Traditional methods of mineral exploration are mainly based on very expensive drilling and seismic methods. The proposed approach assumes the preliminary recognition of prospecting areas using satellite remote sensing methods. Maps of mineral groups created using Landsat 8 images can narrow the search area, thereby reducing the costs of geological exploration during mineral prospecting. This study focuses on the identification of mineralized zones located in the southeastern part of Europe (Kosovo, area of Selac) where hydrothermal mineralization and alterations can be found. The article describes all the stages of research, from collecting in-situ rock samples, obtaining spectral characteristics with laboratory measurements, preprocessing and analysis of satellite images, to the validation of results through field reconnaissance in detail. The authors introduce a curve-index fitting technique to determine the degree of similarity of a rock sample to a given pixel of satellite imagery. A comparison of the reflectance of rock samples against surface reflectance obtained from satellite images allows the places where the related type of rock can be found to be determined. Finally, the results were compared with geological and mineral maps to confirm the effectiveness of the method. It was shown that the free multispectral data obtained by the Landsat 8 satellite, even with a resolution of 30 meters, can be considered as a valuable source of information that helps narrow down the exploration areas.

The object of the study is the processing of space images on the territory of the Carpathian territory in the Lviv region, obtained from the Landsat-8 satellite. The work aims to determine the area of deforestation in the Carpathian territory of the Lviv region from different time-space images obtained from the Landsat-8 satellite. Methods of cartography, photogrammetry, aerospace remote sensing of the Earth and GIS technology were used in the experimental research. The work was performed in Erdas Imagine software using the unsupervised image classification module and the DeltaCue difference detection module. The results of the work are classified as three images of Landsat-8 on the territory of the Carpathian territory in the Lviv region. The areas of forest cover for each of them for the period of 2016-2018 have been determined. During the three years, the area of forests has decreased by 14 hectares. Our proposed workflow includes six stages: analysis of input data, band composition of space images on the research territory, implementation of unsupervised classification in Erdas Imagine software and selection of forest class and determination of implementing this workflow, the vector layers of the forest cover of the Carpathians in the Lviv region for 2016, 2017, 2018 were obtained, and on their basis, the corresponding areas were calculated and compared.

The development of cities and peri-urban areas is exerting an increasingly strong impact on the natural environment and, at the same time, on the living conditions and health of people. Problems and challenges that need to be addressed include increasing air pollution in these areas, formation of a surface urban heat island (SUHI), water management disruptions (water scarcity or excess), and the destruction of natural habitats. One of the solutions that contributes to climate change mitigation is the introduction of blue-green infrastructure into the city space and urbanised areas. The research objective was to identify spatial features (geodata) that determine the optimum location of selected blue-green infrastructure (BGI) components, acquire them, and then use the Geographical Information System (GIS) to determine their optimum locations. As the first step, cartographic models were developed which indicated areas that enable the development of selected blue-green infrastructure components in the Olsztyn city area, Warmińsko-Mazurskie Province, Poland. The models were juxtaposed with other two models developed by the authors, i.e. a surface urban heat island model and a demographic model that showed the age structure of the city’s population. Consequently, maps with potential locations for the blue-green infrastructure were developed, while taking into account reference data from the National Land Surveying and Cartographic Resource and Landsat 8 images.

The marshes are the most abundant water sources and ecological rich communities. They have a significant impact on the ecological and economic well-being of the communities surrounding them. However, climatic changes directly impact these bodies of water, especially those marshes which depend on rainwater and flooding for their survival. The Al-Sannya marsh is used as the example of marshes in Southern Iraq for this study between 1987–2017. The research takes place throughout the winter season due to the revival of marshes in southern Iraq at this time of year. The years 1987, 1990, 1995, 2000, 2007, 2014, 2017 are the focus of this study. Satellite imagery from the Landsat 5 (TM) and Landsat 8 (OLI) and the meteorological parameters affecting the marsh were acquired from NASA. The calculation of the areas of water bodies after classification using satellite imagery is done using the maximum likelihood method and comparing it with meteorological parameters. These results showed that these marshes are facing extinction due to the general change of climate and the interference of humans in utilising the drylands of the marsh for agricultural purposes. The vegetation area can be seen to have decreased from 51.15 km2 in 2000 to 8.77 km2 in 2017.