This research aims to test a scalable and transferable Geographic Information System (GIS)-based evaluation methodology for the identification, quantification and assessment of multi-functional landscape features. The evaluation of multi-functional features is one of the key tasks required when it comes to identifying the values that people attribute to landscapes, according to the principles of the European Landscape Convention. Mapping the static distribution of Landscape Services (LS) through data-derived estimates and performing spatial composite indicators are fundamental steps in understanding the current state of the Social–Ecological System (SES) of threatened or resilient landscapes. The methodological process is structured in four phases: intelligence (i), design (ii), choice (iii) and outcome (iv), according to the framework of the Multi-Criteria Spatial Decision Support System (MC-SDSS). This process has been implemented in the case study of the National Park of Cilento, Vallo di Diano and Alburni (Italy). The weighting of the spatial indicators, which simulates the model of LS-functioning for the study area, derives from an entropy-based method. Such a method, by which the weights are estimated without decisional agents, concerns a key-concept of information entropy theory, whereby the amount of information for each criterion determines its relative importance within a defined set of spatial criteria. The output of the model concerns mapping composite indicators of the LS; this involves the macro-categories of Regulating, Provisioning, and Cultural Services.