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This study aims to identify areas that present similar spectral characteristics to collected rock samples through the use of satellite imagery and spectral analysis. The results indicate that pixels marked in the satellite images exhibit similarities to the spectral characteristics of the samples. Misclassified objects or areas may be included in the results due to mixed pixels and spatial resolution limitations. The similarities identified could result from the region’s mineral composition of building materials, bare land, or dry vegetation. The averaged spectral curve patterns of the samples show similarity overall, but they are not identical, as reflected by the tenth quantile of the similarity coefficient. This research provides a valuable support tool for preliminary geological assessment, and information relating to vast and challenging-to-access parts of prospective areas for further investigation.

Information about vegetation condition is needed for the effective management of natural resources and the estimation of the effectiveness of nature conservation. The aim of the study was to analyse the condition of non-forest mountain communities: synanthropic communities and natural grasslands. UNESCO’s M&B Karkonosze Transboundary Biosphere Reserve was selected as the research area. The analysis was based on 40 field test polygons and APEX hyperspectral images. The field measurements allowed the collection of biophysical parameters - Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and chlorophyll content - which were correlated with vegetation indices calculated using the APEX images. Correlations were observed between the vegetation indices (general condition, plant structure) and total area of leaves (LAI), as well as fraction of Absorbed Photosynthetically Active Radiation (fAPAR). The outcomes show that the non-forest communities in the Karkonosze are in good condition, with the synanthropic communities characterised by better condition compared to the natural communities.

Actual land cover maps are a very good source of information on present human activities. It increases value of actual spatial databases and it is a key element for decision makers. Therefore, it is important to develop fast and cheap algorithms and procedures of spatial data updating. Every day, satellite remote sensing deliver vast amount of new data, which can be semi-automatically classified.

This research aims to discover the potential of hyperspectral remote sensing data for mapping mountain vegetation ecosystems. First, the importance of mountain ecosystems to the global system should be stressed due to mountainous ecosystems forming a very sensitive indicator of global climate change. Furthermore, a variety of biotic and abiotic factors influence the spatial distribution of vegetation in the mountains, producing a diverse mosaic leading to high biodiversity.

The main objectives of the research work were to determine the usefulness of Landsat and SPOT data for monitoring various forest parameters, and to assess the impact of changeable climatic conditions with the use of vegetation indices derived from remotely sensed data. Vegetation indices describing various aspects of plant condition and vegetation structure were derived from satellite images, and their values were analysed in a temporal profile in different vegetation seasons, in conjunction with meteorological parameters. The results of analyses proved that dedicated vegetation indices of water stress in plants - the disease water stress index (DSWI) and normalized difference infrared index (NDII) - are able to detect changeable climatic conditions, especially the impact of drought on forest ecosystems. The indices are also useful for characterising types of forest site and tree stand mixture, in particular differentiating dry, fresh and humid forest sites. Results of analysis of satellite-based indices were supported by conclusions drawn from a study of vegetation parameters obtained in the course of field campaigns. [ABSTRACT FROM AUTHOR]