A computer-implemented method of assessing forest inventory, the computer-implemented method comprising: receiving, by one or more computers one or more radar images of a geographical area of land, one or more spectral images of the land, and actual tree survey data physically collected using one or more ground-based measures; dividing, by one or more computers, the received one or more radar and spectral images into one or more raster image pixels corresponding to the land, each image pixel being a uniform grid unit corresponding to a known geolocation and area of the land, and capturing a reflectance energy gathered by one or more sensors located at a known location in space above the land; extracting, by one or more computers, one or more values associated with one or more feature of the land from the one or more image pixels; determining, by one or more computers, using the extracted one or more values and the actual tree survey data, at least one importance value, including a crown dominance value of a tree genus in the one or more image pixels, the at least one importance value representing an index for site dominance by a corresponding plurality of tree genera on the land, each importance value being a relative measure that corresponds to an amount of crown dominance that the tree genus has in the one or more image pixels for the image corresponding to the land; determining, by one or more computers, for the image pixel a plurality of nearest neighbour pixels in terms of statistical distance; wherein data regarding the tree species and tree sizes corresponding to the nearest neighbour pixels are known, the plurality of nearest neighbour pixels is determined based on a statistical analysis of one or more variables selected from elevation data, slope data, aspect data, importance values, number, shape, scale, and a combination thereof; using, by one or more computers, the nearest neighbour pixels to generate a probability function for predicting the probability that a tree of a particular species, when present on the land represented by the image pixel, would have a given size; using, by one or more computers, the probability function to predict the tree species for a plurality of trees within the land represented by the image pixel; and selecting, by one or more computers, an optimal tree species that maximizes the sum of said probabilities resulting from the predicted tree species and inserting those values into the probability functions for each image pixel, to generate the forest inventory of the geographical area of land subject to the condition that known or estimated proportions of species on said area of land must be satisfied. receiving, by one or more computers one or more radar images of a geographical area of land, one or more spectral images of the land, and actual tree survey data physically collected using one or more ground-based measures; dividing, by one or more computers, the received one or more radar and spectral images into one or more raster image pixels corresponding to the land, each image pixel being a uniform grid unit corresponding to a known geolocation and area of the land, and capturing a reflectance energy gathered by one or more sensors located at a known location in space above the land; extracting, by one or more computers, one or more values associated with one or more feature of the land from the one or more image pixels; determining, by one or more computers, using the extracted one or more values and the actual tree survey data, at least one importance value, including a crown dominance value of a tree genus in the one or more image pixels, the at least one importance value representing an index for site dominance by a corresponding plurality of tree genera on the land, each importance value being a relative measure that corresponds to an amount of crown dominance that the tree genus has in the one or more image pixels for the image corresponding to the land; determining, by one or more computers, for the image pixel a plurality of nearest neighbour pixels in terms of statistical distance; wherein data regarding the tree species and tree sizes corresponding to the nearest neighbour pixels are known, the plurality of nearest neighbour pixels is determined based on a statistical analysis of one or more variables selected from elevation data, slope data, aspect data, importance values, number, shape, scale, and a combination thereof; using, by one or more computers, the nearest neighbour pixels to generate a probability function for predicting the probability that a tree of a particular species, when present on the land represented by the image pixel, would have a given size; using, by one or more computers, the probability function to predict the tree species for a plurality of trees within the land represented by the image pixel; and selecting, by one or more computers, an optimal tree species that maximizes the sum of said probabilities resulting from the predicted tree species and inserting those values into the probability functions for each image pixel, to generate the forest inventory of the geographical area of land subject to the condition that known or estimated proportions of species on said area of land must be satisfied.