Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system

There is an increasing need of effective monitoring systems for habitat quality assessment. Methods based on remote sensing (RS) features, such as vegetation indices, have been proposed as promising approaches, complementing methods based on categorical data to support decision making. Here, we evaluate the ability of Earth observation (EO) data, based on a new automated,knowledge- driven system, to predict several indicators for oak woodland habitat quality in a Portuguese Natura 2000 site. We collected in-field data on five habitat quality indicators in vegetation plots from woodland habitats of a landscape undergoing agricultural abandonment. Forty-three predictors were calculated, and a multi-model inference framework was applied to evaluate the predictive strength of each data set for the several quality indicators.