This paper discusses the continuous field view of representing geography and its role in forest inventory. Describing all forest attributes at any location or point and storing this information in digital databases, as an alternative to store forest compartments as vector polygons and associated attributes, is considered to be important for development of remote sensing and GIS applications for forest inventories. Main results achieved following this approach during the last decade in Lithuanian university of agriculture are described. Special research polygon near Kaunas was developed. Four auxiliary data sources (Spot Xi, Landsat TM, digital aerial photos and stand-wise inventory material), three estimators (two-phase sampling with stratification, the k-nearest neighbours and regression) as well as different methods for auxiliary data integration were examined to get point-wise forest characteristics. The lowest root mean square errors at a level of virtual sample point using optimal implementation tactics are – for mean diameter 24 %, height 18%, age 28%, basal area 37%, volume per 1 ha 40% and percentage of coniferous trees 29% of the mean value of corresponding forest characteristic. Integrating additional auxiliary information – characteristics of forest compartments, estimated during the conventional stand-wise inventory – and satellite images improved the overall estimation accuracy. Pre-stratification of sample plots using the attributes of compartments improved the estimation accuracy for certain stand groups. A new approach of segmentation, aimed to construct conventional forest compartments – estimation of point-wise forest characteristics for every pixel of satellite imagery and using them instead of original image values – was suggested and investigated.

Researching new remote sensing and data processing methods is very important subject in forestry. The objectives of research are to explore methods to determine single tree characteristics using LIDAR and adapt them for Latvian forest conditions. Different algorithms and mathematical relations for automatic calculation of tree species, coordinates, height and diameter at breast height are described. Within the project four different clustering methods for tree identification were evaluated. The first method's construction is based on reflection point count in certain height range. The second and third methods are searching for global and local maximums on height axis of LIDAR data collection. In the fourth method segmentation of aerial photography is done by using the user selected sample data. Tree tops were s discovered by searching similarly coloured regions. Field measurements were used for the calibration of LIDAR data and analysis. Sample plots were fitted in the study area with different species composition, age and density. The total number of measured trees in sample plots is 1844. Results show that height can be found mainly for I, II, III craft class trees with average error 2.5%. Stem diameter estimation error of pine is 28%, spruce 17%, birch 4.2% and second storey trees 5.4% using linear equations D = 0.6616*H + 4.6969 (for coniferous trees) and D = 0.7756*H + 3.7132 (for deciduous trees). Dividing trees in classes of coniferous and deciduous can be done by using near infra red photography. The total number of first storey trees identified by LIDAR is 91%, by aerial photographic method 94%.

Tree species identification is important for a variety of natural resource management and monitoring activities especially in forest inventory. The objective of research is to identify tree species using digital aerial photography and LIDAR data in Latvian forest conditions. The study outlines a number of tree species identification possibilities: the ability to identify conifers and deciduous trees; the ability to identify pine and spruce; the ability to identify birch, aspen and black alder. The study site is a forest in the middle part of Latvia at Jelgava district (56º39’ N, 23º47’ E). Aerial photography camera (ADS 40) and laser scanner (ALS 50 II) were used to capture the data. LIDAR resolution is 9 points m-2 (500 m altitude). The image data is RGB, NIR and PAN spectrum with 20 cm pixel resolution. During the study a modified region growing algorithm was developed to determine tree canopy and tree species identification using threshold segmentation, Fourier transform, frequency filtering and reverse Fourier transform. Tree species classification of coniferous and deciduous trees is possible in 82% of the cases; the first storey of the trees can be classified correctly in 96% of the cases, but the second storey of the trees only in 49% of the cases. Spruce identification is possible in 81.1% of the cases, for first storey trees in 89.6% of the cases and for the second storey trees in 72.9% of cases. Deciduous tree correct classification is possible in 63% of the cases, birch 75%, black alder 60% and aspen only in 41% of the cases.

The most important part in forest inventory based on remote sensing data is individual tree identification, because only when the tree is identified, we can try to determine its characteristic features. The objective of research was to explore remote sensing methods to determine individual tree position using LiDAR and digital aerial photography in Latvian forest conditions. The study site was a forest in the middle of Latvia – in Jelgava district (56º39’ N, 23º47’ E). Aerial photography camera (ADS 40) and laser scanner (ALS 50 II) were used to capture the data. LiDAR resolution was 9p m2 (500 m altitude). The image data is RGB, NIR and PAN spectrum with 20 cm pixel resolution. Image processing was made using Fourier transform, frequency filtering, and reverse Fourier transform. LiDAR data processing methods was based on canopy height model, Gaussian mask, and local maxima. Field measurements were tree coordinates, species, height, diameter at breast height, crown width and length. Using combined LiDAR and optical imagery data allows detecting at least 63% of all trees and about 85% of the dominant trees.

The aim of the study was to investigate the properties of hyperspectral reflectance data of Scots pine (Pinus Sylvestris L.) and Norway spruce (Picea Abies L.). The hyperspectral reflectance data was obtained under laboratory conditions from the last season’s needles of healthy 20 year-old trees from the same site. Hyperspectral data was acquired using Themis Vision Systems LLC VNIR 400H portable scanning hyperspectral imaging camera in 400-1000 nm range. Methods of analysis of variance, discriminant analysis and principal component analysis were applied for the hyperspectral data analysis. Differences between Scots pine and Norway spruce reflection data were examined. The most informative spectral range for Norway spruce – Scots pine spectral separation was determined at 666.5 nm – 668.4 nm, most informative waveband - 667.1 nm. Reflectance variations among individual trees of the same species as well as differences in spectral response between needles from northern – southern crown exposition were tested. A significant variation in spectral response of needles of Norway spruce was detected across the whole measured spectral range (955 wavebands) for each sample tree. However, significant variation of spectral response of needles of Scots pine was detected only in 356 out of 955 wavebands for each sample tree. Depending on the crown exposition to the North or South, the reflectance of Scots pine needles differed significantly in 900 spectral bands. No significant differences were detected in 833 wavebands for Norway spruce.

In Lithuania, forests are managed by Lithuanian State Forest Enterprise, municipalities, ministries, etc. and private forest owners. About 50% of all forest land is State importance, privately owned forests cover 40% of forest land, and about 10% of forest land belongs to forests reserved for restitution. Forest management of private ownership force many challenges, because private forest owners are people, who have purchased or received the property after restitution, and often lacks knowledge about forest resources, its dynamics and sustainable forest management. As remote sensing is a valuable source for forest monitoring, because it provides periodic data on forest resource and condition status, these methods are gaining increased attention worldwide. In this context, more scientific efforts are made at developing remote sensing derived geo-spatial data services for sustainable forest management through a web service platform, which would integrate geo-information into daily decision making processes and operation for private forest owners. This article presents a review of privately owned forests’ statistics, questionnaire-based survey about GIS usage and demand for forest owners in Lithuania and links available sources of open geo-spatial data useful for sustainable forest management.

Latvia University of Life Sciences and Technologies, Latvia Precision of the forest inventory planning is still one of the most important problems in the forestry nowadays. The aim of this research was to estimate the sample tree height results of the combined forest inventory (LiDAR CFI) and LiDAR (Light Identification Detection and Ranging) height data by calculating an average value from sample tree neighbouring pixel values in the ripening Scotch pine forest stands, comparing the results with the measurements of the height in the area. For the update of LiDAR calculated data and LiDAR CFI height results, the increment algorithms of the Latvian State Forest Research Institute ‘Silava’ were used, comparing the results with the sample plot measurements. Both results showed a close correlation – in the case of LiDAR CFI with R2 =0.82, LiDAR data with R2 =0.93, demonstrating a standard deviation: 2.40 and 2.75, accordingly and standard error: 0.11 and 0.13, accordingly. The results indicate that both technologies can be used in the forest management, offering reliable information about the forest inventory. Positive values were reached by minimizing the human error factor, which is problematic for the field inventory.

Wet areas in agricultural lands are usually not fully or properly managed due to problematic accessibility by heavy machinery and are associated with lower crop yields. There are neither studies regarding spatial distribution of wet agricultural areas in Latvia nor large scale soil maps. Being aware of these wet areas, it would be possible to plan actions for effective management of these areas, starting with a scale of landscape. A geographic information system model could serve as an assistant for decision-making, such as, a direct support for the management of amelioration systems, change of land use and management patterns or granting support payments. Remote sensing data like Sentinel-2 satellite images and LiDAR (Light detecting and ranging) technology can be used to identify local wet areas. The focus of this article is to evaluate different remote sensing indices and methods that can be used to identify wet areas in agricultural lands using open access data and software. From 52 indices, which were analysed with soil moisture field measurements in 33 sample plots, only two of them showed statistical significance in linear regression model (p is less than 0.05): normalized height model in resolution of 25 meters (r2 =0.45) and visible blue spectral band in April (r2 =0.39). Results from this study help to focus on different aspects of remote sensing data usage and methodology for future improvements in order to fully implement LiDAR and Sentinel-2 data for identification of wet areas in agricultural lands.

The study represents results on remote sensing methods based evaluation of land use and land use changes in former and existing peat extraction areas in Latvia. The aim of the study is to elaborate activity data set for the National GHG inventory for the wetlands remaining wetlands for peat extraction. The study results provide sufficient data for application of the default emission factors for the peat extraction sites and flooded lands. Abandoned peat extraction fields, which are not yet afforested, flooded or rewetted, should be reported as peat extraction sites following a conservative approach in application of the emission factors. The study results can be used to report land use changes since 1990; however, linearized approach in calculation of the land use change may result in overestimation or underestimation of GHG emissions in certain periods of time. According to study results, the area of peat extraction sites is considerably bigger than currently reported in the National GHG inventory, mainly due to considerable areas of abandoned peat extraction fields. Flooded lands may be a significant source of emissions and should be introduced in the National GHG inventory to secure consistency of reporting. Methodology for calculation of GHG emissions from flooded lands should be also elaborated. It is also necessary to elaborate emission factors for fertile and no fertile peat extraction sites and continue work on separation of different soils in the inventory to increase accuracy of calculations.

Increase in use of biomass as renewable source of energy in Europe is tightly linked to the policies aimed at mitigation of climate changes i.e. reductions of greenhouse gas emissions. Both for assessment of the carbon sequestration and emissions as well as for assessment of potential amounts of biomass for renewable energy, information of land cover dynamics are essential. Therefore, the aim of our study was to improve accuracy of estimates of the land use changes in the time period between 1990 and 2014. Land use categories were determined in accordance to UNFCCC: wetland, cropland (arable land, bare field), forest, grassland, settlements (urban/suburban area), and other land. Combination of data from National forest inventory (NFI) sample plots and analysis of Landsat images were used. For the classification based on Landsat images vegetation index (NDVI) was estimated and linked to known information on the land use type from NFI sample plot data. In the analysed period, the most significant changes were found for forest lands – the total area of forest land during the last two decades had increased by 1% (64.5 thousand ha). Similar increase (1.2%) was observed also in the area of cropland. Both of these tendencies were primarily the result of marginal field area reduction (by 2.6%). Increase in forest area and thus annual increment has led to an increase in above-ground biomass by 10.2 m**3 haE-1.