Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion

Amplicon-based assessment of bacterial diversity and community structure in three tropical forest soils in Kenya

Forest soils provide a multitude of habitats for diverse communities of bacteria. In this study, we selected three tropical forests in Kenya to determine the diversity and community structure of soil bacteria inhabiting these regions. Kakamega and Irangi are rainforests, whereas Gazi Bay harbors mangrove forests. The three natural forests occupy different altitudinal zones and differ in their environmental characteristics. Soil samples were collected from a total of 12 sites and soil physicochemical parameters for each sampling site were analyzed. We used an amplicon-based Illumina high-throughput sequencing approach. Total community DNA was extracted from individual samples using the phenol-chloroform method. The 16S ribosomal RNA gene segment spanning the V4 region was amplified using the Illumina MiSeq platform. Diversity indices, rarefaction curves, hierarchical clustering, principal component analysis (PCA), and non-metric multidimensional scaling (NMDS) analyses were performed in R software. A total of 13,410 OTUs were observed at 97% sequence similarity. Bacterial communities were dominated by Proteobacteria, Bacteroidetes, Firmicutes, Actinobacteria, and Acidobacteria in both rainforest and mangrove sampling sites. Alpha diversity indices and species richness were higher in Kakamega and Irangi rainforests compared to mangroves in Gazi Bay. The composition of bacterial communities within and between the three forests was also significantly differentiated (R = 0.559, p = 0.007). Clustering in both PCA and NMDS plots showed that each sampling site had a distinct bacterial community profile. The NMDS analysis also indicated that soil EC, sodium, sulfur, magnesium, boron, and manganese contributed significantly to the observed variation in the bacterial community structure. Overall, this study demonstrated the presence of diverse taxa and heterogeneous community structures of soil bacteria inhabiting three tropical forests of Kenya. Our results also indicated that variation in soil chemical parameters was the major driver of the observed bacterial diversity and community structure in these forests.

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1.