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The present study aimed to analyse changes in the land cover of Vilnius city and its surrounding areas and propose a scenario for their future changes using an Artificial Neural Network. The land cover dynamics modelling was based on a multilayer perceptron neural network. Landscape metrics at a class and landscape level were evaluated to determine the amount of changes in the land uses. As the results showed, the Built-up area class increased, while the forest (Semi forest and Dense forest) classes decreased during the period from 1999 to 2019. The predicted scenario showed a considerable increase of about 60 % in the Built-up area until 2039. The vegetation plant areas consist about 47 % of all the area in 2019, but it will be 36 % in 2039, if this trend (urban expansion) continues in the further. The findings further indicated the major urban expansion in the vegetation areas. However, Built-up area would expand over Semi forest land and Dense forest land, with a large part of them changed into built- up areas.

The paper analyses the intensity changes of three pollution parameter vectors in space and time. The RGB raster pollution data of the Lithuanian territory used for the research were prepared according to the digital images of the Sentinel-2 Earth satellites. The numerical vectors of environmental pollution parameters CH4 (methane), NO2 (nitrogen dioxide) and for direct comparison O2 (oxygen gas) were used for the calculations. The covariance function theory was used to perform the analysis of intensity changes in digital vectors. Estimates of the covariance functions of the numerical vectors of pollution parameters and O2 or the auto-covariance functions of single vectors are calculated from random functions consisting of arrays of measurement parameters of all parameters vectors. Correlation between parameters vectors depends on the density of parameters and their structure. Estimates of covariance functions were calculated by changing the quantization interval on a time scale and using a compiled computer program using the Matlab procedure package. The probability dependence between the environmental pollution parameter vectors and trace gas of the territory in Lithuania and their change in time scale was determined.

Landfill operators must collect data on the topography of their landfills, their biological and hydrological characteristics, and local meteorological conditions. These data can be collected by satellite, using Unmanned Aerial Vehicles, or by traditional methods such as static flux chambers or modelling. They serve as the basis for landfill monitoring, including the identification and measurement of methane (CH4) gas emissions. Here, we present an approach for landfill mapping using sensor data from unmanned aerial drone systems (UADS) based on DJI Matrice 200 UAVs with Zenmuse X4S sensors and Trimble UX5 UAVs with Sony NEX-5R sensors. RGB (Red, Green, Blue) and near infrared (NIR) data from these sensors were processed using a Geographic Information System (GIS) to generate orthoimages, digital elevation models (DEMs), and normalized difference vegetation index (NDVI) maps. These were then used to evaluate changes in the surface structure and topography of the study area (Kariotiškės landfill, Lithuania). The NDVI maps were used to identify areas of sparse vegetation cover that may indicate localized CH4 emissions. Surface temperature maps based on thermal infrared (TIR) images were then prepared for analysis of these problematic areas. Finally, the presence of CH4 in these areas was investigated using a prototype lightweight gas sensor array. The structure of the Kariotiškės landfill site remained unchanged over three years, but there is evidence of possible CH4 gas influence at the landfill cover’s surface. The combination of UADS-mounted imaging systems and the prototype gas sensor array enabled rapid analysis of emission hotspots and of landfill topography.

Land requirements of wind power (WP) are often seen as a constraint to future broadscale deployment. The aim of the study is to evaluate the eligibility of abandoned agricultural land (AAL) areas, covered with woody plants, for the development of wind energy (WE) in Lithuania. Agricultural land abandonment (ALA) has numerous negative aspects and its use for WE must be a profitable choice for the landowner, as for the energy developers also. A newly developed methodological approach, a multi-criteria decision-making (MCDM) method known as TOPSIS (the Technique for Order Preference by Similarity to Ideal Solution) was applied to select suitable areas for wind power plants (WPP). The authors have used various data sets, as follows: protected areas (reserves, parks) combined into one common geographic information system (GIS) layer, forest cadaster data, water (lakes, rivers) area layer; abandoned land area layer; buildings layer, taken from the Lithuania Georeferenced Data Base. The results were generated for the entire territory of Lithuania and separately for AAL using the algorithms of the open source QGIS program. The results showed that the central part of Lithuania is most suitable for the development of WE. However, ALA in this part is low, because of the high soil yield potential and suitable conditions for farming. According to the selection criteria, about 7% of AAL are suitable for WE, and more than 18% of AAL have an average suitability. Keywords: wind energy; abandoned agricultural land; MCDA method; renewable energy.

Global warming, as the result of the negative impact of humans on climate change, has been observed based on various data sources. Various measures have aimed to reduce anthropogenic factors, and also to lower carbon dioxide (CO2) and methane CH4 emissions. One of the main contributors to anthropogenic factors is organic waste in municipal solid waste landfills. There are many landfills where cost-effective rapid technologies for the identification and quantification of CH4 emission sites are not applied. There is still a need for the development of accessible and cost-effective methods that react in a real-time manner for the rapid detection and monitoring of methane emissions. This paper’s main goal is to create a prototype sensor suitable for operational measurement of the gas value, suitable for integration into geodetic equipment or an unmanned aerial vehicle system. A sensor system (device) was developed, which consisted of three semiconductor sensors—MQ2, MQ4, and MQ135—which aimed to capture flammable gases (CO2, CH4, O2 purity) and to evaluate the averages of the measured values from the components mounted on the board—the semiconductor sensors. The sensors were calibrated in a laboratory and tested in a closed landfill. The measurement data consisted of the read resistances (analog signal) from the MQ2, MQ4, and MQ135 sensors, and the relative humidity and the temperature (digital signal) of the DHT2 sensor with a timestamp calculated by the RTC module. The use of the method was confirmed because the sensors reacted as expected when placed in the vicinity of the gas collection well. Furthermore, the sensor will be tested and improved for field work in landfill sites.

According to the official statistics the areas of abandoned agricultural land in Lithuania are gradually decreasing, but very slightly. The aim of this study is to research spatial determination and abandoned land classification in the territory of Vilnius District Municipality. Vilnius District Municipality was chosen for the research because it, although located near the capital of the country and has a high population density, it is still the district having the largest percent of abandoned land plots. A fast, cost-effective and sufficiently accurate method for determination of abandoned land plots would allow to constantly monitor, to fix changes and foresee the abandoned land plots reduction possibilities. In the study there was used the multispectral RGB and NIR color Sentinel-2 satellite images, the layer of the administrative boundary of Vilnius County and layer of abandoned agriculture land, which is available in Lithuanian Spatial Information Portal (www.geoportal.lt). The data was processed by Geographic Information System (GIS) techniques using classical classification Region Growing Algorithm. The research shows that NIR image classification result is more reliable than the result from RGB images.

Models for land cover/land use simulation are appropriate and important tools for decision-makers, helping them build future plausible landscape scenarios. Due to the fact that the simulation results of different models may be different, it is sometimes difficult for users to choose a suitable model. Therefore, in this study, an integrated approach is used, combining the data obtained from remote sensing and GIS with Land Change Modeler (LCM) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models to simulate and predict land cover/land use changes for 2028 in Karaj metropolis (Northern Iran as a poor region-in terms of data-which is under intense and rapid urbanization. In this sense, three land cover/land use maps related to the study area were primarily generated using satellite image data for the period 2006, 2011, and 2017. They were used as a basis to define two scenarios: business-as-usual (BAU) scenario and participatory plausible scenario (PPS) for 2028. Afterwards, the necessary input data used in running of both models were prepared and, then, the outputs of the models were interpreted and compared. According to the results, while human-made coverage and low-density grasslands increased by about 74% and 12%, respectively, it was from 2006 to 2017 that agricultural lands, gardens, and high-density grasslands decreased by 42%, 34%, and 7%, respectively. According to the business-as-usual scenario, which was projected using the LCM model, the increase in human-made cover will continue by about 29% by 2028, and the reduction rate of agricultural lands, gardens, and low-dense and dense grasslands will experience decrease by about 20%, 3%, 11%, and 9%, respectively. The participatory plausible scenario for 2028, which was defined using the InVEST model, confirmed the same results, but having different quantities. Accordingly, while human-made cover will increase by about 73%, the reduction rate of agricultural lands, gardens, and low-dense and dense grasslands will decrease by about 41%, 10%, 16%, and 1%, respectively. The output quantities of InVEST scenario model seem to be closer to reality with less uncertainty, because this model estimates the quantity of demand for land and its suitability for different uses, based on the views of different stakeholders, and considers landscape development future policies and plans. In contrast, the LCM model is based solely on trend extrapolation from the past to current time and changes in the landscape structure.

The present study aims to evaluate the effect of vegetation on land surface temperature (LST) in different land uses and covers in Vilnius district in 1999 and 2019. To that end, in addition to mono-window and split-window algorithms that help estimate the LST, the variables digital elevation model (DEM), slope, heat load index (HLI), distances from the road and the water, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) affecting the surface temperature were used. Furthermore, the random forest regression (RFR) method was applied to evaluate the effect of the mentioned variables on the LST. The performance model was also assessed by using the mean absolute (MAE), mean squared (MSE), and root mean square error (RMSE). Based on the results, NDVI and NDWI indexes had the greatest impact on the temperature of Vilnius city, respectively. The study area images were categorized as built-up area, cropland, semi-forest land, dense forest land, water bodies, pastures, and green urban areas. It was found that the pastures in 1999 and the built-up class in 2019 received the highest temperature from the land surface and that the classes characterized by natural land cover such as forest land and agricultural and water bodies had a relatively low surface temperature. NDVI response curves in both 1999 and 2019 indicated that the higher the density of vegetation on the land surface, the lower the surface temperature. A lower rate of urbanization, a higher density of vegetation and consequently, a lower the temperature of the land surface were recorded for 1999 in comparison with 2019. Therefore, urbanization was demonstrated to play a significant role in changes in LULC and the increase in LST.

The present study aims to investigate the relationship between reduced air pollution and ecosystem services in Karaj metropolis, Iran. To the end, the trends in the concentrations of O3, NO2, CO, SO2, PM10, and PM2.5 as the main atmospheric pollutants of Karaj were studied. Five time series models of autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) were used to predict changes in air pollutant concentrations. Air pollution zoning is conducted via ArcGIS10.3 by using spline tension interpolation method. Then, normalized difference vegetation index (NDVI) was obtained from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to analyze vegetation dynamics as an index of ecosystem functioning. NDVI thresholds were selected to present guidelines for qualitative and quantitative changes in green cover and were divided into five different categories. Based on the results, AR (1) and ARIMA (1,2,1) were recognized as appropriate models for predicting the concentration of air pollutants in the study area. A decrease in very dense vegetation coverage and increase in poor vegetation areas, followed by an increase in air pollution, revealed that the loss of urban green coverage and decreased ecosystem services were positively related. Furthermore, the expansion of urban lands toward the north and the west from the baseline to future condition led to great changes in the land cover and losses in vegetation along these axes, which finally resulted in increased air pollution in these areas. Thus, the results of this study can be directly used in decision-making in the area of air pollution.

The article analyses the impact of systemic errors in coordinates on the strain tensors and on internal deformation measures (dilatation and elongation) calculated applying the finite element metho...

Different types of disasters such as draughts, floods, landslides, debris flows, earthquakes, and extreme temperatures have struck the region of Central Asia and other parts of the World as well. Spatial Information Science and Technologies, including Geographical Information Systems, Remote Sensing, and Spatial Data Infrastructures have proven to be crucial for environmental protection and disaster risk management. “Environmental Protection in Central Asia (EPCA): Disaster Risk Management using Spatial Methods (EPCA)” is a European Commission funded project under ERASMUS+: Higher Education – International Capacity Building programme (Project No 585382-EPP-1-2017-1-SE-EPPKA2-CBHE-JP). It began in October 2017 and will run until October 2020. Initiated by the Lund University and partners from the Central Asia countries, the EPCA project is an ambitious project aiming to match labour market needs with geospatial education offer both in Europe and Central Asia countries. The aim of this three-year project is to build capacity using spatial methods for better environmental protection and disaster risk management in Central Asia. The objectives of the project are: • Developing innovative and blended courses in Spatial Information Science and Technology (SIST) for environmental protection and disaster risk management (EP-DiRiM): The developed courses will be taught at the partner universities with the aim of skill formation and filling knowledge gaps. The graduated students will be skilled professionals, who can potentially be employed by relevant stakeholders to develop and improve the application of spatial methods in EP-DiRiM. • Training of trainers: Faculty members at the regional partner universities will be trained on how to teach the developed courses. • Improving quality of education and teaching: Online learning techniques/tools have revolutionized the pedagogic world. CA partners will be equipped with e-learning and open network learning (ONL) tools. • System development: Internet-based information Management System for Environmental Protection and disaster risk management (iMSEP) will be developed and implemented. The aim is to make a GIS system widely available for stakeholders to be able to use it for data collection, storage, analysis, and decision-making. • Dissemination of the outcomes: Environmental protection and disaster management authorities, at policy-making, planning, and operational levels, will become aware of the advantages and applications of SIST in environmental protection and disaster risk management to support the development and use of SIST in their countries. In total 3 distance learning courses will be developed jointly by partner institutions in Europe and the Central Asia. The main aim of the network is to promote the use of spatial information and earth observation for environment protection and disaster risk management and modelling through capacity building and institutional development, via a network in which all partners would contribute from their own positions of strength. All 3 courses to be developed will follow EU higher education standards regarding e.g. ECTS, and learning outcomes. The outcome of the project, in terms of courses, will be freely used among the partners. At the end of the project, all produced material will be evaluated and quality controlled by an external evaluation group.

This article describes an agricultural application of remote sensing methods. The idea is to aid in eradicating an invasive plant called Sosnowskyi borscht (H. sosnowskyi). These plants contain strong allergens and can induce burning skin pain, and may displace native plant species by overshadowing them, meaning that even solitary individuals must be controlled or destroyed in order to prevent damage to unused rural land and other neighbouring land of various types (mostly violated forest or housing areas). We describe several methods for detecting H. sosnowskyi plants from Sentinel-2A images, and verify our results. The workflow is based on recently improved technologies, which are used to pinpoint exact locations (small areas) of plants, allowing them to be found more efficiently than by visual inspection on foot or by car. The results are in the form of images that can be classified by several methods, and estimates of the cross-covariance or single-vector auto-covariance functions of the contaminant parameters are calculated from random functions composed of plant pixel vector data arrays. The correlation of the pixel vectors for H. sosnowskyi images depends on the density of the chlorophyll content in the plants. Estimates of the covariance functions were computed by varying the quantisation interval on a certain time scale and using a computer programme based on MATLAB. The correlation between the pixels of the H. sosnowskyi plants and other plants was found, possibly because their structures have sufficiently unique spectral signatures (pixel values) in raster images. H. sosnowskyi can be identified and confirmed using a combination of two classification methods (using supervised and unsupervised approaches). The reliability of this combined method was verified by applying the theory of covariance function, and the results showed that H. sosnowskyi plants had a higher correlation coefficient. This can be used to improve the results in order to get rid of plants in particular areas. Further experiments will be carried out to confirm these results based on in situ fieldwork, and to calculate the efficiency of our method.

In Lithuania, the old archival topographic maps of locations of small and medium scale and 1 : 10,000 scale contour photo plans were created on the basis of aerial images. The material of these scales provides documented records on the condition of the objects in the location and the landscape at the moment of aerial photography. Those maps can be used to restore the accurate topographic image of the terrain and buildings in 1945–1951 and in later years and perform the necessary topographic, photogrammetric, and geodetic measurements of the parameters of objects, to identify the location of objects and changes. This information is necessary for a variety of disputed property issues and for building farmhouses in reserves, in areas of forestry land parcels, and for assessment of changes in the landscape. Studies of landscape changes are needed for the assessment of renaturalization processes. This paper presents the geodetic method used for geomorphological changes at the channel of River Žalesa during the period 1942–2017, based on the collected archival cartographic materials: historical topographic maps from archive of the Lithuanian State Company Land Fund, Land Information System, and land management projects and orthophoto maps from Lithuanian spatial information portal and free open-access Sentinel-1 satellite data from the Copernicus Open Access Hub.

Recently, the aircrafts tend to be replaced by unmanned, light, simple, aerial vehicles (UAVs), same can be said about the field of aerial photogrammetry. UAV system besides the standard airframe and engine includes main processor board, Inertial Navigation System (INS), Global Positions Systems (GPS), telemetry module. For areal imaging purposes there can be a photo camera mounted too. The images taken by the areal camera can later be processed by software which is specialized for photogrammetry. The project of testing Pix4d Mapper (Switzerland) and PhotoMod (Russia) software programs is described in this paper. Pix4d Mapper is software specialized for UAV images proceedings meanwhile PhotoMod is designed for common photogrammetry proceedings. PhotoMod request some special photogrammetric knowledge from the user. There were 36 images processed producing ortho-mosaic and surface model. The results are further described in the paper.

The construction and development of aerial vehicle or flying robot systems with remote sensing have become a vision of environmental monitoring in agriculture, ice condition, pollution, military, and territory. The new concept of environmental monitoring is superior to satellite imagery because of its accessibility, flexibility, applicability, and economy. Resulting from the expectable advantages, aerial vehicles have been extensively researched for their potential to carry out various missions. Recently, several developments have been engaged for aerial vehicles to conduct tasks in battlefield, transportation, exploration, and disaster prevention. Although the aforementioned researches can verify the applicability of aerial vehicle systems, most aerial vehicles lack the flexibility to accomplish other than predefined tasks. Moreover, the limitations of aerial vehicle performance and photographic technique also impede the development of such system. Therefore, with the introducing of multi-vehicle system, human intelligence, novel aerial vehicle design, and real-time photogrammetry system in outdoor environments, the efficiency and quality of monitoring can be improved significantly.