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Meteorological hazards, such as floods, can develop rapidly and are usually a local phenomenon. On the other hand, drought is a phenomenon arising over longer periods. Moreover, it occurs in areas that are remarkably diverse in terms of surface area. Drought has a massive impact on agriculture, socio-economic activities, and the natural environment. However, determining the losses associated with the phenomenon of drought and its identification is complicated. The aim of this paper is to identify and quantify droughts using climatic indices, which include the combined climatologic deviation index, groundwater drought index, water storage deficit index and multivariate standardized drought index. Based on the research, it was concluded that the CCDI, GGDI, WSDI, and MSDI indicators can be a useful tool, on the basis of which it was possible to analyze drought periods. These periods were not related to changes and loss of groundwater, but resulted from low rainfall and snowfall. [ABSTRACT FROM AUTHOR]

The number of people living in cities is continuously growing, and the buildings in topographic maps are in need of frequent updates, which are costly to perform manually. This makes automatic building extraction a significant research subject. Transfer learning, on the other hand, offers solutions in situations where the data of a target area are scarce, making it a profitable research subject. Moreover, in previous studies, there was a lack of metrics in quantifying the accuracy improvement with transfer learning techniques. This paper investigated various transfer learning techniques and their combinations with U-Net for the semantic segmentation of buildings from true orthophotos. The results were analyzed using quantitative methods. Open-source remote sensing data from Poland were used for pretraining a model for building segmentation. The fine-tuning techniques including a fine-tuning contracting path, a fine-tuning expanding path, a retraining contracting path, and a retraining expanding path were studied. These fine-tuning techniques and their combinations were tested with three local datasets from the diverse environment in Finland: urban, suburban, and rural areas. Knowledge from the pretrained model was transferred to the local datasets from Helsinki (urban), Kajaani (suburban), and selected areas in Finland (rural area). Three models with no transfer learning were trained from scratch with three sets of local data to compare the fine-tuning results. Our experiment focused on how various transfer learning techniques perform on datasets from different environments (urban, suburban, and rural areas) and multiple locations (southern, northern, and across Finland). A quantitative assessment of performance improvement by using transfer learning techniques was conducted. Despite the differences in datasets, the results showed that using transfer learning techniques could achieve at least 5% better accuracy than a model trained from scratch with several different transfer learning techniques. In addition, the effect of the sizes of training datasets was also studied. [ABSTRACT FROM AUTHOR]

Modern public transportation in urban areas increasingly relies on high-capacity buses. At the same time, the share of electric vehicles is increasing to meet environmental standards. This introduces problems when charging these vehicles from chargers at bus stops, as untrained drivers often find it difficult to execute docking manoeuvres on the charger. A practical solution to this problem requires a suitable advanced driver-assistance system (ADAS), which is a system used to automatise and make safer some of the tasks involved in driving a vehicle. In the considered case, ADAS supports docking to the electric charging station, and thus, it must solve two issues: precise positioning of the bus relative to the charger and motion planning in a constrained space. This paper addresses these issues by employing GNSS-based positioning and optimisation-based planning, resulting in an affordable solution to the ADAS for the docking of electric buses while recharging. We focus on the practical side of the system, showing how the necessary features were attained at a limited hardware and installation cost, also demonstrating an extensive evaluation of the fielded ADAS for an operator of public transportation in the city of Poznań in Poland. [ABSTRACT FROM AUTHOR]

The properties of soil constitute one of the most important features of the environment that determine the potential for food production in a given region. Knowledge of the soil texture and agroclimate allows for the proper selection of species and agrotechnics in plant production. However, in contrast to the agroclimate, the soil may show a large spatial variation of physical and chemical characteristics within the plot. In regions where the soil diversity is so high that the available soil maps are not sufficient, the only method that allows for precise mapping of the soil mosaic is remote sensing. This paper presents the concepts of using Sentinel-2 multispectral satellite images to detail the available soil-agriculture map at a scale of 1:25,000. In the presented work, the following research hypothesis has been formulated: spatial and temporal analysis of high-resolution satellite images can be used to improve the quality of a large-scale archival soil-agriculture map. It is possible due to the spatial differentiation of the spectral reflection from the field (canopy), which is influenced by soil conditions—especially the differentiation of physical properties (granulometric composition) in soil profiles which determine the possibility of water retention during drought conditions. The research carried out as a case study of maize remote sensing confirmed the hypothesis. It was based on the selection of the most appropriate term (maize development period: BBCH 79, 6-decade drought index: CBW = −206 mm) and the vegetation index (NDVI). This made it possible to make the scale of the map 10 times more detailed. The obtained results are the first step in developing a general model (based on remote sensing) for detailing the soil-agriculture map for Poland, which will significantly improve the accuracy of the drought monitoring system developed by the Institute of Soil Science and Plant Cultivation (Poland). [ABSTRACT FROM AUTHOR]

Landslides, as one of the main problems in mountainous areas, are a challenging issue for modern geophysics. The triggers that cause these phenomena are diverse (including geological, geomorphological, and hydrological conditions, climatic factors, and earthquakes) and can occur in conjunction with each other. Human activity is also relevant, undoubtedly contributing to the intensification of landslide phenomena. One of these is the production of artificial snow on ski slopes. This paper presents a multimethod approach for imaging the landslide structure in Cisiec, in southwestern Poland, where such a situation occurs. In the presented work, the integration of remote sensing with multi-method geophysical imaging was used to visualize landslide zones, and to estimate ground motion. To verify the uncertainty of the obtained data, the combination of electrical resistivity tomography (ERT), multi-channel analysis of surface waves (MASW), and seismic refraction method (SRT) was supported by synthetic modeling. Using geophysical data with accurate GPS-based topography and a terrestrial laser scanning-based digital terrain model (DTM), it was possible to model the spatial variability and surface area of the landslide more precisely, as well as to estimate the velocity field in the nearest surface more accurately. The final result shows displacement up to 1 m on the ground surface visible on the DTM models, while the geophysical methods confirm the change in internal structure. The proposed methodology is fast, cost-effective, and can be used to image the structure of landslides, where the shallowest parts are usually complex and thus difficult to observe seismically. [ABSTRACT FROM AUTHOR]

The paper investigates the usage of LiDAR (light detection and ranging) data for the automation of mapping vegetation with respect to the evaluation of the ecological succession process. The study was performed for the repository of the "Fryderyk" mine (southern Poland). The post-flotation area analyzed is a unique refuge habitat—Natura2000, PLH240008—where a forest succession has occurred for several dozen years. Airborne laser scanning (ALS) point clouds were used for deriving detailed information about the morphometry of the spoil heap and about the secondary forest succession process—mainly vegetation parameters i.e., height and canopy cover. The area of the spoil heap is irregular with a flat top and steep slopes above 20°. Analyses of ALS point clouds (2011 and 2019), confirmed progression in the forest succession process, and land cover changes especially in wooded or bushed areas. Precise vegetation parameters (3D LiDAR metrics) were calculated and provided the following parameters: mean value of vegetation height as 6.84 m (2011) and 8.41 m (2019), and canopy cover as 30.0% (2011) and 42.0% (2019). Changes in vegetation volume (3D area) were shown: 2011—310,558 m3, 2019—325,266 m3, vegetation removal—85,136 m3, increasing ecological succession—99,880 m3. [ABSTRACT FROM AUTHOR]

Multibeam echo sounders (MBES), side-scan sonars (SSS), and remotely operated vehicles (ROVs) are irreplaceable devices in contemporary hydrographic works. However, a highly reliable method of identifying detected wrecks is visual inspection through diving surveys. During underwater research, it is sometimes hard to obtain images in turbid water. Moreover, on-site diving operations are time-consuming and expensive. This article presents the results of the remote sensing surveys that were carried out at the site of a newly discovered wreck, in the southern part of the Baltic Sea (Poland). Remote sensing techniques can quickly provide a detailed overview of the wreckage area and thus considerably reduce the time required for ground truthing. The goal of this paper is to demonstrate the process of identification of a wreck based on acoustic data, without involving a team of divers. The findings, in conjunction with the collected archival documentation, allowed for the identification of the wreck of a Junkers Ju-88, a bomber from World War II. [ABSTRACT FROM AUTHOR]

Nowadays, Poland is one of the European countries most affected by agricultural land abandonment (ALA). Though considered to be a negative phenomenon, ALA opens up several options for planning future land uses critical for biodiversity conservation or future carbon sequestration. So far, many studies of ALA have been done in the mountainous areas in Poland, but less is known about the magnitude of ALA in other regions. In this paper we use the declassified CORONA satellite imagery (1969) to backdate the information on land cover and land abandonment from topographic maps from 1970s for the region located in central Poland and currently affected by widespread ALA. The information from archival materials is compared with current High-Resolution Layers and airborne laser scanning products, indicating that a forest cover increase of 23% was observed. The output of vegetation height analysis confirmed significant land use transformation from non-forest and ALA into forest area. Additionally, analysis of forest pattern change revealed that although forest core areas have increased since 1970, its share in total forest cover decreased due to newly established small forest patches. Our research shows the importance of archival remote sensing materials and indicates their role in understanding ALA-related forest cover change in Poland over the last 50 years. [ABSTRACT FROM AUTHOR]

The COVID-19 lockdown in 2020–2021 and the refugee crisis in 2021–2022 were two new and unexpected social and political events in Poland in recent years. These "wildcards" will certainly have major effects on individuals and cities, both directly and indirectly, through the influence of "externalities." The paper examines trends in the spatial development of Polish cities during the last five years (2016–2021), focusing on residential suburbanization and urban sprawl. The study aims to reveal the elements that determine the spatial scale of suburbanization, as well as "wildcards" that may have an indirect impact on the process but are difficult to quantify and include in spatial analysis. The use of location quotient (LQ) metrics, as well as a subset of the Global Human Settlement Layer in the spatial analysis allow for comparisons of locations with intensified urbanization throughout different periods, serving a task that is comparable to feature standardization from a time and space viewpoint. The analysis provides evidence of growing suburbanization surrounding major Polish cities from 2016 to 2021, while also exposing distinct elements of spatial development during a period that was marked by social and political stress (2021). [ABSTRACT FROM AUTHOR]

This paper presents the results of investigations on the beamforming of a low-frequency radio-telescope LOFAR which can be used as a receiver in passive coherent location (PCL) radars for aerial and space object detection and tracking. The use of a LOFAR radio-telescope for the passive tracking of space objects can be a highly cost-effective solution due to the fact that most of the necessary equipment needed for passive radiolocation already exists in the form of LOFAR stations. The capability of the radiolocation of planes by a single LOFAR station in Borowiec is considered to be 'proof of concept' for future research focused on the localization of space objects. Beam patterns of single sets of LOFAR antennas (known as tiles), as well as for the entire LOFAR station, are presented and thoroughly discussed in the paper. Issues related to grating lobes in LOFAR beam patterns are also highlighted. A beamforming algorithm used for passive radiolocation purposes, exploiting data collected by a LOFAR station, is also discussed. The results of preliminary experiments carried out with real signals collected by the LOFAR station in Borowiec, Poland confirm that the appropriate beamforming can significantly increase the radar's detection range, as well as the detection's certainty. [ABSTRACT FROM AUTHOR]

Indicator 11.3. 1 of the 2030 sustainable development goals (SDG) 11, i.e., the ratio of the land use to the population growth rate, is currently classified by the United Nations as a Tier II indicator, as there is a globally-accepted methodology for its calculation, but the data are not available, nor are not regularly updated. Recently, the increased availability of remotely sensed data and products allows not only for the calculation of the SDG 11.3. 1, but also for its monitoring at different levels of detail. That is why this study aims to address the interrelationships between population development and land use changes in Poland and Lithuania, two neighboring countries in Central and Eastern Europe, using the publicly available remotely sensed products, CORINE land cover and GHS-POP. The paper introduces a map modelling process that starts with data transformation through GIS analyses and results in the geovisualisation of the LCRPGR (land use efficiency), the PGR (population growth rate), and the LCR (land use rate). We investigated the spatial patterns of the index values by utilizing hotspot analyses, autocorrelations, and outlier analyses. The results show how the indicators' values were concentrated in both countries; the average value of SDG 11.3. 1, from 2000 to 2018 in Poland amounted to 0.115 and, in Lithuania, to −0.054. The average population growth ratio (PGR) in Poland equaled 0.0132, and in Lithuania, it was −0.0067, while the average land consumption ratios (LCRs) were 0.0462 and 0.0067, respectively. Areas with an increase in built-up areas were concentrated mainly on the outskirts of large cities, whereas outliers of the LCRPGR index were mainly caused by the uncertainty of the source data and the way the indicator is interpreted. [ABSTRACT FROM AUTHOR]

Recent advances in search and rescue methods include the use of unmanned aerial vehicles (UAVs), to carry out aerial monitoring of terrains to spot lost individuals. To date, such searches have been conducted by human observers who view UAV-acquired videos or images. Alternatively, lost persons may be detected by automated algorithms. Although some algorithms are implemented in software to support search and rescue activities, no successful rescue case using automated human detectors has been reported on thus far in the scientific literature. This paper presents a report from a search and rescue mission carried out by Bieszczady Mountain Rescue Service near the village of Cergowa in SE Poland, where a 65-year-old man was rescued after being detected via use of SARUAV software. This software uses convolutional neural networks to automatically locate people in close-range nadir aerial images. The missing man, who suffered from Alzheimer's disease (as well as a stroke the previous day) spent more than 24 h in open terrain. SARUAV software was allocated to support the search, and its task was to process 782 nadir and near-nadir JPG images collected during four photogrammetric flights. After 4 h 31 min of the analysis, the system successfully detected the missing person and provided his coordinates (uploading 121 photos from a flight over a lost person; image processing and verification of hits lasted 5 min 48 s). The presented case study proves that the use of an UAV assisted by SARUAV software may quicken the search mission. [ABSTRACT FROM AUTHOR]

The main purpose of the paper is to determine changes in transport behaviour of users of the public bike-share (PBS) scheme in a large Polish city, Łódź. By tracking GPS signals for individual trips taken by PBS users, it was possible to analyse their changeability (time and spatial) for periods before the implementation of statutory Sunday retail restrictions (2017) and after their partial introduction (2018). The study also took into account weather conditions, namely maximum and minimum daily temperatures and daily totals of precipitation recorded by a weather station in Łodź. In order to determine the correlations between certain weather conditions and PBS trips, the authors applied regression analysis. The results of the study showed that weekend cycling is less susceptible to the impact of weather than cycling on weekdays. At the same time, a comparative analysis of trading and non-trading Sundays proved that, during Sundays with retail restrictions, public bikes were used for longer, farther, and slower trips. These observations were confirmed by analyses of maps of traffic structure. [ABSTRACT FROM AUTHOR]

Highly accurate and extensive datasets are needed for the practical implementation of precision forestry as a method of forest ecosystem management. Proper processing of huge datasets involves the necessity of the appropriate selection of methods for their analysis and optimization. In this paper, we propose a concept for and implementation of a data preprocessing algorithm, and a method for the empirical verification of selected individual tree detection (ITD) algorithms, based on Airborne Laser Scanning (ALS) data. In our study, we used ALS data and very extensive dendrometric field measurements (including over 21,000 trees on 522 circular sample plots) in the economic and protective coniferous stands of north-eastern Poland. Our algorithm deals well with the overestimation problems of tree top detection. Furthermore, we analyzed segmentation parameters for the two currently dominant ITD methods: Watershed (WS) and Local Maximum Filter with Growing Region (LMF+GR). We optimized them with respect to minimizing the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Additionally, our results show the crucial importance of the quality of empirical data for the correct evaluation of the accuracy of ITD algorithms. [ABSTRACT FROM AUTHOR]

The article presents the results of research on multi-SBAS (multi-satellite-based augmentation system) positioning in UAV (unmanned aerial vehicle) technology. For this purpose, a new solution was developed for combining the UAV position navigation solution from several SBAS systems. In this particular case, the presented linear combination algorithm is based on the fusion of EGNOS (European geostationary navigation overlay service) and SDCM (system of differential correction and monitoring) positioning to determine the resultant UAV coordinates. The algorithm of the mathematical model uses weights of measurements in three ways, i.e., Variant I, the reciprocal of the number of tracked satellites from a single SBAS solution; Variant II, the inverse square of mean coordinate errors from a single SBAS solution; and Variant III, the reciprocal of UAV flight speed from a single SBAS solution. The research experiment used real GNSS (global navigation satellite system) navigation data recorded by the VTOL unmanned platform. The test flight was made in April 2020 in Poland, near Warsaw. Based on the developed research results, it was found that the highest accuracy of UAV positioning was obtained when using the weighting model for Variant II. In the weight model of Variant II, the accuracy of the solution of the UAV position increased by 1–2% for the horizontal components and 19–22% for the vertical component h, concerning the results obtained from the weighing Variants I and III. It is worth noting that the proposed research model significantly improves the results of determining the ellipsoidal height h. Compared to the arithmetic mean model, determining the h component in the Variant II weight model is improved by about 23%. The paper also shows the advantage of EGNOS+SDCM positioning over EGNOS positioning alone in determining the accuracy of the vertical component h. The obtained research results show the significant advantages of the multi-SBAS positioning model in UAV technology. [ABSTRACT FROM AUTHOR]

Peatlands play an important role in the global carbon cycle due to the high carbon storage in the substrate. Ecosystem production depends, for example, on the solar energy amount that reaches the vegetation, however the diffuse component of this flux can substantially increase ecosystem net productivity. This phenomenon is observed in different ecosystems, but the study of the atmosphere optical properties on peatland production is lacking. In this paper, the presented methodology allowed us to disentangle the diffuse radiation impact on the net ecosystem production (NEP) of Rzecin peatland, Poland. It allowed us to assess the impact of the atmospheric scattering process determined by the aerosol presence in the air mass. An application of atmospheric radiation transfer (ART) and ecosystem production (EP) models showed that the increase of aerosol optical thickness from 0.09 to 0.17 caused NEP to rise by 3.4–5.7%. An increase of the diffusion index (DI) by 0.1 resulted in an NEP increase of 6.1–42.3%, while a DI decrease of 0.1 determined an NEP reduction of −49.0 to −10.5%. These results show that low peatland vegetation responds to changes in light scattering. This phenomenon should be taken into account when calculating the global CO2 uptake estimation of such ecosystems. [ABSTRACT FROM AUTHOR]

Tillage of arable fields, using for instance a smoothing harrow, may increase the magnitude of albedo of such soil surfaces depending on the location, the sun's illumination and atmospheric components. As these soil surfaces absorb less shortwave radiation compared to plowed soils, the result is an atmospheric cooling and a positive effect on the Earth's climate. This paper is the follow-on of a previous study aimed at quantifying the seasonal dynamics of net shortwave radiation reflected by bare air-dried arable land areas located in contrasting environments, i.e. Poland and Israel. Soil tillage includes a plow, a disk harrow, and a smoothing harrow. Previous work concentrated on the estimate of net shortwave radiation under clear-sky theoretical scenarios, whereas the present study deals with a realistic atmosphere throughout the year 2014. This latter is characterized by the observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the Meteosat Second Generation (MSG). The variations of the net shortwave radiation for the selected bare arable land areas were assessed in combining observations from Landsat 8 images and digital maps of land use and soil, plus model equations that calculate the diurnal variations of the broadband blue-sky albedo with roughness inclusive. The daily amount of net shortwave radiation for air-dried bare arable land in Poland and Israel for the time their spatial coverage is the largest was found to be about 40–50% and 10% lower, respectively, in cloudy-sky conditions compared to clear-sky conditions. [ABSTRACT FROM AUTHOR]

This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (Poland–AOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (June–September 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48–0.83). The mean bias is 0.10–0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with Poland–AOD correlation values are 0.55 to 0.71. The mean bias is 0.04–0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33–0.39). Biases of −0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the ground–stations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the Poland–AOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts. [ABSTRACT FROM AUTHOR]

Automatic building extraction from remote sensing data is a hot but challenging research topic for cadastre verification, modernization and updating. Deep learning algorithms are perceived as more promising in overcoming the difficulties of extracting semantic features from complex scenes and large differences in buildings' appearance. This paper explores the modified fully convolutional network U-Shape Network (U-Net) for high resolution aerial orthoimagery segmentation and dense LiDAR data to extract building outlines automatically. The three-step end-to-end computational procedure allows for automated building extraction with an 89.5% overall accuracy and an 80.7% completeness, which made it very promising for cadastre modernization in Poland. The applied algorithms work well both in densely and poorly built-up areas, typical for peripheral areas of cities, where uncontrolled development had recently been observed. Discussing the possibilities and limitations, the authors also provide some important information that could help local authorities decide on the use of remote sensing data in land administration. [ABSTRACT FROM AUTHOR]

Landslides and rock falls are among the many phenomena that have an impact on sustainable construction and infrastructure safety. The main causes of landslides are natural meteorological and hydrological phenomena. In building design and construction, environmental monitoring by identifying geotechnical hazards must be taken into account, as appropriate hazard assessment contributes to ensuring future construction safety. The Carpathian region in southern Poland is particularly predisposed to landslide formation. This may be favored by the nature of the shapes associated with the high and steep slopes of the region's valleys. Another reason for concern is the flysch geological structure, which is characterized by alternating layers of water-permeable sandstones and poorly permeable shales, clays, and marls. Furthermore, the presence of a quaternary weathering cover makes the geological structure more susceptible to landslide processes and tectonic formations. The paper presents the results of a study whose aim was to elaborate a detailed landslide hazard map for a selected area in the Polish Carpathians, using statistical methods. The approach is based on the Hellwig method, which seems particularly useful in the assessment of susceptibility and landslide hazards on a local scale for a relatively small area. A two-stage study was conducted. The first stage of the research involved the creation of a database associated with environmental parameters and triggering factors, whereas the second stage consisted of the adoption of weights for seven thematic sections and their special features on the basis of expert knowledge. The hazard map developed as a result was compared to the mapping made using the weight-of-evidence method. The proposed data normalization method allows the use and analysis of both qualitative and quantitative data collected from various sources. The advantage of this method is the simple calculation procedure. A large-scale (1:2000) map might be used to assess the landslide hazard for specific cadastral units. Such a map becomes the basis for municipal spatial planning and may be able to influence investment decisions. Detailed landslide hazard maps are crucial for more precise risk evaluation for specific cadastral units. This, in turn, allows one to reduce serious economic and social losses, which might be the future results of landslides. [ABSTRACT FROM AUTHOR]

Geographic object-based image analysis (GEOBIA) is a primary remote sensing tool utilized in land-cover mapping and change detection. Land-cover patches are the primary data source for landscape metrics and ecological indicator calculations; however, their application to visual landscape character (VLC) indicators was little investigated to date. To bridge the knowledge gap between GEOBIA and VLC, this paper puts forward the theoretical concept of using viewpoint as a landscape imageability indicator into the practice of a multi-temporal land-cover case study and explains how to interpret the indicator. The study extends the application of GEOBIA to visual landscape indicator calculations. In doing so, eight different remote sensing imageries are the object of GEOBIA, starting from a historical aerial photograph (1957) and CORONA declassified scene (1965) to contemporary (2018) UAV-delivered imagery. The multi-temporal GEOBIA-delivered land-cover patches are utilized to find the minimal isovist set of viewpoints and to calculate three imageability indicators: the number, density, and spacing of viewpoints. The calculated indicator values, viewpoint rank, and spatial arrangements allow us to describe the scale, direction, rate, and reasons for VLC changes over the analyzed 60 years of landscape evolution. We found that the case study nature reserve ("Kózki", Poland) landscape imageability transformed from visually impressive openness to imageability due to the impression of several landscape rooms enclosed by forest walls. Our results provide proof that the number, rank, and spatial arrangement of viewpoints constitute landscape imageability measured with the proposed indicators. Discussing the method's technical limitations, we believe that our findings contribute to a better understanding of land-cover change impact on visual landscape structure dynamics and further VLC indicator development. [ABSTRACT FROM AUTHOR]

Analyses of riverbed shape evolution are crucial for environmental protection and local water management. For narrow rivers located in forested, mountain areas, it is difficult to use remote sensing data used for large river regions. We performed a study of the Prądnik River, located in the Ojców National Park (ONP), Poland. A multitemporal analysis of various data sets was performed. Light detection and ranging (LiDAR)-based data and orthophotomaps were compared with classical survey methods, and 78 cross-sectional profiles were done via GNSS and tachymetry. In order to add an extra time step, the old maps of this region were gathered, and their content was compared with contemporary data. The analysis of remote sensing data suggests that they do not provide sufficient information on the state and changes of riverbanks, river course or river depth. LiDAR data sets do not show river bottoms, and, due to plant life, do not document riverbanks. The orthophotomaps, due to tree coverage and shades, cannot be used for tracking the whole river course. The quality of old maps allows only for general shape analysis over time. This paper shows that traditional survey methods provide sufficient accuracy for such analysis, and the resulted cross-sectional profiles can and should be used to validate other, remote sensing, data sets. We diagnosed problems with the inventory and monitoring of such objects and proposed methods to refine the data acquisition. [ABSTRACT FROM AUTHOR]

Detecting land cover changes requires timely and accurate information, which can be assured by using remotely sensed data and Geographic Information System(GIS). This paper examines spatiotemporal trends in land cover changes in the Polish Baltic coastal zone, especially the urbanisation, loss of agricultural land, afforestation, and deforestation. The dynamics of land cover change and its impact were discussed as the major findings. The analysis revealed that land cover changes on the Polish Baltic coast have been consistent throughout the 1990–2018 period, and in the consecutive inventories of land cover, they have changed faster. As shown in the research, the area of agricultural land was subject to significant change, i.e., about 40% of the initial 8% of the land area in heterogeneous agriculture was either developed or abandoned at about equal rates. Next, the steady growth of the forest and semi-natural area also changed the land cover. The enlargement of the artificial surface was the third observed trend of land cover changes. However, the pace of land cover changes on the Baltic coast is slightly slower than in the rest of Poland and the European average. The region is very diverse both in terms of land cover, types of land transformation, and the pace of change. Hence, the Polish national authorities classified the Baltic coast as an area of strategic intervention requiring additional action to achieve territorial cohesion and the goals of sustainable development. [ABSTRACT FROM AUTHOR]

Reservoirs are formed through the artificial damming of a river valley. Reservoirs, among others, capture polluted load transported by the tributaries in the form of suspended and dissolved sediments and substances. Therefore, reservoirs are treated in the European Union (EU) as "artificial" or "heavily modified" surface water bodies. The reservoirs' pollutant load depends to a large extent on the degree of anthropogenic impact in the respective river catchment area. The purpose of this paper is to assess the mutual relation between the catchment area and the reservoirs. In particular, we focus on the effects of certain land use/land cover on reservoirs' water quality. For this study, we selected twenty Polish reservoirs for an in-depth analysis using 2018 CORINE Land Cover data. This analysis allowed the identification of the main triggering factors in terms of water quality of the respective reservoirs. Moreover, our assessment clearly shows that water quality of the analysed dam reservoirs is directly affected by the composition of land use/land cover, both of the entire total reservoir catchment areas and the directly into the reservoir draining sub-catchment areas. [ABSTRACT FROM AUTHOR]

Due to the pathogenic nature of asbestos, a statutory ban on asbestos-containing products has been in place in Poland since 1997. In order to protect human health and the environment, it is crucial to estimate the quantity of asbestos–cement products in use. It has been evaluated that about 90% of them are roof coverings. Different methods are used to estimate the amount of asbestos–cement products, such as the use of indicators, field inventory, remote sensing data, and multi- and hyperspectral images; the latter are used for relatively small areas. Other methods are sought for the reliable estimation of the quantity of asbestos-containing products, as well as their spatial distribution. The objective of this paper is to present the use of convolutional neural networks for the identification of asbestos–cement roofing on aerial photographs in natural color (RGB) and color infrared (CIR) compositions. The study was conducted for the Chęciny commune. Aerial photographs, each with the spatial resolution of 25 cm in RGB and CIR compositions, were used, and field studies were conducted to verify data and to develop a database for Convolutional Neural Networks (CNNs) training. Network training was carried out using the TensorFlow and R-Keras libraries in the R programming environment. The classification was carried out using a convolutional neural network consisting of two convolutional blocks, a spatial dropout layer, and two blocks of fully connected perceptrons. Asbestos–cement roofing products were classified with the producer's accuracy of 89% and overall accuracy of 87% and 89%, depending on the image composition used. Attempts have been made at the identification of asbestos–cement roofing. They focus primarily on the use of hyperspectral data and multispectral imagery. The following classification algorithms were usually employed: Spectral Angle Mapper, Support Vector Machine, object classification, Spectral Feature Fitting, and decision trees. Previous studies undertaken by other researchers showed that low spectral resolution only allowed for a rough classification of roofing materials. The use of one coherent method would allow data comparison between regions. Determining the amount of asbestos–cement products in use is important for assessing environmental exposure to asbestos fibres, determining patterns of disease, and ultimately modelling potential solutions to counteract threats. [ABSTRACT FROM AUTHOR]

The main goal of the paper is to verify the impact of the absorption of European Union (EU) grants on land cover changes in Polish municipalities in the years 2012–2018. The selection of the research area was justified by the fact that Poland is the largest and significantly spatially differentiated transition economy in Central-Eastern Europe, recognised as a substantial beneficiary of EU accession in 2004. The time range of analysis was set as the result of a comparison of data availability in Corine Land Cover (CLC) and Statistics Poland. The CLC dataset referring to land cover and land use changes between 2012 and 2018 was used. The focus on modifications taking place within one of the main land cover groups at level 3 of detail was applied in this research. These changes were analysed as percentages referring to the area of the municipality and to the total area of changes in the investigated period. Two categories of EU grants were considered: total and infrastructural (granted under EU Operational Programme "Infrastructure and Environment"). Moreover, some control economic, social, demographic, institutional, infrastructural, and environmental variables were applied to better explain land cover changes. Moran's local statistic was employed to detect spatial hot-spots of EU grants absorption, as well as hot-spots of land cover changes. Then, a collection of various variables related to determinants of land cover changes was set. Economic factors, including EU grants absorption, as well as factors related to accessibility, agrarian structure, demography, environment, and spatial planning were investigated. Principal Component Analysis (PCA) was employed to convert the set of all considered variables into a set of few uncorrelated predictors. Finally, Geographically Weighted Regression (GWR) was applied to describe the spatially varied impact of investigated determinants, including EU grants, represented by estimated principal components on land cover changes. [ABSTRACT FROM AUTHOR]