The recognition and monitoring of vegetation and habitats for nature conservation is a vital point of research within the remote sensing community. It has been agreed on that there is no general solution on deriving information on habitats due to different data availability and spectral as well as textural behaviour of habitat main types (e.g. woodlands, grasslands, etc.). Therefore the monitoring should be rather multi-scale, versatile, user-friendly, and cost-efficient for predefined indicators.
In the presented study, five Central European test sites of natural vegetation communities in an Alpine area (1), a temperate forest (2), a Pannonian grassland (3), a shallow lake (4) and a Carpathian grassland (5) have been investigated by multi-temporal remote sensing. For these studies, different time-series of RapidEye images from the years 2009 to 2011 were acquired. The amount of the images was depending on required acquisitions dates as well as weather conditions. The definition of the indicators was relying on the available ground truth data as well as the demands and judgement of the managing authorities in the nature conservation areas. The selected methods for deriving of the indicators depend on the time-series as well as the available calibration and validation data. The techniques vary between unsupervised classification, object-based approaches and supervised classification methods with algorithms such as support vector machines (e.g. SVM) or classification trees (e.g. See5). Often the named methods are utilized in combined approaches.
The resulting indicators for the monitoring are shrub encroachment (for 1), share of naturally occurring tree type (for 2), differentiation of grassland types (for 3 and 5) and the changing extent of a reed belt (for 4). All indicators seem to be valid and useful. However, a transferability of the methods or a general statement on good-practice remote sensing applications can hardly be derived from these specific case studies.