Abstract: 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.

Abstract: A remote sensing and probabilistic sampling based method for determining carbon dioxide volume of a forest 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 an accurate forest inventory, and corresponding carbon dioxide volume thereof.

Abstract: 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.

Abstract: A remote sensing and probabilistic sampling based method for determining carbon dioxide volume of a forest 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 an accurate forest inventory, and corresponding carbon dioxide volume thereof.

Abstract: 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.

Abstract: 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.

Abstract: A method for efficiently and accurately inventorying image features such as timber, including steps of segmenting digital images into tree stands, segmenting tree stands into tree crowns, each tree crown having a tree crown area, classifying tree crowns based on species, and analyzing the tree crown classification to determine information about the individual tree crowns and aggregate tree stands. The tree crown area is used to determine physical information such as tree diameter breast height, tree stem volume and tree height. The tree crown area is also used to determine the value of timber in tree stands and parcels of land using tree stem volume and market price of timber per species.