A method was developed for crop area mapping inspired by spectral matching techniques (SMTs) and based on phenological characteristics of different crop types applied using 100-m Proba-V NDVI data for the season 2014–2015. Ten-daily maximum value NDVI composites were created and smoothed in SPIRITS (spirits.jrc.ec.europa.eu). The study sites were globally spread agricultural areas located in Flanders (Belgium), Sria (Russia), Kyiv (Ukraine) and Sao Paulo (Brazil). For each pure pixel within the field, the NDVI profile of the crop type for its growing season was matched with the reference NDVI profile based on the training set extracted from the study site where the crop type originated. Three temporal windows were tested within the growing season: green-up to senescence, green-up to dormancy and minimum NDVI at the beginning of the growing season to minimum NDVI at the end of the growing season. Post classification rules were applied to the results to aggregate the crop type at the plot level. The overall accuracy (%) ranged between 65 and 86, and the kappa coefficient changed from 0.43–0.84 according to the site and the temporal window. In order of importance, the crop phenological development period, parcel size, shorter time window, number of ground-truth parcels and crop calendar similarity were the main reasons behind the differences between the results. The methodology described in this study demonstrated that 100-m Proba-V has the potential to be used in crop area mapping across different regions in the world.

According to Monteith’s theory, crop biomass is linearly correlated with the amount of absorbed photosynthetically active radiation (APAR) and a constant radiation use efficiency (RUE) down-regulated by stress factors such as CO₂ fertilisation, temperature and water stress. The objective was to investigate the relative importance of these stress factors in relation to regional biomass production and yield. The production efficiency model Copernicus Global Land Service-Dry Matter Productivity (CGLS-DMP), which follows Monteith’s theory, was modified and evaluated for common wheat and silage maize in France, Belgium and Morocco using SPOT VEGETATION for the period 1999–2012. For each study site the stress factor that has the highest correlation with crop yield was retained. The correlation between crop yield data and cumulative modified DMP, CGLS-DMP, fAPAR, and NDVI values were analysed for different crop growth stages. A leave-one-year-out cross validation was used to test the robustness of the model. On average, R² values increased from 0.49 for CGLS-DMP to 0.68 for modified DMP, RMSE (t/ha) decreased from 0.84–0.61, RRMSE (%) reduced from 13.1–8.9, MBE (t/ha) decreased from 0.05–0.03 and the index of model performance (E₁) increased from 0.08–0.28 for the selected sites and crops. The best results were obtained by including combinations of the most appropriate stress factors for each selected region and cumulating the modified DMP during part of the growing season that includes the reproductive stage. Though no single solution to the improvement of a global product could be demonstrated, our findings encourage an extension of the methodology to other regions of the world.

Climate change is increasing the risk for extreme weather events such as heatwaves, including in northern countries like Sweden, which until recent years has had limited experiences of coping with extreme heat. Based on predictions that Sweden will be more frequently exposed to heatwaves in the future, it is imperative to increase the societal resilience and adaptation measures. This paper presents a qualitative interview study involving 19 participants and their experiences of caring for vulnerable people during the heatwave in 2018. The participants represent four different organizations (working directly or indirectly with vulnerable people) in two municipalities in Sweden, including preschools, homes for the elderly, homecare services, and care homes for people with functional impairments, which were all impacted during the heatwave. This study contributes new empirical insights about the heatwave in 2018 and, in particular, similarities and differences in both experiences and adaptation measures across the four organizations. The findings show how both staff and vulnerable people suffered from the consequences of heat which increased vulnerability, how some organizations lacked enough (qualified) staff to secure routines, and that few evaluations and formal changes were done after the heatwave.  

This review article analyzes factors affecting emergency response to hazardous events on highways and their bridges, with focus on man-made and natural scenarios: heavy vehicle collision with a bridge, earthquake, heavy snowfall, and flooding. For each disaster scenario, selected historical events were compiled to determine influential factors and success criteria for efficient emergency response, both related to organizational and technical measures. This study constituted a part of a resilience management process, recently developed and demonstrated within the European Union (EU)-funded H2020 project IMPROVER and can be a useful approach in aiding operators of transportation infrastructure to improve their resilience to emergency incidents.

Global warming is increasingly causing extreme weather events, such as heat waves. One-way heat waves are problematic in society, particularly in urban areas, is because of their negative impact on vulnerable groups including elderly people and children. Organizations such as municipalities that are responsible for local schools, day care centres and/or elderly homes, may struggle to provide the necessary care and function during extreme heat waves. This work explores how remote sensing images providing historical data of land surface temperatures (LST) can be used to create summer urban heat island maps and heat wave intensity, and in the longer run, how such information could be used by municipalities and other actors to mitigate effects of future heat waves. The method presented in this study was used to detect the “hot spots” in two participating Swedish municipalities and identify the municipal services located within these spots based on MODIS (1 km) and Landsat (30 m) LST datasets. Furthermore, this study showed that urban heat island phenomenon existed in the two participating municipalities. In general, this methodology can be applicable at both local and regional scale, although it might require additional site-specific data.