ABSTRACT Background: Process-based modeling is made to simulate the interactions of the soil-plant-atmosphere system providing information and estimates for forest management and services. Through bibliometric evaluation we identify the quantitative state of the art on the use of Process-Based Models (PBMs) applied in forestry studies, contextualizing research fronts, and analyzing the main topics and emerging themes. Data from the Web of Science and Scopus were used for the bibliometric survey of 533 scientific records. Results: We verified that (i) the number of publications related to the PBMs was boosted in the year 2000, and is growing; (ii) Forest Ecology and Management (74 publications) is the journal that most publishes on the subject; (iii) Annikki Mäkelä is the most productive researcher (20 publications) and most cited (h-index: 12); (iv) The USA (131 records) is the country with the highest number of research; (v) there are eight collaborative networks between the authors, three workings together, and five producing in isolation; (vi) the keywords are grouped into five clusters: (a) the effect of climate change on forests; (b) carbon studies; (c) physiology of trees; (d) silvicultural practices and; (e) analysis of model uncertainties. The separation of themes showed that the new search hotspots are the PBMs using of remote detection tools. Conclusion: The results presented can be a fundamental theoretical tool for understanding the trends and development perspectives of this research line in future studies.

ABSTRACT This paper aimed to estimate the basic density (DB) of the wood of Cerrado species using mixed linear models. For performing the DBH measurement, the sampling of 334 individuals was carried out. By keeping the Pilodyn apparatus in the DBH position, two measurements were made on opposite sides. Further, for determining DB, the trees were knocked down, followed by removal of five wood discs at different height of stem positions. For this purpose, two sets of modeling alternatives were proposed, which take into account with and without random effects, employing species as a random effect grouping variable. Thus, it was elucidated that, for the estimation of DB, the mixed model that considered the random effects performed better as compared to the alternative model without random effects. The inclusion of random effects leads to the estimation of DB with high accuracy.

ABSTRACT The objective of this study was to evaluate the use of the MSI Sentinel-2 and SRTM data to estimate the volume of wood in a Semidecidual Seasonal Forest. Regression equations were fitted based on the remote sensing data, taking into consideration the individual bands and vegetation index of the MSI, elevation values and their derivatives obtained from the SRTM mission and the combination of the data drawn from the MSI and SRTM. RMSE and graphic analysis of residues were used to assess the accuracy of the fitted equations. The best model revealed values of 0.6508 and RMSE of 20.41% in the fit, and of 0.5680 and RMSE of 26.61% in the validation, using the combined MSI and SRTM data as predictors. The volume estimation using spectral data showed satisfactory results, highlighting the importance of topography in the prediction of the volume of wood for the area under investigation.