Spatial distribution of aboveground biomass stock in tropical dry forest in Brazil

Climate change is being intensified by anthropogenic emission of greenhouse gasses, highlighting the value forests for carbon dioxide storing in their biomass. Seasonally dry tropical are a neglected, threatened, but potentially critical biome helping mitigate climate change. In South America, knowing amount and distribution Caatinga seasonally vegetation essential to understand its contribution global cycle subsequently design strategic plan conservation. The present study aimed model map spatial potential forest biomass stock across 32 fragments Caatinga, state Bahia, Brazil, using regression kriging Inverse Square Distance techniques, building from point measurements made on-the-ground ecological plots. First, estimating was fitted as function environmental variables apply kriging, then applied maps selected components. Elevation, temperature, precipitation explained 46% variations Caatinga. residuals showed strong dependence were mapped based on geostatistical criteria, selecting spherical semivariogram interpolation ordinary kriging. Biomass also approach. quality suggests that there good here variables. greater detail revealed trend increasing north south domain. Additional studies with sampling intensity use other explanatory suggested improve model, well maximize technique’s ability capture actual behavior this newly studied ecosystem.