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This paper aims to provide an illustrative assessment of the functioning of labour law enforcement in Hungary from a collective labour law perspective, based on selected topical regulatory case studies. In doing so, the paper intends to demonstrate the generally and largely deficient nature of the collective dimension of labour law enforcement in Hungary. Namely, the first case study describes the situation of amicable settlement of collective labour disputes in Hungary and the prevailing institutional vacuum in this regard. The second case study deals with the very limited possibilities of “actio popularis” and strategic litigation in Hungarian labour law and the potential of these legal mechanisms to further labour law enforcement. The third case study outlines the meagre legal and practical room for manoeuvre of alt-labour organisations in the Hungarian context and points out that their chance for collective bargaining is extremely small. The paper formulates some policy pointers and proposals on how the collective dimension of labour law enforcement (especially concerning these three regulatory fields illustrated by the case studies) could be further promoted and enhanced in Hungary, especially in the context of a constantly changing, new world of work.

The economic viability of orchard sanitation practices, is crucial for sustainable apple production. However, our knowledge in this area is limited, particularly after the fruit is stored in the high-energy-consuming ultra-low oxygen (ULO) storage system. The objective of this 3-year study was to investigate the cost-benefit ratios of five sanitation treatments (lime sulfur-Lime-S, leaf collection-Collect-L, mulching-Mulch-C, lime sulfur + leaf collection, leaf collection + mulching) in integrated and organic apple orchards, considering the sale of apples after 6-month ULO storage. Cost-benefit analyses determined cost, total revenue, revenue for class 1 fruit (fruits without scab infection) and income surplus. Costs of ULO storage were twice higher in the integrated orchard (mean 3,064 EUR ha−1) than in the organic one (mean 1,512 EUR ha−1). Direct costs of the two combined sanitation treatments were significantly higher than the Lime-S and Collect-L treatments across all years and orchard systems. Analysis of variance for total revenue, revenue for class 1 fruit and income surplus revealed significant differences among years, sanitation treatments, and orchard systems. The total revenue and revenue for class 1 fruit were significantly higher in the integrated orchard (10,787 and 10,557 EUR ha−1, respectively) than in the organic one (8,713 and 7,742 EUR ha−1, respectively). The lowest total revenue and revenue for class 1 fruit were obtained in the non-sanitized control, while highest were recorded in the Collect-L or Collect-L + Mulch-C treatments. Collect-L and Collect-L + Mulch-C treatments provided the highest income surplus in all years and orchard systems. Kernel density estimations and frequency distributions indicated the widest variability for total revenue and revenue for class 1 fruit in the integrated orchard system. Correlation and linear regression analyses revealed significant relationship between total revenue and revenue for class 1 fruit in both orchard systems. In conclusion, our study demonstrates that Collect-L and Collect-L + Mulch-C treatments offer the greatest economic benefit after 6-month ULO storage regardless of the orchard system employed.

Abstract The amount of dust deposited on tree leaves is a cost-effective indicator of air quality. Our aim was to explore the leaf surface deposition, and chlorophyll content of leaves along a road section that started at an intersection, and ended in a less disturbed suburban area in Debrecen, Hungary. We also assessed the impact of meteorological conditions on the amount of deposited dust. Leaf samples were collected in July, and September 2022 from Celtis occidentalis, a frequent species in green urban areas of Debrecen. We found a significant negative correlation between dust deposition, and the distance from the intersection in July. In September, dust deposition decreased considerably compared to July, due to rainfall before the second sampling. Surprisingly, we found a positive correlation between dust deposition and chlorophyll content in July. Our findings suggest that dust deposition on leaves serves as a reliable indicator of traffic intensity, because the excess dust caused by the proximity of vehicle traffic can be detected on the leaf surface. Although, rainfall can disrupt the patterns in dust deposition that have developed over an extended period through wash-off and resuspension. Hence, it is advisable to consider these effects while selecting the sampling time and evaluating the results.

Abstract Species composition of forests is a very important component from the point of view of nature conservation and forestry. We aimed to identify 10 tree species in a hilly forest stand using a hyperspectral aerial image with a particular focus on two invasive species, namely Ailanthus tree and black locust. Deep learning‐based training data augmentation (TDA) and post‐classification techniques were tested with Random Forest and Support Vector Machine (SVM) classifiers. SVM had better performance with 81.6% overall accuracy (OA). TDA increased the OA to 82.5% and post‐classification with segmentation improved the total accuracy to 86.2%. The class‐level performance was more convincing: the invasive Ailanthus trees were identified with 40% higher producer's and user's accuracies (PA and UA) to 70% related to the common technique (using a training dataset and classifying the trees). The PA and UA did not change in the case of the other invasive species, black locust. Accordingly, this new method identifies well Ailanthus, a sparsely distributed species in the area; while it was less efficient with black locust that dominates larger patches in the stand. The combination of the two ancillary steps of hyperspectral image classification proved to be reasonable and can support forest management planning and nature conservation in the future.

Societal Impact statement Reduced fungicide use lowers environmental pollution and enables safer food production. The usage of fungicides in apple orchards can be reduced through the application of sanitation practices which decrease the inoculum sources of apple scab disease on fallen leaves. This study found two non‐chemical sanitation practices, namely the collection of fallen leaves (CFL) and CFL combined with straw mulch in tree rows, were beneficial. These two practices are not only biologically and environmentally valuable, as they reduce disease levels and can replace chemical fungicides, but they are also economically efficient options for integrated and organic orchards compared to non‐sanitized ones. Summary Severe fungicide use can be reduced by applications of sanitation practices in order to reduce scab incidence, yield and fruit quality losses in apple orchards. In a 5‐year study, we aimed to investigate the effect of sanitation practices on biological and cost–benefit parameters in two sustainable apple management systems, and to find significant correlations among the parameters. We investigated the effect of five sanitation treatments (lime sulphur, leaf collection, mulching, lime sulphur + leaf collection, leaf collection + mulching) on four biological (scab incidence, fruit parameters: total yield, yield class I and II) and seven cost–benefit (three cost types, three annual revenue types, income surplus/deficit) parameters in integrated and organic apple orchards. Correlation, linear regression and principal component analyses (PCA) were performed to find correlations among biological and cost–benefit parameters. Results showed that fruit scab incidence was 3.4–8.1 times higher, while total yield was 1.4–1.8 times lower in the organic management system than in the integrated one. The treatment of leaf collection and/or leaf collection + mulching showed higher total cost (180.3 and 675.2 EUR ha−1) but lower scab incidence (5.3 and 27.3%; 4.8 and 26.7%, integrated and organic, respectively) and higher yield with greater total revenues (10,235 and 10,329 EUR ha−1; 8,136 and 8,230 EUR ha−1, integrated and organic, respectively) and income surpluses (851 and 451 EUR ha−1; 897 and 496 EUR ha−1, integrated and organic, respectively) compared to non‐sanitized control treatments in most cases. Other sanitation treatments provided fewer biological and/or no financial benefits. Results from correlation and linear regression analyses indicated strong relationships among the factors of total yield vs surplus, class I vs surplus, and fruit scab vs class II) in both management systems. Further relationships were detected among almost all parameters in the PCA. Overall, our study demonstrated that two non‐chemical sanitation treatments could not only reduce scab incidence and increase fruit yield, but could show positive cost–benefit outcomes in both management systems.

Building extraction from remote sensing data is an important topic in urban studies and the deep learning methods have an increasing role due to their minimal requirements in training data to reach outstanding performance. We aimed to investigate the original U-Net architecture’s efficiency in building segmentation with different number of training images and the role of data augmentation based on multisource remote sensing data with varying spatial and spectral resolutions (WorldView-2 [WV2], WorldView-3 [WV3] images and an aerial orthophoto [ORTHO]). When the trainings and predictions were conducted on the same image, U-Net provided good results with very few training images (validation accuracies: 94-97%; 192 images). Combining the ORTHO’s and WV2’s training data for prediction on WV3 provided poor results with low F1-score (0.184). However, the inclusion of only 48 WV3 training images significantly improved the F1-score (0.693), thus, most buildings were correctly identified. Accordingly, using only independent reference data (other than the target image) is not enough to train an accurate model. In our case, the reference from WW2 and ORTHO images did not provide an acceptable basis to train a good model, but a minimal number of training images from the targeted WV3 improved the accuracy (F1-score: 69%).

Reducing environmental pollution is a critical goal in global environmental economics and economic development. The European Union (EU) faces environmental challenges due to its development activities. Here we present a comprehensive approach to assess the impact of carbon dioxide (CO2) emissions, energy consumption (EC), population structure (POP), economy (GDP), and policies on the environment within the EU using the environmental Kuznets curve (EKC). Our research reveals that between 1990 and 2019, the EU-27 experienced an increase of +1.18 million tonnes of oil equivalent (Mtoe) per year in energy consumption (p

Abstract Introduction Crop production contribution to food security faces unprecedented challenge of increasing human population. This is due to the decline in major cereal crop yields including maize resulting from climate change and declining soil infertility. Changes in soil nutrient status and climate have continued to occur and in response, new fertilizer recommendations in terms of formulations and application rates are continuously developed and applied globally. In this sense, this review was conducted to: (i) identify the key areas of concentration of research on fertilizer and climate change effect on maize grain yield, (ii) assess the extent of the effect of climate change on maize grain yield, (iii) evaluate the extent of the effect of fertilization practices on maize grain yield, and (iv) examine the effect of interaction between climate change factors and fertilization practices on maize grain yield at global perspective. Methodology Comprehensive search of global literature was conducted in Web of Science (WoS) database. For objective 1, metadata on co-authorship (country, organisation), and co-occurrence of keywords were exported and analysed using VOSviewer software. For objective 2–4, yield data for each treatment presented in the articles were extracted and yield increment calculated. Results The most significant keywords: soil fertility, nutrient use efficiency, nitrogen use efficiency, integrated nutrient management, sustainability, and climate change adaptation revealed efforts to improve maize production, achieve food security, and protect the environment. A temperature rise of 1–4 °C decreased yield by 5–14% in warm areas and increased by

Abstract Tree species’ composition of forests is essential in forest management and nature conservation. We aimed to identify the tree species structure of a floodplain forest area using a hyperspectral image. We proposed an efficient novel strategy including the testing of three dimension reduction (DR) methods: Principal Component Analysis, Minimum Noise Fraction (MNF) and Indipendent Component Analysis with five machine learning (ML) algorithms (Maximum Likelihood Classifier, Support Vector Classification, Support Vector Machine, Random Forest and Artificial Neural Network) to find the most accurate outcome; altogether 300 models were calculated. Post-classification was applied by combining the multiresolution segmentation and filtering. MNF was the most efficient DR technique, and at least 7 components were needed to gain an overall accuracy (OA) of > 75%. Forty-five models had > 80% OAs; MNF was 43, and the Maximum Likelihood was 19 times among these models. Best classification belonged to MNF with 10 components and Maximum Likelihood classifier with the OA of 83.3%. Post-classification increased the OA to 86.1%. We quantified the differences among the possible DR and ML methods, and found that even > 10% worse model can be found using popular standard procedures related to the best results. Our workflow calls the attention of careful model selection to gain accurate maps.

Abstract Livestock depredation by brown bears is one of the main source of human–wildlife conflict in rural Eastern Europe. Thus, identifying environmental and anthropogenic drivers of human–bear conflict, and developing spatial predictions for predation intensity are critical to mitigate such conflicts. We used 756 records of bear‐caused livestock predation collected between 2008 and 2016 in the Romanian Carpathians and evaluated predictors and spatial distribution of bear livestock predation events (BPEs) using separate binomial generalized linear mixed models for cows, sheep, and other livestock. Despite differences in the direction and magnitude of the effect, the prevalence of BPE for all livestock was driven by the interaction between environmental drivers along with relative bear abundance. Distance from forest was a strong negative predictor for cows and sheep, while distance to villages was a strong negative predictor for cows. Landscape heterogeneity was positively associated with cow and other livestock predation and negatively associated with sheep. Relative bear abundance data collected by wildlife managers was a positive predictor for predation on all livestock. Livestock damage was more prevalent near villages, showcasing plasticity of food resources sought by bears. Our work informs brown bear and livestock management strategies to develop awareness and implement damage prevention measures.

Deriving the thematic accuracy of models is a fundamental part of image classification analyses. K-fold cross-validation (KCV), as an accuracy assessment technique, can be biased because existing built-in algorithms of software solutions do not handle the high autocorrelation of remotely sensed images, leading to overestimation of accuracies. We aimed to quantify the magnitude of the overestimation of KCV-based accuracies and propose a method to overcome this problem with the example of rooftops using a WorldView-2 (WV2) satellite image, and two orthophotos. Random split to training/testing subsets, independent testing and different types of repeated KCV sampling strategies were used to generate input datasets for classification. Results revealed that applying the random splitting of reference data to training/testing subsets and KCV methods had significantly biased the accuracies by up to 17%; overall accuracies (OAs) can incorrectly reach >99%. We found that repeated KCV can provide similar results to independent testing when spatial sampling is applied with a sufficiently large distance threshold (in our case 10 m). Coarser resolution of WV2 ensured more reliable results (up to a 5–9% increase in OA) than orthophotos. Object-based pixel purity of buildings showed that when using a majority filter for at least of 50% of objects the final accuracy approached 100% with each sampling method. The final conclusion is that KCV-based modelling ensures better accuracy than single models (with better pixel purity on the object level), but the accuracy metrics without spatially filtered sampling are not reliable.

Hazánk számos határ menti térségében tapasztalhatjuk a társadalmi-gazdasági folyamatok kedvezőtlen alakulását, melyek a jövőbeni fejlődés ütemére, a településeken várható folyamatokra is hatással vannak. A határok, különösen az ország keleti részén, akár több alkalommal is megváltoztak, újra és újra megakasztva egy-egy térség fejlődését (Pénzes et al. 2011). A határok elszigetelhetik egymástól a településeket, így inkább gátolják fejlődést, mint a kapcsolatokat ösztönzik (Reichman, 1993). Számos esetben magának a határnak az átjárhatatlansága, a határátkelők hiánya, vagy távoli elhelyezkedése miatt a kapcsolatok nehezen tehetők újra funkcionális egységgé (Pénzes, 2012).

The objective was to parameterize a modified water cloud model using crop coefficients (A and B). These crop coefficients were derived from Landsat-8 and Sentinel-2 data. Whereas the coefficients C and D are of soil parameters. The water cloud model was modified using crop coefficients by minimizing the RMSE between observed VVσ0 and Sentinel-1 based simulated VVσ0. The comparison with observed and simulated VV polarized σ0 showed low RMSE (0.81 dB) and strong R2 of 0.98 for NDVI-EVI combination. However, based on other possible combinations of vegetation indices VVσ0 and simulated VVσ0 do not show a good statistical agreement. It was observed that the errors in crop coefficients (A and B) are sensitive to errors in initial vegetation/canopy descriptor parameters.

Urban green spaces of cities are crucial elements of city structure that ensure habitat for species and ecological functionality of habitat patches, maintain biodiversity, and provide environmental services. However, detailed maps intended for planning and improving the existing network require a quick and effective technique for assessing the possibilities. Multispectral imagery is an accessible source for species-level classification of urban trees. Using a multispectral image from the WorldView–2 satellite sensor, we classified six of the most common urban tree species in Debrecen, Hungary. Maximum Likelihood (ML) and Support Vector Machine (SVM) classifiers were applied to different numbers of the MNF-transformed bands. The best overall accuracy was achieved with the ML algorithm applied to the first four transformed bands (75.1%), and with the SVM algorithm applied to eight bands (71.0%). In general, ML performed better than SVM. Despite the relatively low number of spectral bands, we achieved moderately good accuracy for basic vegetation mapping, which can be used in spatial planning and decision making. In a future interdisciplinary research study, we could merge the classification results with the dust adsorption capacity of individual species to assess the reduction of dust pollution by urban trees

Bentonite fining is one of the generally applied wine-making technological elements that may seriously affect wine components. The aim of this study was (i) to investigate the effect of 21 bentonite products on eight oenological parameters, 19 elements, 21 volatile organic compounds (VOCs) and 10 organoleptic properties of white wine; and (ii) to quantify intercorrelations among the parameters separately for each of the four quality attributes. Among oenological parameters, sugar, acidity, malic-, lactic-, citric acid and total phenol contents were significant among several bentonite products. The amounts of elements were the lowest in the control wine treatments (with exceptions of, e.g., Ni and Cu); and these values were significantly different from several bentonite products. The relative presence of the VOCs was above 100% for most VOCs, but it was below 100% for 1-propanol, 4-amino-1,5-pentandioic acid and butane-dioic acid, and diethyl ester in all treatments. For organoleptic parameters, the values of clearness, colour, flavour intensity and taste persistency was the lowest in the control wine treatment, while the values of flavour character, flavour quality, taste intensity, taste character, and overall harmony were the highest for the bentonite products of AP, EBE, M-SA, EBE, EBE, respectively. Results of correlation and factor analyses showed strong intercorrelative effects of bentonite fining on the four quality attributes. In conclusion, this study can help in the proper choice of a specific bentonite product in relation to complexity effects of bentonite fining.

Climate change is one of the most important issues of anthropogenic activities. The increasing drought conditions can cause water shortage and heat waves and can influence the agricultural production or the water supply of cities. The Carpathian region is also affected by this phenomenon; thus, we aimed at identifying the tendencies between 1960 and 2010 applying the CarpatClim (CC) database. We calculated the trends for each grid point of CC, plotted the results on maps, and applied statistical analysis on annual and seasonal level. We revealed that monthly average temperature, maximum temperature and evapotranspiration had similar patterns and had positive trends in all seasons except autumn. Precipitation also had a positive trend, but it had negative values in winter. The geospatial analysis disclosed an increasing trend from West to East and from north to west. A simple binary approach (value of 1 above the upper quartile in case of temperature and evapotranspiration, value of 1 below the lower quartile; 0 for the rest of the data) helped to identify the most sensitive areas where all the involved climatic variables exceeded the threshold: Western Hungary and Eastern Croatia. Results can help to prepare possible mitigation strategies to climate change and both landowners and planners can draw the conclusions.

Extreme precipitation events can trigger flash flood, mass movements, pluvial flood and accelerated soil erosion. As soil structures are highly degraded due to intensive improper cultivation water infiltration can considerably decrease during the vegetation period. Additional changes in canopy coverage on the soil surface cause relevant variability in infiltration and hence vulnerability against runoff related disasters. Most researchers agree that the frequency of extreme precipitations increases, however, in the Carpathian Basin the uncertainties are quite high. This study aims to compare daily maximum mean precipitation amounts (MMPA) predicted by the Goda-method for June and August as the most probable months of extremities. We used the CarpatClim database as input and predicted MMPAs for two periods, 1960–1985 and 1986–2010. The Goda-method uses monthly data and calculates daily results on given probability. A general increase was found between the first and second half of the period regarding daily maximum precipitation amount in both investigated months. For August the 1-day precipitation amount increased from 56.1 mm to 61.8 mm, whereas 6-days amount from 93.8 mm to 103.2 mm at 1 per cent probability (r = 0.53; p < 0.001). Beyond this change, relevant spatial differences were found. Comparing the macro regions plains had lower increase compared to the mountains, whereas the highest increase was at the. The most endangered location is the southern part of the Transdanubian Hills where parallel with the intensive increase in MMPA both in June and August the environmental conditions such as loose parent material and the high percentage of crop fields also emphasize the potential hazard.

Landscape aesthetic research that emerged from the second half of the 20th century has become increasingly appreciated and popular in the last few decades. There are two main reasons for this. On the one hand, it was recognized the role of landscape aesthetics in land use and environmental planning, management and conservation. On the other hand, its definition among Cultural Ecosystem Services has made it clear that landscape aesthetics has significant impact on human well-being and there is a need to examine it in the concept of Ecosystem Services and, in particular, Cultural Ecosystem Services (CES). The mapping of landscape aesthetics is mostly based on the exclusive evaluation of objective, biophysical landscape factors. The aim of the research was to create the landscape aesthetic map of Hungary with a novel method based on human perception. For this, a questionnaire survey and a GIS approach were used. In order to better understand the role of factors influencing the aesthetic value of the landscape, value maps separately for land cover and elevation that are decisive for the landscape experience were prepared. To validate the results of the maps, and contribute a better understanding of the interrelationship between CES, a certain tourism product was chosen, and the connection between landscape aesthetics and the offer of rural tourism was examined in Hungary and in the Danube Bend priority tourism development area. Our findings show that there is a difference in the results of the objective (GIS-based) and subjective (questionnaire-based) assessment of landscape aesthetic value with the more important role of elevation in the latter. According to our tourism product-based analysis, which represents a niche approach in its kind, landscape values are higher in the areas with rural accommodation. At the same time, based on the results of the Danube Bend region, it can also be concluded that elevation and land cover together are crucial factors in landscapes considered to be the most valuable in aesthetic terms. The most direct practical application of our research is to orientate further tourism development of the new Danube Bend area designated in 2017.

The aim of this paper was to study reliable automated delineation possibilities of karst sinkholes using a LiDAR-based digital terrain model (DTM) with pixel-based classifications. We applied two approaches to extract sinkholes: (1) general linear modeling (GLM) with morphometric indices derived from DTM; (2) and a local minima-based delineation using only LiDAR DTM as the input layer. The outcome of the local minima was significantly different from the reference ones but found all the sinkholes without previous knowledge of the area. The GLM-based outcome did not differ statistically from the reference. Results showed that these approaches were efficient in detecting sinkholes based on LIDAR derivatives, and can be used for risk assessment and hazard preparedness in karst areas: GLM had an overall accuracy of 89.5% and local minima had an accuracy of 92.3%; both methods identified sinkholes but also had commission errors, identifying depressions as sinkholes.

Understanding climate change and revealing its future paths on a local level is a great challenge for the future. Beside the expanding sets of available climatic data, satellite images provide a valuable source of information. In our study we aimed to reveal whether satellite data are an appropriate way to identify global trends, given their shorter available time range. We used the CARPATCLIM (CC) database (1961–2010) and the MODIS NDVI images (2000–2016) and evaluated the time period covered by both (2000–2010). We performed a regression analysis between the NDVI and CC variables, and a time series analysis for the 1961–2008 and 2000–2008 periods at all data points. The results justified the belief that maximum temperature (TMAX), potential evapotranspiration and aridity all have a strong correlation with the NDVI; furthermore, the short period trend of TMAX can be described with a functional connection with its long period trend. Consequently, TMAX is an appropriate tool as an explanatory variable for NDVI spatial and temporal variance. Spatial pattern analysis revealed that with regression coefficients, macro-regions reflected topography (plains, hills and mountains), while in the case of time series regression slopes, it justified a decreasing trend from western areas (Transdanubia) to eastern ones (The Great Hungarian Plain). This is an important consideration for future agricultural and land use planning; i.e. that western areas have to allow for greater effects of climate change.

Soil erosion is one of the main threats facing the agriculture and natural resources sector all over the world, and the same is true for Syria. Several empirical and physically based tools have been proposed to assess erosion induced soil losses and runoff driving the processes, from plot to regional spatial scales. The main goal of this research is to evaluate the performance of the Water Erosion Prediction Project (WEPP) model in predicting runoff in comparison with field experiments in the Al-Sabahia region of Western Syria in three ecosystems: agricultural lands (AG), burned forest (BF) and forest (FO). To achieve this, field experimental plots (2∗1.65∗0.5 m) were prepared to obtain runoff observation data between September 2012 and December 2013. In addition, the input data (atmospheric forcing, soil, slope, land management) were prepared to run the WEPP model to estimate the runoff. The results indicate that the average observed runoffs in the AG, BF and FO were 12.54 ± 1.17, 4.81 ± 0.97 and 1.72 ± 0.16 mm/event, respectively, while the simulated runoffs in the AG, BF and FO were 15.15 ± 0.89, 9.23 ± 1.48 and 2.61 ± 0.47mm/event, respectively. The statistical evaluation of the model's performance showed an unsatisfactory performance of the WEPP model for predicting the run-offs in the study area. This may be caused by the structural flaws in the model, and/or the insufficient site-specific input parameters. So, to achieve good performance and reliable results of the WEPP model, more observation data is required from different ecosystems in Syria. These findings can provide guidance to planners and environmental engineers for proposing environmental protection and water resources management plans in the Coastal Region in Syria.

Temporal and spatial variability of annual and seasonal precipitation from 71 stations located in Western Balkan (WB) countries (Serbia, Bosnia and Herzegovina, and Montenegro) and their correlations with nine atmospheric circulation patterns was examined for the period 1950–2016. Annual precipitation increased significantly throughout the WB (from 2% to 8% per decade) on 20% of stations located mainly in the mountainous western Serbia and eastern Bosnia and Herzegovina. Winter was characterized by non-significant precipitation changes in most of the studied area, with only a few stations characterized by significant precipitation increase (up to 12% per decade) in the mountainous area of WB, and a few stations characterized by significant decrease (up to –6% per decade) in the Pannonian plain. Significant precipitation increase was noticed on 15% of the stations in spring, while it was noticed on 17% of the stations in autumn. Summer precipitation decreased significantly (up to –5% per decade) on a limited area of northern Serbia (6% of the stations), while the majority of stations showed non-significant increase. The strongest influences on annual precipitation in WB region are of the Arctic Oscillation (AO) and Mediterranean Oscillation (MO), leading to the precipitation decrease during their positive phases. Winter precipitation is significantly negatively correlated with AO, East Atlantic/Western Russia oscillation (EA/WR), and North Atlantic Oscillation (NAO) and has a significant positive correlation with Western Mediterranean Oscillation (WeMO) on the majority of stations. MO has the strongest influence on summer precipitation in WB region leading to precipitation decrease, while AO has the dominant influence on precipitation in the region during autumn.

Shot hole disease (SHD) can cause severe epidemics in plum orchards, depending on cultivar susceptibility and training system; however, the combined effect on the progress of temporal disease and on the possible reduction in SHD in the disease management was not investigated. The aim of this 3-year study was (i) to monitor and analyze the temporal dynamics of SHD progress under four training systems (4 × 1.5, 4 × 2, 5 × 2.5 and 6 × 3 m) and on four plum cultivars (‘Čačanska lepotica’, ‘Bluefre’, ‘Stanley’ and ‘President’) in an integrated plum orchard; (ii) to identify those time periods when training system and cultivar combinations can reduce the disease development. Both SHD incidences and the area under the disease progress curves (AUDPC) were significantly affected by the training system, cultivar and year. Plum cultivars with high or mid–high susceptibility to SHD showed continuous SHD development from May to November, while cultivars with low susceptibility to SHD showed no symptoms until mid-summer and then progressed slowly until November. High (4 × 1.5 m) vs. low (6 × 3 m) density training systems reduced SHD incidence and AUDPC consistently for three cultivars (‘Čačanska lepotica’, ‘Stanley’ and ‘President’) in September, October and November, compared to the high-density training system. Only cv. ‘Bluefre’ showed no effect either on disease incidence or AUDPC, due to very high disease incidences in all training systems from September to November. In conclusions, combinations of training system and cultivar can significantly reduce SHD incidence, which may be successfully used as a part of the integrated pest management approach during the establishment new plantations.

Identification of roofing material is an important issue in the urban environment due to hazardous and risky materials. We conducted an analysis with Discriminant Function Analysis (DFA) and Random Forest (RF) on WorldView-2 imagery. We applied a three- and a six-class approach (red tile, brown tile and asbestos; then dividing the data into shadowed and sunny roof parts). Furthermore, we applied pan-sharpening to the image. Our aim was to reveal the efficiency of the classifiers with a different number of classes and the efficiency of pan-sharpening. We found that all classifiers were efficient in roofing material identification with the classes involved, and the overall accuracy was above 85 per cent. The best results were gained by RF, both with three and with six classes; however, quadratic DFA was also successful in the classification of three classes. Usually, linear DFA performed the worst, but only relatively so, given that the result was 85 per cent. Asbestos was identified successfully with all classifiers. The results can be used by local authorities for roof mapping to build registers of buildings at risk.

We used the Air Pollution Tolerance Index (APTI), the amount of PM5 and PM10, and the elemental analysis of leaves to explore the sensitivity of tree species to air pollution. We assessed the tolerance of Robinia pseudoacacia, Acer saccharinum, Tilia × europaea, Acer platanoides, Fraxinus excelsior, Betula pendula, Celtis occidentalis, and Platanus × acerifolia to the amount of dust, APTI, and the elemental concentration of leaves. Leaves were collected in Debrecen (Hungary), which has a high intensity of vehicular traffic. The highest amount of PM (both PM10 and PM5) was found on the leaves of A. saccharinum and B. pendula. Our results demonstrated that A. saccharinum was moderately tolerant, while P. acerifolia was intermediate, based on the APTI value. There was a significant difference in the parameters of APTI and the elemental concentration of leaves among species. We found that tree leaves are reliable bioindicators of air pollution in urban areas. Based on the value of APTI, A. saccharinum and P. acerifolia, and based on PM, A. saccharinum and B. pendula are recommended as pollutant-accumulator species, while other studied species with lower APTI values are useful bioindicators of air pollution. The results support landscape engineers and urban developers in finding the best tree species that are tolerant to pollution and in using those as proxies of urban environmental health.

Climate mitigation and adaptation planning (CMAP) has recently been implemented across the EU-28 to reduce GHG emissions (CO2, CH4, N2O). Thus, the aim of this study was to provide an overview of GHG emissions from the agricultural sector in the EU-28 from 1990 to 2019, and cluster the EU-28 countries regarding their total GHG emissions. The results emphasize the positive impact of CMAP through a negative trend of the total GHG emissions (−2653.01 thousand tons/year, p < 0.05). Despite the positive and not significant trend of the total CO2 emissions, both CH4 and N2O exhibited a negative and significant trend. At the country scale, Italy, the United Kingdom, and the Netherlands showed the highest reduction in total GHG emissions, by −282.61thousand tons/year (p < 0.05), −266.40 thousand tons/year (p < 0.05), and −262.91 thousand tons/year (p < 0.05), respectively. The output of the multivariate analysis approach indicates changes in the pattern of GHG emissions between 1990 and 2019, where CO2 emissions decreased in the case of Poland and Czechia. The output of this study highlights the positive impact of CMAP, adopted by EU countries, in minimizing GHG emissions. Despite some fluctuations in CO2 emissions, strategies for attaining carbon neutrality in the agricultural sector, across the European Union, should be pursued.

The aim of this paper was to analyze the ground and low vegetation points of a Light Detection and Ranging (LiDAR) point cloud from the aspect of the generated digital terrain model (DTM). We determined the height difference between the surveyed surface and the DTM and the level of interspersion of ground and low vegetation points in a floodplain. Finally, we performed a supervised classification with topographic (elevation, slope and aspect) variables and an Normalized Difference Vegetation Index (NDVI) layer to identify swales and point bars as floodplain forms. Cross sections of field surveys provided reference data to express the magnitude of the bias on the DTM caused by the vegetation, and we proved that the bias can reach the 60% of the relative height and depth of the floodplain forms (mean error was 0.15 ± 0.12 m). A landscape metric, the Aggregation Index, provided an appropriate tool to analyze and quantify the interspersion of the ground and vegetation points: indicating a high level of interspersion of the classified points, i.e. proved that vegetation points where the last echoes reflected from the vegetation became ground points. Floodplain classification performed best with the common use of DTM, slope, aspect and NDVI coverages, with 71% overall accuracy.

Revealing the driving forces of changes in landscape pattern is a key question of landscape ecology and landscape analysis. Temperature and precipitation as climatic variables have a dominant role in triggering vegetation changes; thus, a database, which contain their interaction, can support the understanding of spatio-temporal changes in vegetation patterns even on a large scale. The dataset provided in this article contain the R-squared values of bivariate linear regression analysis between the Normalized Difference Vegetation Index (target variable; as a general quantitative descriptor of surface greenness) of the TERRA satellite's MODIS sensor and the climatic variables of the CarpatClim database (predictor variables; maximum monthly temperature, aridification index, evapotranspiration and precipitation). Environmental variables are also included to support further analysis: terrain height, macro regions, land cover classes. The dataset has a spatial projection (i.e. maps) and covers the area of Hungary. Tabular version provides the possibility of traditional statistical analysis, while maps allow the investigation to involve the spatial characteristics of absolute and relative position of the data points. This data article is related to the paper “NDVI dynamics as reflected in climatic variables: spatial and temporal trends – a case study of Hungary” (Szabo et al., 2019). Keywords: NDVI, Trend, Climatic factors, R-squared, Pattern

Irrigation is a key factor for different physiological aspects of fruit trees. Therefore, such irrigation protocols that can save water consumption during irrigation and maintain fruit trees productivity are an essential goal especially under semiarid climate conditions. The aim of this 3-year apricot study was to investigate the effect of four deficit irrigation (DI) treatments (control, moderate regulated deficit irrigation: RDIm, severe RDI: RDIs and continuous DI: CDI) on 15 tree physiological properties (chilling requirement—CR, heat requirement—HR, days from end—dormancy until fruit harvest—DEDFH, sum of growing degree days—sGDD, total number of buds—TNB, number of flower buds—NFB, number of vegetative buds—NVB, starting date of flowering—SDF, number of opened flower buds—NOFB, flower bud abscission—FBA, fruit set—FS, seasonal vegetative growth—SVG, fruit number per tree—FNT, fruit weight—FW, fruit yield—FY), and on two tree chemical properties (total soluble carbohydrates—TSC and total proline content—TPC) on apricot cultivars ‘Ninfa’ and ‘Canino’ in Egypt. Results showed that both DI treatments and cultivars significantly influenced the values of CR, HR, TNB, SDF, NOFB, FS, SVG, FNT, FY, TSC, and TPC. Values of FBA were significantly affected by years and DI treatments, while sGDD by years and cultivars. Values of DEDFH, NFB, and FW were significantly influenced only by cultivars, while NVB only by DI treatments. The RDIm treatment gave the most acceptable values for most measured properties compared to the fully irrigated control treatment. Prediction based model analysis demonstrated that generalized linear models (GLMs) can be predictors for the measured tree properties in the DI treatments. The best goodness-of-fit of the predicted GLMs was reached for HR, NOFB, FS, SVG, FNT, TSC, and TPC. In all the four DI treatments, 22 pair-variables (TNB versus (vs.) NFB, TNB vs. NOFB, TNB vs. NOFB, NFB vs. NOFB, NFB vs. FNT, NFB vs. FY, NFB vs. FW, NOFB vs. SVG, NOFB vs. FNT, NOFB vs. FY, FS vs. FNT, FS vs. FY, SVG vs. FNT, SVG vs. FY, SVG vs. TSC, FNT vs. FY, FY vs. FW, CR vs. TSC, HR vs. TNB, HR vs. NFB, HR vs. FNT, HR vs. FY, and NOFB vs. FBA) correlated significantly in Pearson correlation and regression analyses. Principal component analyses explained 82% of the total variance and PC1, PC2, and PC3 explained 23, 21, and 15% of the total variance and correlated with the HR, TNB, FS, FNT and FY; FBA, SVG, TSC, and TPC; and NFB, NVB and NOFB, respectively, indicating strong connections among tree physiological and chemical properties. In conclusion, DI techniques using moderate water deficits can be managed successfully in apricot production under semiarid Mediterranean climate conditions such as the one in Egypt.

The availability of aerial and satellite imageries has greatly reduced the costs and time associated with gully mapping, especially in remote locations. Regardless, accurate identification of gullies from satellite images remains an open issue despite the amount of literature addressing this problem. The main objective of this work was to investigate the performance of support vector machines (SVM) and random forest (RF) algorithms in extracting gullies based on two resampling methods: bootstrapping and k-fold cross-validation (CV). In order to achieve this objective, we used PlanetScope data, acquired during the wet and dry seasons. Using the Normalized Difference Vegetation Index (NDVI) and multispectral bands, we also explored the potential of the PlanetScope image in discriminating gullies from the surrounding land cover. Results revealed that gullies had significantly different (p < 0.001) spectral profiles from any other land cover class regarding all bands of the PlanetScope image, both in the wet and dry seasons. However, NDVI was not efficient in gully discrimination. Based on the overall accuracies, RF’s performance was better with CV, particularly in the dry season, where its performance was up to 4% better than the SVM’s. Nevertheless, class level metrics (omission error: 11.8%; commission error: 19%) showed that SVM combined with CV was more successful in gully extraction in the wet season. On the contrary, RF combined with bootstrapping had relatively low omission (16.4%) and commission errors (10.4%), making it the most efficient algorithm in the dry season. The estimated gully area was 88 ± 14.4 ha in the dry season and 57.2 ± 18.8 ha in the wet season. Based on the standard error (8.2 ha), the wet season was more appropriate in gully identification than the dry season, which had a slightly higher standard error (8.6 ha). For the first time, this study sheds light on the influence of these resampling techniques on the accuracy of satellite-based gully mapping. More importantly, this study provides the basis for further investigations into the accuracy of such resampling techniques, especially when using different satellite images other than the PlanetScope data.

In both arid and semiarid regions, erosion by wind is a significant threat against sustainability of natural resources. The objective of this work was to investigate the direct impact of various soil moisture levels with soil texture and organic matter on soil crust formation and evaporation. Eighty soil samples with different texture (sand: 19, loamy sand: 21, sandy loam: 26, loam: 8, and silty loam: 6 samples) were collected from the Nyírség region (Eastern Hungary). A wind tunnel experiment was conducted on four simulated irrigation rates (0.5, l.0, 2.0, and 5.0 mm) and four levels of wind speeds (4.5, 7.8, 9.2, and 15.5 m s−1). Results showed that watering with a quantity equal to 5 mm rainfall, with the exception of sandy soils, provided about 5–6 h protection against wind erosion, even in case of a wind velocity as high as 15.5 m s−1. An exponential connection was revealed between wind velocities and the times of evaporation (R2 = 0.88–0.99). Notably, a two-way ANOVA test revealed that both wind velocity (p < 0.001) and soil texture (p < 0.01) had a significant effect on the rate of evaporation, but their interaction was not significant (p = 0.26). In terms of surface crusts, silty loamy soils resulted in harder and more solid crusts in comparison with other textures. In contrast, crust formation in sandy soils was almost negligible, increasing their susceptibility to wind erosion risk. These results can support local municipalities in the development of a local plan against wind erosion phenomena in agricultural areas.

Monilinia laxa causes serious postharvest damage on apricot fruits under shelf-life storage conditions. Plant elicitors of methyl jasmonate (MeJA) and salicylic acid (SA) can reduce this damage, and their research can explain the background of the plant defense physiological processes in M. laxa-infected fruits. The aims of this study were: (i) to evaluate the effect of various concentrations of MeJA and SA on brown rot incidence (BRI) and lesion diameter (LD) of apricot fruits; (ii) to measure the temporal patterns for the effect of 0.4 mmol L−1 MeJA and 2 mmol L−1 SA treatments on BRI, LD and seven fruit measures (fruit firmness (FF), lignin content (LC), total soluble phenol content (TSPC), total antioxidant capacity (TAC) and enzyme activities of PAL, POD and SOD) in treatments of M. laxa-inoculated versus (vs.) non-inoculated fruits over an eight-day shelf-life storage period; and (iii) to determine inter-correlations among the seven fruit measures for MeJA and SA treatments. Both MeJA and SA significantly reduced BRI and LD. LC, FF, TAC, TSPC, as well as SOD and PAL activities in the MeJA and SA treatments were higher than the water-treated control in most assessment days and both inoculation treatments. In both inoculation treatments, the activity of POD in the SA-treated fruits was higher than MeJA-treated and control fruits at all dates. In MeJA vs. SA and inoculated vs. non-inoculated treatments, six variable pairs (FF vs. TSPC, FF vs. TAC, TAC vs. PAL, PAL vs. POD, PAL vs. SOD, and POD vs. SOD) showed significant inter-correlation values. Principal component analyses explained 96% and 93% of the total variance for inoculated and non-inoculated treatments, respectively. In inoculated treatments, both PC1 and PC2 explained 41% of the total variance and correlated with FF, TSPC and TAC and with PAL, SOD and POD, respectively. In non-inoculated treatments, PC1 and PC2 explained 49% and 44% of the total variance and correlated with LC, PAL, POD and SOD and with FF, TSPC and TAC, respectively. It can be concluded that MeJA and SA are useful in the practice to enhance the plant defense system against brown rot by reducing fungal growth and by improving physical and antioxidant attributes (FF, LC, TAC and TSPC) and the activity of defense-related enzymes (PAL, POD and SOD) in apricot fruits during shelf-life storage conditions.

Gullies are responsible for detaching massive volumes of productive soil, dissecting natural landscape and causing damages to infrastructure. Despite existing research, the gravity of the gully erosion problem underscores the urgent need for accurate mapping of gullies, a first but essential step toward sustainable management of soil resources. This study aims to obtain the spatial distribution of gullies through comparing various classifiers: k-dimensional tree K-Nearest Neighbor (k-d tree KNN), Minimum Distance (MD), Maximum Likelihood (ML), and Random Forest (RF). Results indicated that all the classifiers, with the exception of ML, achieved an overall accuracy (OA) of at least 0.85. RF had the highest OA (0.94), although it was outperformed in gully identification by MD (0% commission), but the omission error was 20% (MD). Accordingly, RF was considered as the best algorithm, having 13% error in both adding (commission) and omitting pixels as gullies. Thus, RF ensured a reliable outcome to map the spatial distribution of gullies. RF-derived gully density map reflected the agricultural areas most exposed to gully erosion. Our approach of using satellite imagery has certain limitations, and can be used only in arid or semiarid regions where gullies are not covered by dense vegetation as the vegetation biases the extracted gullies. The approach also provides a solution to the lack of laser scanned data, especially in the context of the study area, providing better accuracy and wider application possibilities.

Monitoring air pollution and environmental health are crucial to ensure viable cities. We assessed the usefulness of the Air Pollution Tolerance Index (APTI) as a composite index of environmental health. Fine and coarse dust amount and elemental concentrations of Celtis occidentalis and Tilia × europaea leaves were measured in June and September at three sampling sites (urban, industrial, and rural) in Debrecen city (Hungary) to assess the usefulness of APTI. The correlation between APTI values and dust amount and elemental concentrations was also studied. Fine dust, total chlorophyll, and elemental concentrations were the most sensitive indicators of pollution. Based on the high chlorophyll and low elemental concentration of tree leaves, the rural site was the least disturbed by anthropogenic activities, as expected. We demonstrated that fine and coarse dust amount and elemental concentrations of urban tree leaves are especially useful for urban air quality monitoring. Correlations between APTI and other measured parameters were also found. Both C. occidentalis and T. europaea were sensitive to air pollution based on their APTI values. Thus, the APTI of tree leaves is an especially useful proxy measure of air pollution, as well as environmental health.

Floodplains are valuable scenes of water management and nature conservation. A better understanding of their geomorphological characteristic helps to understand the main processes involved. We performed a classification of floodplain forms in a naturally developed area in Hungary using a Digital Terrain Model (DTM) of aerial laser scanning. We derived 60 geomorphometric variables from the DTM and prepared a geomorphological map of 265 forms (crevasse channels, point bars, swales, levees). Random Forest classification was conducted with Recursive Feature Elimination (RFE) on the objects (mean pixel values by forms) and on the pixels of the variables. We also evaluated the classification probabilities (CP), the spatial uncertainties (SU), and the overfitting in the function of the number of the variables. We found that the object-based method had a better performance (95%) than the pixel-based method (78%). RFE helped to identify the most important 13–20 variables, maintaining the high model performance and reducing the overfitting. However, CP and SU were not efficient measures of classification accuracy as they were not in accordance with the class level accuracy metric. Our results help to understand classification results and the specific limits of laser scanned DTMs. This methodology can be useful in geomorphologic mapping.

Urban sprawl related increase of built-in areas requires reliable monitoring methods and remote sensing can be an efficient technique. Aerial surveys, with high spatial resolution, provide detailed data for building monitoring, but archive images usually have only visible bands. We aimed to reveal the efficiency of visible orthophotographs and photogrammetric dense point clouds in building detection with segmentation-based machine learning (with five algorithms) using visible bands, texture information, and spectral and morphometric indices in different variable sets. Usually random forest (RF) had the best (99.8%) and partial least squares the worst overall accuracy (~60%). We found that >95% accuracy can be gained even in class level. Recursive feature elimination (RFE) was an efficient variable selection tool, its result with six variables was like when we applied all the available 31 variables. Morphometric indices had 82% producer’s and 85% user’s Accuracy (PA and UA, respectively) and combining them with spectral and texture indices, it had the largest contribution in the improvement. However, morphometric indices are not always available but by adding texture and spectral indices to red-green-blue (RGB) bands the PA improved with 12% and the UA with 6%. Building extraction from visual aerial surveys can be accurate, and archive images can be involved in the time series of a monitoring.

Observing wetland areas and monitoring changes are crucial to understand hydrological and ecological processes. Sedimentation-induced vegetation spread is a typical process in the succession of lakes endangering these habitats. We aimed to survey the tendencies of vegetation spread of a Hungarian lake using satellite images, and to develop a method to identify the areas of risk. Accordingly, we performed a 33-year long vegetation spread monitoring survey. We used the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) to assess vegetation and open water characteristics of the basins. We used these spectral indices to evaluate sedimentation risk of water basins combined with the fact that the most abundant plant species of the basins was the water caltrop (Trapa natans) indicating shallow water. We proposed a 12-scale Level of Sedimentation Risk Index (LoSRI) composed from vegetation cover data derived from satellite images to determine sedimentation risk within any given water basin. We validated our results with average water basin water depth values, which showed an r = 0.6 (p < 0.05) correlation. We also pointed on the most endangered locations of these sedimentation-threatened areas, which can provide crucial information for management planning of water directorates and management organizations.