Restoration interventions to combat land degradation are carried out in arid and semi-arid areas to improve vegetation cover and land productivity. Evaluating the success of an intervention over time is challenging due to various constraints (e.g. difficult-to-access areas, lack of long-term records) and the lack of standardised and affordable methodologies. We propose a semi-automatic methodology that uses remote sensing data to provide a rapid, standardised and objective assessment of the biophysical impact, in terms of vegetation cover, of restoration interventions. The Normalized Difference Vegetation Index (NDVI) is used as a proxy for vegetation cover. Recognising that changes in vegetation cover are naturally due to environmental factors such as the inter-annual climate variability and the seasonal vegetation development cycle, conclusions about the success of the intervention cannot be drawn by focussing on the intervention area only. We therefore use a comparative method that analyses the temporal variations (before and after the intervention) of the NDVI of the intervention area with respect to multiple control sites that are automatically and randomly selected from a set of candidates that are similar to the intervention area. Similarity is defined in terms of class composition as derived from an ISODATA classification of the imagery before the intervention. The method provides an estimate of the magnitude of the differential change in the intervention area and the statistical significance of the no-change hypothesis test. As a case study, the methodology is applied to 15 restoration interventions carried out within the framework of the Great Green Wall for the Sahara and the Sahel Initiative in Senegal. The impact of the interventions is analysed using data at two different resolutions: 250 m for the Moderate Resolution Imaging Spectroradiometer and 30 m for the Landsat mission. Results show that a significant improvement in vegetation cover was detectable only in one third of analysed interventions, which is consistent with independent qualitative assessments based on field observations and visual analysis of high resolution imagery. The pros and cons of using the two data sources are discussed.