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Several studies reveal that there is a strong interconnection between climate change and biodiversity. Indeed, estimating plant biodiversity is an important issue under forest ecosystem monitoring, which allows the evaluation of carbon storage and sequestration capacity. To this end, a two-phase strategy, suitably compatible with the most adopted sampling designs in large-scale forest inventories, is proposed. In the first phase, tessellation stratified sampling is performed by partitioning the study area into a grid of quadrats and by randomly selecting a point in each quadrat. The first-phase points are classified as forest or nonforest using remotely sensed imagery. In the second phase, a sample of points is selected from those classified as forest by means of simple random sampling without replacement. The second-phase points constitute the centers of circular plots that are visited in the field to record plant species (usually trees) and their abundance. Estimators of abundance and diversity and estimators of their variances are presented. The proposed strategy is applied in a forest area from Central Italy, as a case study. With respect to the sampling effort, the resulting estimates of relative standard errors are satisfactory, especially those regarding the overall total and diversity index estimators. The proposed statistical approach represents a suitable reference for integrated forest inventory frameworks effectively supporting biodiversity monitoring and assessment.