article
With their dense tropical forests and vast woody savannas, African ecosystems play a crucial role in global carbon regulation. However, recent findings show their role is shifting from carbon sink to source. Assessing changes in aboveground biomass (AGB) requires consistent time series to reflect temporal continuity and epoch comparability. Here, we leverage machine learning and satellite time series data to 1) produce temporally consistent annual maps of AGB in Africa for the period 2015-2021 and 2) provide a spatially and temporally detailed assessment AGB changes and their drivers at a resolution relevant for land management (100 m). Our retrieval algorithm estimates AGB using a combination of metrics that reflects canopy structure and cover fraction from spaceborne Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR) and optical data, as well as additional covariates influencing tree size and biomass (woody plant functional traits and prevailing moisture and topographic conditions). Trend analysis and breakpoint change detection from LandTrendR are used to evaluate overall AGB changes and to differentiate periods of gradual (e.g. natural growth, degradation) versus abrupt changes (e.g. disturbances, deforestation, replanting). Drivers of the changes are further inferred from temporally aligned land cover dynamics, alongside other ancillary data reflecting vegetation alterations (e.g. fire occurrence, management). AGB changes, quantified in terms of direction, magnitude, rate, and duration are finally summarized across multiple stratification levels (i.e. by driver, biome, country, etc.) to estimate carbon gains and losses. The results provide observation-based carbon stock trajectories over time, which are useful and timely to inform policy decisions on forest restoration and climate mitigation and support Measurement, Reporting and Verification (MRV) frameworks for REDD+, the new Tropical Forests Forever Facility (TFFF) and other policy instruments.
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DOI: 10.5194/egusphere-egu26-21337
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