Narrow Results

The visual uniformity of tropical peat swamp forest masks the considerable variation in forest structure that has evolved in response to differences and changes in peat characteristics over many millennia. Details are presented of forest structure and tree composition of the principal peat swamp forest types in the upper catchment of Sungai Sebangau, Central Kalimantan, Indonesia, in relation to thickness and hydrology of the peat. Consideration is given to data on peat geochemistry and age of peat that provide evidence of the ombrotrophic nature of this vast peatland and its mode of formation. The future sustainability of this ecosystem is predicted from information available on climate change and human impact in this region.

In geochemical data analysis, chemical elements are often clustered by statistical means first to reveal any established association of some elements due to similar originality, colocation, chemical bonding, or contamination. Statistical clustering ranks a predominately single association among elements very highly and the double or multiple associations of elements lowly according to their correlation coefficients. These lowly ranked double/multiple associations of elements in fact may also be useful in identifying new relationships among the associated elements. We propose a new intuitive method to fast verify statistical clusters of geochemical elements using self-organizing maps (SOM) of neural networks. SOM clustering offers an independent approach that can not only reaffirm the highly ranked statistical clusters of elements, but also further classify those lowly ranked statistical clusters into double or multiple associations of elements.