An Inventory of the Above Ground Biomass in the Mau Forest Ecosystem, Kenya

Abstract

Biomass assessment of the Mau Forest Ecosystem (MFE) was done as part of Kenya’s greenhouse gas inventory. Trans Mara and Mount Londiani forest blocks representing extremes of vegetation types in the MFE were selected for ground data. Based on canopy closure, four forest strata were identified as very dense, moderately dense, open and bamboo. In each stratum, 5 clusters each with 4 plots measuring 30 m × 30 m were located. Big trees (D 1.3 ≥ 10 cm) were measured per species for diameter at breast height (D 1.3 ) in the whole plot while height was measured for every 5 th tree. Poles (10 cm > D 1.3 ≤ 5) were measured for D 1.3 in a 10 × 10 m concentric sub plot. Saplings (5 cm > D 1.3 ; ht ≥ 1.5 m) and seedlings (ht < 1.5 m) were enumerated per species within 5 × 5 m and 2 × 2 m concentric sub plots, respectively. Data were recorded in a Personal Digital Assistant (PDA) and quality checked with Open Foris Collect software. Allometric equations that have been used for similar vegetation in Kenya were used to relate D 1.3 and height with biomass. The tree data were uploaded to ArboWebForest (AWF) cloud-service and using the AWF-SIMO calculation tool, average values of diameter, height, and biomass were calculated for each plot. The data were generalised to cover all areas for each block using the Sparse Bayesian linear regression process on the vegetation characteristics with 10 m resolution ALOS-AVNIR-2 images of the MFE. ANOVA was used to compare biomass generated from several allometric equations. Results show that the average biomass of the MFE was 236 Mg·ha 1 . Degradation that converts dense forests into open and moderately dense forests contributed to a biomass loss of 228 Mg·ha 1 and 194 Mg·ha 1 respectively. Four allometric equations gave no significant difference (P < 0.05) in biomass for the 80 plots implying that costly processes of developing new equations may not improve accuracy. The study offers a learning lesson in Kenya’s forest inventory processes and the biomass values may show the estimates of stocking in similar forests of Kenya.