The Upper Guinea Forest (UGF; West Africa), a global biodiversity hotspot, has lost more than 90% of its original area since 1900, threatening endemic species such as the endangered pygmy hippopotamus (Choeropsis liberiensis). However, little is known about the proximate causes of this deforestation. We classified Sentinel-2 data using the random forest algorithm to differentiate between three main human processes (shifting agriculture, intensive agriculture or urban expansion) driving deforestation between 2000 and 2019 across the pygmy hippopotamus distribution area. Out of c. 89 600 km2 in the year 2000, 15 900 km2 (17%) of forest were lost, primarily to shifting agriculture (14 900 km2). Côte d’Ivoire and Liberia accounted for 14 900 km2 (94%) of the net area of forest lost, c. 15 times greater than deforestation in Sierra Leone and Guinea combined (953 km2). Forest loss inside protected areas is pervasive, and it is essential to prioritize conservation efforts in areas where deforestation is still low (e.g., Taï, Sapo and Gola Rainforest national parks). We suggest that the preservation of the UGF will face challenges associated with people’s demand for food and income. Continued landscape-scale planning and action to reduce deforestation are urgently needed to limit the impact of shifting agriculture on pygmy hippopotamus habitat.

This study summarises the scenario of maritime traffic anomalies, like the increased congestion and U-turn of ships caused by the ship grounding in the Suez Canal in March 2021. Here, satellite automatic identification system based ship trajectories, and Sentinel-1 and Sentinel-2 images based ship positions are analysed after subdividing the study area into seas, lakes and canals. The results show that the blockage affected the maritime traffic for more than three weeks, waiting ship numbers increased from 5 to 122, and daily one to three ships made a U-turn between 23 and 31 March in the Gulf of Suez. Ship density also increased to more than double in Bitter Lakes with a minimum waiting time of 7 days. Hence, to avoid such prolonged waiting of ships, we propose a warning method based on the sharp speed decrease rate, U-turn and congestion.

We used Sentinel-2 satellite imagery at 10 m resolution to map the extent of Norway's glaciers and ice-marginal lakes over 2018–19. We applied a standardized semi-automated band ratio method to derive glacier outlines and ice-marginal lakes. To optimise the results, we manually edited the ice-lake interfaces, debris, snow and parts of the glaciers situated under shadow. We compared our Sentinel-2 derived outlines with very high-resolution aerial orthophotos and Pléiades satellite orthoimages. Glaciers larger than 0.3 km2 have area differences within 7%, whereas values are larger for smaller glaciers. The orthophotos and orthoimages provide more details and a higher mapping accuracy for individual glaciers, but require manual digitisation, have smaller spatial and temporal coverage and can have adverse snow conditions. We found a total glacier area of 2328 ± 70 km2 of which the ten largest glaciers accounted for 52%. The glacier area decreased 15% since the previous inventory (Landsat data from 1999 to 2006), the reduction being largest in northern Norway (22%) compared to southern Norway (10%). We detected more than 2000 previously undetected smaller glaciers and ice patches (covering 37 km2) and 360 new ice-marginal lakes.

Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is the Sentinel-2 Earth Observation mission, developed by the European Space Agency as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10 m) with unprecedented revisit time (every 5 days at the equator). In this data-rich scenario, the remote sensing community is showing a growing interest toward modern supervised machine learning techniques (e.g., deep learning) to perform information extraction, often underestimating the need for reference data that this framework implies. Conversely, few attention is being devoted to the use of network analysis techniques, which can provide a set of powerful tools for unsupervised information discovery, subject to the definition of a suitable strategy to build a network-like representation of image data. The aim of this work is to provide clues on how Satellite Image Time Series can be profitably represented using complex network models, by proposing a methodology to build a multilayer network from such data. This is the first work to explore the possibility to exploit this model in the remote sensing domain. An example of community detection over the provided network in a real-case scenario for the mapping of complex land use systems is also presented, to assess the potential of this approach.

The project “FArming Tools for external nutrient Inputs and water Management” (FATIMA, H2020-SFS2) is developing satellite-based methodologies and information to support effective and efficient water and nitrogen input recommendations in agricultural production. This paper focuses on nitrogen recommendation for winter cereals in Austria and presents preliminary findings from the 2015/16 crop growing season. The Nitrogen Nutrition Index was applied using an empirical relationship to derive dry mass from Leaf Area Index (LAI) and %Na from a chlorophyll index. Results showed a very high correlation between LAI and above ground dry mass (R2=0.95) but a lower correlation between the chlorophyll index and %Na (R2=0.24). Despite various indices tested, the relationship to estimate %Na remains weak. Additional field data and research are needed to further study this aspect.

New remote sensing technologies have provided unprecedented results in vineyard monitoring. The aim of this work was to evaluate different sources of images and processing methodologies to describe spatial variability of spectral-based and canopy-based vegetation indices within a vineyard, and their relationship with productive and qualitative vine parameters. Comparison between image-derived indices from Sentinel 2 NDVI, unfiltered and filtered UAV NDVI and with agronomic features have been performed. UAV images allow calculating new non-spectral indices based on canopy architecture that provide additional and useful information to the growers with regards to within-vineyard management zone delineation.