Analysis of Congo Basin rainforest regrowth trajectories by land use history

As the world’s second largest rainforest, the Congo Basin rainforest plays a crucial role in the global carbon cycle. Furthermore, recent data suggests that it is more carbon-dense and more resistant to climate change than the Amazon (White et al, 2021). It is also a vital resource for local livelihoods and regional climate regulation. Increasing human disturbance to this rainforest due to demographic growth is generating large uncertainties in the regional carbon balance, mainly due to a lack of understanding of forest regrowth trajectories. The Afrocards consortium (U. Gent, U. Liege and U. catholique de Louvain) works to better understand regional regrowth trajectories following slash-and-burn agriculture, which is the dominant cultivation system in the region. In particular, we aim to shed light on the role of land use history and environmental variables in determining forest regrowth. To that end, we work to develop a regional land surface model calibrated on field, airborne, and satellite remote sensing data.

 

Here we present results related to the calculation of regrowth curves based on satellite remote sensing data using a space-for-time approach, where forest patches of different age are coupled with their above ground biomass (AGB). Building on a methodology initially established at the Laboratoire des Sciences du Climat et de l’Environnement (P. Ciais, Y. Xu), we use the time since last disturbance as a proxy for forest age, derived from the Tropical Moist Forest dataset, paired with gridded AGB estimates as our input data. The coupled age/AGB data is grouped by land use history classes and used as input to fit local sigmoidal (Richard-Chapman) regrowth curves using a Bayesian approach at the 1-degree grid cell level, across the Congo Basin. By using a Bayesian modeling approach, we can better account for uncertainties on the input data and output model parameter estimates. We use the posterior distributions of the fit parameters for all 312 grid cells and 3 land use history classes together with gridded bioclimactic variable datasets to carry out an exploratory analysis of variable importance and interaction by means of machine learning techniques, including Decision Tree ensemble methods and clustering methods. Ultimately, we aim to use such local regrowth curves to calibrate the Ecosystem Demography Biosphere model (version 2) to carry out mechanistic modeling of forest regrowth in the Congo Basin under different climate change and demographic growth scenarios.