Assessment and prediction of above-ground biomass in selectively logged forest concessions using field measurements and remote sensing data: Case study in South East Cameroon

Within the framework of the current REDD + initiative,1 there is an urgent need for information to guide the development and implementation of strategies for the reduction of GHG2 emissions from developing countries. Selective logging is one of the main sources of GHG emissions; but few studies in Cameroon have analyzed the impact of selective logging activities on above-ground biomass (AGB). This has resulted in a gap in essential information needed for the design of suitable forest management policies that can guarantee reductions in GHG emissions. In this study, we assessed the impact of selective logging on AGB in a forest concession in South-East Cameroon by quantifying AGB logged and AGB damaged by logging. We equally investigated through linear regression modeling whether the density of logging roads and NDVI3 values (from MODIS 250 m) can be used to predict the quantity of AGB logged. Allometric equations were used to estimate AGB of trees, while the surface area of logging infrastructures and the unit area value (ha−1) of AGB for the forest zone of Cameroon permitted the calculation of AGB damaged by logging. The study reveals that 0.78 trees ha−1; an equivalence of 6.97 Mg ha −1 of AGB was logged. Logging affected a surface area of 85.04 ha; approximately 2% of the study area. This is equivalent to 0.02 ha ha−1 and 5.65 Mg ha−1 of AGB damaged. The density of the logging roads explained 66% of the variation in AGB logged, while the density of the logging roads and NDVI values together explained 73% of the variation. This study concludes as follows: (i) selective logging reduces AGB stock of the forest and the magnitude of the impact varies with the different activities of selective logging, (ii) ground-based measurements facilitated by GIS permitted to quantify the impact of selective logging on AGB, (iii) logging roads and NDVI (which can either be field measured or captured remotely) can be used to indirectly determine AGB logged, hence can contribute in the measurement and monitoring of forest degradation caused by selective logging, and (iv) the findings from this study can usefully support the design of sustainable forest management policies, which are beneficiary to the REDD + process in Cameroon.