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High-pulse-rate laser scanners are capable to detect single trees in boreal forest zone, since significant amount of laser pulses reflect directly from the ground without any interaction with the canopy. This allows detailed investigation of forest areas and the creation of a 3-dimensional tree height model. By extracting the height, location and crown dimension of the trees from the 3-dimensional tree height model and by using the tree species information available in aerial photographs and in laser scanner data, important tree attributes, such as stem volume, basal area, and age, can be estimated for single trees. By knowing the characteristics of single trees, forest characteristics for sample piots, stands and larger areas, such as stem volume per hectare [m3/ha}, basal area per hectare [m2lha], mean height, dominant height, mean age, number of stems [pc/ha' and development class, can be calculated. The advantage of the method is the capability to measure physical dimensions from the trees directly and the capability to use existing conversion formulas for stand attributes. This paper describes the methods and gives a first indication of the performance of the developed method. It is shown that tree heights of individual trees in the dominating storey can be obtained with less than 1 m standard error. In addition, the following standard errors were obtained for mean height, basal area and stem volume at stand level: 2.3 m (13.6 %), 1.9 m2/ha (9.6 %), and 16.5 m3/ha (9.5 %), respectively, even without using the tree species information. The accuracy was better than the accuracy of conventional standwise field inventory. It was also demonstrated that laser scanner is significantly more accurate than imaging spectrometer AISA in the stand attributes retrieval.

In this study a remote sensing method for compartmentwise forest inventories is described. The method is based on radar derived stand profiles and aerial photographs. Standard multivariate statistical methods are used for data analysis. The obtained accuracies (22 % for stem volume, 20 ?6 for basal area and 13% for mean height) are compared with other forest inventory methods.