ABSTRACT:Cultural heritage is an invaluable example of human culture and creativity. The majority of them can become unstable or can be destroyed due to a combination of human and natural disturbances. In order to restore, preserve, and systematize data about architectural heritage objects, it is necessary to have geodetic, photogrammetric measurements of such data and to constantly monitor condition of the objects. The data of immovable cultural objects for many years are stored in photogrammetric data archives. Such archives have Germany, Lithuania, England and other countries. The article gives a brief introduction of the history of data archives formation and presents a photogrammetric and modern methods of modelling the spatial geometric properties of objects currently used to reveal immovable cultural properties and to evaluate geometric sizes. The pilot work was done with the Concept Capture simulation program that was developed by the Bentley company with photos of the Blessed Virgin Mary painting in Pivašiūnai of Trakai district. A shot from the ground with 12.4 MP resolution Pentax K-x camera was done using lenses with different focal lengths. The painting of the Blessed Virgin Mary is coordinated by 4 reference geodesic points and therefore after the modelling work it was possible to evaluate the accuracy of the created model. Based on the results of the spatial (3D) model, photo shooting and modelling recommendations are presented, the advantages of the new technology are distinguished.

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.

The problems of management of abandoned agricultural land as well as their effective use are relevant for any country to a greater or lesser extent. The endeavours to tackle the problems of effective utilization of abandoned agricultural land and in various ways are made in Lithuania as well as elsewhere. While analyzing the issues related to abandoned agricultural land, a clear definition of an abandoned area is important to perceive as well as potential methods for the identification of such areas are needed to analyse. Also, in order to suggest an effecticve utilisation of abandoned agricultural land for sustainable land use in the country, the analysis and statistics of such land is important to undertake. The paper discusses the analysis of abandoned agricultural land in Lithuania, providing the dynamics of changes of abandoned agricultural land and the the percentage distribution of such land across Lithuania. Also, the factors, which caused the abonded agricultural land appearance in Lithuania identified and described. The Remote Sensing method identified and analysed as the most effective methodology for abandoned agricultural land identification. A collection of spatial data on abandoned agricultural land was formed on the base of spectral images of the terrene obtained from an artificial Earth satellite and a map of abandoned agricultural areas was created upon applying remote cartographic methods.

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.

H. sosnowskyi (Heracleum sosnowskyi) is a plant that is widespread both in Lithuania and other countries and causes abundant problems. The damage caused by the population of the plant is many-sided: it menaces the biodiversity of the land, poses risk to human health, and causes considerable economic losses. In order to find effective and complex measures against this invasive plant, it is very important to identify places and areas where H. sosnowskyi grows, carry out a detailed analysis, and monitor its spread to avoid leaving this process to chance. In this paper, the remote sensing methodology was proposed to identify territories covered with H. sosnowskyi plants (land classification). Two categories of land cover classification were used: supervised (human-guided) and unsupervised (calculated by software). In the application of the supervised method, the average wavelength of the spectrum of H. sosnowskyi was calculated for the classification of the RGB image and according to this, the unsupervised classification by the program was accomplished. The combination of both classification methods, performed in steps, allowed obtaining better results than using one. The application of authors’ proposed methodology was demonstrated in a Lithuanian case study discussed in this paper.

It has been recently observed that aircrafts tend to be replaced by light, simple structure unmanned aerial vehicles (UAV) or mini unmanned aerial vehicles (MUAV) with the purpose of updating the field of aerial photogrammetry. The built-in digital photo camera takes images of the Earth’s surface. To satisfy the photogrammetric requirements of the photographic images, it is necessary to carry out the terrestrial project planning of the flight path before the flight, to select the appropriate flying height, the time for acquiring images, the speed of the UAV, and other parameters. The paper presents the results of project calculations concerning the UAV flights and the analysis of the terrestrial images acquired during the field-testing flights. The experience carried out so far in the Lithuanian landscape is shown. The taken images have been processed by PhotoMod photogrammetric system. The paper presents the results of calculation of the project values of the UAV flights taking the images by digital camera Canon S100 and the analysis of the possibilities of the UAV orthophoto images’ mode.

Abstract. Using remote sensing methods to capture environmental contamination is very relevant not only to Lithuania, but also to the whole of Europe. The article examines the Remotely Piloted Aircraft System (RPAS) and its components, in particular aircraft (UAV) mounted camera sensors. From the type of sensor depends what can be identified in the photo. The article presents the geographic informational (GIS) modeling system CALMIM with which the experimental modeling of the landfill territory has been performed. UAV aerial photos captured, modeling described and data analysis carried out.

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