Assessing the effects of climate and land use land cover changes on recent carbon storage in terrestrial ecosystem using model-satellite approach over Wallonia, Belgium

The use of a dynamic vegetation model, CARAIB, to estimate carbon sequestration from land-use
and land-cover change (LULCC) offers a new approach for spatial and temporal details of carbon
sink and for terrestrial ecosystem productivity affected by LULCC. Using the remote sensing
satellite imagery (Landsat) we explore the role of land use land cover change (LULCC) in modifying
the terrestrial carbon sequestration. We have constructed our LULCC data over Wallonia, Belgium,
and compared it with the ground-based statistical data. However, the results from the satellite
base LULCC are overestimating the forest data due to the single isolated trees. We know forests
play an important role in mitigating climate change by capturing and sequestering atmospheric
carbon. Overall, the conversion of land and increase in urban land can impact the environment.
Moreover, quantitative estimation of the temporal and spatial pattern of carbon storage with the
change in land use land cover is critical to estimate. The objective of this study is to estimate the
inter-annual variability in carbon sequestration with the change in land use land cover. Here, with
the CARAIB dynamic vegetation model, we perform simulations using remote sensing satellitebased
LULCC data to analyse the sensitivity of the carbon sequestration. We propose a new
method of using satellite and machine learning-based observation to reconstruct historical LULCC.
It will quantify the spatial and temporal variability of land-use change during the 1985-2020
periods over Wallonia, Belgium at high resolution. This study will give the space to analyse past
information and hence calibrate the dynamic vegetation model to minimize uncertainty in the
future projection (until 2070). Further, we will also analyse the change in other climate variables,
such as CO2, temperature, etc. Overall, this study allows us to understand the effect of changing
land-use patterns and to constrain the model with an improved input dataset which minimizes the
uncertainty in model estimation.