The characterisation and evaluation of biodiversity and land-cover in Southern Africa’s Savannas is a major prerequisite for suitable and sustainable land management and conservation purposes. However, mechanisms for frequent update of the status and trends of biodiversity and land change processes are still missing. The knowledge of the spatial distribution of vegetation types is an important information source for all social benefit areas. Remote sensing techniques are essential tools for mapping and monitoring of land-cover. The development and evaluation of concepts’ for integrated land-cover assessments attracted increased interest in the remote sensing community since evolving standards for the characterisation of land-cover enable an easier access and inter-comparability of earth observation data. Regarding the complexity of the savanna biome in terms of the spatiotemporal heterogeneity of the vegetation structure and rainfall variability, the main research needs are addressing the assessment of the capabilities and limitations of using satellite data for land-cover and vegetation mapping purposes. While integrating Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for mapping vegetation types in Namibia, the temporal characteristics of semi-arid life-forms types were used for the classification of vegetation types in Namibia. The Random Forest framework was applied and evaluated for classifying vegetation types using MODIS time series metrics as input features. The study region comprised the Kalahari in the north-eastern communal lands of Namibia. Regarding of the evolving global standardisation process for land-cover, there is the need to report the capabilities and limitations of the FAO and UNEP Land Cover Classification System (LCCS) in regional case studies. LCCS is evaluated in terms of the applicability in open savanna ecosystems and as ontology for the semantic integration of an in-situ vegetation database in a coarse scale mapping framework based on MODIS data. Further, the capabilities of the methodological setups of global land-cover mapping initiatives are assessed while using the results of the integrated vegetation type mapping framework. In order to assess the existing accuracy uncertainties of mapping savannas at global scales, the effects of composite length and varying observation periods were compared in terms of mapping accuracy. The implications for global monitoring were discussed. The determinants of precipitation amount and mapping accuracy were evaluated by comparing MODIS and Tropical Rainfall Measuring Mission (TRMM) time series data. Integrating multi-scale land-cover information, such as life form, cover, and height of vegetation types (in-situ), vegetation physiognomy and local patterns (Landsat), and phenology (MODIS) in an ecosystem assessment framework, resulted in a flexible land-cover map including a broad structural-physiognomic and a phytosociological legend. The principle of classifiers and modifiers in LCCS proved to be applicable in dry savanna ecosystems and can be confirmed as overarching land-cover ontology. Analyses of time series classifications showed that mapping accuracy increases with increasing observation period. Small composite period lengths lead to increased mapping accuracies. The relationship between mapping accuracy and observation period was observed as a function of precipitation input and the magnitude of change between land-cover stages. The integration of in-situ data in a multi-scale framework leads to improved knowledge of the regionalisation of Namibian vegetation types. On the one hand, the case study in the north-eastern Kalahari showed that multi-data mapping approaches using in-situ to medium resolution MODIS time series data bear the potential of the wall-to-wall update of existing vegetation type maps. On the other hand, the global remote sensing community can extend the reference databases by integrating regional standardised biodiversity and ecotype assessments in calibration and validation activities. The studies point on the uncertainties of mapping savannas at global scales and suggest possible solutions for improvements by adapting the remotely sensed feature sets, classification methods, and integrating dynamic processes of semi-arid ecosystems in the mapping framework.