Predicting the Distribution of Tree Species and Their Biomass in Yangambi Biosphere Using Spatial Interpolation

Background and Objective: Knowledge of the spatial distribution of trees and stands is very important in forest management strategies. This study investigated whether spatial interpolation methods could predict the spatial distribution of tree species and their biomass in a mixed forest of Yangambi Biosphere, Democratic Republic of Congo. Materials and Methods: A 90×90 m grid was installed in a mixed forest, the coordinates of each selected tree were recorded with a GPS and the Diameter at Breast Height (DBH) measured. The 3 biomass was estimated with an allometric equation. Data was transferred to ArcGIS 10.3 software where maps predicting the spatial distribution of tree species and biomass were made using ArcGIS-Geostatistical Analyst Extension. The 7 spatial interpolation methods were tested: Inverse Distance Weighting (IDW), Simple, Ordinary and Universal Kriging (SK, OK and UK), Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI) and Kernel Interpolation (KI). Results:Scorodophloeus zenkeri was the most dominant plant species (31%), followed by Strombosia pustulata (12%) and Microdesmis yafungana (8%). The 3 biomass ranged from 100.32 to 8777.30 kg with a mean value of 2866.70 kg. The coefficient of variation was 72.98% with a standard deviation of 2092.30 kg, suggesting that forest biomass was highly variable. The LPI and GPI methods on the one hand, OK and UK on the other gave similar predictions of tree species. The species spatial distribution verification was nearly consistent with IDW. Conclusion: There is a need to expand the study area later and conduct further investigations to refine the predictions.