Using Dap-Rpa Point Cloud Derived Metrics to Monitor Restored Tropical Forests

Abstract

Therefore, the objective of this study was to use DAP-RPA point cloud-derived metrics to estimate the aboveground biomass (AGB) values, species diversity and structural variables in restored secondary tropical forest areas. The study was carried out in three different active and one passive forest restoration systems in a secondary forest in the state of Sergipe, Brazil. Tree individuals from 36 plots (0.0625 ha each) were identified and their total height (ht) and diameter at breast height (dbh) values ​​measured in the field. Regression models were fitted to estimate the values ​​of AGB, Shannon diversity index (H), ht, dbh and basal area (ba). The best model for AGB explained 88% (R2 = 0.88) of the variation in biomass at the plot level, with an RMSE error of 8.8 Mg ha-1 (31.9%). The best model for ht explained 72% (R2 = 0.72) of the variation in height at the plot level, with an RMSE error of 0.8 m (11.0%). In dbh, the model explained 70% (R2 = 0.70) of the variation in diameter at breast height at the plot level, with an RMSE error of 0.7 cm (11.0%). For ba, the model explained the most with 90% (R2 = 0.90) of the variation in area at the plot level, with an RMSE error of 1.6 m3 (24.8%). In relation to H’, the model explained 67% (R2 = 0.67) of the variation in H’ at the plot level, with an RMSE error of 0.3 (20.1%).

Keywords: DRONE, Ecosystem and environmental services, Secondary forest, Fourier, Atlantic Forest