Modeling and Spatialization of Biomass and Carbon Stock Using LiDAR Metrics in Tropical Dry Forest, Brazil

Abstract

In recent years, with the growing environmental concern regarding climate change, there has been a search for efficient alternatives in indirect methods for the quantification of biomass and forest carbon stock. In this article, we seek to obtain pioneering results of biomass and carbon estimates from forest inventory data and LiDAR technology in a dry tropical forest in Brazil. We use forest inventory data in two areas together with data from the LiDAR flyby, generating estimates of local biomass and carbon levels obtained from local species. We approach three types of models for data analysis: Multiple linear regression with principal components (PCA), conventional multiple linear regression and stepwise multiple linear regression. The best fit total above ground biomass (TAGB) and total above ground carbon (TAGC) model was the stepwise multiple linear regression, 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. Our finding provides 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.

Crown Structure Metrics to Generalize Aboveground Biomass Estimation Model Using Airborne Laser Scanning Data in National Park of Hainan Tropical Rainforest, China

“…Oliveira et al used ALS data to estimate total aboveground carbon (TAGC) and total aboveground biomass (TAGB) of dry tropical forests in Brazil, and established a model using multiple stepwise linear regression methods. The results showed that LiDAR can be used for estimating biomass and total carbon in dry tropical forests (R 2 = 0.533, RMSE = 14.76 t/ha) [17].…”

Effects of point density on interpretability of lidar-derived forest structure metrics in two temperate forests

“…The advent of lidar (light detection and ranging) has made it possible to quickly and accurately measure forest structure at plot, landscape, and even global scales [15,16]. Lidar has been used successfully to estimate biomass in both temperate [17] and tropical forests [18,19], which has been essential for measurement reporting and verification of programs such as REDD+ [20]. Additionally, land managers have used lidar data to estimate fuel loadings [21–23], including ladder fuels [24,25], in forests, allowing them to better maintain forests through prescribed burns and respond to wildfires.…”

Carbon storage estimation of mountain forests based on deep learning and multisource remote sensing data

“…In 2019, based on Landsat 8 images and five remote sensing variables, Ding et al 17 used the regression kriging (RK) method to estimate grassland carbon storage in Northeast China; the results showed that, compared with the linear regression method, the RK method improved the performance of the model, and the improved model accuracy was attributed to the improved estimation accuracy in typical grasslands. In 2021, de Oliveira et al 18 used lidar data combined with continuous forest inventory data in two regions to fit the models of aboveground biomass and carbon storage through linear and nonlinear models, and each indicator show a strong correlation. In 2021, Lefsky et al 19 conducted a comprehensive study and discussion on the aboveground biomass and carbon storage of nine forest types in Pakistan and determined that coniferous forests have a better ability to sequester carbon than other forest types.…”

“…used the regression kriging (RK) method to estimate grassland carbon storage in Northeast China; the results showed that, compared with the linear regression method, the RK method improved the performance of the model, and the improved model accuracy was attributed to the improved estimation accuracy in typical grasslands. In 2021, de Oliveira et al 18 . used lidar data combined with continuous forest inventory data in two regions to fit the models of aboveground biomass and carbon storage through linear and nonlinear models, and each indicator show a strong correlation.…”