Forest damage has become more frequent in Hungary in the last decades, and remote sensing offers a powerful tool for monitoring them rapidly and cost-effectively. A combined approach was developed to utilise high-resolution ESA Sentinel-2 satellite imagery and Google Earth Engine cloud computing and field-based forest inventory data. Maps and charts were derived from vegetation indices (NDVI and Z∙NDVI) of satellite images to detect forest disturbances in the Hungarian study site for the period of 2017–2020. The NDVI maps were classified to reveal forest disturbances, and the cloud-based method successfully showed drought and frost damage in the oak-dominated Nagyerdő forest of Debrecen. Differences in the reactions to damage between tree species were visible on the index maps; therefore, a random forest machine learning classifier was applied to show the spatial distribution of dominant species. An accuracy assessment was accomplished with confusion matrices that compared classified index maps to field-surveyed data, demonstrating 99.1% producer, 71% user, and 71% total accuracies for forest damage and 81.9% for tree species. Based on the results of this study and the resilience of Google Earth Engine, the presented method has the potential to be extended to monitor all of Hungary in a faster, more accurate way using systematically collected field-data, the latest satellite imagery, and artificial intelligence.

The land use sector is a crucial pillar in achieving the EU climate goals set for 2050. A significant part of the climate change mitigation potential of the land use sector is inherent to agroforestry. Windbreaks are important agroforestry elements of Hungarian agricultural landscapes. The new and improved agroforestry subsidy system may positively affect the extension of windbreaks in Hungary, making it relevant to assess their carbon sequestration potential. In our study, we examined the carbon sequestration of windbreaks at the country level and in two sample areas of 24,000 hectares based on National Forestry Database volume stock data, as well as information collected from the Hungarian Forest Cover Map using orthophoto interpretation. We estimated the total annual carbon sequestration realized in the aboveground biomass pool of Hungarian windbreaks to be −33.1 ktCO₂/year, which is 0.67% of the total annual carbon sequestration of the aboveground biomass pool of all Hungarian forests, as reported by the Hungarian Greenhouse Gas Inventory. On the other hand, according to our estimate, the weighted mean annual carbon sequestration in the aboveground biomass of windbreaks was −2.4 tCO₂/ha/year in the 2010–2020 period. This value is very close to the average mean annual carbon sequestration per hectare value of all forests, as reported by the Hungarian Greenhouse Gas Inventory. This means that planting a given area of windbreaks in between agricultural fields can have similar climate change mitigation effects as planting forests in the same given area.

The effects of the changing frequency and severity of drought events in Central Europe may become a growing concern for its forests. In this study, we looked into how Hungary’s forests have been affected by the 2022 compound heatwave and drought, following an arid period from 2018 to 2021. We used our active intensive monitoring plots of the Forest Protection Measuring and Monitoring System (Level II in the ICP Forests) across the country between 2017 and 2022. We analyzed satellite images to support a survey of the large-scale drought utilizing moderate and high-resolution data. The health state of the forest calculated and mapped on the NDVI, ZNDVI, and NDWI indices showed damage and regeneration throughout the period studied. Overall, the forest stands observed tolerated the negative impacts of the drought (126–204 mm water deficit in 2022) based on our biomass data (the summer leaf loss was 14% in each monitoring plot). However, the classified Z-NDVI values of the Sentinel-2 satellite imagery for the period 2017–2022 showed a severe drought in 2022, which was followed by some improvement in 2023.

Gypsy (or spongy) moth (Lymantria dispar) outbreaks have imposed significant threats to European forests for centuries. While traditional field-based research has provided detailed insights, it remains time-consuming, labour-intensive, and spatially limited. With the advancement of Earth observation satellite technology, forest monitoring has become more efficient and flexible. This study examined the impact of the most extensive gypsy moth outbreak (2003–2006) on the forest dynamics in Hungary using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived indices: the Normalised Difference Vegetation Index (NDVI), Standardised NDVI (Z NDVI), and Leaf Area Index (LAI). Our results show that while the gypsy moth population in Hungary peaked in 2004, based on light trap data, and in 2005, according to field damage reports, the most severe defoliation occurred in 2005 and 2006, as detected by satellite-based decreases in the NDVI and LAI. MODIS-based vegetation indices proved effective in quantifying the extent and severity of defoliation, showing temporal and spatial patterns that aligned with ground observations. The LAI and NDVI metrics also captured varying degrees of defoliation and partial recovery. These findings underscore the value of integrating satellite data with field observations to improve early warning systems and enhance the forecasting and management of gypsy moth outbreaks.

Forest damage has been increasingly recorded over the past decade in both Europe and Hungary, primarily due to prolonged droughts, causing a decline in forest health. In the framework of ICP Forests, the forest damage has been monitored for decades; however, it is labour-intensive and time-consuming. Satellite-based remote sensing offers a rapid and efficient method for assessing large-scale damage events, combining the ground-based ICP Forests datasets. This study utilised cloud computing and Sentinel-2 satellite imagery to monitor forest health and detect anomalies. Standardised NDVI (Z NDVI) maps were produced for the period from 2017 to 2023 to identify disturbances in the forest. The research focused on seven active ICP Forests Level II and 78 Level I plots in Hungary. Z NDVI values were divided into five categories based on damage severity, and there was agreement between Level II field data and satellite imagery. In 2017, severe damage was caused by late frost and wind; however, the forest recovered by 2018. Another decline was observed in 2021 due to wind and in 2022 due to drought. Data from the ICP Forests Level I plots, which represent forest condition in Hungary, indicated that 80% of the monitored stands were damaged, with 30% suffering moderate damage and 15% experiencing severe damage. Z NDVI classifications aligned with the field data, showing widespread forest damage across the country.

Forest damage has become more frequent in Europe in the last decades, and remote sensing offers a powerful tool for monitoring them rapidly and cost-effectively. A novel approach was developed to utilize high-resolution ESA Sentinel-2 satellite imagery and Google Earth Engine cloud computing. The processing, analysing, and visualization of vegetation and water index (NDVI, NDVIch, Z NDVI, EVI, NDWI) maps and charts derived from satellite images took place in the cloud to ensure the detection of forest disturbances in the Hungarian study site for the period 2017-2020. The index maps were classified to reveal forest disturbances, and the cloud-based method successfully showed drought and frost damage in the oak-dominated Nagyerdő forest of Debrecen. Differences in the reaction to damage between tree species were visible in the index values, therefore a Random Forest Machine Learning classifier was applied to show the spatial distribution of dominant species. Accuracy assessment was accomplished with confusion matrices that compared classified index maps to field-surveyed data, demonstrating 71% Total Accuracy for forest damage and 76% for tree species. Based on the results of this study and the resilience of Google Earth Engine, the presented method has the potential to be extended to monitor the entire Hungary.