The impact of land cover change on the carbon stock of moist afromontane forests in the Majang Forest Biosphere Reserve.

BACKGORUND: Forest plays an important role in the global carbon cycle by sequestering carbon dioxide and thereby mitigating climate change. In this study, an attempt was made to investigate the effects of land use/land cover (LULC) change (1989-2017) on carbon stock and its economic values in tropical moist Afromontane forests of the Majang Forest Biosphere Reserve (MFBR), south-west Ethiopia. Systematic sampling was conducted to collect biomass and soil data from 140 plots in MFBR. The soil data were collected from grassland and farmland. InVEST modelling was employed to investigate the spatial and temporal distribution of carbon stocks. Global Voluntary Market Price (GVMP) and Tropical Economics of Ecosystems and Biodiversity (TEEB) analysis was performed to estimate economic values (EV) of carbon stock dynamics. Correlation and regression analyses were also employed to identify the relationship between environmental and anthropogenic impacts on carbon stocks. RESULTS: The results indicated that the above-ground carbon and soil organic carbon stocks were higher than the other remaining carbon pools in MFBR. The mean carbon stock (32.59 M tonne) in 2017 was lower than in 1989 (34.76 Mt) of MFBR. Similarly, the EV of carbon stock in 2017 was lower than in 1989. Elevation, slope, and harvesting index are important environmental and disturbance factors resulting in major differences in carbon stock among study sites in MFBR. CONCLUSIONS: Therefore, the gradual reduction of carbon stocks in connection with LULC change calls for urgent attention to implement successful conservation and sustainable use of forest resources in biosphere reserves.

Analysis of the Current and Future Prediction of Land Use/Land Cover Change Using Remote Sensing and the CA-Markov Model in Majang Forest Biosphere Reserves of Gambella, Southwestern Ethiopia.

This study aimed to evaluate land use/land cover changes (1987-2017), prediction (2032-2047), and identify the drivers of Majang Forest Biosphere Reserves. Landsat image (TM, ETM+, and OLI-TIRS) and socioeconomy data were used for the LU/LC analysis and its drivers of change. The supervised classification was also employed to classify LU/LC. The CA-Markov model was used to predict future LU/LC change using IDRISI software. Data were collected from 240 households from eight kebeles in two districts to identify LU/LC change drivers. Five LU/LC classes were identified: forestland, farmland, grassland, settlement, and waterbody. Farmland and settlement increased by 17.4% and 3.4%, respectively; while, forestland and grassland were reduced by 77.8% and 1.4%, respectively, from 1987 to 2017. The predicted results indicated that farmland and settlement increased by 26.3% and 6.4%, respectively, while forestland and grassland decreased by 66.5% and 0.8%, respectively, from 2032 to 2047. Eventually, agricultural expansion, population growth, shifting cultivation, fuel wood extraction, and fire risk were identified as the main drivers of LU/LC change. Generally, substantial LU/LC changes were observed and will continue in the future. Hence, land use plan should be proposed to sustain resource of Majang Forest Biosphere Reserves, and local communities' livelihood improvement strategies are required to halt land conversion.