Novel Techniques for Mapping of Soil Carbon

This thesis presents novel techniques for spatial prediction of soil carbon. Chapter 1 introduces a method to incorporate the local scale spatial variability of soil organic carbon into regional scale mapping. Different to the conventional approach of using globally calibrated single model for the entire region, this method uses a combination of locally and globally calibrated models to predict soil organic carbon at regional scale, using a moving widow approach. Chapter 2 studies how diverse spatial modelling techniques perform under varying training sample sizes, in terms of soil carbon predictions. The study explores the behaviour of various algorithms ranging from simple linear models to complex machine learning techniques trained under numerous sample sizes. Chapter 3 investigates how to optimally use infrared spectroscopy inferred soil carbon data for mapping. Infrared soil spectroscopic data is considered as a timely, low-cost input for spatial modelling of soil carbon. However these data are associated higher measurement errors compared to the standard dry combustion technique. This study establishes a methodology in the model-based geostatistics to filter out the measurement error variability through the inclusion of the error information in the covariance structure of the spatial model. In disaggregating soil information, uncertainty of the disaggregation process is not often discussed. Underestimation of inferential or predictive uncertainty in statistical modelling leads to inaccurate statistical summaries and overconfident decisions. The use of Bayesian inference allows for quantifying the uncertainty associated with disaggregation process. Chapter 4 introduces Bayesian area-to-point regression kriging with a case study of downscaling regional scale soil organic carbon map to farm scale information

Variation of floristic diversity, community composition, endemism, and conservation status of tree species in tropical rainforests of Sri Lanka across a wide altitudinal gradient

Abstract Tropical rainforests in Sri Lanka are biodiversity hotspots, which are sensitive to anthropogenic disturbance and long-term climate change. We assessed the diversity, endemism and conservation status of these rainforests across a wide altitudinal range (100–2200 m above sea level) via a complete census of all trees having ≥ 10 cm diameter at breast height in ten one-hectare permanent sampling plots. The numbers of tree families, genera and species and community-scale tree diversity decreased with increasing altitude. Tree diversity, species richness and total basal area per ha across the altitudinal range were positively associated with long-term means of maximum temperature, annual rainfall and solar irradiance. Percentage of endangered species increased with increasing altitude and was positively associated with cumulative maximum soil water deficit, day-night temperature difference and high anthropogenic disturbance. Percentage of endemic species was greater in the lowland rainforests than in high-altitude montane forests. Nearly 85% of the species were recorded in three or less plots, which indicated substantial altitudinal differentiation in their distributions. Less than 10 individuals were recorded in 41% of the endemic species and 45% of the native species, which underlined the need for urgent conservation efforts across the whole altitudinal range