This paper discusses the usability of non-parametric knn (k-nearest neighbour) method to detect changes in forest areas from satellite images. Spot Xi images acquired 1999, main forest characteristics from field measured sample plots and data of conventional stand-wise forest inventory from the year 1988 were used to estimate the grids of following forest characteristics: mean age of main forest storey, diameter, basal area, height, volume per 1 ha, as well as the percentages of coniferous, soft and hard deciduous tree species. The differences of grids, created using stand-wise forest attributes from the 1988 inventory and estimated using the k-nearest neighbour methods were experimented to detect changes in the forest. 68.7-75.5% of areas, classified as the potential felling areas, were detected to be clear cut areas or young stands less than 15 years according to the data of stand-wise inventory of year 2003. Different settings for the methods investigated are evaluated, too.

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%.

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.

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.

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.

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.

Free radicals can rapidly and irreversibly oxidize various structures, including unsaturated fatty acids in vegetable oils, which affect the sensory properties. Spectrophotometry is the most widely used method for the determination of free radical scavenging activity (RSA) using 2,2-diphenyl-1-picrylhydrazyl (DPPH). Barrier to the further use of classical analytical methods to analyse biologically active compounds in foodstuffs is that equipment requires high cost and has limited mobility. One of solutions is to replace classical methods, such as spectroscopy, with smartphonebased colorimetry. Huawei P30 Lite smartphone was used for colorimetric detection. The free radical scavenging activity (RSA) in vegetable oil was detected using an application ‘Color Picker’, with image matching algorithm for red, green, and blue (RGB) model. RSA was expressed as percentage and measured by the DPPH method. The aim of the study was to determinate the total free radical scavenging activity with smartphone-based colorimetry. For the data comparison and accuracy spectrophotometer as analytical optical instrument was used. Eleven vegetable oils: sea buckthorn, sunflower, rice, macadamia nut, hemp, corn, grape, linseed, rapeseed, olive and milk thistle oils were selected for analysis. The best results with no significant differences (p is greater than 0.05) compared to smartphone-based colorimetry from spectrophotometry were determined using RG values. The poor results were detected by using B value (p is less than 0.05) and were not suitable for determination of RSA. Smartphone-based colorimetry can be used in the determination of the RSA in vegetable oils.

Geophysical studies in mapping and screening applications are widely applied for archaeological, environmental, geological, hydrological and many other applications. Ground-penetrating radar (GPR) is one of methods from geophysical toolbox that is also called a ground-probing radar, subsurface radar, surface-penetrating radar and ‘georadar’ or impulse radar – it is a non-invasive and non-destructive technique. Pulsed electromagnetic signal is recording the reflected energy and scattering from subsurface objects. Studies were performed in former Littorina Sea lagoons that became lakes after the further Limnea Sea stage in the Baltic Sea established with comparatively lower absolute sea level that is close to present day situation. Characterization of sediments as well as full sediment core description for comparison with GPR signals were performed. Major results show that GPR as non-destructive method in combination with geological coring followed by laboratory analysis of sediment properties can be successfully used to describe layering conditions, topography and depth of shallow lakes. Although there are some limitations regarding the electromagnetic (EM) noise and similar EM properties of analysed sediments, proper treatment of data gives complementary insight thus diminishing the necessity of dense coring network establishments in analysed areas of lakes. The aim of this screening study is to analyse potential advantages of GPR use for mapping sediments and topography of sandy bottom in shallow lagoon lakes as well as pinpoint problems during field and cameral works considering electromagnetic, geological and topographical disturbances.

Globally crop pathogens and pests cause significant yield and quality losses in agriculture production systems. Foliar fungal diseases of small grain cereals are economically among the most important diseases worldwide and in the Baltics. Finding an effective, reliable, and easily accessible method for plant disease diagnosis still presents a challenge. Currently used methods include visual examination of the affected plant, morphological characterization of isolated pathogens and different molecular, and serological methods. All of these methods have important limitations, especially for large-area applications. Hyperspectral imaging is a promising technique to assess fungal diseases of plants, as it is a non-invasive, indirect detection method, where the plant’s responses to the biotic stress are identified as an indicator of the disease. Hyperspectral measurements can reveal a relationship between the spectral reflectance properties of plants and their structural characteristics, pigment concentrations, water level, etc., which are considerably influenced by biotic plant stress. Despite the high accuracy of the information obtained from hyperspectral detectors, the interpretation is still problematic, as it is influenced by various circumstances: noise level, lighting conditions, abiotic stress level, a complex interaction of the genotype and the environment, etc. The application of hyperspectral imaging in everyday farming practice will potentially allow farmers to obtain timely and precise information about the development of diseases and affected areas. This review provides an introduction into issues of hyperspectral imaging and data analysis and explores the published reports of worldwide research on the use of hyperspectral analysis in the detection of foliar fungal diseases of small-grain cereals.