Methodology for generating a global forest management layer

The first ever global map of forest management was generated based on remote sensing data. To collect training data, we launched a series of Geo-Wiki (https://www.geo-wiki.org/) campaigns involving forest experts from different world regions, to explore which information related to forest management could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, Sentinel time series and normalized difference vegetation index (NDVI) profiles derived from Google Earth Engine. A machine learning technique was then used with the visually interpreted sample (280K locations) as a training dataset to classify PROBA-V satellite imagery. Finally, we obtained a global wall-to-wall map of forest management at a 100m resolution for the year 2015. The map includes classes such as intact forests; forests with signs of management, including logging; planted forests; woody plantations with a rotation period up to 15 years; oil palm plantations; and agroforestry. The map can be used to deliver further information about forest ecosystems, protected and observed forest status changes, biodiversity assessments, and other ecosystem-related aspects

Analysis of the spectrum of the Pd I-like xenon (Xe IX) and extended interpretation of the Sb VI, Te VII and I VIII spectra

The spectrum of Xe IX has been observed using a fast capillary discharge with inductive storage as a light source and the normal incidence 6.65m spectrograph at Troitsk. This follows extended observations of vacuum spark spectra of Sb VI, Te VII and I VIII at Troitsk and at Meudon. Theoretical studies of [MATH] ([MATH]) configurations by means of the Cowan codes and by generalized-least-squares fits along the Pd I sequence lead to classify 82 lines as 5-5 transitions of Xe IX