Estimating aboveground biomass in forest’s zone of Togo (West Africa).

The estimation of aboveground biomass (AGB) at the landscape level is necessary for estimating carbon pools in forest and provides baseline data for future studies. The objective of this study is to combine national forest inventory and remote sensing data to estimate aboveground forest biomass from remotely sensed data, and assess the accuracy of the method developed. The AGB maps 2015 across forest’s zone in Togo were produced based on secondary data from national forest inventory (NFI) field measurement using open sources Landsat images. The 2015 national inventory data (168 plots) has served as the base for validation of the 2015 biomass map. Three measurements were made to quantify accuracy: root mean square error (RMSE), bias and the coefficient of determination (R2) of the linear regression between predicted and measured AGB values. A complete map of AGB maps at 30 metres spatial resolution was produced over 603'972 ha. The overall model shows 74% of variance. The predicted AGB values across the landscape are between 40.34 and118.71 Mg/ha, with mean equal to 75.83Mg/ha and standard deviation (S.D.) equal to57.93Mg/ha. The model has overestimated biomass of the AGB with low values (Forest plantation and Savanna) and underestimated the AGB with high biomass values (Fallow, Woodland, Dense forest and Gallery forest). The RMSE values vary between27.41 and 35.66 t/ha depending on the forest strata and the overall RMSE value is around 15 t/ha. The estimated mean biomass for the model ischosen from 40.34 (savanna) to 118.71 (dense forest) t/ha. This study can be considered as a reliable, cost-effective and reproducible approach to map AGB in dynamic forest landscapes and can support policy approaches towards reducing emissions from deforestation and degradation (REDD+).