REMOTE SENSING AND PROBABILISTIC SAMPLING BASED FOREST INVENTORY METHOD

A remote sensing and probabilistic sampling based forest inventory method can correlate aerial data, such as LiDAR, CIR, and/or Hyperspectral data with actual sampled and measured ground data to facilitate obtainment, e.g., prediction, of a more accurate forest inventory. The resulting inventory can represent an empirical description of the height, DBH and species of every tree within the sample area. The use of probabilistic sampling methods can greatly improve the accuracy and reliability of the forest inventory.