Comparison of forest structure estimates using discrete and full-waveform LiDAR metrics

Introduction Forest structure variables provide important information for inventories, carbon flux and energy analyses, and fuel properties characterization for wildfire prediction and control. In this study, we compare the accuracy of the models obtained for eight different forest structure and canopy fuel variables based on discrete metrics on one side, and full-waveform metrics on the other. Additionally, we compare the models obtained considering two vegetation strata: Douglas-fir, and mixed forest. Data and methods Data were acquired on the framework of the Panther Creek Cooperative Research Project (Flewelling and McFadden, 2011), on 15 July 2010 by Watershed Sciences, Inc. using a Leica ALS60 system, obtaining a density of â‰¥ 8 points/m2. The Panther Creek study area covers 2258 ha and is located on the coastal mountain range in Yamhill County, Oregon. Douglas-fir (Pseudotsuga menziesii) is the dominant species, completed by other conifers and occasional deciduous species. Field measurements were collected on 84 circular plots (16 m radius), from which eight plot-level parameters were computed following standard methods: aboveground biomass (AGB), basal area (BA), quadratic mean diameter (QMD), volume (V), canopy height (CH), canopy base height (CBH), canopy bulk density (CBD) and canopy fuel load (CFL). All 84 plots constituted the mixed forest or non-discriminated strata, while the Douglas-fir strata was composed of a set of 49 plots with more than 70% of trees of this species. A digital terrain model (DTM) was provided by Watershed Sciences, Inc. with 0.19 m RMSE, and used to normalize the point cloud with respect to the terrain and to obtain the canopy height model (CHM). Discrete LiDAR metrics were obtained from the normalized point cloud (standard statistics and percentiles), the density profiles, the CHM, and intensity values. Two types of full-waveform LiDAR metrics were computed, those based on the height and intensity retrieved from the local maxima peak of the waveforms, consisting on statistics from the point cloud, CHM and intensity; and those based on the waveform modelling after partitioning the aboveground space in 0.25 A— 0.25 A— 0.30 m voxels and assigning to each voxel the maximum waveform amplitude value registered within it (Hermosilla et al., 2014a). These composite waveform metrics were derived from the height of median energy (HOME), waveform distance, height to median ratio, number of peaks, outer canopy roughness, vertical distribution ratio and others (Duong, 2010). All the aforementioned metrics are described in Hermosilla et al. (2014b). Akaike Information Criterion was used to pre-select the variables, then stepwise linear regression was applied to generate the models of the dependent variables, restricting to a maximum of three the independent variables to be included in each model. Models were evaluatedusing all the plots by cross-validation, from which the adjusted coefficient of determination (r2), RMSE and nRMSE (RMSE divided by the range of values) were computed (Table 1). Results and discussion Table 1 shows the results of the assessment of the prediction models for the eight forest structure variables considered in the study. The models considering only Douglas-fir plots are always more accurate than those including all plots with different species and mixed vegetation, as expected. The results point to the convenience of creating specific prediction models for different strata when possible. Full-waveform derived estimates result in higher r2 than discrete based metrics, and slightly lower RMSE values. The model improvement is more prominent using stratified samples, as in the prediction of variables for Douglas-fir. Therefore, the effect of using full-waveform metrics with respect to LiDAR discrete metrics for the generation of the forest structure and canopy fuel variable prediction models increases as the variability of species, mixing, or complexity decreases. Acknowledgements The authors thank the Panther Creek Remote Sensing and Research cooperative program for making available the data used in this research, in particular to George McFadden from Bureau of Land Management, and Jim Flewelling from Seattle Biometrics. Table 1. Comparative accuracy of the models obtained for two strata, Douglas-fir (n=49) and mixed forest (n=84), and using discrete or full-waveform LiDAR metrics. (r2 values refer to adjusted r2). Variables (unit) Mixed Forest Only Douglas-Fir Discrete Full-waveform Discrete Full-waveform r2 RMSE nRMSE r2 RMSE nRMSE r2 RMSE nRMSE r2 RMSE nRMSE AGB (t/ha) 0.78 79.97 0.09 0.84 79.04 0.09 0.80 86.05 0.10 0.86 84.43 0.09 BA (m2/ha) 0.75 11.49 0.11 0.76 10.89 0.11 0.79 11.11 0.11 0.84 9.51 0.09 QMD (m) 0.73 0.06 0.10 0.82 0.056 0.09 0.80 0.06 0.09 0.89 0.05 0.08 V (m3) 0.68 92.73 0.11 0.73 86.37 0.10 0.74 93.68 0.11 0.85 71.03 0.10 CH (m) 0.63 5.08 0.12 0.79 4.00 0.09 0.77 4.48 0.10 0.84 3.82 0.10 CBH (m) 0.64 3.56 0.10 0.78 3.34 0.09 0.76 3.38 0.09 0.85 3.11 0.09 CBD (kg/m3) 0.63 0.05 0.12 0.67 0.05 0.13 0.65 0.05 0.12 0.69 0.05 0.15 CFL (t/ha) 0.77 11.26 0.11 0.79 10.70 0.10 0.79 11.37 0.11 0.84 9.71 0.09