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Agricultural use of sewage sludge is one of the means of sustainable environmental management. In order to monitor the short-term effects of sludge disposal a multi-year, high-resolution data collection was planned on arable land in south-eastern Hungary. Data acquisition was applied at the highest temporal and spatial resolution using Sentinel-2 and PlanetScope satellite imagery observing the vegetation period based on vegetation indices (EVI, NDVI) from 2016 to 2021. There were statistical differences in the case of sunflower and maize biomass productions but the spatial and statistical deviations between the affected and non-affected areas of sludge disposal were generally not significant. The sensitivity of EVI in the dense vegetation period and its applicability might be emphasized in a comparative analysis.

River temperature is a critical parameter influencing aquatic ecosystems and water quality. However, it can be changed by natural (e.g., flow and depth conditions) and human factors (e.g., waste and industrial water drainage). Satellite-based monitoring offers a valuable tool for assessing river temperature on a large scale, elucidating the impacts of various factors. This study aims to analyze the spatiotemporal dynamics of surface water temperature (SWT) in the medium-sized Tisza River in response to natural and anthropogenic influences, employing Landsat satellites and in situ water temperature data. The validity of the Landsat-based SWT estimates was assessed across different channel sections with varying sizes. The longitudinal thermal profile of the Tisza was analyzed by mosaicking, monthly, four Landsat 9 images, covering the entire 962 km length of the Tisza in 2023. The impact of climate change was evaluated by analyzing SWT trends at a specific site from 1984 to 2024, utilizing 483 Landsat 4–9 images. The findings indicated elevated accuracy for Landsat-based SWT estimation (R2 = 0.94; RMSE = 3.66 °C), particularly for channel sizes covering ≥ 3 pixels. Discharge, microclimatic conditions, and channel morphology significantly influence SWT, demonstrating a general increasing trend downstream with occasional decreases during the summer months. Dams were observed to lower the SWT downstream due to cooler bottom reservoir water discharge, with more pronounced differences during the summer months (1–3 °C). Tributaries predominantly (75%) elevated the SWT in the Tisza River, albeit with varying magnitudes across different months. Over the 40-year study period, an increasing trend in SWT was discerned, with an annual rise rate of 0.0684 °C. While the thermal band of Landsat satellites proved valuable for investigating the Tisza River's thermal profile at a broad scale, finer spatial resolution bands are necessary for detecting small-scale phenomena such as thermal plumes and localized temperature variations in rivers. [ABSTRACT FROM AUTHOR]

Classification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

An integrated approach was applied in this article to provide a medium-scale map of land use intensity for Hungary. The main goal was to estimate its value by a small set of parameters, which are freely available and have a high resolution. The basis of the evaluation was the CORINE 2012 dataset, and a matrix method was applied to integrate the ratio of natural/semi-natural vegetation, woody vegetation and the Natural Capacity Index in the assessment to describe the complex approach of land use intensity. The medium level land use intensity map provides information for decision makers/landscape planners on the current status and spatial pattern of anthropogenic impact and indicates those hot-spots where land use intensity is high and should be focused research and management to intervene in order to encourage sustainable land use. 46% of the arable lands in Hungary show the most intensive land use. Comparing the map with the previously published hemeroby map of Hungary, more intensive impact on landscape transformation through human action was found. In agricultural areas both researches agree that the intensity and human activity is really high, and the lowest intensity class is rare in Hungary except for mountain regions and protected areas.

Landscape responses to degradation caused by aridification bring the landscape system into a new equilibrium state. The system transformation may entail irreversible changes to its constituting parameters. This paper analyses the impact of aridification on landscape degradation processes in the sand-covered landscapes of the Hungarian Danube-Tisza Interfluve region at the regional, landscape, and local site scales. Changes in groundwater level (well data), lake surface area (Modified Normalized Difference Water Index) and vegetation cover (Enhanced Vegetation Index) were analysed over time periods of 12–60 years. Significant regional variation in decreasing groundwater levels is observed and limits the regional applicability of this indicator. Applying the lake surface area parameter from remote sensing data demonstrated greater utility, identifying several local lakes in the landscapes which have dried out. Analysis of the vegetation response indicated minor changes over the 2000–2014 time period and did not indicate a landscape system change. Landscape degradation as a result of changes in groundwater, vegetation, land cover and land use is clearly identified exclusively in local lake areas, but at the landscape scale, changes in the water balance are found in phases of system stability and transformation. Thresholds are identified to support policy and management towards landscape degradation neutrality.

Inland excess water (IEW) is a type of flood where large flat inland areas are covered with water during a period of several weeks to months. The monitoring of these floods is needed to understand the extent and direction of development of the inundations and to mitigate their damage to the agricultural sector and build up infrastructure. Since IEW affects large areas, remote sensing data and methods are promising technologies to map these floods. This study presents the first results of a system that can monitor inland excess water over a large area with sufficient detail at a high interval and in a timely matter. The methodology is developed in such a way that only freely available satellite imagery is required and a map with known water bodies is needed to train the method to identify inundations. Minimal human interference is needed to generate the IEW maps. We will present a method describing three parallel workflows, each generating separate maps. The maps are combined to one weekly IEW map. At this moment, the method is capable of generating IEW maps for a region of over 8000 km2, but it will be extended to cover the whole Great Hungarian Plain, and in the future, it can be extended to any area where a training water map can be created.

Rivers transport terrestrial microplastics (MP) to the marine system, demanding cost-effective and frequent monitoring, which is attainable through remote sensing. This study aims to develop and test microplastic concentration (MPC) models directly by satellite images and indirectly through suspended sediment concentration (SSC) as a proxy employing a neural network algorithm. These models relied upon high spatial (26 sites) and temporal (198 samples) SSC and MPC data in the Tisza River, along with optical and active sensor reflectance/backscattering. A feedforward MLP neural network was used to calibrate and validate the direct models employing k-fold cross-validation (five data folds) and the Optuna library for hyperparameter optimization. The spatiotemporal generalization capability of the developed models was assessed under various hydrological scenarios. The findings revealed that hydrology fundamentally influences the SSC and MPC. The indirect estimation method of MPC using SSC as a proxy demonstrated higher accuracy (R2 = 0.17–0.88) than the direct method (R2 = 0–0.2), due to the limitations of satellite sensors to directly estimate the very low MPCs in rivers. However, the estimation accuracy of the indirect method varied with lower accuracy (R2 = 0.17, RMSE = 12.9 item/m3 and MAE = 9.4 item/m3) during low stages and very high (R2 = 0.88, RMSE = 7.8 item/m3 and MAE = 10.8 item/m3) during floods. The worst estimates were achieved based on Sentinel-1. Although the accuracy of the MPC models is moderate, it still has practical applicability, especially during floods and employing proxy models. This study is one of the very initial attempts towards MPC quantification, thus more studies incorporating denser spatiotemporal data, additional water quality parameters, and surface roughness data are warranted to improve the estimation accuracy. [ABSTRACT FROM AUTHOR]

The rate of global urbanization is exponentially increasing and reducing areas of natural vegetation. Remote sensing can determine spatiotemporal changes in vegetation and urban land cover. The aim of this work is to assess spatiotemporal variations of two vegetation indices (VI), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), in addition land cover in and around Erbil city area between the years 2000 and 2015. MODIS satellite imagery and GIS techniques were used to determine the impact of urbanization on the surrounding quasi-natural vegetation cover. Annual mean vegetation indices were used to determine the presence of a spatiotemporal trend, including a visual interpretation of time-series MODIS VI imagery. Dynamics of vegetation gain or loss were also evaluated through the study of land cover type changes, to determine the impact of increasing urbanization on the surrounding areas of the city. Monthly rainfall, humidity and temperature changes over the 15-year-period were also considered to enhance the understanding of vegetation change dynamics. There was no evidence of correlation between any climate variable compared to the vegetation indices. Based on NDVI and EVI MODIS imagery the spatial distribution of urban areas in Erbil and the bare around it has expanded. Consequently, the vegetation area has been cleared and replaced over the past 15 years by urban growth.

In this study a new remote sensing drought index called Difference Drought Index (DDI) was introduced. DDI was calculated from the Terra satellite’s MODIS sensor surface reflectance data using visible red, near-infrared and short-wave-infrared spectral bands. To characterize the biophysical state of vegetation, vegetation and water indices were used from which drought indices can be derived. The following spectral indices were examined: Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Difference Water Index (DWI), Normalized Difference Water Index (NDWI), Difference Drought Index (DDI) and Normalized Difference Drought Index (NDDI). Regression analysis with the Pálfai Drought Index (PaDi) and average annual yield of different crops has proven that the Difference Drought Index is applicable in quantifying drought intensity. However, after comparison with reference data NDWI performed better than the other indices examined in this study. It was also confirmed that the water indices are more sensitive to changes in drought conditions than the vegetation ones. In the future we are planning to monitor drought during growing season using high temporal resolution MODIS data products.

The restructuring of Hungarian higher education according to European expectations has resulted in fundamental changes. Due to the intensifying market and the decreasing number of applicants, institutions are interested in ensuring the necessary number of students. The objective of the study is to identify those schools where the greatest interest in the courses offered by the University of Szeged can be expected. The selection was based on spatial and temporal analysis of submitted applications using geographical information system methods. The results can contribute to attracting the necessary number of students and to developing a sound basis for future educational planning.