Search Results (8)

In the context of global climate change, eucalyptus stands in the planted forest sector have become a viable alternative for reducing greenhouse gas (GHG) emissions, in addition to presenting great potential for the carbon (C) stock. Thus, the objective of this study was to quantify C stocks in different eucalyptus compartments, in addition to evaluating three mathematical models at the individual tree level. We evaluated four areas of eucalyptus stands located in the Federal District, Brazil. The data were collected from the forest inventory and rigorous cubing procedures using the following statistical models: Spurr, Schumacher–Hall, and adapted Schumacher–Hall. The highest Pearson’s linear modification coefficient, lowest root means square error percentage (RMSE%), and lowest Akaike information criterion (AIC) were used to select the best model. The C content and stock varied between the compartments and areas studied owing to age and, above all, genetic differences. Clone I224 had the highest carbon concentration per acre at 233.35 Mg ha⁻¹ and carbon difference per compartment. The adapted Schumacher–Hall was the best model. It included data on biometric factors, such as the diameter at breast height, height, and age. The contribution of eucalyptus plantations to carbon sequestration is fundamental to socioenvironmental enhancement. Full article

Pinus occidentalis (Swartz) is the primary timber species in the Dominican Republic (DR). Despite its economic importance, studies conducted on this species are scarce, making it difficult to estimate current inventory levels. This study aims to enhance the accuracy of estimating the total bole volume of P. occidentalis in different ecological zones (EZs) within La Sierra, evaluating and comparing two established volume equations—combined variable (CV) and Schumacher and Hall (S&H) across nine modeling variants. An indicator variables analysis determined the necessity of distinct equations for two EZs. Fitting included both linear and nonlinear models. Our comprehensive statistical analysis included goodness-of-fit metrics to evaluate each model variant’s performance rigorously. The second modeling variant (SH02) for the SH equation was most effective in the Dry Ecological Zone, showing superior performance in both the fitting and validation phases. Similarly, the third modeling variant (SH03) for the SH equation emerged as the best fit for the Combined Intermediate and Humid Ecological Zones, achieving the lowest overall ranking sum among tested variants. SH02 and SH03 provide reliable and precise volume estimations, allowing for the optimization of forestry management practices for P. occidentalis trees. The SH models outperformed the CV model variants’ consistency in parameter estimation. This tailored approach ensures more accurate volume predictions, which is crucial for sustainable management and conservation efforts. Full article

Biometric systems are the basis of forest management and consist of a set of equations that describe the relationships between forest attributes and dendrometric variables. A systematic review of the state of the art of biometric systems in Mexico was carried out by a Mexican consortium (10 researchers), covering a period of 50 years ca (1970–2019), using the main scientific literature delivered by a systematic search (WoS, Scopus, Scielo, Redalyc) and a targeted search (theses, technical reports, etc.). A single selection criterion was established for the inclusion of information in the analysis: the document had to present at least one of the equations of interest. We found 376 documents containing 2524 equations for volume (69%), diameter (11%), height (9%) and site index (11%). These equations were developed for forest species mainly from temperate regions (88%), such as pine (66%) and oak (9%). Consequently, the Mexican states with the highest number of equations were Durango (28%), Chihuahua (17%), Hidalgo (13%) and Oaxaca (8%). Although large, the number of equations identified concentrated on a relatively small number of models: Schumacher & Hall and Fang et al. for volume; Chapman-Richards and Schumacher for site index and diameter; and Chapman-Richards and the allometric equation for height. An analysis of model fit, measured through R², showed that, on average, the volume, diameter and site index models show high fit (R² = 0.96), although this pattern was more consistent in the volume models. Publication bias was evaluated by means of a funnel plot analysis, with no apparent bias identified. A limitation of our study is that the information obtained is not updated to the present year; however, the 50-year trends allow us to assume that no recent significant changes in the patterns exist. Finally, we highlight the need to assess the predictive ability of the models to ensure accurate estimates to support better forest management decisions. Full article

Accurate estimation of aboveground carbon stock for individual trees is important for evaluating forest carbon sequestration potential and maintaining ecosystem carbon balance. Airborne light detection and ranging (LiDAR) data has been widely used to estimate tree-level carbon stock. However, few studies have explored the potential of combining LiDAR and hyperspectral data to estimate tree-level carbon stock. The objective of this study is to explore the potential of integrating unmanned aerial vehicle (UAV) LiDAR with hyperspectral data for tree-level aboveground carbon stock estimation. To achieve this goal, we first delineated individual trees by a CHM-based watershed segmentation algorithm. We then extracted structural and spectral features from UAV LiDAR and hyperspectral data respectively. Then, Pearson correlation analysis was conducted to assess the correlation between LiDAR features, hyperspectral features, and tree-level carbon stock, based on which, features were selected for model development. Finally, we developed tree-level carbon stock estimation models based on the Schumacher–Hall formula and stepwise multiple regression. Results showed that both LiDAR and hyperspectral features were strongly correlated to tree-level carbon stock. Both tree height (H, r = 0.75) and Green index (GI, r = 0.83) had the highest correlation coefficients with tree-level carbon stock in LiDAR and hyperspectral features, respectively. The best model using LiDAR features alone includes the metrics of H, the 10th height percentile of points (PH10), and mean height of points (H_mean), and can explain 74% of the variations in tree-level carbon stock. Similarly, the best model using hyperspectral data includes GI and modified normalized differential vegetation index (mNDVI), and has similar explanatory power (r² = 0.75). The model that integrates predictors, namely, GI and the 95th height percentile of points (PH95) from hyperspectral and LiDAR data, substantially improves the explanatory power (r² = 0.89). These results indicated that while either LiDAR data or hyperspectral data alone can estimate tree-level carbon stock with reasonable accuracy, combining LiDAR and hyperspectral features can substantially improve the explanatory power of the model. Such results suggested that tree-level carbon stock estimation can greatly benefit from the complementary nature of LiDAR-detected structural characteristics and hyperspectral-captured spectral information of vegetation. Full article

The prediction of biological processes, which involve growth and plant development, is possible via the adjustment of mathematical models. In forest areas, these models assist in management practices, silviculture, harvesting, and soil fertility. Diameter, basal area, and height are predictors of volume and biomass estimates in forest stands. This study utilized different non-linear models for estimating biomass and nutrient values in the aerial biomass and roots of an unmanaged eucalypt stand in Cerrado dystrophic soil. It was hypothesized that the models would estimate the nutrients of the aboveground biomass and roots after meeting the selection and validation criteria. By statistical analysis of the parameters and subsequent validation, the Schumacher–Hall model was presented to be the best fit for biomass and nutrients. This result confirmed the ability of different variables, including diameter, basal area, and height, to be predicted. Estimating the nutrient values in the aboveground biomass and roots allowed a better understanding of the quality of the vegetal residues that remained in the soil. For dystrophic soils, which occur in the Cerrado, these estimates become even more relevant. Full article

Quantifying biomass is important for determining the carbon stores in land ecosystems. The objective of this study was to predict aboveground biomass (AGB) of Agave lechuguilla Torr., in the states of Coahuila (Coah), San Luis Potosí (SLP) and Zacatecas (Zac), Mexico. To quantify AGB, we applied the direct method, selecting and harvesting representative plants from 32 sampling sites. To predict AGB, the potential and the Schumacher–Hall equations were tested using the ordinary least squares method using the average crown diameter (Cd) and total plant height (Ht) as predictors. Selection of the best model was based on coefficient of determination (R² adj.), standard error (S_xy), and the Akaike information criterion (AIC). Studentized residues, atypical observations, influential data, normality, variance homogeneity, and independence of errors were also analyzed. To validate the models, the statistic prediction error sum of squares (PRESS) was used. Moreover, dummy variables were included to define the existence of a global model. A total of 533 A. lechuguilla plants were sampled. The highest AGB was 8.17 kg; the plant heights varied from 3.50 cm to 118.00 cm. The Schumacher–Hall equation had the best statistics (R² adj. = 0.77, Sxy = 0.418, PRESS = 102.25, AIC = 632.2), but the dummy variables revealed different populations of this species, that is, an equation for each state. Satisfying the regression model assumptions assures that the predictions of A. lechuguilla AGB are robust and efficient, and thus able to quantify carbon reserves of the arid and semiarid regions of Mexico. Full article

Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collected in Eucalyptus urophylla x Eucalyptus grandis even-aged forest stands located in the mountainous region of the Rio Doce Valley, southeastern Brazil. We tested five methods to estimate volume from LiDAR-derived metrics using ABA: Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and linear and Gompertz models. LiDAR-derived canopy metrics were selected using the Recursive Feature Elimination algorithm and Spearman’s correlation, for nonparametric and parametric methods, respectively. For the ITD, we tested three ITD methods: two local maxima filters and the watershed method. All methods were tested adding our proposed procedure of Tree Buffer Exclusion (TBE), resulting in 35 possibilities for treetop detection. Stem volume for this approach was estimated using the Schumacher and Hall model. Estimated volumes in both ABA and ITD approaches were compared to the field observed values using the F-test. Overall, the ABA with ANN was found to be better for stand volume estimation ( $r_{y\widehat{y}}$ = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision ( $r_{y\widehat{y}}$ = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (<25%). Stem volume maps also differed between the approaches; ITD represented the stand variability better. In addition, we discuss the importance of LiDAR metrics as input variables for stem volume estimation methods and the possible issues related to the ABA and ITD performance. Full article

The Eschweilera genus has great ecological and economic importance due to its wide abundance in the Amazon basin. One potential use for the Eschweilera genus is in forest management, where just a few trees are removed per hectare. In order to improve the forest management in the Amazon, this study assessed two critical issues: volume equations fitted for a single genus and the development of a non-destructive method using climbing techniques. The equipment used to measure the sample trees included: climbing rope, ascenders, descenders, and carabiners. To carry out the objectives of this study, 64 trees with diameter at breast height (DBH) ≥ 10 cm were selected and measured in ZF-2 Tropical Forestry Station near the city of Manaus, Brazil. Four single input models with DBH and four dual input models with DBH and merchantable height (H) were tested. The Husch model (V = a × DBH^b) presented the best performance (R² = 0.97). This model does not require the merchantable height, which is an important advantage, because of the difficulty in measuring this variable in tropical forests. When the merchantable height data are collected using accurate methods, the Schumacher and Hall model (V = a × DBH^b × H^c) is the most appropriated. Tree climbing techniques with the use of ropes, as a non-destructive method, is a good alternative to measure the merchantable height, the diameter along the stem, and also estimate the tree volume (m³) of the Eschweilera genus in the Amazon basin. Full article