Classifying hyperspectral airborne imagery for vegetation survey along coastlines

This paper studies the potential of airborne hyperspectral imagery for classifying vegetation along the Belgian coastlines. Here, the aim is to build vegetation maps using automatic classification. Besides a general linear multiclass classifier (Linear Discriminant Analysis), several strategies for combining binary classifiers are proposed: one based on a hierarchical decision tree, one based on the Hamming distance between the codewords obtained by binary classifiers and one based on the coupling of posterior probabilities. In addition, a new procedure is proposed for spatial classification smoothing. This procedure takes into account spatial information by letting the decision for classification of a pixel depend on the classification probabilities of neighboring pixels. This is shown to render smoother classification images

Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions

Although satellite-based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The growth of interest in biodiversity variables observable from space has been partly underpinned by the development of the essential biodiversity variable (EBV) framework by the Group on Earth Observations – Biodiversity Observation Network, which itself was guided by the process of identifying essential climate variables. This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing (SRS) EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space. Progress toward the identification of SRS-EBVs will require a clear understanding of what makes a biodiversity variable essential, as well as agreement on who the users of the SRS-EBVs are. Technological and algorithmic developments are rapidly expanding the set of opportunities for SRS in monitoring biodiversity, and so the list of SRS-EBVs is likely to evolve over time. This means that a clear and common platform for data providers, ecologists, environmental managers, policy makers and remote sensing experts to interact and share ideas needs to be identified to support long-term coordinated actions

Europe's lost forests: a pollen-based synthesis for the last 11,000 years

8000 years ago, prior to Neolithic agriculture, Europe was mostly a wooded continent. Since then, its forest cover has been progressively fragmented, so that today it covers less than half of Europe’s land area, in many cases having been cleared to make way for fields and pasture-land. Establishing the origin of Europe’s current, more open land-cover mosaic requires a long-term perspective, for which pollen analysis offers a key tool. In this study we utilise and compare three numerical approaches to transforming pollen data into past forest cover, drawing on >1000 14C-dated site records. All reconstructions highlight the different histories of the mixed temperate and the northern boreal forests, with the former declining progressively since ~6000 years ago, linked to forest clearance for agriculture in later prehistory (especially in northwest Europe) and early historic times (e.g. in north central Europe). In contrast, extensive human impact on the needle-leaf forests of northern Europe only becomes detectable in the last two millennia and has left a larger area of forest in place. Forest loss has been a dominant feature of Europe’s landscape ecology in the second half of the current interglacial, with consequences for carbon cycling, ecosystem functioning and biodiversity

The farther, the safer: a manifesto for securely navigating synthetic species away from the old living world

Biotechnology has empirically established that it is easier to construct and evaluate variant genes and proteins than to account for the emergence and function of wild-type macromolecules. Systematizing this constructive approach, synthetic biology now promises to infer and assemble entirely novel genomes, cells and ecosystems. It is argued here that the theoretical and computational tools needed for this endeavor are missing altogether. However, such tools may not be required for diversifying organisms at the basic level of their chemical constitution by adding, substituting or removing elements and molecular components through directed evolution under selection. Most importantly, chemical diversification of life forms could be designed to block metabolic cross-feed and genetic cross-talk between synthetic and wild species and hence protect natural habitats and human health through novel types of containment

A review of applying second-generation wavelets for noise removal from remote sensing data.

The processing of remotely sensed data includes compression, noise reduction, classification, feature extraction, change detection and any improvement associated with the problems at hand. In the literature, wavelet methods have been widely used for analysing remote sensing images and signals. The second-generation of wavelets, which is designed based on a method called the lifting scheme, is almost a new version of wavelets, and its application in the remote sensing field is fresh. Although first-generation wavelets have been proven to offer effective techniques for processing remotely sensed data, second-generation wavelets are more efficient in some respects, as will be discussed later. The aim of this review paper is to examine all existing studies in the literature related to applying second-generation wavelets for denoising remote sensing data. However, to make a better understanding of the application of wavelet-based denoising methods for remote sensing data, some studies that apply first-generation wavelets are also presented. In the part of hyperspectral data, there is a focus on noise removal from vegetation spectrum

30-Day morbidity and mortality of bariatric metabolic surgery in adolescence during the COVID-19 pandemic – The GENEVA study

Background: Metabolic and bariatric surgery (MBS) is an effective treatment for adolescents with severe obesity. Objectives: This study examined the safety of MBS in adolescents during the coronavirus disease 2019 (COVID-19) pandemic. Methods: This was a global, multicentre and observational cohort study of MBS performed between May 01, 2020, and October 10,2020, in 68 centres from 24 countries. Data collection included in-hospital and 30-day COVID-19 and surgery-specific morbidity/mortality. Results: One hundred and seventy adolescent patients (mean age: 17.75 ± 1.30 years), mostly females (n = 122, 71.8%), underwent MBS during the study period. The mean pre-operative weight and body mass index were 122.16 ± 15.92 kg and 43.7 ± 7.11 kg/m2, respectively. Although majority of patients had pre-operative testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (n = 146; 85.9%), only 42.4% (n = 72) of the patients were asked to self-isolate pre-operatively. Two patients developed symptomatic SARS-CoV-2 infection post-operatively (1.2%). The overall complication rate was 5.3% (n = 9). There was no mortality in this cohort. Conclusions: MBS in adolescents with obesity is safe during the COVID-19 pandemic when performed within the context of local precautionary procedures (such as pre-operative testing). The 30-day morbidity rates were similar to those reported pre-pandemic. These data will help facilitate the safe re-introduction of MBS services for this group of patients