In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scales.

The thermodynamic characterization of the soil organic matter could be achieved by different enthalpic models little explored for soil. This paper compares two of them for calculating the enthalpy change, the Gibbs energy change and the entropy change of the soil organic matter combustion reaction, by simultaneous differential scanning calorimetry and thermogravimetry. Soil samples were collected in beech and oak forests from different geographical areas, and at different depths to represent theL/Fhorizon and the mineral soil, at each sampling site. The thermodynamic state variables were calculated using two different enthalpic models, to examine how they differed in relation to different types of SOM and different forest ecosystems. Both models yielded thermodynamic variables, which although closely and significantly correlated, were statistically significantly different. All the thermodynamic variables depended on the different forest types and the different nature of the soil organic matter under study. Results allowed to discern which of the models applied better to the SOM combustion reaction designed.

Terrestrial Laser Scanning (TLS) enables rapid, automatic, and detailed 3D representation of surfaces with an easily handled scanner device. TLS, therefore, shows great potential for use in Forest Inventories (FIs). However, the lack of well-established algorithms for TLS data processing hampers operational use of the scanner for FI purposes. Here, we present FORTLS, which is an R package specifically developed to automate TLS point cloud data processing for forestry purposes. The FORTLS package enables (i) detection of trees and estimation of their diameter at breast height (dbh), (ii) estimation of some stand variables (e.g., density, basal area, mean, and dominant height), (iii) computation of metrics related to important tree attributes estimated in FIs at stand level, and (iv) optimization of plot design for combining TLS data and field measured data. FORTLS can be used with single-scan TLS data, thus, improving data acquisition and shortening the processing time as well as increasing sample size in a cost-efficient manner. The package also includes several features for correcting occlusion problems in order to produce improved estimates of stand variables. These features of the FORTLS package will enable the operational use of TLS in FIs, in combination with inference techniques derived from model-based and model-assisted approaches.

Plot design is one of the key elements that must be defined in forest inventories. This is particularly challenging in large-scale inventories, as both forest variability and measurement uncertainty generally increase with scale. Nonetheless, plot design is usually based exclusively on targeting (i) an average minimum number of trees to reduce the random measurement errors, and (ii) an average maximum number of trees to optimize the efficiency of fieldwork, while ignoring the uncertainty in tree position measurement. The present study focused on the effect of horizontal distance measurement errors on stand level estimates in large-scale forest inventories including circular fixed-area plots. The error was characterized in forests with non-regular (natural stands) and regular (plantations) patterns of tree positions. The effect on stand volume, stand basal area, and stand density estimates was simulated using Monte Carlo techniques. Different horizontal distance measurement uncertainty was observed in natural stands and plantations. However, similar effects were observed in the three stand variables estimates for both tree spacing patterns, with stabilization of errors for radii between 12 and 20 m. Doubling or halving the error uncertainty yielded similar results. The proposed method can help with selecting plot size in forest inventories based on circular fixed-area plots.