This paper introduces the first test results to use laser scanning and high resolution digital colour infrared aerial image data to estimate average tree crown density at a sample plot level. General methodological framework based on two-phase sampling schemes, non-parametric estimators and satellite images as the auxiliary data sets was adopted for the use with airborne data sources. More than 400 circular sample plots were established and measured in a special research forest area near Kaunas, the central part of Lithuania. The tree crown density was visually estimated for every coniferous tree belonging to the 500 square m plot together with other conventional forest parameters. Two variants of digital colour infrared aerial images (ground sampling density 15 and 40 cm), LiDAR point clouds, based on 1 point/square m scanning density and two phase sampling approach with non-parametric k-nearest neighbour and most similar neighbour estimators were used to test the accuracies of tree crown density estimation at a sample plot level. Reliable estimates were found to be possible on pure coniferous stands only. Average tree crown density was estimated with the root mean square error around 17.5-18% at a sample plot level, bearing in mind average crown density around 64% for the whole study area. The estimates were unbiased. Integration of laser scanning based variables with the ones available from digital aerial images resulted in lowest estimation root mean square errors. Laser scanning based variables used as the auxiliary data set independently resulted in better estimation errors than the variables available from digital colour infrared images.

Precision of the forest inventory still is one of the most important problems in the forestry nowadays. The aim of this research was to estimate the results of the combined forest inventory (CFI), using high spatial resolution aerial images in the planned areas of clear-cuts, comparing the results with the calipering and production files of harvesters. Testing of algorithms showed considerable difference in results between the CFI, forest inventory data and harvester production data. CFI results and production data had a close correlation with R2 =0.83. Comparing CFI calculated growing stock with production data, the average relative error amounted to 10.7%, which means the possibility for integration of these results into the forest inventory system. Comparing to CFI, there is a weak correlation between forest inventory and production data with R2 =0.34. The results indicate that LiDAR CFI technology can be used in the forecasting of the forest management, offering precise information about potential amount and economic value of assortments.

Nowadays, the use of Unmanned Aerial Vehicle flying at a low altitude in conjunction with photogrammetric and LiDAR technologies allows to collect images of very high-resolution to generate dense points cloud and to simulate geospatial data of territories. The technology used in experimental research contains reconstruction of topography of surface with historical structure, observing the recreational infrastructure, obtaining geographic information for users who are involved in preservation and inspection of such unique cultural/ heritage object as are mounds in Lithuania. In order to get reliable aerial mapping products of preserved unique heritage object, such photogrammetric/ GIS procedures were performed: UAV flight for taking images with the camera; scanning surface by LiDAR simultaneously; processing of image data, 3D modelling and generation of orthophoto. Evaluation of images processing results shows that the accuracy of surface modelling by the use of UAV photogrammetry method satisfied requirements – mean RMSE equal to 0.031 m. The scanning surface by LiDAR from low altitude is advisable, relief representation of experimental area was obtained with mean accuracy up to 0.050 m. Aerial mapping by the use of UAV requires to specify appropriate ground sample distance (GSD) that is important for reducing number of images and time duration for modelling of area. Experiment shows that specified GSD of 1.7 cm is not reasonable; GSD size increased by 1.5 times would be applicable. The use of different software in addition for DSM visualization and analysis is redundant action.