In the framework of an investigation about frost damage that occurred 1987 in Northern Greece a new index, the modified NDVI (mNDVI), was developed. Within a joint bilateral German-Greek-Cooperation project the Greek partner investigated SPOT images and the German partner mainly evaluated Landsat TM images to estimate frost damages in agriculture with a remote sensing approach. The first applications of the new mNDVI are described in this paper and show very promising results.

The rapid development of world‐wide urbanization causes many problems, such as infrastructure planning, uncontrolled urban sprawl and a concentration of resources at the expense of the surrounding countryside. Hence, planners and resource managers need a reliable mechanism to assess the consequences of urbanization quickly and effectively. Today, remote sensing and GIS are the new technologies that can help to solve these problems with up‐to‐date spatial information. This paper examines remote sensing of urban areas from the point of view of developed and developing countries.

Since the COVID-19 (coronavirus disease 2019) pandemic is a global phenomenon, many scientists and research organizations create thematic maps to visualize and understand the spatial spread of the disease and to inform mankind. Nowadays, Geographic Information Systems (GIS) and web mapping technologies enable people to create digital maps on demand. This fosters the permanent update of COVID-19 map products, even by non-cartographers, and their publication in news, media and scientific publications. With the ease and speed of map-making, many map creators seem to forget about the fundamental principles of good and easy-to-read thematic choropleth maps, which requires geo-spatial data literacy. Geo-spatial data literacy is an important skill, to be able to judge the reliability of spatial data, and to create ingenuous thematic maps. This contribution intends to make people of disciplines other than those that are map-related aware of the power of thematic maps and how one can create trustworthy thematic maps instead of misleading thematic maps which could, in a worst case, lead to misinterpretation.

By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning.