Protected forests provide essential resources for climate-related, ecological and social functions. To ensure the continued protection and improvement of their functional status, classification and monitoring of these areas are of high importance. In addition, legal requirements and international agreements mandate the classification and monitoring of protected areas with significant importance. Different biodiversity classification schemes (like Natura 2000) are often characterized by a great variety of factors like species compositions, vegetation structure, water availability, soil and bedrock, topography and elevation and differ amongst classes. Combining remote sensing data of different spatial, temporal and spectral resolution, e.g. airborne laser scanning (ALS), image-based point clouds (IM), Sentinel 1 (S1) and Sentinel 2 (S2) and train machine learning models for classification and monitoring shows potential for small to medium scale areas on a pixel level (Iglseder et al., 2023). However, pixel-based classification is facing inevitable challenges with class boundaries, different spatial resolution of input data and classification uncertainties. One way to overcome these challenges is to create segments as primary observation units upstream of classification and monitoring. The presented research focuses on gathering, testing and adapting segmentation strategies based on combinations of various remote sensing data (ALS, IM, S1, S2) regarding their applicability to designate homogeneous areas for further assessment in the context of biodiversity analysis. Therefore, it is of particular interest to find remote sensing data-based features that make it possible to delineate these areas, whose internal homogeneity is defined on the basis of different factors. In this contribution, results from a study in East-Austria are presented and discussed. Preliminary results show that distance water bodies, aspect, slope, nDSM (ALS) and various S2 bands are features of great relevance.