Abstract: In this paper, we investigate methods to estimate plant population size and intensity (also known as density) from presence/absence data. Presence/absence sampling is a useful and relatively simple method for monitoring state and change of plant species communities. Moreover, it has advantages compared to traditional plant cover assessment, the latter being more prone to surveyor judgement error. We use inhomogeneous Poisson point process models concerning plant locations, and generalised linear models (GLM) with a complementary log-log link function for linking presence/absence data to plant intensity. In these models, auxiliary covariate information coming from remote sensing (i.e. wall-to-wall data) are used. We propose an estimator of plant intensity, as well as a variance of this estimator (and how to estimate this variance). For evaluating these estimators, we use both Monte-Carlo simulations, where we create artificial plant populations, and empirical data from the Swedish National Forest Inventory (NFI). We also develop a test for our models, to check the underlying Poisson point process model assumption and protect inference against model misspecification. The suggested hypothesis test is evaluated through Monte-Carlo. Some models could be produced for a selection of forest plant species and passed the Poisson test. Estimation of plant density and its related variance estimation could be performed for these species.
