to a fixed time span allocated for detection, the sensor’s variable outgoing pulse signal, receiver impulse response, and system noise. Theoretically, such a loss of resolution can be recovered by deconvolving the system response from the measured signal. In this paper, we present a comparative controlled study of three deconvolution techniques, namely, Richardson–Lucy, Wiener filter, and nonnegative least squares, in order to verify which method is quantitatively superior to others. These deconvolution methods were compared in terms of two use cases: 1) ability to recover the true cross-sectional profile of an illuminated object based on the waveform simulation of a virtual 3-D tree model and 2) ability to differentiate herbaceous biomass based on the waveform simulation of virtual grass patches. All the simulated waveform data for this study were derived via the “Digital Imaging and Remote Sensing Image Generation” radiative transfer modeling environment. Results show the superior performance for the Richardson–Lucy algorithm in terms of small root mean square error for recovering the true cross section, low false discovery rate for detecting the unobservable local peaks in the stretched raw waveforms, and high classification accuracy for differentiating herbaceous biomass levels.
    IEEE Transactions on Geoscience and Remote Sensing 01/2011; 49(6):2402-2414. · 3.47 Impact Factor

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May 30, 2014