No abstract

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.