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Automatic processing of remotely sensed data, like ALS point clouds, is crucial for modern economy, including forestry. The aim of the study was to develop automated procedures for digital forest map (LMN) revision and automated verification of the attributes (height) stored in the forest descriptive database (SILP), both based on airborne laser scanner datasets. The study areas were the Piasek (Chojna) and Milicz management forest districts, covering about 6,000 ha (80% Scots pine stands). The workflow of verifying and updating a digital map started with updating the compartment borders, which was based on nDSM (created from classified point cloud) and digital ortophoto (RGB+NIR) as well. The developed method, based on normalized ALS point cloud and GIS analysis, provided instant possibility for compartment border update, revealing additional objects like gaps or tree biogroups. The total area of automatically detected objects was around 15% lower when compared to the reference data for Chojna forest district and 10% higher regarding Milicz forest district. Around 84.0% and 85.5% of the gaps matched the reference for Chojna and Milicz forest districts, respectively. A method based on point cloud distribution (95th percentile) within compartment borders to assess its height was presented in the study. The results were compared to a height model (GRID) generated from descriptive database. For both the study areas the height stored in SILP database was lower than the height value derived from ALS data. The difference was equal to +0.9 m (Chojna; absolute difference 2.1 m) and +2.3 m (Milicz; absolute difference 3.2 m). When the stand area was used as a weight in the difference calculation, the difference values (HDiff) changed to +0.6 m (Chojna; absolute difference1.5 m) and +2.4 m (Milicz; absolute difference 2.7 m). Concerning the deciduous stands, the difference was higher (~+1 m) than for the Scots pine stands. The analysis performed confirms the possibility of using airborne laser scanning for geometrical (LMN) and descriptive (SILP/height) database updating. Periodical stand monitoring based on ALS technology can guarantee keeping the databases up to date without the necessity of costly and time consuming field measurements.
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