A remote sensing and probabilistic sampling based method for determining the carbon dioxide volume of a forest is disclosed. The carbon dioxide volume is a volume of carbon sequestered by the forest. The method comprises processing remote sensing data indicative of tree attribute information for the forest. The remote sensing data (52) comprises at least one of LiDAR data and digital images. The method further comprises defining a sampling frame within the remote sensing data determining a field plot corresponding to the sampling frame and collecting field plot data (54) therefrom. The field plot data comprises actual tree attribute information. The method further comprises generating a correlated model (56) by combining the field plot data with the remote sensing data corresponding to the sample frame applying the correlated model to all the remote sensing data to produce a probabilistic forest inventory and determining a probabilistic carbon dioxide volume of the forest utilizing the probabilistic forest inventory. Also disclosed is a remote sensing and probabilistic sampling based method for determining the carbon dioxide volume of a forest. The carbon dioxide volume is a volume of carbon sequestered by the forest and the method comprises processing imagery data, the imagery data indicative of tree attribute information for the forest classifying tree polygons within the imagery data to derive the tree attribute information correlating field data, the field data comprising at least one of actual tree attribute formation and plot centre location generating a correlated model utilizing the tree attribute information derived from the imagery data and the actual tree attribute information generating a probabilistic forest inventory by applying the correlated model to all the imagery data and determining a probabilistic carbon dioxide volume of the forest utilizing the probabilistic forest inventory.
