Nitrogen dioxide (NO2) is one of the main air quality pollutants of concern in many urban and industrial areas worldwide, and particularly in the European region, where in 2017 almost 20 countries exceeded the NO2 annual limit values imposed by the European Commission Directive 2008/50/EC (EEA, 2019). NO2 pollution monitoring and regulation is a necessary task to help decision makers to search for a sustainable solution for environmental quality and population health status improvement. In this study, we propose a comparative analysis of the tropospheric NO2 column spatial configuration over Europe between similar periods in 2019 and 2020, based on the ESA Copernicus Sentinel-5P products. The results highlight the NO2 pollution dynamics over the abrupt transition from a normal condition situation to the COVID-19 outbreak context, characterized by a short-time decrease of traffic intensities and industrial activities, revealing remarkable tropospheric NO2 column number density decreases even of 85% in some of the European big cities. The validation approach of the satellite-derived data, based on a cross-correlation analysis with independent data from ground-based observations, provided encouraging values of the correlation coefficients (R2), ranging between 0.5 and 0.75 in different locations. The remarkable decrease of NO2 pollution over Europe during the COVID-19 lockdown is highlighted by S-5P products and confirmed by the Industrial Production Index and air traffic volumes.

Abstract. Strong winds may uproot and break trees and represent a major natural disturbance for European forests. Wind disturbances have intensified over the last decades globally and are expected to further rise in view of the effects of climate change. Despite the importance of such natural disturbances, there are currently no spatially explicit databases of wind-related impact at a pan-European scale. Here, we present a new database of wind disturbances in European forests (FORWIND). FORWIND is comprised of more than 80 000 spatially delineated areas in Europe that were disturbed by wind in the period 2000–2018 and describes them in a harmonized and consistent geographical vector format. The database includes all major windstorms that occurred over the observational period (e.g. Gudrun, Kyrill, Klaus, Xynthia and Vaia) and represents approximately 30 % of the reported damaging wind events in Europe. Correlation analyses between the areas in FORWIND and land cover changes retrieved from the Landsat-based Global Forest Change dataset and the MODIS Global Disturbance Index corroborate the robustness of FORWIND. Spearman rank coefficients range between 0.27 and 0.48 (p value < 0.05). When recorded forest areas are rescaled based on their damage degree, correlation increases to 0.54. Wind-damaged growing stock volumes reported in national inventories (FORESTORM dataset) are generally higher than analogous metrics provided by FORWIND in combination with satellite-based biomass and country-scale statistics of growing stock volume. The potential of FORWIND is explored for a range of challenging topics and scientific fields, including scaling relations of wind damage, forest vulnerability modelling, remote sensing monitoring of forest disturbance, representation of uprooting and breakage of trees in large-scale land surface models, and hydrogeological risks following wind damage. Overall, FORWIND represents an essential and open-access spatial source that can be used to improve the understanding, detection and prediction of wind disturbances and the consequent impacts on forest ecosystems and the land–atmosphere system. Data sharing is encouraged in order to continuously update and improve FORWIND. The dataset is available at https://doi.org/10.6084/m9.figshare.9555008 (Forzieri et al., 2019).

In this article, we processed Sentinel-2 images in order to obtain high accuracy land cover maps for two complementary study areas. The first is represented by the Romanian Subcarpathians, a hilly highly fragmented area with heterogeneous land cover pattern and the second by Romanian Carpathians, a mountain area with homogenous structure of vegetation cover. The aim of this article is to evaluate the potential of a singledate in comparison with multi-date images for which a complete calibration and an iterative process of supervised classification using Maximum Likelihood (ML) and Support Vector Machine (SVM) algorithms were applied for the both study areas. The results show that in the case of Subcarpathian area, the SVM classification on multi-date images has better accuracy due to high complexity of the land cover pattern and spectral similarities between classes, while in the Carpathians, the ML returns good accuracy, consequence of high spectral separabilities between compact features. The validation process based on ground reference data shows good accuracies, about 92.41% for the Subcarpathians and 98.65% for the Carpathians. It is clearly noticed that the land cover pattern determines the use of different algorithms and the multi-date images enhance the overall accuracy of the classification.

In this study, Bucharest, the capital city of Romania was selected as a case study. Based on time series of Landsat TM imagery and statistical data, an analysis on urban growth from 1984 to 2010 was performed, using an integrated approach of remote sensing and GIS techniques. The land cover data were validated by CORINE Land Cover maps. The results revealed that rapid urban growth of the Bucharest region led to accelerated land use conversion from cropland to built-up land. The processes of deindustrialization in the core city and industrialization to the ring road represent other driving factors for spatiotemporal pattern of built-up land. The paper will discuss these processes and their impact on economic growth and residential suburbanization of the studied region.

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