Prediction of Biodiversity - Correlation of Remote Sensing Data with Lichen Field Samples

The objective of the present study was to develop a model to predict lichen species richness for six test sites in the Swiss Pre-Alps following a gradient of land use intensity combining airborne remote sensing data and regression models. This study ties in with the European Union Project BioAssess which aimed at quantifying patterns in biodiversity and developing “Biodiversity Assessment Tools” that can be used to rapidly assess biodiversity. For this study lichen surveys were performed on a circular area of 1ha on 96 sampling plots in the six test sites. Lichen relevés were carried out on three different substrates: trees, rocks and soil. In a first step, ecological meaningful variables derived from CIR orthoimages were calculated using both spatial and spectral information and additional lichen expert knowledge. In a second step, all variables were calculated for each sampling plot and correlated with the different lichen relevés. Multiple linear regression models were built containing all extracted variables and a stepwise variable selection was applied to optimize the final models. The predictive power of the models (r ranging from 0.79 for lichens on trees to 0.48 for lichens) can be regarded as good to satisfactory, respectively. Species richness for each pixel within the six test sites was then calculated. The present ecological modelling approach also reveals two main restrictions 1) this method only indicates the potential presence or absence of species and 2) the models may only be useful for calculating species richness in neighboring regions with similar landscape structures.