Combining remotely sensed spectral data and digital surface models for fine-scale modelling of mire ecosystems

Abstract

The detection and evaluation of changes in vegetation patterns is a prerequisite for monitoring programs. The Swiss mire monitoring program aims to assess the changes in mire vegetation in order to examine the efficiency of the management measures. A promising way to explore and detect vegetation structure and vegetation change is the application of predictive vegetation mapping that combines image classification and predictive habitat distribution models. These models deal with predictor variables derived from remotely sensed spectral data and from environmental variables such as a digital surface model (DSM). Low accuracy of environmental data to predict vegetation at the local scale is due to the difficulties to capture dominant fine-scale enironmental gradients. Using high resolution spectral and topographical data sets of 50 cm pixel size and below, the study presented here aims to improve the simulation of local-scale vegetation properties.