The detection of possible actual changes with scenario-based models was aggregated to a map of habitat locations especially vulnerable to climate changes. When detecting very sensitive areas, measures were elaborated for the implementation of climate change-related habitat modifications in the available management plans of protected areas.

Multi-temporal satellite imagery was used to prepare phenologic profiles, which describe the current situation of habitats. These phenologic profiles rely on several images acquired during the vegetation period. Satellite images or alternative remote sensing data of comparable quality, supplemented by an external provider and selected based on the public procurement rules were compared to a series of terrestrial spectral measurements of target habitats.

At a first stage the existing habitats were classified based on additional information from existing Land Use Land Cover data sets, such as CORINE land cover, Global Service Element Land or regional biotope maps, as well as other Geoinformation, such as soil data or digital elevation models. In addition, some specific issues had to be approved by field mapping. Because of the wide variation of phenologic patterns in different habitats a high level of differentiation was expected. At a second stage, changes in habitats related to damage or stress were distinguished from undisturbed habitats. Additionally, long-term multi-temporal studies, which detect changes at a minimum interval of a year, based on satellite image archives were carried out. Furthermore, the findings were evaluated and compared with existing historical data on habitat and species distribution.

The results provided insights into the development of habitats during the last three decades, leading to management suggestions on a long term basis. With regard to abiotic drivers, mainly changes in hydrological processes and water resources were analysed (soil water regime, groundwater recharge and run-off regimes) taking into account soil type, elevation, land use, and climate. A suitable model applied for those analyses is SWIM (Soil and Water Integrated Model), which operates on the spatial resolution of hydrotopes. After validation for the target areas, the model was used to transform changes in climate and land use into spatially distributed changes in hydrology and water resources under scenario conditions.

Based on the results from the modelling and the earth observation approaches regional risk maps were generated. These maps provided further input to the implementation of climate change adapted management strategies in WP 6.