Modeling tropical montane forest biodiversity – The potential of multispectral remote sensing

Abstract

In this thesis, the potential of multispectral remote sensing data to model taxonomic and functional aspects of biodiversity in a tropical mountain rainforest in southern Ecuador was analyzed. In particular, vegetation indices from multispectral reflectances and their textural information were used. For this purpose (i) different taxonomic groups and diversity measures (e.g. alpha/beta diversity) were investigated, (ii) a comparison to topographic metrics was made, and (iii) sensor data with high and moderate spatial resolution were considered. The three studies showed that the potential of multispectral remote sensing is closely related to the environmental filters of the respective biodiversity measures, which are responsible for spatial patterns of taxonomic and functional diversity. The taxon-specific resource requirements and their specific adaptation strategies to the environment are decisive for the importance of the predictors used here. In particular texture metrics, as proxies for habitat structure, explained a high proportion of diversity in addition to topographic metrics. However, their potential depended both on the spatial resolution of the multispectral sensor and on the complexity of the texture calculation. The robustness of multispectral image textures as an important driver of taxonomic and functional diversity should therefore be further investigated