Understanding the environmental and disturbance determinants of tree species dominance and community composition in an ecosystem, is important for informing management and conservation decisions, through maintaining or improving the existing forest composition and structure. This study was carried out to quantify the relationship between forest tree composition structure and environmental and disturbance gradients, in a tropical sub-montane forest of Eastern Usambara. Vegetation, environmental, and anthropogenic disturbance data for 58 plots across Amani and Nilo nature forest reserves were obtained. Agglomerative hierarchical cluster analysis and canonical correspondence analysis (CCA) were used to identify plant communities and analyze the influence of environmental variables and anthropogenic disturbances on tree species and community composition respectively. Four communities were identified and CCA results showed that the variation was significantly related to elevation, pH, Annual mean temperature, temperature seasonality, phosphorus nutrients and pressures from adjacent villages and roads. Likewise, environmental factors (climate, soil and topography) explained the most variation (14.5%) of tree and community composition in relation to disturbance pressure (2.5%). The large and significant variation in tree species and community patterns explained by environmental factors suggests a need for site-specific assessment of environmental properties for biodiversity conservation plans. Similarly, the intensification of human activities and associated impacts on natural environment should be minimized to maintain forest species composition patterns and communities. The findings are useful in guiding in policy interventions that focus on minimizing human disturbances in the forests and could aid in preserving and restoring the functional organization and tree species composition of the sub-tropical montane forests.
Accurate information on above-ground biomass (AGB) is important for sustainable forest management as well as for global initiatives aimed at combating climate change in the Tropics. In this study, AGB was estimated using a combination of field and Sentinel-2 earth observation data. The study was conducted at Magamba Nature Reserve in Lushoto district, Tanzania. Field plot-based AGB values were regressed against eighteen Sentinel-2 remote sensing variables (bands and vegetation indices) using Random Forest (RF) models based on centroid and weighted approaches. Results showed that the weighted model had the highest fit and precision (pseudo-R 2 = 0.21, rRMSE = 68.23%). A prediction map was produced with a mean AGB of 223.47 Mg ha -1 which was close to that of the field (225.19 Mg ha -1 ). Furthermore, the standard deviation of the AGB obtained from the map (i.e 174.04 Mg ha -1 ) was relatively lower as compared to the one obtained from the field-based measurements (i.e 97.42 Mg ha -1 ). This study demonstrated that Sentinel-2 imagery and RF-based regression techniques have potential to effectively support large scale estimation of forest AGB in the tropical rainforests.
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