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Due to extreme weather, occasionally Hungary's main rivers and lakes grow an ice cover causing severe damage to infrastructure and increased flood hazard. During cold periods in 2017 and 2022, a dangerous layer of ice developed on the main rivers in the country. Since river ice is rare in this region, no permanent ice monitoring system is in operation. Due to their all weather capabilities, active remote sensing instruments provide a good opportunity to monitor ice coverage. ESA's Sentinel-1 radar satellites acquire data with a relatively high spatial and temporal resolution. A method was developed to provide ice coverage information at a regular interval; depending on the satellite revisit, at least once every 5 days, but often also on a daily basis. In 2017, maps were created for sections along the Danube and in 2022 for another section of the Tisza river. The ice coverage was calculated with a spatial resolution of 10 metre and visualised with a spatial density of 100 metre along the rivers. The mapping procedure provides visual information to give a fast overview of the spatial extent of ice coverage and quantitative , tabular information for operational activities to mitigate the damage due to ice packs and ice jams.

Agricultural production in greenhouses shows a rapid growth in many parts of the world. This form of intensive farming requires a large amount of water and fertilizers, and can have a severe impact on the environment. The number of greenhouses and their location is important for applications like spatial planning, environmental protection, agricultural statistics and taxation. Therefore, with this study we aim to develop a methodology to detect plastic greenhouses in remote sensing data using machine learning algorithms.

Soil erosion is a main problem in sloping vineyards, which can dramatically affect soil quality and fertility. The present study aimed to evaluate the spatial patterns of selected physico-chemical soil characteristics and the soil’s potentially toxic element (PTE) contents in the context of erosion. The study was conducted in a 0.4 ha vineyard plot on a steep slope in Tállya, part of the wine-growing region of Tokaj-Hegyalja (Hungary). A total of 20 topsoil samples (0-10 cm) were collected and analysed for PTEs (B, Co, Ba, Sr, Mn, Ni, Cr, Pb, Zn, and Cu), soil pH (deionized water and KCl solution), particle-size distribution, soil organic matter (SOM), (nitrate+nitrite)-N, P2O5, and carbonate content. Among the selected PTEs, only Cu (125±27 mg/kg) exceeds the Hungarian standards set for soils and sediments (75 mg/kg) due to the long-term use of Cu-based pesticides in the vineyard. Examined PTEs are negatively correlated with the sand content of the topsoil, except for Mn, while the significant positive relationship with the clay content shows the role of clay in retaining PTEs in soil. SOM seems to play a minor role in binding PTEs, as Cu is the only element for which a significant correlation with the SOM content can be detected. The spatial distribution maps prepared by inverse distance weighting (IDW) and lognormal kriging (LK) methods show higher PTE contents at the summit and the shoulder of the hillslope and lower contents at the backslope and the footslope zones. The low slope gradients (0-5 degree) and the high contents of the coarse fraction (> 35%) likely protect the soil at the summit and the hillslope’s shoulder from excessive erosion-induced losses. While the reraising PTE contents at the toeslope are likely due to the deposition of fine soil particles (silt and clay). The highest SOM contents at the summit and the toeslope areas, and increased contents of the coarse fraction at the backslope, confirm the effects of soil erosion on the spatial distribution patterns of main soil quality indicators. Overall, the LK outperformed the IDW method in predicting the soil parameters in unsampled areas.

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.

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.

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.

Wildfire poses a serious risk in several regions of the world threatening urban, agricultural areas and natural ecosystems as well. Nature conservation has important role to be prepared for the management of postfire environmental degradation and restoration for protected areas preserving valuable ecosystems. The improving temporal and spatial resolution of remote sensing and GIS methods significantly contributes to map the changes for accelerating management steps of restoration. In this study a severe wildfire and its impacts were assessed in case of a protected area of the Kiskunság National Park in Hungary, which was partly burnt down in 2012. The aim of this research was to efficiently and accurately assess the damages and to plan and execute the restoration works using remote sensing tools. Aerial data collection was performed one month, and one year after the fire. In 2014 the regenerated vegetation was surveyed and mapped in the field. Using the aerial photographs and the field data, the degree and extent of the fire damages, the types and the state of the vegetation and the presence and proportion of the invasive species were determined. Semi-automatic methods were used for the classification of completely, partially damaged and undamaged areas. Based on the results, the reforestation of the burnt area is suggested to prevent the overspreading of white poplar against common junipers and to clean the area from the most frequent invasive species. To monitor the regeneration of the vegetation and the spreading of the invasive species, further aerial photography and field campaigns are planned.

The most obvious characteristics of urban climate are higher air and surface temperatures compared to rural areas and large spatial variation of meteorological parameters within the city. This research examines the long term and seasonal development of urban surface temperature using satellite data during a period of 30 years and within a year. The medium resolution Landsat data were (pre)processed using open source tools. Besides the analysis of the long term and seasonal changes in land surface temperature within a city, also its relationship with changes in the vegetation cover was investigated. Different urban districts and local climate zones showed varying strength of correlation. The temperature difference between urban surfaces and surroundings is defined as surface urban heat island (SUHI). Its development shows remarkable seasonal and spatial anomalies. The satellite images can be applied to visualize and analyze the SUHI, although they were not collected at midday and early afternoon, when the phenomenon is normally at its maximum. The applied methodology is based on free data and software and requires minimal user interaction. Using the results new urban developments (new built up and green areas) can be planned, that help mitigate the negative effects of urban climate.

Inland excess water floodings are a common problem in the Carpathian Basin. Nearly every year large areas are covered by water due to lack of natural runoff of superfluous water. To study the development of this phenomenon it is necessary to determine where these inundations are occurring. This research evaluates different methods to classify inland excess water occurrences on a study area covering south-east Hungary and northern Serbia. The region is susceptible to this type of flooding due to its geographical circumstances. Three separate methods are used to determine their applicability to the problem. The methods use the same input data set but differ in approach and complexity. The input data set consists of a mosaic of RapidEye medium resolution satellite images. The results of the classifications show that all three methods can be applied to the problem and provide high quality satellite based inland excess water maps over a large area.

The wildlife and richness of species in Hungary is greatly endangered by invasive plants mostly coming from other countries and continents. These invasive plants are spreading aggressively and they are taking over massive territories. One of the biggest ecological risk is caused by the common milkweed (Asclepias syriaca), which is spreading unstoppably in the sand ridges of the Duna-Tisza Interfluve in Hungary, harming the native flora and the agricultural plough fields. This massive spreading is causing serious nature conservational harm as well as economical problems. It's very important to continuously monitor this invasive species, to map the ways of spreading, and to terminate the infected and the potentially compromised territories. This ecological problem has not yet been examined via a combination of GIS and remote sensing tools and methods. Using the LUCAS dot based land use and land cover database, made by Eurostat, spreading of the milkweed on the southern territories of the Great Hungarian Plain was identified and categorised by measure of infection. The outcome was correlated with the genetic soil maps of the region. Furthermore as part of the research a mobile application was created with a real time map on ArcGIS Online platform, based on VGI (Volunteered geographic information) data collection of the topic. With this solution fast, accurate and up-to-date data can be provided from greater regions, consuming minimal time and energy. This method is indispensable to monitor these rapid and dynamic ecological changes. Simultaneously with the fieldworks, from an UAV (unmanned aerial vehicle) platform high resolution hyperspectral aerial images with 138 spectral bands were taken. Spectral information from the visible and near infra-red range enabled to specify the spectral characteristics of common milkweed. Using field training and validation data different classification methods (Spectral Angle Mapper, Support Vector Machine, etc.) were tested with a best Producer accuracy of 76%. However false positive results were also appeared on open grassland areas, which problem have to be solved in the future [ABSTRACT FROM AUTHOR]