The study presents the analysis of the possible use of limited number of the Sentinel-2 and Sentinel-1 to check if crop declarations that the EU farmers submit to receive subsidies are true. The declarations used in the research were randomly divided into two independent sets (training and test). Based on the training set, supervised classification of both single images and their combinations was performed using random forest algorithm in SNAP (ESA) and our own Python scripts. A comparative accuracy analysis was performed on the basis of two forms of confusion matrix (full confusion matrix commonly used in remote sensing and binary confusion matrix used in machine learning) and various accuracy metrics (overall accuracy, accuracy, specificity, sensitivity, etc.). The highest overall accuracy (81%) was obtained in the simultaneous classification of multitemporal images (three Sentinel-2 and one Sentinel-1). An unexpectedly high accuracy (79%) was achieved in the classification of one Sentinel-2 image at the end of May 2018. Noteworthy is the fact that the accuracy of the random forest method trained on the entire training set is equal 80% while using the sampling method ca. 50%. Based on the analysis of various accuracy metrics, it can be concluded that the metrics used in machine learning, for example: specificity and accuracy, are always higher then the overall accuracy. These metrics should be used with caution, because unlike the overall accuracy, to calculate these metrics, not only true positives but also false positives are used as positive results, giving the impression of higher accuracy. Correct calculation of overall accuracy values is essential for comparative analyzes. Reporting the mean accuracy value for the classes as overall accuracy gives a false impression of high accuracy. In our case, the difference was 10–16% for the validation data, and 25–45% for the test data.

In response to the escalating demand for mineral resources and the imperative for sustainable management of natural assets, the development of effective methods for monitoring mining excavations is essential. This study presents an innovative decision-making model that employs a suite of spectral indices for the sustainable monitoring of mining activities. The integration of the Combinational Build-up Index (CBI) with additional spectral indices such as BRBA and BAEI, alongside multitemporal analysis, enhances the detection and differentiation of mining areas, ensuring greater stability and reliability of results, particularly when applied to single datasets from the Sentinel-2 satellite. The research indicates that the average accuracy of excavation detection (overall accuracy, OA) for all test fields and data is approximately 72–74%, varying with the method employed. Utilizing a single CBI index often results in a significant overestimation of producer’s accuracy (PA) over user’s accuracy (UA), by about 10–14%. Conversely, the introduction of a set of three complementary indices achieves a balance between PA and UA, with discrepancies of approximately 1–3%, and narrows the range of result variations across different datasets. Furthermore, the study underscores the limitations of employing average threshold values for excavation monitoring and suggests the adoption of dedicated monthly thresholds to diminish accuracy variability. These findings could have considerable implications for the advancement of autonomous and largely automated systems for the surveillance of illegal mining excavations, providing a predictable and reliable methodology for remote sensing applications in environmental monitoring.

The utilization of Effective Microorganisms (EMs) for lake restoration represents a sustainable approach to enhancing water quality and rebalancing the ecology of aquatic ecosystems. The primary objective of this study was to evaluate the effects of two bioremediation treatment cycles employing EM-enriched biopreparations on water quality in the Siemiatycze lakes. Specifically, this research analyzed various parameters, including dissolved oxygen, transparency, chlorophyll-a, pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total phosphorus, total nitrogen, and suspended matter (SM), across eleven designated sampling locations. Additionally, this study employed remote sensing techniques, leveraging Sentinel-2 satellite imagery and the Maximum Chlorophyll Index (MCI), to detect and quantify algal blooms, with a particular focus on elevated chlorophyll-a concentrations. This comprehensive approach aimed to provide a holistic understanding of the impact of biotechnological reclamation on aquatic ecosystem restoration and sustainability. The study’s findings indicated a significant improvement in water quality in all lakes, with enhanced water clarity and oxygen profiles. Further, remote sensing studies indicated a reduction in algal blooms, particularly those with high chlorophyll-a concentrations. A considerable decrease in water eutrophication intensity was observed due to diminished nutrient concentrations. The improvements in water parameters are likely to enhance the living conditions of aquatic organisms. These results demonstrate the effectiveness of using EM-enriched biopreparations in the bioremediation of lakes, providing a sustainable approach to enhancing water quality and balancing aquatic ecosystems.

Understanding processes that affect changes in the coastal zone and the ability to predict these processes in the future depends on the period for which detailed monitoring is carried out and on the type of coast. This paper analyzes a southern fragment of the Baltic coast (30 km), where there has been no anthropogenic impact (Slowinski National Park). The study was carried out covering a time interval of 65 years. Historic and current aerial photographs (orthophotomaps) from the following years: 1951, 1964, 1975, 1984, 1995, 2004, and 2016 were used. Changes in the position of the foredune toe line (FTL) in each years’ images were used. For each time interval (1951–1964, 1964–1975, 1975–1984, 1984–1995, 1995–2004, 2004–2016), the coastal area over which morphodynamic processes (erosion and accumulation) took place was calculated. The calculated RL (reference line)—FTL positions allowed us to determine differences in the shoreline course in subsequent years and to determine the extent of shifts/changes of the coastline in each time period. The study results showed an equilibrium between the processes of accumulation and erosion, proving that the development of the studied natural coastline is balanced. There was only a change in the trend of the characteristics of changes from erosive into accumulative ones and vice versa. Moreover, along the studied coast section, a certain periodicity in the coastline changes can be observed. The intervals where predominant erosion occurs alternate with those when accumulation prevails, and then the cycle repeats. The analysis of historic/current aerial images and orthophotomaps from 1951–2016 indicated that strong storms have a significant impact on the magnitude of change (accumulation/erosion) and the formation of the studied coastline.

The specific aim of the European project named "Cultural Heritage Through Time" (CHT2) and reported in this paper is to fully integrate the fourth dimension (4D) into Cultural Heritage studies for analysing structures and landscapes over time. Krakow - the Fortress City (Poland) is the one of four case studies of the CHT2, which are used for the time varying reconstruction, analysis, visualization, and preservation. The goal of the project is produce time-varying 3D products, from landscape to architectural scales, to envisage and analyse lost scenarios or visualize changes due to anthropogenic activities or intervention, pollution, wars, earthquakes, or other natural hazards. The results of the case studies will be the basis for web publication.

Urban green spaces provide measurable cooling that can mitigate urban heat islands, yet few studies have quantified these effects over multiple decades. This study analyzed Landsat imagery from four epochs (1990, 2000, 2013, 2018) to derive land surface temperature (LST) and vegetation indices—NDVI for greenness and NDMI for moisture content—for four large urban parks in Krakow. Late spring/summer LST in parks was compared with that of urban areas within 0–150 m and 150–300 m of park boundaries. Statistical significance was evaluated using bootstrapped confidence intervals, long-term trends were assessed via the Mann–Kendall test, and correlation analysis was used to examine relationships between LST and each vegetation index. Results show a persistent park cooling effect, with park interiors ~2–3 °C cooler than adjacent urban areas in all years. Despite an overall city-wide LST rise of ~5–6 °C from 1990 to 2018, the park cool island intensity (temperature difference between park and city) remained stable (no significant long-term trend, p > 0.7). Bootstrapped 95% confidence intervals confirmed that each park’s cooling effect was statistically significant in each year analyzed. NDMI (vegetation moisture content) correlated more strongly with LST (r ~ −0.90) than NDVI (r ~ −0.7 to −0.9), highlighting the importance of vegetation moisture in park cooling. These findings demonstrate that well-watered urban parks can sustain substantial cooling benefits over decades of urban development. The persistent ~2–3 °C daytime cooling observed underscores the value of water-sensitive green space planning as a long-term urban heat mitigation strategy.