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Abstract Aims To characterize and identify upland vegetation composition and height from a satellite image, and assess whether the resulting vegetation maps are accurate enough for predictions of bird abundance. Location South-east Scotland, UK. Methods Fine-taxa vegetation data collected using point samples were used for a supervised classification of a Landsat 7 image, while linear regression was used to model vegetation height over the same image. Generalized linear models describing bird abundance were developed using field-collected bird and vegetation data. The satellite-derived vegetation data were substituted into these models and efficacy was examined. Results The accuracy of the classification was tested over both the training and a set of test plots, and showed that more common vegetation types could be predicted accurately. Attempts to estimate the heights of both dwarf shrub and graminoid vegetation from satellite data produced significant, but weak, correlations between observed and predicted height. When these outputs were used in bird abundance–habitat models, bird abundance predicted using satellite-derived vegetation data was very similar to that obtained when the field-collected data were used for one bird species, but poor estimates of vegetation height produced from the satellite data resulted in a poor abundance prediction for another. Conclusions This pilot study suggests that it is possible to identify moorland vegetation to a fine-taxa level using point samples, and that it may be possible to derive information on vegetation height, although more appropriate field-collected data are needed to examine this further. While remote sensing may have limitations compared with relatively fine-scale fieldwork, when used at relatively large scales and in conjunction with robust bird abundance–habitat association models, it may facilitate the mapping of moorland bird abundance across large areas.