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Quantitative data obtained from native forests is costly and time-consuming. Thus, alternative measurement methods need to be developed to provide reliable information, especially in Atlantic Rain Forests. In this study we evaluated the hypothesis that the combination of an Airborne Laser Scanner (ALS) and an Unmanned Aerial Vehicle (UAV) can provide accurate quantitative information on tree height, volume, and aboveground biomass of the Araucaria angustifolia species. The study was carried out in Atlantic Rain forest fragments in southern Brazil. We tested and evaluated 3 digital canopy height model (CHM) scenarios: 1) CHM derived from ALS models; 2) CHM derived from UAV models; and 3) CHM from a combined ALS digital terrain model and UAV digital surface model. The height value at each tree coordinate was extracted from the pixel in the three evaluated scenarios and compared with the field measured values. ALS and UAV+ALS obtained RMSE% of 6.38 and 12.82 for height estimates, while UAV was 49.91%. Volume and aboveground biomass predictions are more accurate by ALS and UAV+ALS, while the UAV produced biased estimates. Since the ALS is currently used, periodic monitoring can be carried out by a combination of active (ALS) and passive (UAV) sensors.

Understanding how studies have been carried out in the region helps to understand the Amazon rainforest potential in mitigating climate change. In addition, evaluating scientific production is essential to verify the main methods and places researched. Thus, this study objective was to build an overview and identify the main gaps regarding research related to biomass and carbon stocks with the coverage limit of the Amazon rainforest. Therefore, an analysis of the publications indexed in the Scopus database was developed until 2020, performing a bibliometric analysis and a systematic and state-of-the-art review. Initially 2042 publications were obtained, of which 786 met the inclusion criteria. The first work indexed in the Scopus database related to the theme was published in 1982. Over time, it was possible to observe an increasing behavior in relation to the interest in the theme. Natural environments were the most researched and change in the land use and occupation of the Amazon Rainforest is still poorly evaluated. Brazil is the country with the highest number of studies, followed by Peru and Colombia. Guyana and Suriname appear as possible scientific gaps and potential environments to be studied. Studies preferentially explore the biomass carbon, with the soil being little evaluated when compared to the total amount of papers found. When observing only the biomass studies, the aboveground biomass is the most evaluated, while the roots and necromass are little studied. The main variables used in biomass equations were diameter at 1.3 m above ground and total tree height. The biomass to carbon conversion factor has been widely used, but it can generate unreliable results. It is recommended to carry out local assessments of the carbon content, especially using the dry combustion method, which generates less waste, with more precise results and shorter execution time of the analyses. Such assessments present values for the location that can avoid false or misinterpretations of the biomass and carbon stocks in the Amazon Rainforest., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Cattle ranching is the primary land-use of deforested areas in the Brazilian Amazon. Deforestation precedes pasture establishment, implying tremendous amounts of greenhouse gas emissions caused by carbon stock losses. Despite several studies addressing carbon storage in forests, there is a lack of data regarding cultivated pastures. Hence, the estimation of greenhouse gas emissions associated with land-use change becomes uncertain. In this study, we assessed the carbon stock of cultivated pastures located in Rondônia, southwestern Brazilian Amazon. A total of 50 squared plots of 1 m² were randomly allocated in cattle ranching farms covered by Oxisols (Dystrophic Yellow and Dystrophic Red-Yellow Latosols). Carbon fraction ranged from 0.36 for belowground biomass to 0.45 gC.g-1 d.m. for aboveground biomass. The average total carbon stock was 5.17 MgC.ha-1, with non-significant differences when stratifying data by soil types. Considering data from the III Brazilian Inventory of Anthropogenic Emissions and Removals of Greenhouse Gases, our results suggested that land-use change from primary forests to cultivated pastures resulted in a loss of 192.54 MgC.ha-1, which corresponds to a net emission of 705.98 MgCO2eq.ha-1 to the atmosphere. This study provides valuable information to improve the Brazilian Inventory of Anthropogenic Emissions and Removals of Greenhouse Gases.

"Bracatingais" are common forest formations formed by bracatinga (Mimosa scabrella) and secondary species, which have replaced bracatinga over time; these forests are an important source of income for small farmers. The objective of this study was to model the growth and yield volume of firewood per unit of area and to evaluate the dynamics of the stock across the years. Data from 320 plots were used to fit 12 mathematical models separately addressing the data for bracatinga and secondary species and total species. The Clutter model presented better results for bracatinga (IA = 0.954 and Syx% = 8.54) and for total species (IA = 0.917 and Syx% = 11.56). The modified Clutter model was the best for the secondary species, with IA = 0.952 and Syx% = 25.08. The volumetric estimation of these equations was used to compare the estimated volume of the bracatinga with that of the secondary species, identifying the age as between 13 and 14 years when the volume of the bracatinga is supplanted by the volume of the secondary species. Furthermore, 8 years of age was ideal for clearcutting the bracatingais.

Background: 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., Results: The adjusted coefficient of determination ([Formula: see text]) 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., Conclusions: 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.

Background: We analyzed the dynamics of carbon (C) stocks and CO 2 removals by Brazilian forest plantations over the period 1990-2016. Data on the extent of forests compiled from various sources were used in the calculations. Productivities were simulated using species-specific growth and yield simulators for the main trees species planted in the country. Biomass expansion factors, root-to-shoot ratios, wood densities, and carbon fractions compiled from literature were applied. C stocks in necromass (deadwood and litter) and harvested wood products (HWP) were also included in the calculations., Results: Plantation forests stocked 231 Mt C in 1990 increasing to 612 Mt C in 2016 due to an increase in plantation area and higher productivity of the stands during the 26-year period. Eucalyptus contributed 58% of the C stock in 1990 and 71% in 2016 due to a remarkable increase in plantation area and productivity. Pinus reduced its proportion of the carbon storage due to its low growth in area, while the other species shared less than 6% of the C stocks during the period of study. Aboveground biomass, belowground biomass and necromass shared 71, 12, and 5% of the total C stocked in plantations in 2016, respectively. HWP stocked 76 Mt C in the period, which represents 12% of the total C stocked. Carbon dioxide removals by Brazilian forest plantations during the 26-year period totaled 1669 Gt CO 2- e., Conclusions: The carbon dioxide removed by Brazilian forest plantations over the 26 years represent almost the totality of the country´s emissions from the waste sector within the same period, or from the agriculture, forestry and other land use sector in 2016. We concluded that forest plantations play an important role in mitigating GHG (greenhouse gases) emissions in Brazil. This study is helpful to improve national reporting on plantation forests and their GHG sequestration potential, and to achieve Brazil's Nationally Determined Contribution and the Paris Agreement.

Taper functions and volume equations are essential for estimation of the individual volume, which have consolidated theory. On the other hand, mathematical innovation is dynamic, and may improve the forestry modeling. The objective was analyzing the accuracy of machine learning (ML) techniques in relation to a volumetric model and a taper function for acácia negra. We used cubing data, and fit equations with Schumacher and Hall volumetric model and with Hradetzky taper function, compared to the algorithms: k nearest neighbor (k-NN), Random Forest (RF) and Artificial Neural Networks (ANN) for estimation of total volume and diameter to the relative height. Models were ranked according to error statistics, as well as their dispersion was verified. Schumacher and Hall model and ANN showed the best results for volume estimation as function of dap and height. Machine learning methods were more accurate than the Hradetzky polynomial for tree form estimations. ML models have proven to be appropriate as an alternative to traditional modeling applications in forestry measurement, however, its application must be careful because fit-based overtraining is likely.

Floristic surveys and diversity indices are often applied to measure tree species diversity in mixed tropical forest remnants. However, these analyses are frequently limited to the overall results and do not allow to evaluate the spatial variability distributions of tree diversity, leading to develop additional tools. This study aimed to estimate the spatial variability of tree diversity and map their spatial patterns in a Brazilian mixed tropical forest conservation area. We used indices to measure the tree species diversity (dbh ≥ 10 cm) in 400 sampling units (25 m x 25 m) from a continuous forest inventory. Semivariograms were fitted to estimate spatial dependences and punctual kriging was applied to compose maps. Mean diversity values were constant in the continuous inventories, indicating a forest remnant in an advanced stage of ecological succession. On the other hand, tree diversity presented spatial patterns identified by geostatistics, in which the dynamics were composed of heterogeneous mosaics spatially influenced by tree species with different ecological features and densities, gap dynamics, advancement of forest succession, mortality, and Araucaria angustilofia's cohorts.

It is presented the theme additivity of biomass of tree components. To evaluate and discuss this context, experimental information collected in forests of Acacia mearnsii De Wild. was used. Equations for components (stem and crown) and total biomass were fitted by means of two procedures: 1) generalized nonlinear least squares and 2) weighted-nonlinear seemingly unrelated regressions. Analyzing the performance of the estimators, it can be concluded that the two tested procedures are equivalent. On the other hand, this conclusion differs when evaluated the consistency and efficiency of the estimators. Fitting equations for the components and for the total biomass by an independent way is not realistic, because from a biological point of view the estimates of biomass are inconsistent, i.e., are not additive. The biomass estimates of the components and of the total, resulting from equations adjusted by means of systems of equations, provided narrower confidence intervals in relation to the equations adjusted independently, and is therefore more efficient. The second procedure presents better biological properties and statistics to estimate allometric equations for biomass of the components and for the total when compared with the independent estimation, thus it should be the method to be used.

The spatial distribution of tropical tree species can affect the consistency of the estimators in commercial forest inventories, therefore, appropriate sampling procedures are required to survey species with different spatial patterns in the Amazon Forest. For this, the present study aims to evaluate the conventional sampling procedures and introduce the adaptive cluster sampling for volumetric inventories of Amazonian tree species, considering the hypotheses that the density, the spatial distribution and the zero-plots affect the consistency of the estimators, and that the adaptive cluster sampling allows to obtain more accurate volumetric estimation. We use data from a census carried out in Jamari National Forest, Brazil, where trees with diameters equal to or higher than 40 cm were measured in 1,355 plots. Species with different spatial patterns were selected and sampled with simple random sampling, systematic sampling, linear cluster sampling and adaptive cluster sampling, whereby the accuracy of the volumetric estimation and presence of zero-plots were evaluated. The sampling procedures applied to species were affected by the low density of trees and the large number of zero-plots, wherein the adaptive clusters allowed concentrating the sampling effort in plots with trees and, thus, agglutinating more representative samples to estimate the commercial volume.

The objective is to study the dynamics of photosynthetic radiation reaching the soil surface in stands of Acacia mearnsii De Wild and its influence on height growth in stands. This fact gives rise to the formulation of the following hypothesis for this study: "The reduction of the incidence of light inside the stand of black wattle will cause the inflection point in its height growth when this reaches 4 to 5 m in height, i.e. when the stand is between 2 and 3 years of age". The study was conducted in stands in the state of Rio Grande do Sul, Brazil, where diameters at breast height, total height and photosynthetically active radiation available at ground level were measured. The frequency tended to be more intense when the age of the stands increases. It was evident that a reduction of light incidence inside the forest occurred, caused by canopy closure. Consequently, closed canopy propitiated the competition of plants. This has affected the conditions for growth in diameter and height of this species, reason why it becomes possible to conceive the occurrence of an inflection point in the growth of these two variables, confirming the formulated hypothesis.

Background: 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., Results: Various DM approaches were examined regarding distance, number of neighbors and weighting, by using 180 trees coming from environmental restoration plantations in the Atlantic Forest biome. The best results were attained using the Chebishev distance, 1/d weighting and 5 neighbors. Increasing number of neighbors did not improve estimates. We also analyze the effect of the size of data set and number of variables in the results. The complete data set and the maximum number of predicting variables provided the best fitting. We compare DM to Schumacher-Hall model and the results showed a gain of up to 16.5% in reduction of the standard error of estimate., Conclusion: It was concluded that Data Mining can provide accurate estimates of tree biomass and can be successfully used for this purpose in environmental restoration plantations in the Atlantic Forest. This technique provides lower standard error of estimate than the Schumacher-Hall model and has the advantage of not requiring some statistical assumptions as do the regression models. Flexibility, versatility and simplicity are attributes of DM that corroborates its great potential for similar applications.

This article discusses the dynamics of a diameter distribution in stands of black wattle throughout its growth cycle using the Weibull probability density function. Moreover, the parameters of this distribution were related to environmental variables from meteorological data and surface soil horizon with the aim of finding a model for diameter distribution which their coefficients were related to the environmental variables. We found that the diameter distribution of the stand changes only slightly over time and that the estimators of the Weibull function are correlated with various environmental variables, with accumulated rainfall foremost among them. Thus, a model was obtained in which the estimators of the Weibull function are dependent on rainfall. Such a function can have important applications, such as in simulating growth potential in regions where historical growth data is lacking, as well as the behavior of the stand under different environmental conditions. The model can also be used to project growth in diameter, based on the rainfall affecting the forest over a certain time period.

Forests contribute to climate change mitigation by storing carbon in tree biomass. The amount of carbon stored in this carbon pool is estimated by using either allometric equations or biomass expansion factors. Both of the methods provide estimate of the carbon stock based on the biometric parameters of a model tree. This study calls attention to the potential advantages of the data mining technique known as instance-based classification, which is not used currently for this purpose. The analysis of the data on the carbon storage in 30 trees of Brazilian pine (Araucaria angustifolia) shows that the instance-based classification provides as relevant estimates as the conventional methods do. The coefficient of correlation between the estimated and measured values of carbon storage in tree biomass does not vary significantly with the choice of the method. The use of some other measures of method performance leads to the same result. In contrast to the convention methods the instance-based classification does not presume any specific form of the function relating carbon storage to the biometric parameters of the tree. Since the best form of such function is difficult to find, the instance-based classification could outperform the conventional methods in some cases, or simply get rid of the questions about the choice of the allometric equations.

The Biomass Expansion Factor (BEF) and the Root-to-Shoot Ratio (R) are variables used to quantify carbon stock in forests. They are often considered as constant or species/area specific values in most studies. This study aimed at showing tree size and age dependence upon BEF and R and proposed equations to improve forest biomass and carbon stock. Data from 70 sample Pinus spp. grown in southern Brazil trees in different diameter classes and ages were used to demonstrate the correlation between BEF and R, and forest inventory data, such as DBH, tree height and age. Total dry biomass, carbon stock and CO2 equivalent were simulated using the IPCC default values of BEF and R, corresponding average calculated from data used in this study, as well as the values estimated by regression equations. The mean values of BEF and R calculated in this study were 1.47 and 0.17, respectively. The relationship between BEF and R and the tree measurement variables were inversely related with negative exponential behavior. Simulations indicated that use of fixed values of BEF and R, either IPCC default or current average data, may lead to unreliable estimates of carbon stock inventories and CDM projects. It was concluded that accounting for the variations in BEF and R and using regression equations to relate them to DBH, tree height and age, is fundamental in obtaining reliable estimates of forest tree biomass, carbon sink and CO2 equivalent.