Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m²), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m² and 2300 m²) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R², Bias, and residual charts. The basal area values ranged from 7.40 m² ha⁻¹ (C1380) to 8.14 m² ha⁻¹ 281 (C2300), about −5.9% less than the real value (8.65 m² ha⁻¹). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m³) to the real total value (683.88 m³) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m² is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time.

Biomass models are useful for several purposes, especially for quantifying carbon stocks and dynamics in forests. Selecting appropriate equations from a fitted model is a process which can involves several criteria, some widely used and others used to a lesser extent. This study analyzes six selection criteria for models fitted to six sets of individual biomass collected from woody indigenous species of the Tropical Atlantic Rain Forest in Brazil. Six models were examined and the respective fitted equations evaluated by the residual sum of squares, adjusted coefficient of determination, absolute and relative estimates of the standard error of estimate, and Akaike and Schwartz (Bayesian) information criteria. The aim of this study was to analyze the numeric behavior of these model selection criteria and discuss the ease of interpretation of them. The importance of residual analysis in model selection is stressed. The adjusted coefficient of determination ( $$ R^{2}_{adj.} $$ R a d j . 2 ) and the standard error of estimate in percentage (Syx%) are relative model selection criteria and are not affected by sample size and scale of the response variable. The sum of squared residuals (SSR), the absolute standard error of estimate (Syx), the Akaike information criterion and the Schwartz information criterion, in turn, depend on these quantities. The best fit model was always the same within a given data set regardless the model selection criteria considered (except for SSR in two cases), indicating they tend to converge to a common result. However, such criteria are not always closely related across different data sets. General model selection criteria are indicative of the average goodness of fit, but do not capture bias and outlier effects. Graphical residual analysis is a useful tool to this detection and must always be used in model selection. It is concluded that the criteria for model selection tend to lead to a common result, regardless their mathematical formulation and statistical significance. Relative measures of goodness of fitting are easier to interpret than the absolute ones. Careful graphical residual analysis must always be used to confirm the performance of the models.

The traditional method used to estimate tree biomass is allometry. In this method, models are tested and equations fitted by regression usually applying ordinary least squares, though other analogous methods are also used for this purpose. Due to the nature of tree biomass data, the assumptions of regression are not always accomplished, bringing uncertainties to the inferences. This article demonstrates that the Data Mining (DM) technique can be used as an alternative to traditional regression approach to estimate tree biomass in the Atlantic Forest, providing better results than allometry, and demonstrating simplicity, versatility and flexibility to apply to a wide range of conditions.

Oil palm (Elaeis guineensis Jacq.) is a major raw material for biofuel and food industries in the world. In Brazil, cultivation of this species has evolved much in recent years, but basic information on its growth in the country is still scarce. This study analyzes the biomass and carbon storage in plants selected in three stands located in southern state of Bahia, northeastern Brazil, considering the full rotation cycle. Plants aging 3 to 36 years were cut and measured at biometric variables: Diameter at 50 cm from the ground level, crown diameter, stipe length and total height. Relationships among the biometric variables and the stipe, foliage, root, and total biomasses were analyzed. The Chapman & Richards model was fitted to the total biomass and carbon as a function of age. All the linear correlations between variables were significant at 95% probability. Total height and stipe length were more strongly correlated with age than with the diameter at 50 cm and crown diameter. The total biomass was highly correlated with the stipe variables and age. The percentage participation of stipe and total biomasses increases with age unlike the biomass foliage. The proportion of roots does not change with age. The total dry biomass and carbon stocks at the age of 25 years were estimated at ca. 90 and 35 t.ha-1, respectively. It was concluded that oil palm, because of its rapid growth and due to the fact that it is a permanent culture, is able to stock a high amount of biomass and carbon per unit area. If implemented in appropriate places, oil palm cannot be considered a carbon debt crop and represent an important alternative to regional socioeconomics. Key words: Growth, oil palm, biomass expansion factor, biomass partitioning, biometric relationships.

Necromass play a significant ecological function, including the deposition of organic matter, which is essential for carbon cycling in forest ecosystems. Despite the devoted attention to the role of forest necromass into the global carbon cycle, international organizations such as IPCC and FAO have reported the lack of compatible data concerning the carbon stocks in forest necromass among different regions. This paper reviews the terminologies and sampling methods regarding the quantification of carbon stock in forest necromass. Volume, biomass and carbon content determination methods were examined into the literature and then notably in Brazil. We address the problems associated with sampling methods and spatial distribution in related studies. Conflict in nomenclatures, components and diameter threshold confound the comparison of regional results. Meanwhile, we present a standard terminology for similar studies. Coarse woody and non-woody (litter) necromass are frequently computed, although fine woody necromass stands ignored in most studies and reports. Finally, we provide recommendations on specific sampling and measurement methods, taking into account necromass size and spatial pattern for both necromass and litter.