Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales

Abstract

Abstract:Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R 2 of 0.76 and RMSE of 125 g/m 2 for shrub biomass and a pseudo R 2 of 0.74 and RMSE of 141 g/m 2 for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77-79% of the variance, with RMSE ranging from 120 to 129 g/m 2 for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem.

Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data

“…Vertical forest structure has been estimated with ALS data for several applications, such as forest inventory [16][17][18], forest structural heterogeneity [19][20][21][22], fuel type mapping [23,24] fuel modelling [23][24][25][26] or tree damage detection after natural disasters [27][28][29] for several height strata. However, few studies have focused on shrub biomass characterization with ALS data [30][31][32][33]. Some studies have used low density ALS data to estimate forest biomass [25,[34][35][36][37][38], but little research has been performed including shrub vegetation because of the inherent difficulty in the estimation related to its low height and uniform surface [30].…”

“…Besides, when shrub and tree vegetation cover is high [43] and density of ALS data is low, the accuracy of digital elevation models (DEM) used to normalize return heights decreases [30]. The performed studies use an approach that combines ALS data and harvesting field measurements for biomass estimation [30,33]. In this sense, the lack of more studies to characterize shrub vegetation might have been associated with the necessary destructive sampling to generate forest structure equations, the assumption of simple geometric shapes [44,45], and the additional difficulty to estimate biomass at a regional scale using low-density ALS data.…”

“…In this sense, LASSO selection and the varimax rotation for Principal component analysis (PCA) selection are proposed as novel selection processes for biomass estimation. Some studies have compared different regression models to estimate forest parameters using high point cloud density [16,34,[50][51][52], but only Li et al [33] have compared two regression models to estimate shrub biomass.The main objective of this study is to estimate the total biomass (TB) in heterogeneous Pinus halepensis Miller (hereinafter Aleppo pine) forest stands located in the Aragón Region (Spain). In this manuscript, TB refers to the dry weight of the plant material from trees, including roots, stems, bark, branches, and leaves from the ground to the apex, as well as the aboveground plant material from shrubs.…”

“…In this sense, LASSO selection and the varimax rotation for Principal component analysis (PCA) selection are proposed as novel selection processes for biomass estimation. Some studies have compared different regression models to estimate forest parameters using high point cloud density [16,34,[50][51][52], but only Li et al [33] have compared two regression models to estimate shrub biomass.…”

Appling the One-Class Classification Method of Maxent to Detect an Invasive Plant Spartina alterniflora with Time-Series Analysis

“…The output is the class voted by most trees [91]. The input data selected for training each of the trees are in-bag observations, and the remaining are Out-Of-Bag (OOB) observations used for estimating OOB errors [38,91]. The prediction accuracy of random forests models is evaluated by the OOB error.…”