Abstract. Observed bimodal distributions of woody cover in western Africa provide evidence that alternative ecosystem states may exist under the same precipitation regimes. In this study, we show that bimodality can also be observed in mean annual shortwave radiation and above-ground biomass, which might closely relate to woody cover due to vegetation–climate interactions. Thus we expect that use of radiation and above-ground biomass enables us to distinguish the two modes of woody cover. However, through conditional histogram analysis, we find that the bimodality of woody cover still can exist under conditions of low mean annual shortwave radiation and low above-ground biomass. It suggests that this specific condition might play a key role in critical transitions between the two modes, while under other conditions no bimodality was found. Based on a land cover map in which anthropogenic land use was removed, six climatic indicators that represent water, energy, climate seasonality and water–radiation coupling are analysed to investigate the coexistence of these indicators with specific land cover types. From this analysis we find that the mean annual precipitation is not sufficient to predict potential land cover change. Indicators of climate seasonality are strongly related to the observed land cover type. However, these indicators cannot predict a stable forest state under the observed climatic conditions, in contrast to observed forest states. A new indicator (the normalized difference of precipitation) successfully expresses the stability of the precipitation regime and can improve the prediction accuracy of forest states. Next we evaluate land cover predictions based on different combinations of climatic indicators. Regions with high potential of land cover transitions are revealed. The results suggest that the tropical forest in the Congo basin may be unstable and shows the possibility of decreasing significantly. An increase in the area covered by savanna and grass is possible, which coincides with the observed regreening of the Sahara.
Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest
