Remote sensing is a suitable candidate for monitoring rapid changes in Polar regions, offering high-resolution spectral, spatial and radiometric data. This paper focuses on the spectral properties of dominant plant species acquired during the first week of August 2015. Twenty-eight plots were selected, which could easily be identified in the field as well as on RapidEye satellite imagery. Spectral measurements of individual species were acquired, and heavy metal contamination stress factors were measured contemporaneously. As a result, a unique spectral library of dominant plant species, heavy metal concentrations and damage ratios were achieved with an indication that species-specific changes due to environmental conditions can best be differentiated in the 1401–2400 nm spectral region. Two key arctic tundra species, Cassiope tetragona and Dryas octopetala, exhibited significant differences in this spectral region that were linked to a changing health status. Relationships between field and satellite measurements were comparable, e.g., the Red Edge Normalized Difference Vegetation Index (RENDVI) showed a strong and significant relationship (R² = 0.82; p = 0.036) for the species Dryas octopetala. Cadmium and Lead were below detection levels while manganese, copper and zinc acquired near Longyearbyen were at concentrations comparable to other places in Svalbard. There were high levels of nickel near Longyearbyen (0.014 mg/g), while it was low (0.004 mg/g) elsewhere.

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.

The use of effective methods for large-area drought monitoring is an important issue; hence, there have been many attempts to solve this problem. In this study, the Drought Information Satellite System (DISS) index is presented, based on the synergistic use of meteorological data and information derived from satellite images. The index allows us to monitor drought phenomena in various climatic and environmental conditions. The approach utilizes two indices for constructing a drought index: (1) the hydrothermal coefficient (HTC), which characterizes meteorological conditions across the study area over a long-term period; and (2) the temperature condition index (TCI) derived from Moderate-resolution Imaging Spectroradiometer (MODIS) data, which refers instantaneous land surface temperature (LST) to long-term extreme values. The model for drought assessment based on the DISS index was applied for generating drought index maps for Poland for the 2001–2019 vegetation seasons. The performance of the index was verified through comparison of the extent of agricultural drought to the reduction in cereal and maize yield. Analysis of variance revealed a significant relationship between the area of drought determined by the drought index and the decrease in cereal yield due to unfavorable growth conditions. The presented study proves that the proposed drought index can be an effective tool for large-area drought monitoring under variable environmental conditions.

Contemporary satellite Earth Observation systems provide growing amounts of very high spatial resolution data that can be used in various applications. An increasing number of sensors make it possible to monitor selected areas in great detail. However, in order to handle the volume of data, a high level of automation is required. The semi-automatic change detection methodology described in this paper was developed to annually update land cover maps prepared in the context of the Geoland2. The proposed algorithm was tailored to work with different very high spatial resolution images acquired over different European landscapes. The methodology is a fusion of various change detection methods ranging from: (1) layer arithmetic; (2) vegetation indices (NDVI) differentiating; (3) texture calculation; and methods based on (4) canonical correlation analysis (multivariate alteration detection (MAD)). User intervention during the production of the change map is limited to the selection of the input data, the size of initial segments and the threshold for texture classification (optionally). To achieve a high level of automation, statistical thresholds were applied in most of the processing steps. Tests showed an overall change recognition accuracy of 89%, and the change type classification methodology can accurately classify transitions between classes.

Heavy metals and radioactive compounds are potentially hazardous substances for plants, animals and humans in the Arctic. A good knowledge of the spatial variation of these substances in soil and primary producers, and their sources, is therefore essential. In the samples of lichen Thamnolia vermicularis, Salix polaris and Cassiope tetragona, and the soil samples collected in 2014 in Svalbard near Longyearbyen, the concentrations of the following heavy metals were determined: Mn, Ni, Cu, Zn, Cd, Pb and Hg, as well as the activity concentrations of the following: K-40, Cs-137, Pb-210, Pb-212, Bi-212, Bi-214, Pb-214, Ac-228, Th-231 and U-235 in the soil samples. The differences in the concentrations of the analytes accumulated in the different plant species and soil were studied using statistical methods. Sea aerosol was indicated as the source of Pb, Hg, Cs-137, Pb-210 and Th-231 in the studied area. A relatively high concentration of nickel was determined in the biota samples collected near Longyearbyen, compared to other areas of Svalbard. It was supposed that nickel may be released into the atmosphere as a consequence of the local coal mining around Longyearbyen.