Spatial distribution of wood volume in Brazilian savannas

Abstract

Here we model and describe the wood volume of Cerrado Sensu Stricto, a highly heterogeneous vegetation type in the Savanna biome, in the state of Minas gerais, Brazil, integrating forest inventory data with spatial-environmental variables, multivariate regression, and regression kriging. Our study contributes to a better understanding of the factors that affect the spatial distribution of the wood volume of this vegetation type as well as allowing better representation of the spatial heterogeneity of this biome. Wood volume estimates were obtained through regression models using different environmental variables as independent variables. Using the best fitted model, spatial analysis of the residuals was carried out by selecting a semivariogram model for generating an ordinary kriging map, which in turn was used with the fitted regression model in the regression kriging technique. Seasonality of both temperature and precipitation, along with the density of deforestation, explained the variations of wood volume throughout Minas gerais. The spatial distribution of predicted wood volume of Cerrado Sensu Stricto in Minas gerais revealed the high variability of this variable (15.32 to 98.38 m 3 ha-1) and the decreasing gradient in the southeast-northwest direction.

Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil

“…This high variability is expected for the Caatinga dry vegetation because the ecophysiological conditions that promote tree trunk growth and shape are functionally related to specific environmental factors, such as water stress, temperature, soil type, and topography (Chaturvedi et al, 2013;Dexter et al, 2018;García-Cervigón et al, 2020;Mattos et al, 2015). Therefore, the combination of these factors can result in locations with high volume concentration while other areas may have a lower volumetric stock distribution (Reis et al, 2020;Silveira et al, 2019).…”

“…For the predictive validation of ordinary kriging, Standardized Mean (MS), Root Mean Standardized Error Square (RMSS), Mean Standard Error (ASE), Square Root of Mean Error (RMS) were calculated from crossvalidation, which provided accuracy of the estimates, based on the data set (Barni et al, 2016;Silveira et al, 2019). All computations and analyses were performed with the support of R's geoR package (Ribeiro Junior et al, 2001) and the final maps were generated in ArcGIS version 10.5 (ESRI, 2019).…”

“…Geostatistical modeling is an alternative mainly because it can predict a variable of interest in unsampled locations and map its spatial distribution using interpolated information from a sampled area (Seidel and Oliveira, 2016). As the volumetric stock of seasonally dry vegetation across the Caatinga is correlated with environmental covariates (Silveira et al, 2019), this study aims to quantify and spatialize volumetric wood stock for Caatinga vegetation in Bahia State to support more sustainable forest management. We analyzed the potential influence of key environmental variables on the spatial behavior of ecosystem wood volume and created predictive maps of the potential volume distribution for the entire Caatinga area of Bahia State.…”

Flora Do Amapá: Vitaceae Juss

“…O Cerrado brasileiro é um dos biomas mais ricos do planeta (MACHADO et al, 2004;SILVEIRA et al, 2019). Sua vegetação possui uma fisionomia bastante diversificada, apresentando desde formas campestres abertas, como campos limpos de vegetação rasteira sem arbustos ou árvores, até formas relativamente densas, denominadas cerradões, caracterizadas por matas fechadas com árvores altas e solo seco (Figura 1).…”

Establishment of native seedlings species as an indicator of ecological restoration of riparian forest, Cerrado, DF, Brazil

“…The Cerrado biome occupies 25% of the Brazilian territory (Silveira et al, 2019) and occurs in 11 states, from Paraná to Maranhão (Brandão Junior et al, 2020). The biome consists of flora and fauna grouped by vegetation types, with similar geological conditions, climate and landscape formation (Passaretti et al, 2020).…”