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Habitat surveillance and subsequent monitoring at a national level is usually carried out by recording data from in situ sample sites located according to predefined strata. This paper describes the application of remote sensing to the extension of such field data recorded in 1-km squares to adjacent squares, in order to increase sample number without further field visits. Habitats were mapped in eight central squares in northeast Estonia in 2010 using a standardized recording procedure. Around one of the squares, a special study site was established which consisted of the central square and eight surrounding squares. A Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image was used for correlation with in situ data. An airborne light detection and ranging (lidar) vegetation height map was also included in the classification. A series of tests were carried out by including the lidar data and contrasting analytical techniques, which are described in detail in the paper. Training accuracy in the central square varied from 75 to 100 %. In the extrapolation procedure to the surrounding squares, accuracy varied from 53.1 to 63.1 %, which improved by 10 % with the inclusion of lidar data. The reasons for this relatively low classification accuracy were mainly inherent variability in the spectral signatures of habitats but also differences between the dates of imagery acquisition and field sampling. Improvements could therefore be made by better synchronization of the field survey and image acquisition as well as by dividing general habitat categories (GHCs) into units which are more likely to have similar spectral signatures. However, the increase in the number of sample kilometre squares compensates for the loss of accuracy in the measurements of individual squares. The methodology can be applied in other studies as the procedures used are readily available.

Abstract. Geodesy is currently experiencing a rapid and very diverse development of surveying technologies. Therefore it is important to evaluate the suitability of one or another technology in different situations for different tasks. This is especially needed for geodesy companies to make the right investment decisions. One of the innovative measurement technologies is mobile laser scanning, a rapidly evolving method of collecting survey data. It is mainly used for surveying objects such as streets, roads, railways and rivers. The results of mobile laser scanning can be used in everyday geodesy as well as for making three-dimensional models. As the development of other technologies makes it possible to process large amounts of mobile laser scanning data, this measuring method will soon become increasingly more attractive in Estonia. The paper analyses the technology of mobile laser scanning in one specific situation pointing out the dependence of elevation accuracy on the nature of the vegetation. It also analyses the amount of accuracy of mobile laser scanning technology that it is possible to increase by using cross-section profiles. For accuracy assessment, the mobile laser scanning elevation data and control points measured with GNSS device were compared. The study found that the elevation accuracy of mobile laser scanning depends substantially on the density and height of vegetation. Drawing ground profiles increases the accuracy of the final result. The result of RMSE of mobile laser scanning elevation data was 0.70 meters, the result of the RMSE of mobile laser scanning elevation data using drawing ground profile was 0.53 meters. It can be concluded that the most reasonable time for conducting mobile laser scanning would be during the season when vegetation is sparsest.