Prediction of biodiversity - regression of lichen species richness on remote sensing data

Waser, L.T. and Stofer, S. and Schwartz, M. and Küchler, M. and Ivits, E. and Scheidegger, C. (2004) Prediction of biodiversity - regression of lichen species richness on remote sensing data. Community Ecology, 5 (1). pp. 121-133. ISSN 1585-8553

Text comec.5.2004.1.12.pdf Restricted to Repository staff only until 30 June 2024. Download (1MB)

Abstract

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 1 ha in 96 sampling plots in the six test sites. Lichen relevés were made on three different substrates: trees, rocks and soil. In the first step, ecologically meaningful variables derived from airborne remote sensing data were calculated using two levels of detail. 1'st level variables were processed using both spatial and spectral information of the CIR orthoimages. 2'nd level variables - based on 1'st level variables - were implemented using additional lichen expert knowledge. In the 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 (correlation between predicted and measured diversity) in a reference data set can be regarded as good. The obtained R ranging from 0.48 for lichens on soil to 0.79 for lichens on trees can be regarded as satisfactory to good, respectively. The accuracy of models could be further improved by adapting the model and by using additional calibration data and sampling plots. Species richness for each pixel within the six test sites was then calculated. This ecological modeling 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.

Actions (login required)

Edit Item