Abstract
We tested metrics from full-waveform (FW) LiDAR (light detection and ranging) as predictors for forest basal area (BA) and aboveground biomass (AGB), in a tropical moist forest. Three levels of metrics are tested: (i) peak-level, based on each return echo; (ii) pulse-level, based on the whole return signal from each emitted pulse; and (iii) plot-level, simulating a large footprint LiDAR dataset. Several of the tested metrics have significant correlation, with two predictors, found by stepwise regression, in particular: median distribution of the height above ground (nZmedian) and fifth percentile of total pulse return intensity (i_tot5th). The former contained the most information and explained 58% and 62% of the variance in AGB and BA values; stepwise regression left us with two and four predictors, respectively, explaining 65% and 79% of the variance. For BA, the predictors were standard deviation, median and fifth percentile of total return pulse intensity (i_totstdDev, i_totmedian and i_tot5th) and nZmedian, whereas for AGB, only the last two were used. The plot-based metric showed that the median height of echo count (HOMTC) performs best, with very similar results as nZmedian, as expected. Cross-validation allowed the analysis of residuals and model robustness. We discuss our results considering our specific case scenario of a complex forest structure with a high degree of variability in terms of biomass.
Highlights
The ability of recent LiDAR sensors to record the full-waveform (FW) has inspired discussion amongst the scientific community on what is the effective advantage that this data can contribute with respect to standard discrete-return (DR) LiDAR data
The paper is organized as follows: (i) we provide a brief background on LiDAR applications for estimating aboveground biomass (AGB) in tropical forests and highlight some differences between existing systems; (ii) we describe our study site and the dataset that was used; (iii) we present the method and the results of using only direct FW metrics and of adding derived height metrics; and (iv) we discuss results in relation to other efforts to estimate tropical forest structure and future developments of the research
Results of investigations on large footprint LiDAR agree that biomass is significantly correlated to height-/energy-related metrics
Summary
The ability of recent LiDAR sensors to record the full-waveform (FW) has inspired discussion amongst the scientific community on what is the effective advantage that this data can contribute with respect to standard discrete-return (DR) LiDAR data. Numerous related investigations argue the pros and cons [1], but all agree on improvements from FW related to two aspects: (i) improved detection of weak return echoes, which results in an increase in overall point density [2] by a factor of two over high vegetation [3]; and (ii) metrics extracted from the distribution of the return energy Ei over time f(ti) in the FW (e.g., amplitude, echo width, backscatter cross-section, backscatter cross-section per illuminated area and backscatter coefficient) Both DR and FW LiDAR data have proven to be popular among scientists dealing with natural resources on the Earth’s surface, in particular high vegetation (i.e., forests) due to the unique capability of penetrating the canopy structure [4]. Such metrics have been used in [19], where different regression methods were tested to find the best combination of predictors avoiding collinearity
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