Abstract
The new Moment Distance (MD) framework uses the backscattering profile captured in waveform LiDAR data to characterize the complicated waveform shape and highlight specific regions within the waveform extent. To assess the strength of the new metric for LiDAR application, we use the full-waveform LVIS data acquired over La Selva, Costa Rica in 1998 and 2005. We illustrate how the Moment Distance Index (MDI) responds to waveform shape changes due to variations in signal noise levels. Our results show that the MDI is robust in the face of three different types of noise—additive, uniform additive, and impulse. In effect, the correspondence of the MDI with canopy quasi-height was maintained, as quantified by the coefficient of determination, when comparing original to noise-affected waveforms. We also compare MDIs from noise-affected waveforms to MDIs from smoothed waveforms and found that windows of 1% to 3% of the total wave counts can effectively smooth irregularities on the waveform without risking of the omission of small but important peaks, especially those located in the waveform extremities. Finally, we find a stronger positive relationship of MDI with canopy quasi-height than with the conventional area under curve (AUC) metric, e.g., r2 = 0.62 vs. r2 = 0.35 for the 1998 data and r2 = 0.38 vs. r2 = 0.002 for the 2005 data.
Highlights
The active remote sensing using LiDAR has seen rapid developments in the past two decades
We looked at the Moment Distance Index (MDI) as a function of the original quasi-height to assess how the introduction of the levels of uncertainty to the waveforms using three different types of noise models changed the initial observed trend of the original MDI against the canopy quasi-height
Our results reveal that the MDI can capture aspects of temporal dynamics of canopy quasi-heights and group them based on the curve shapes
Summary
The active remote sensing using LiDAR has seen rapid developments in the past two decades. With a promise of improved accuracy of biophysical measurements and the spatial analysis done in the third dimension, LiDAR could play an important role in atmospheric and environmental field of studies. The full-waveform LiDAR system has the ability to record many returns per emitted pulse, as a function of time, within the vertical structure of the illuminated object, showing position of individual targets, and finer details of the signature of intercepted surfaces or the proportion of the canopy complexity. The richness of the LiDAR waveform holds the promise to address the challenge of characterizing in detail the geometric and reflection characteristics of vegetation structure, e.g. the vertical canopy volume distribution [5]
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