Monitoring of plant populations has become more and more important, especially in the current context of environmental change. In this paper, we propose methods to estimate plant density from presence/absence surveys, wherein the presence or absence of each species is recorded on sample plots. Presence/absence sampling is a useful and relatively simple method for monitoring state and change of plant communities. Moreover, it has advantages compared to traditional plant cover assessment, the latter being more prone to observer bias. We present a hybrid estimation framework, that combines model- and design-based inference features, in which a generalized linear model (for binary presence/absence data) and an inhomogeneous Poisson model (for plant locations) are used to estimate plant density in a region of interest. We look at two different cases, the first one with a known area and the second one where the area is unknown and must be estimated. Our methods are applied to real data on Vaccinium vitis-idaea from the Swedish National Forest Inventory as well as simulated data to assess the performance of our estimators of plant density and corresponding variance estimators. The results obtained are promising and indicate that this method has a potential to add considerable analytic strength to monitoring programmes that collect presence/absence data.