Modeling and Spatialization of Biomass and Carbon Stock Using Lidar Metrics in Tropical Dry Forest, Brazil: Preliminary Results

Abstract

Background: In recent years, with the growing environmental concern regarding climate change, there has been a search for efficient alternatives in indirect methods for studies on the quantification of biomass and forest carbon stock. In this article, we seek to obtain pioneering and preliminary results of estimates of biomass and carbon using data from conventional forest inventory and LiDAR technology in a dry tropical forest in Brazil. We used data from conventional forest inventory in two areas together with data from the LiDAR overflight, generating local biomass estimates from a developed local equation and the carbon levels obtained from local species. With data from LiDAR technology, we extracted the metrics from the point cloud and were used as an independent variable. For the construction of the biomass and carbon allometric models per hectare, we approach three types of models for data analysis: Multiple linear regression with Principal Components - PCA, Conventional multiple linear regression and Multiple linear regression with Stepwise, the generated equations were analyzed by comparisons of statistical criteria (R²aj and RMSE). After selecting the best equation, we generate the carbon estimates by area by assessing the plot level.Results: The best fit TAGB and TAGC model was the multiple linear regression with Stepwise, concluding, then, that LiDAR data can be used to estimate biomass and total carbon in dry tropical forest, proven by an adjustment considered in the models employed, with a significant correlation between the LiDAR metrics.Conclusions: Our preliminary results provide important information about the spatial distribution of TAGB and TAGC in the study area, which can be used to manage the reserve for optimal carbon sequestration.