Recently, the interest of Unmanned Aerial Vehicle application in photogrammetric environment for roads observation and monitoring has increased in many countries, in Lithuania as well. The experimental object for demonstration of capability and efficiency of aerial vehicle-based remote sensing technology for road data collection was a western bypass of Vilnius. The platform of the model UX5 Trimble with mounted camera Sony NEX-5R was applied for gaining images. The implemented means are mobile and not expensive. Photogrammetric technique with software package Business Center Photogrammetry Module was applied for the modelling of images. The correctness of digital surface model generally depends on camera resolution, flight height and accuracy of ground control points. The coordinates of control points were determined using Global Positioning System Trimble R4. Paper demonstrates results of a new technology application possibilities for linear object (road) mapping and accuracy evaluation of spatial models. The road points positioning accuracy investigation was carried out in consideration with geodetic control measurements. The average root mean square error for the points coordinates is 2.94 cm, and standard deviations – 2.78 cm. Analyzing coincidence or mismatches of Vilnius western bypass project data with photogrammetric product, not significant discrepancies of road section features were determined. The cost consideration of Unmanned Aerial Vehicle in conjunction with photogrammetry employment at experimental object is presented.

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.

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.

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.

The citation and text in the sentences of the 1.1 chapter should be corrected:The line 39: Such a classification for civilian UAVs is taken from the military (Fig. 2) [3,4]. The line 44: Fig. 2. Examples of fixed-wing unmanned aerial systems (UAS) platforms for trace gas monitoring [5]. The line 47: The aircraft's flight height, duration, load lift capacity is influenced by its size. These aircrafts can be considered as the category of MINI UAVs (MUAV) [4,5,6]. The line 61: For each pixel of each photo they usually capture 5-7 spectral bands, of which 1-2 bands captures part of the NIR wave spectrum, the red and green spectra, and spectrum corresponding color red is divided into several parts, since in case of plant investigation, the so-called Red Edge range is extremely informative (helpful) [7]. The line 67: In this case, the information is captured by a slightly different principle ("Push-broom") -not by capturing a rectangular picture, but one line of pixels, that represent one band spectrum instead and the entire sensor travels with the aircraft in the desired direction, simultaneously covering same pixels with all different possible bands [4,7,9]. The line 68: There are also attempts to produce small lightweight camera-type spectrometers [8, 9] (Fig. 3). The line 78: The suitability of these systems for further research, industrial or everyday life application depends on that [8,9]. The line 82: The more complex main sensor used, the more important is the overall calibration of the system [9, 10]. The line 86: From relevant (just captured) images, orthophotographic images are created for mapping the area with precise contours, topographic material is updated with generated height information (DSM -Digital Surface Model, Fig. 4).The line 88: Fig.3. Airborne Imaging Sensor products [http://www.itres.com/]. The line 95: Collected material is processed by specialized software, capable of performing classical photogrammetric and previously mentioned SfM photo processing and simulation (modeling) works in GIS [6]. The citation and text in the sentences of the 1.2 chapter should be corrected:The line 104: During review of the literature noted CALMIM software, that calculates the annual methane emission estimate of landfills for individual sites, based on the key processes that determine the emissions [11, 12, 13]. The line 151: Published literature on this topic over decade points out that due to specific local characteristics, the previous reliance on first-class kinetic models for the formation of methane of a landfill as a basis for predicting emissions is not precise, it is necessary to replace better, more scientifically based methodology for the following reasons [11][12][13][14][15]:3. The citation and text in the sentences of the 2.