Small-footprint Full Waveform Research Articles

Towards Forest Condition Assessment: Evaluating Small-Footprint Full-Waveform Airborne Laser Scanning Data for Deriving Forest Structural and Compositional Metrics

Spatial data on forest structure, composition, regeneration and deadwood are required for informed assessment of forest condition and subsequent management decisions. Here, we estimate 27 forest metrics from small-footprint full-waveform airborne laser scanning (ALS) data using a random forest (RF) and automated variable selection (Boruta) approach. Modelling was conducted using leaf-off (April) and leaf-on (July) ALS data, both separately and combined. Field data from semi-natural deciduous and managed conifer plantation forests were used to generate the RF models. Based on NRMSE and NBias, overall model accuracies were good, with only two of the best 27 models having an NRMSE > 30% and/or NBias > 15% (Standing deadwood decay class and Number of sapling species). With the exception of the Simpson index of diversity for native trees, both NRMSE and NBias varied by less than ±4.5% points between leaf-on only, leaf-off only and combined leaf-on/leaf-off models per forest metric. However, whilst model performance was similar between ALS datasets, model composition was often very dissimilar in terms of input variables. RF models using leaf-on data showed a dominance of height variables, whilst leaf-off models had a dominance of width variables, reiterating that leaf-on and leaf-off ALS datasets capture different aspects of the forest and that structure and composition across the full vertical profile are highly inter-connected and therefore can be predicted equally well in different ways. A subset of 17 forest metrics was subsequently used to assess favourable conservation status (FCS), as a measure of forest condition. The most accurate RF models relevant to the 17 FCS indicator metrics were used to predict each forest metric across the field site and thresholds defining favourable conditions were applied. Binomial logistic regression was implemented to evaluate predicative accuracy probability relative to the thresholds, which varied from 0.73–0.98 area under the curve (AUC), where 11 of 17 metrics were >0.8. This enabled an index of forest condition (FCS) based on structure, composition, regeneration and deadwood to be mapped across the field site with reasonable certainty. The FCS map closely and consistently corresponded to forest types and stand boundaries, indicating that ALS data offer a feasible approach for forest condition mapping and monitoring to advance forest ecological understanding and improve conservation efforts.

A Method for Quantifying Understory Leaf Area Index in a Temperate Forest through Combining Small Footprint Full-Waveform and Point Cloud LiDAR Data

Understory vegetation plays an important role in the structure and function of forest ecosystems. Light detection and ranging (LiDAR) can provide understory information in the form of either point cloud or full-waveform data. Point cloud data have a remarkable ability to represent the three-dimensional structures of vegetation, while full-waveform data contain more detailed information on the interactions between laser pulses and vegetation; both types have been widely used to estimate various forest canopy structural parameters, including leaf area index (LAI). Here, we present a new method for quantifying understory LAI in a temperate forest by combining the advantages of both types of LiDAR data. To achieve this, we first estimated the vertical distribution of the gap probability using point cloud data to automatically determine the height boundary between overstory and understory vegetation at the plot level. We then deconvolved the full-waveform data to remove the blurring effect caused by the system pulse to restore the vertical resolution of the LiDAR system. Subsequently, we decomposed the deconvolved data and integrated the plot-level boundary height to differentiate the waveform components returned from the overstory, understory, and soil layers. Finally, we modified the basic LiDAR equations introducing understory leaf spectral information to quantify the understory LAI. Our results, which were validated against ground-based measurements, show that the new method produced a good estimation of the understory LAI with an R2 of 0.54 and a root-mean-square error (RMSE) of 0.21. Our study demonstrates that the understory LAI can be successfully quantified through the combined use of point cloud and full-waveform LiDAR data.

Decomposition of small-footprint full waveform LiDAR data based on generalized Gaussian model and grouping LM optimization

Full waveform airborne Light Detection And Ranging(LiDAR) data contains abundant information which may overcome some deficiencies of discrete LiDAR point cloud data provided by conventional LiDAR systems. Processing full waveform data to extract more information than coordinate values alone is of great significance for potential applications. The Levenberg–Marquardt (LM) algorithm is a traditional method used to estimate parameters of a Gaussian model when Gaussian decomposition of full waveform LiDAR data is performed. This paper employs the generalized Gaussian mixture function to fit a waveform, and proposes using the grouping LM algorithm to optimize the parameters of the function. It is shown that the grouping LM algorithm overcomes the common drawbacks which arise from the conventional LM for parameter optimization, such as the final results being influenced by the initial parameters, possible algorithm interruption caused by non-numerical elements that occurred in the Jacobian matrix, etc. The precision of the point cloud generated by the grouping LM is evaluated by comparing it with those provided by the LiDAR system and those generated by the conventional LM. Results from both simulation and real data show that the proposed algorithm can generate a higher-quality point cloud, in terms of point density and precision, and can extract other information, such as echo location, pulse width, etc., more precisely as well.

Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests

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.

Effective LAI and CHP of a Single Tree From Small-Footprint Full-Waveform LiDAR

This letter has tested the canopy height profile (CHP) methodology as a way of effective leaf area index (LAIe) and vertical vegetation profile retrieval at a single-tree level. Waveform and discrete airborne LiDAR data from six swaths, as well as from the combined data of six swaths, were used to extract the LAIe of a single live Callitris glaucophylla tree. LAIe was extracted from raw waveform as an intermediate step in the CHP methodology, with two different vegetation-ground reflectance ratios. Discrete point LAIe estimates were derived from the gap probability using the following: 1) single ground returns and 2) all ground returns. LiDAR LAIe retrievals were subsequently compared to hemispherical photography estimates, yielding mean values within ±7% of the latter, depending on the method used. The CHP of a single dead Callitris glaucophylla tree, representing the distribution of vegetation material, was verified with a field profile manually reconstructed from convergent photographs taken with a fixed-focal-length camera. A binwise comparison of the two profiles showed very high correlation between the data reaching R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.86 for the CHP from combined swaths. Using a study-area-adjusted reflectance ratio improved the correlation between the profiles, but only marginally in comparison to using an arbitrary ratio of 0.5 for the laser wavelength of 1550 nm.

Stepwise decomposition and relative radiometric normalization for small footprint LiDAR waveform

As an advanced generation instrument of earth observation, small footprint full waveform light detection and ranging (LiDAR) technology has been widely used in the past few years. Decomposition and radiative correction is an important step in waveform data processing, it influences the accuracy of both information extraction and further applications. Based on a stepwise strategy, this study adopts Gaussian mixture model to approximate the LiDAR waveform. In addition to waveform decomposition, a relative correction model is proposed in this paper, the model considers the transmit pulses as well as the different of the travel path for implementing LiDAR waveform relative correction. Validation of the stepwise decomposition and relative correction model are carried out on LiDAR waveform acquired over Zhangye, China. The results indicate that stepwise decomposition identified the number of peaks in LiDAR waveforms, center position and width of each peak well. The relative radiometric correction also improves the similarity of waveforms which acquired at the same target.