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Changes in forest areas have great impact on a range of ecosystem functions, and monitoring forest change across different spatial and temporal resolutions is a central task in forestry. At the spatial scales of municipalities, forest properties and stands, local inventories are carried out periodically to inform forest management, in which airborne laser scanner (ALS) data are often used to estimate forest attributes. As local forest inventories are repeated, the availability of bitemporal field and ALS data is increasing. The aim of this study was to assess the utility of bitemporal ALS data for classification of dominant height change, aboveground biomass change, forest disturbances, and forestry activities. We used data obtained from 558 field plots and four repeated ALS-based forest inventories in southeastern Norway, with temporal resolutions ranging from 11 to 15 years. We applied the k-nearest neighbor method for classification of: (i) increasing versus decreasing dominant height, (ii) increasing versus decreasing aboveground biomass, (iii) undisturbed versus disturbed forest, and (iv) forestry activities, namely untouched, partial harvest, and clearcut. Leave-one-out cross-validation revealed overall accuracies of 96%, 95%, 89%, and 88% across districts for the four change classifications, respectively. Thus, our results demonstrate that various changes in forest structure can be classified with high accuracy at plot level using data from repeated ALS-based forest inventories.

Cut-to-length harvesters collect detailed information on the dimensions and characteristics of individual harvested trees. When equipped with global navigation satellite system (GNSS) receivers and motion sensors, the obtained measurements can be linked to locations of single harvested trees, benefitting a range of forest inventory applications. We propose a way of georeferencing harvested trees using a Komatsu 931XC harvester, which measures and records the machine's bearing, crane angle and crane length for each harvested tree. We replaced the harvester's standard GNSS receiver with a dual-antenna differential GNSS receiver. From the coordinates obtained, rotations calculated from the GNSS receiver and data on crane length, we determined the location of 285 trees harvested in eight final fellings in Norway. We compared the obtained locations to control measurements taken on the corresponding stumps directly after harvest using a differential GNSS receiver. The mean distance between planimetric coordinates of trees measured by the harvester and corresponding control measurements was 0.88 m with a standard deviation of 0.38 m. By correcting the crane lengths for systematic deviations between harvester and control locations, the mean distance was reduced to 0.79 m. This study shows that measurements of single harvested trees can be georeferenced with sub-meter accuracy, by mounting a differential GNSS receiver on a harvester and without installing additional sensors. The results also suggest that the positional accuracy can be further improved by measuring and recording the length of the telescopic boom, and that with minor adjustments, the system could be fully automated.