Modeling and mapping basal area of Pinus taeda L. plantation using airborne LiDAR data

Abstract

Basal area (BA) is a good predictor of timber stand volume and forest growth. This study developed predictive models using field and airborne LiDAR (Light Detection and Ranging) data for estimation of basal area in Pinus taeda plantation in south Brazil. In the field, BA was collected from conventional forest inventory plots. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil.

Integration of field sampling and LiDAR data in forest inventories: comparison of area-based approach and (lognormal) universal kriging

“…FMU 4 presents the greater amount of variance explained by autocorrelation for the analyzed variables, possibly due to the lack of silvicultural treatments in this FMU covering high altitude protection areas, where variability in forest structure is due to changes in the ecological conditions and the natural dynamics, in contrast to the man-induced forest structure in the other managed FMUs, where group shelterwood is applied at forest compartment level, leading to a shorter spatial autocorrelation range. The Pearson coefficients from the cross-validation for N, BA, and QMD attained through ABA are in general within the range reported in other studies (from 0.38 to 0.67 for N (Silva et al, 2018;Hall et al 2005), from 0.78 to 0.93 for BA (Hall et al 2005;Reutebuch et al, 2005;Silva et al, 2017), or from 0.39 to 0.78 for QMD (Naesset 2002). However, in FMU 4, which covers the high elevation areas with a more complex forest structure (due to the natural disturbance regime), ABA showed poorer correlations.…”

Important Airborne Lidar Metrics of Canopy Structure for Estimating Snow Interception

“…The CrownMetrics function in the 'rLiDAR' package [46] was used to produce 11 structural canopy metrics (e.g., maximum height, mean height, etc.) for each sample tree.…”

“…The normalmixEM function from the 'mixtools' R package [47] was employed to calculate the crown base height from the vertical profiles of heights within the crown polygon. The chullLiDAR3D function in 'rLiDAR' [46] was used to produce 3-D convex hulls derived from ALS returns within the crown segments and calculate the crown surface area and volume. The 'alphashape3D' package [43] was utilized to calculate whole-tree volumes for three arbitrary α values (α = 0.25, 0.50, 0.75) spanning the α range (0 -1).…”

“…Removal of these values from the final model increased model fit (R 2 = 0.88; +0.22 compared to final model) and reduced model error (RMSE = 0.19 m 3 ; −0.33 m 3 compared to the final model) (Figure 8). These results imply that future research that applies our modeling approach to a larger area, particularly one with dense forest, may necessitate (1) calibration of individual tree detection algorithms that allow detection of individual trees from ATLS data (e.g., FindTreesCHM function in 'rLiDAR' package [46]), and (2) validation of how accurately these algorithms partition individual trees from clumps of trees (e.g., randomized visual inspection of ALS data or randomized field sampling). If there are limitations applying our modeling approach in dense forests due to challenges related to individual tree classification, or reduced canopy penetration by the laser beam (e.g., [63,64]), this may lend further credence to our suggestion that snow interception models include both canopy structural metrics and forest spacing metrics.…”

A novel smartphone-based activity recognition modeling method for tracked equipment in forest operations

“…Remotely sensed data may support activity recognition model development by providing descriptive information related to site and stand conditions within the work environment. Light detection and ranging (lidar) is used extensively in forest, range, and fire management to quantify vertical forest structure, succession, and other forest attributes, as well as terrain morphology [ 62 – 70 ]. Use of lidar to conduct inventories and to characterize geophysical and ecological conditions across temporal and spatial scales can reduce inventory costs and improve the resolution of data for land management agencies and the forest industry [ 71 ].…”