A method of counting the number of coniferous trees by species within forest compartments was developed by combining an individual tree crown delineation technique with a treetop detection technique, using high spatial resolution optical sensor data. When this method was verified against field data from the Shinshu University Campus Forest composed of various cover types, the accuracy for the total number of trees per stand was higher than 84%. This shows improvements over the individual tree crown delineation technique alone which had accuracies lower than 62%, or the treetop detection technique alone which had accuracies lower than 78%. However, the accuracy of the number of trees classified by species was less than 84%. The total number of trees by species per stand was improved with exclusion of the understory species and ranged from 45.2% to 93.8% for Chamaecyparis obtusa and C. pisifera and from 37.9% to 98.1% for broad-leaved trees because many of these were understory species. The better overall results are attributable primarily to the overestimation of Pinus densiflora, Larix kaempferi and broad-leaved trees compensating for the underestimation of C. obtusa and C. pisifera. Practical forest management can be enhanced by registering the output resulting from this technology in a forest geographical information system database. This approach is mostly useful for conifer plantations containing medium to old age trees, which have a higher timber value.

A major shift in the forest inventory and management paradigm toward the use of semi-automated analysis realized on an individual tree crown basis has been made possible by recent developments in high-resolution remote sensing. This paper discusses issues related to the fusion of high-resolution satellite imagery and LIDAR (light detection and ranging) data and their application in the classification of individual trees for precision forest management. The proposed methodological approach consists in the combination of spatial filtering object detection and reconstruction methods with a rule-based individual tree crown (ITC) system. Examples using QuickBird imagery combined with LIDAR data from an Alberta site (both boreal and mixed forest) demonstrate the advantages of the proposed fusion approach