Land fragmentation is a problem for many post-communist countries. Different aspects of land fragmentation have been investigated by many researchers. However, there is little attention paid to the issues of internal fragmentation of agricultural parcels. In this study, internal fragmentation is understood as the following phenomenon: a parcel consists of different types of land plots or one land type (e.g. arable land) is split into separated plots. In this study the empirical test of internal fragmentation of arable land inside agricultural parcels has been made. The aim of the study was to examine the existence and extent of internal fragmentation of agricultural parcels. The digital map of boundaries of arable land parcels and data about land types (arable land, roads, etc.) from the Estonia National Topographic Database were the data sources of the study. The following characteristics were calculated in a GIS environment for agricultural parcels: compactness coefficient; number of pieces of arable land inside a parcel; Januszewski index for characterization of internal land fragmentation and the ratio (in percent) of arable land in a parcel. The results of the study show the existence of internal fragmentation of arable land inside agricultural parcels. Arable land is internally fragmented in about 30 percent of agricultural parcels. The area of the arable land plot inside parcels is 7.7 hectares if there is no internal fragmentation. In the case of internal fragmentation of the arable land, this area is 5.6 hectares respectively.

Wet soils play an important role in hydrological, biological and chemical processes, and knowledge on their spatial distribution is essential in forestry, agriculture and similar fields. Digital elevation models (DEM) and various hydrological indexes are used to perform water runoff and accumulation processes. The prerequisite for the calculation of the hydrological indexes is the most accurate representation of the Earth’s surface in the DEM, which must be corrected as necessary to remove surface artefacts that create a dam effect. In addition, different resolutions for DEM give different results, so it is necessary to evaluate what resolution data is needed for a particular study. The aim of this study is to evaluate the feasibility of using existing ditch vector data for DEM correction and the resulting implications for soil moisture prediction. Applied methodology uses a network of available ditch vectors and creates gaps in the overlapping parts of the DEM. The data were processed using open source GIS software QGIS, GRASS GIS and Whitebox GAT. Ditch vector data were obtained from JSC Latvian State Forests and the Latvian Geospatial Information Agency. The results show that by applying the bottomless ditch approach in forest lands on moraine deposits, depending on the accuracy of the ditch vector data, the values of the prediction of the soil wetness both increase and decrease. On the other hand, in forest lands on graciolimnic sediments it is visible that predicted soil wetness values increase in the close proximity of ditches. For forest lands on glaciofluvial and eolitic sediments there were no visible changes because of lack of ditches.

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.

The distribution and abundance of Barbarea arcuata (Opiz ex J. et C. Presl) Rchb. were investigated throughout the territory of Latvia. The field survey was carried out to estimate the abundance patterns, and the herbarium materials were used to compile a distribution map. In total 411 localities were recorded in the period from 2015 to 2017. The species has been commonly found on roadsides, which accounts for 66% of the localities. Seventeen percent of the localities occurred in grasslands, 10% – in croplands, 4% – in fallows, 2% – on road embankment slopes, and 1% – on railway embankments. The highest density of B. arcuata were found in new fallows where it forms large populations. Whole field localities account for 5% of the total localities. Medium-sized stands are found in about 20% of localities and are mostly found in grasslands, roadsides, as well as croplands which include cereal fields and oilseed rape fields. Individual specimens are mostly found on roadside habitats and grasslands and account for 75% of the total number of localities. As dominant weed species it is found on fields of oilseed rape, cereal fields and fallows. Herbarium data and the Institute of Biology, University of Latvia lists of species show that B. arcuata distribution was frequent during the period from 1970 to 2014.

The most obvious application of satellite images in forestry areas and generating forests maps with particular emphasis on identifying temporarily non-forested areas and mapping forest clear-cuts. The aim of this paper was to investigate the influence of attributes describing forest clear-cut patch size, patch shape, and habitat conditions on classification results and map forest clear-cuts in Latvian and Estonian boundary area. The satellite images used were medium spatial resolution Landsat Thematic Mapper satellite images made in plain snow cover conditions in late winter. The boundary area was represented by Aluksne region in Latvia and by Voru County in Estonia. Clear -cut areas as changed areas in forests were discerned from non-changed areas with image differencing method that has proved itself as one of the most often used methods in land use and land cover change detection.

Aim of the paper is to explore the possibilities of application of orthophoto maps in determination of land degradation. One of the forms of remote sensing is aerial photography. Orthophoto maps are made from aerial photography with specialized software orthophoto maps were analysed in perspective for several years – from 2005 to 2011.The results are based on the expert. With each year possibilities of application of orthophoto maps are expanding. During the research, data of survey and SWOT analysis of determination of land degradation by orthophoto maps. The study results prove that based on orthophoto maps mainly, it can be detected the following land degradation processes – agricultural land overgrowing with bushes and abandonment of built-up areas.

Groundwater is the main drinking water source in Latvia, and Quaternary groundwater is widely used in households due to shallow occurrence. The identification of vulnerable areas is important for better water management and protection of deeper, more intensively used aquifers. The existing groundwater vulnerability map of Latvia does not take into account land use which can be an important factor affecting natural groundwater quality. Multivariate statistical methods - principal component analysis (PCA) and hierarchical cluster analysis (HCA) - were applied to identify groundwater groups with distinct water quality in Quaternary sediments in Latvia. On the basis of major ion concentrations and nitrogen compounds four distinct groundwater groups were identified. First group represents natural and most common calcium- magnesium bicarbonate water type in Latvia with low nitrate and ammonium concentrations. Samples from second and third group both reflect anthropogenic influence: diffuse agricultural contamination mostly with nitrates and/or contamination derived from artificial surfaces. Fourth group belongs to calcium bicarbonate water type and is characterised as a very young groundwater formed in sandy deposits. The results show that the highest concentrations of nitrogen compounds can be found in areas with agricultural land use or in artificial surfaces which are often classified as medium low or low vulnerability areas (mostly samples from group two and three). Meanwhile the lowest values of nitrogen compounds are present in areas where dominant land covers are forests and semi-natural areas or wetlands, and groundwater vulnerability classes are medium to high (samples from the first and fourth group).

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.

In this study, forest type mapping data set taken from UCI (University of California, Irvine) machine learning repository database has been classified using different machine learning algorithms including Multilayer Perceptron, k-NN, J48, Naïve Bayes, Bayes Net and KStar. In this dataset, there are 27 spectral values showing the type of three different forests (Sugi, Hinoki, mixed broadleaf). As the performance measure criteria, the classification accuracy has been used to evaluate the classifier algorithms and then to select the best method. The best classification rates have been obtained 90.43% with MLP, and 89.1013% with k-NN classifier (for k=5). As can be seen from the obtained results, the machine learning algorithms including MLP and k-NN classifier have obtained very promising results in the classification of forest type with 27 spectral features.

The purpose of this study was to predict and compare salt accumulation in the soil profile under drained and undrained conditions. The water management simulation model, Drainmod (Ver. 6.1) was used to determine the optimal drainage system design parameters, which will decrease soil profile salinity and provide maximum crop yields in Ankara-Bala Basin of Turkey. Soil sampling points were coordinated with the Global Positioning System (GPS). Soil, crop and site parameters were obtained as an input. The model was run for 5 years from 2005 to 2010 to simulate optimum drainage design parameters (drain depth, drain spacing) while controlling soil salinity in the root zone. Soil water conditions and soil salinity level were simulated for crop rotation of corn (Zea mays) and winter wheat (Triticum). Yield of individual crops was predicted for each growing season. The results of the simulations were analyzed to identify alternatives of subsurface drainage system that would satisfy maximum crop productions. According to the simulation results, the drain spacing of 130 m and drain depth at 160 cm are recommended for Bala Basin. Soil salinity maps were created for undrained and drained conditions. Results showed that the soil salinity level and salinity stress can be reduced and yield increased by installing a drainage system.