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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.

The accurate and detailed measurement of the vertical temperature, humidity, pressure, and wind profiles of the atmosphere is pivotal for high-resolution numerical weather prediction, the determination of atmospheric stability, as well as investigation of small-scale phenomena such as urban heat islands. Traditional approaches, such as weather balloons, have been indispensable but are constrained by cost, environmental impact, and data sparsity. In this article, we investigate uncrewed aerial systems (UASs) as an innovative platform for in situ atmospheric probing. By comparing data from a drone-mounted semiconductor temperature sensor (TMP117) with traditional radiosonde measurements, we spotlight the UAS-collected atmospheric data’s accuracy and such system suitability for atmospheric surface layer measurement. Our research encountered challenges linked with the inherent delays in achieving ambient temperature readings. However, by applying specific data processing techniques, including smoothing methodologies like the Savitzky–Golay filter, iterative smoothing, time shift, and Newton’s law of cooling, we have improved the data accuracy and consistency. In this article, 28 flights were examined and certain patterns between different methodologies and sensors were observed. Temperature differentials were assessed over a range of 100 m. The article highlights a notable accuracy achievement of 0.16 ± 0.014 °C with 95% confidence when applying Newton’s law of cooling in comparison to a radiosonde RS41’s data. Our findings demonstrate the potential of UASs in capturing accurate high-resolution vertical temperature profiles. This work posits that UASs, with further refinements, could revolutionize atmospheric data collection.

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.

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.