Continental-scale land cover mapping at 10 m resolution over Europe (ELC10)

Widely used European land cover maps such as CORINE are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a high resolution (10 m) land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A Random Forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across 8 land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10-m land cover maps including S2GLC and FROM-GLC10. We found that atmospheric correction of Sentinel-2 and speckle filtering of Sentinel-1 imagery had minimal effect on enhancing classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The conversion of LUCAS points into homogenous polygons under the Copernicus module increased accuracy by <1%, revealing that Random Forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies - the difference between 5K and 50K LUCAS points is only 3% (86 vs 89%). At 10-m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g. tree planting)

Priority maps for pollinator habitat enhancement schemes in semi-natural grasslands

Conserving semi-natural grasslands, a threatened habitat type in European landscapes, is increasingly recognized as a measure to conserve pollinators. Our aim was to test if (a) prediction maps of solitary bee species richness could be used to rank semi-natural grasslands in terms of their potential for supporting wild bees, and (b) if such predictions extend current assessment criteria that determine which grasslands are eligible for being listed under habitat conservation schemes. We sampled wild bee communities in 52 semi-natural grasslands in southeast Norway. We conducted an across-year validation, using data from 2019 (32 sites) to model bee species richness, and used data from 2020 (20 sites) to validate predictions. We then conducted a leave-one-out cross-validation, iteratively using data from 51 sites to parameterize our model, and validating predictions on the withheld site. Finally, we used data from all 52 sites to update the model and tested if predicted species richness within the 1075 grasslands in our region was reflected in current assessment criteria scores assigned to those grasslands. Models from across-year, and leave-one-out cross-validations, predicted 39%, and 43% of bee species richness in semi-natural grasslands, respectively. Model predictions and current criteria of semi-natural grassland quality were not strongly related (R2 adjusted = 0.01), suggesting that prediction models can add a valuable extra dimension when prioritizing between semi-natural grassland for pollinator habitat conservation. Our findings illustrate how spatial prediction models can provide management authorities with a valuable tool for prioritizing where to direct habitat enhancement schemes in order to improve conservation effectiveness. Pollinators Bees Grassland Semi-natural Management ConservationpublishedVersio

Chart Adequacy: Workshop and GEBCO Training

In July, 2015 the first NOAA Chart Adequacy Workshop was held in Silver Spring, Maryland, USA. Following a three-day workshop (14th to 16th July, 2015), four Nippon Foundation GEBCO students stayed at NOAA for an additional 10-day training at Office of Coast Survey’s Marine Chart Division. The key objective of the NOAA Chart Adequacy Workshop was to demonstrate techniques to evaluate the suitability of nautical chart products using chart quality information and publicly-available information. The attendees were cartographers, hydrographers and potential chart producers from hydrographic offices and government agencies around the world. The nations of the participants in the workshop included: Indonesia, Israel, Japan, Kenya, Malaysia, Philippines, South Korea, Sri Lanka, United Kingdom, United States and Venezuela. Through instructor presentations and GIS laboratory exercises (provided by Dr. Shachak Pe’eri and Lt Anthony Klemm), the participants generated the key layers that are involved in the NOAA procedure. A vessel traffic layer was generated by classification of navigational routes using Automatic Identification Systems (AIS) information. A bathymetric difference layer was generated by identifying areas that showed significant bathymetric changes identified by comparing Satellite-Derived Bathymetry (SDB) or other surveys of opportunity, with the existing chart. A hydrographic characteristics layer was generated by classification of chart quality information. Chart data (including the smooth sheet sounding sets) for the procedure were provided in a vector format with the appropriate metadata according to IHO S-57. Raster Navigational Charts were also used as a background and as a reference for the Bathymetric Difference layer

High resolution prediction maps of solitary bee diversity can guide conservation measures

Wild bees are key ecosystem components making their decline a cause for concern. An effective measure to increase wild bee diversity is to enhance plant diversity. However, the effect on bee diversity of augmenting plant diversity depends on site-specific environmental conditions. We aimed to make spatial predictions of where: (a) environmental conditions maximize bee diversity, so that such areas can be prioritized for augmenting plant diversity; and (b) populations of threatened wild bee species are most likely to occur. We surveyed bee communities in traditionally managed hay meadows in SE Norway and modelled bee diversity as a function of climate, habitat area, and distance to nesting substrates. We used independent data to validate our predictions and found that plant and predicted bee species richness together explained 76% and 69% of the variation in observed solitary bee species richness in forested and agricultural ecosystems, respectively. In urban areas, the predicted bee species richness alone explained 31% of the variation in observed solitary bee species richness. Using data from online species occurrence records, we found that – compared to species of lower conservation concern – threatened solitary bee species were more typically recorded in areas with a high predicted solitary bee species richness. We show that spatial predictions of bee diversity can identify sites where augmenting plant diversity is likely to be most effective. Maps of predicted bee diversity can guide species surveys and monitoring projects and increase the chances of locating populations of threatened bees.publishedVersio

The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models

1. Sowing flower strips along field edges is a widely adopted method for conserving pollinating insects in agricultural landscapes. To maximize the effect of flower strips given limited resources, we need spatially explicit tools that can prioritize their placement, and for identifying plant species to include in seed mixtures. 2. We sampled bees and plant species as well as their interactions in a semicontrolled field experiment with roadside/field edge pairs with/without a sown flower strip at 31 sites in Norway and used a regional spatial model of solitary bee species richness to test if the effect of flower strips on bee species richness was predictable from the modelled solitary bee species richness. 3. We found that sites with flower strips were more bee species rich compared to sites without flower strips and that this effect was greatest in areas that the regional solitary bee species richness model had identified to be particularly important for bees. Spatial models revealed that even within small landscapes there were pronounced differences between field edges in the predicted effect of sowing flower strips. 4. Of the plant species that attracted the most bee species, the majority mainly attracted bumblebees and only few species also attracted solitary bees. Considering both the taxonomic diversity of bees and the species richness of bees attracted by plants we suggest that seed mixes containing Hieracium spp. such as Hieracium umbellatum, Pilosella officinarum, Taraxacum spp., Trifolium repens, Lotus corniculatus, Stellaria graminea and Achillea millefolium would provide resources for diverse bee communities in our region 5. Spatial prediction models of bee diversity can be used to identify locations where flower strips are likely to have the largest effect and can thereby provide managers with an important tool for prioritizing how funding for agri-environmental schemes such as flower strips should be allocated. Such flower strips should contain plant species that are attractive to both solitary and bumblebees, and do not need to be particularly plant species rich as long as the selected plants complement each other. agri-environmental schemes, bees, flower strips, networks, pollinators, restoration, spatialpublishedVersio

Acute skin toxicity associated with a 1-week schedule of whole breast radiotherapy compared with a standard 3-week regimen delivered in the UK FAST-Forward Trial

BACKGROUND AND PURPOSE: FAST-Forward is a phase 3 clinical trial testing a 1-week course of whole breast radiotherapy against the UK standard 3-week regimen after primary surgery for early breast cancer. Two acute skin toxicity substudies were undertaken to test the safety of the test schedules with respect to early skin reactions. MATERIAL AND METHODS: Patients were randomly allocated to 40Gy/15 fractions (F)/3-weeks, 27Gy/5F/1-week or 26Gy/5F/1-week. Acute breast skin reactions were graded using RTOG (first substudy) and CTCAE criteria v4.03 (second substudy) weekly during treatment and for 4weeks after treatment ended. Primary endpoint was the proportion of patients within each treatment group with grade ⩾3 toxicity (RTOG and CTCAE, respectively) at any time from the start of radiotherapy to 4weeks after completion. RESULTS: 190 and 162 patients were recruited. In the first substudy, evaluable patients with grade 3 RTOG toxicity were: 40Gy/15F 6/44 (13.6%); 27Gy/5F 5/51 (9.8%); 26Gy/5F 3/52 (5.8%). In the second substudy, evaluable patients with grade 3 CTCAE toxicity were: 40Gy/15F 0/43; 27Gy/5F 1/41 (2.4%); 26Gy/5F 0/53. CONCLUSIONS: Acute breast skin reactions with two 1-week schedules of whole breast radiotherapy under test in FAST-Forward were mild

Mechanical model of the ultra-fast underwater trap of Utricularia

The underwater traps of the carnivorous plants of the Utricularia species catch their preys through the repetition of an "active slow deflation / passive fast suction" sequence. In this paper, we propose a mechanical model that describes both phases and strongly supports the hypothesis that the trap door acts as a flexible valve that buckles under the combined effects of pressure forces and the mechanical stimulation of trigger hairs, and not as a panel articulated on hinges. This model combines two different approaches, namely (i) the description of thin membranes as triangle meshes with strain and curvature energy, and (ii) the molecular dynamics approach, which consists in computing the time evolution of the position of each vertex of the mesh according to Langevin equations. The only free parameter in the expression of the elastic energy is the Young's modulus E of the membranes. The values for this parameter are unequivocally obtained by requiring that the trap model fires, like real traps, when the pressure difference between the outside and the inside of the trap reaches about 15 kPa. Among other results, our simulations show that, for a pressure difference slightly larger than the critical one, the door buckles, slides on the threshold and finally swings wide open, in excellent agreement with the sequence observed in high-speed videos.Comment: Accepted for publication in Physical Review