A better understanding of the carbon cycle is especially important due to the uncertainties of how vegetation will respond to a changing climate. As natural or anthropogenic disturbances such as fires and land cover conversions alter ecosystem functions and eventually can turn carbon sinks into sources, it is crucial to monitor change processes and map related carbon stocks and the changes thereof. Photosynthesis and respiration processes of vegetation are the direct link between biosphere and atmosphere, stressing terrestrial ecosystems’ importance in the global carbon cycle.
Terrestrial ecosystems play a pivotal role in providing regulating ecosystem services related to global and climate change. Our results suggest that metrics relating to the seasonal minimum and maximum values were the most influential variables and bear potential to improve spatially explicit mapping approaches in heterogeneous ecosystems, where both spatial and temporal resolutions are critical. These metrics revealed the relationship between aboveground carbon and the phenology of the observed vegetation.
Phenological metrics were derived from 8-day Landsat time series with a spatial resolution that is sufficient to capture gradual changes in carbon stocks of heterogeneous Savanna ecosystems. Aboveground carbon distributions could be mapped and revealed comprehensible spatial patterns. Comparable relationships between remote sensing based land surface phenological metrics and aboveground carbon were observed in all study areas. Random Forest model performances varied between the three study areas with RMSE values of 1.64 t/ha (mean relative RMSE 30%), 2.35 t/ha (46%) and 2.18 t/ha (45%). The gap filling approach enabled to accurately approximate the original Landsat ETM+ and OLI EVI values and the subsequent derivation of annual phenological metrics. Here phenological metrics from the season 2014/2015 were combined with aboveground carbon field samples of cerrado sensu stricto vegetation using Random Forest regression models to map the regional carbon distribution and to analyze the relation between phenological metrics and aboveground carbon. We selected three areas located in different subregions in the central Brazil region, which is a prominent example of a Savanna with significant carbon stocks that has been undergoing extensive land cover conversions. In order to account for these challenges we analyzed the potential of land surface phenological metrics derived from gap-filled 8-day Landsat time series for carbon mapping. Especially in heterogeneous ecosystems, such as Savannas, accurate carbon quantifications are still lacking, where highly variable vegetation densities occur and a strong seasonality hinders consistent data acquisition.
The quantification and spatially explicit mapping of carbon stocks in terrestrial ecosystems is important to better understand the global carbon cycle and to monitor and report change processes, especially in the context of international policy mechanisms such as REDD+ or the implementation of Nationally Determined Contributions (NDCs) and the UN Sustainable Development Goals (SDGs).