Wall-to-wall information about the state and change of vegetation is needed in many ecological applications, such as the monitoring of large conservation areas. In support of this task, remote sensing can provide valuable information that is complementary to the results from field work. Remote sensing is also well suited for change detection, but the question arises how a rate of change can be expressed in a generalized and objective way that allows comparisons between different areas. We think that true comparability can hardly be achieved by using conventional vegetation classification approaches, which are not transferable if they take account of the individuality of areas. To reach such comparability, an approach would be needed that combines generality with flexibility to adapt to local conditions.

Therefore, we propose that the local vegetation is broken down into basic strategy types as proposed by Phil Grime in 1974. He observed general rules in the occurrence of three general plant strategies: competitive ability (C), stress tolerance (S), and ruderal strategy (R). Our research question is whether these strategy types can be used to derive functional signatures of landscapes as a basis for comparison between conservation areas.

We used the CSR concept to map plant strategies in a heath landscape based on remote sensing data. Average Grime CSR values of vegetation samples were regressed against airborne hyperspectral imagery, resulting in spatial representations of C, S, and R (val. r² of 0.55, 0.59, and 0.28, respectively). Based on this continuous information we created functional signatures for two subareas of the study site, the ‘CSR-fingerprints’.

We found clear differences in the CSR signatures of different parts of the investigated area. We think that similar differences in time can also be assessed using the same approach. This could provide a simple but powerful expression of the state of vegetation that would be comparable across regions and time. We therefore assume that the method is suitable for comparative studies with a focus on vegetation functioning. While it does not explicitly take into account differences in species composition, it can also work as an early warning system with follow-up investigations in areas subjected to change.