Preface 54There is much interest in using Earth Observation (EO) technology to track biodiversity, 55 ecosystem functions, and ecosystem services, understandable given the fast pace of 56 biodiversity loss. However, because most biodiversity is invisible to EO, EO-based 57 indicators could be misleading, which can reduce the effectiveness of nature 58 conservation and even unintentionally decrease conservation effort. We describe an 59 approach that combines automated recording devices, high-throughput DNA Meeting the Aichi Biodiversity Targets 64From Google Earth to airborne sensors, the Copernicus Sentinels, and cube satellites, 65Earth Observation is undergoing a rapid expansion in capacity, accessibility, resolution, 66and signal-to-noise ratio, resulting in a recognised shift in our capability for using 67 remote-sensing technologies to monitor biophysical processes on land and water [1][2][3] . 68These advances are motivating calls to use Earth Observation products to manage our 69 natural environment and to track progress toward global and national policy targets on 70 biodiversity and ecosystem services [4][5][6] . Foremost among these policies are the Strategic 71Plan for Biodiversity and the Aichi Biodiversity Targets, which were adopted in 2010 by products (net primary productivity and fire incidence) that could serve as Essential 108Biodiversity Variables for the Sahara, despite this biome's suitability for remote sensing 109 due to its visible biodiversity hotspots, remoteness, and availability of long time series. 110Many of the Aichi Targets require data with species-level resolution, either because some 111 species are direct policy targets (e.g. Target 9: "invasive species controlled or eradicated") 112 or because species compositional data define the metric (e.g. Target 11: "protected areas 113 are ecologically representative and conserved effectively"). species, but information could be 'borrowed' from data-rich species to increase the 294 precision of predictions for rare species. These procedures were able to compensate for 295 the fact that only 134 total bird species had been detected in the survey, which is less The GDM was parameterised with a training dataset of 2280 surveys and fourteen 303 environmental variables and explained 57% of the variation in beta diversity. In addition, for linking pure-EO data to biodiversity. 382The major remaining components of uncertainty relate to generalisability, because only a 383 single FSC-certified reserve was sampled; the applicability of results to arboreal species, 384 which tend to be detected more frequently in forests with disturbed canopy but are not 385 necessarily more widespread in these forests; and wide confidence intervals around 386 parameter estimates for some species as a consequence of sparse data and a fairly 394Another example of the CEOBE approach is the use of Generalised Dissimilarity 395Modelling to connect EO-derived metrics of habitat degradation and fragmentation 89,90 396 to over 300 million records of more ...

Abstract:The European CORINE land cover mapping scheme is a standardized classification system with 44 land cover and land use classes. It is used by the European Environment Agency to report large-scale land cover change with a minimum mapping unit of 5 ha every six years and operationally mapped by its member states. The most commonly applied method to map CORINE land cover change is by visual interpretation of optical/near-infrared satellite imagery. The Sentinel-1A satellite carries a C-band Synthetic Aperture Radar (SAR) and was launched in 2014 by the European Space Agency as the first operational Copernicus mission. This study is the first investigation of Sentinel-1A for CORINE land cover mapping. Two of the first Sentinel-1A images acquired during its ramp-up phase in May and December 2014 over Thuringia in Germany are analysed. 27 hybrid level 2/3 CORINE classes are defined. 17 of these were present at the study site and classified based on a stratified random sample of training pixels from the polygon-eroded CORINE 2006 map. Sentinel-1A logarithmic radar backscatter at HH and HV polarisation (May acquisition), VV and VH polarisation (December acquisition), and the HH image texture are used as input bands to the classification. In addition, a Digital Terrain Model OPEN ACCESSRemote Sens. 2015, 7 14877(DTM), a Canopy Height Model (CHM) and slope and aspect maps from the Shuttle Radar Topography Mission (SRTM) are used as input bands to account for geomorphological features of the landscape. In future, elevation data will be delivered for areas with sufficiently high coherence from the Sentinel-1A Interferometric Wide-Swath Mode itself. When augmented by elevation data from radar interferometry, Sentinel-1A is able to discriminate several CORINE land cover classes, making it useful for monitoring of cloud-covered regions. A bistatic Sentinel-1 Convoy mission would enable single-pass interferometric acquisitions without temporal decorrelation.

Over recent decades, Land Use and Cover Change (LUCC) trends in many regions of Europe have reconfigured the landscape structures around many urban areas. In these areas, the proximity to landscape elements with high forest fuels has increased the fire risk to people and property. These Wildland-Urban Interface areas (WUI) can be defined as landscapes where anthropogenic urban land use and forest fuel mass come into contact. Mapping their extent is needed to prioritize fire risk control and inform local forest fire risk management strategies. This study proposes a method to map the extent and spatial patterns of the European WUI areas at continental scale. Using the European map of WUI areas, the hypothesis is tested that the distance from the nearest WUI area is related to the forest fire probability. Statistical relationships between the distance from the nearest WUI area, and large forest fire incidents from satellite remote sensing were subsequently modelled by logistic regression analysis. The first European scale map of the WUI extent and locations is presented. Country-specific positive and negative relationships of large fires and the proximity to the nearest WUI area are found. A regional-scale analysis shows a strong influence of the WUI zones on large fires in parts of the Mediterranean regions. Results indicate that the probability of large burned surfaces increases with diminishing WUI distance in touristic regions like Sardinia, Provence-Alpes-Côte d'Azur, or in regions with a strong peri-urban component as Catalunya, Comunidad de Madrid, Comunidad Valenciana. For the above regions, probability curves of large burned surfaces show statistical relationships (ROC value > 0.5) inside a 5000 m buffer of the nearest WUI. Wise land management can provide a valuable ecosystem service of fire risk reduction that is currently not explicitly included in ecosystem service valuations. The results re-emphasise the importance of including this ecosystem service in landscape valuations to account for the significant landscape function of reducing the risk of catastrophic large fires.

No abstract

We identified three different classes of P450c17-expressing interstitial cells in the human fetal ovary, each having a different spatiotemporal pattern of P450c17 expression and, presumably, a different set of physiological functions.

No abstract

Abstract:Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the habitat condition. Using an empirical modelling approach, this paper aims to use airborne hyperspectral image data with ground vegetation survey data to model vegetation abundance for a degraded upland blanket bog in the United Kingdom (UK), which is undergoing restoration. A predictive model for vegetation abundance of Plant Functional Types (PFT) was produced using a Partial Least Squares Regression (PLSR) and applied to the whole restoration site. A sensitivity test on the relationships between spectral data and vegetation abundance at PFT and single species level confirmed that PFT was the correct scale for analysis. The PLSR modelling allows selection of variables based upon the weighted regression coefficient of the individual spectral bands, showing which bands have the most influence on the model. These results suggest that the SWIR has less value for monitoring peatland vegetation from hyperspectral images than initially predicted. RMSE values for the validation data range between 10% and 16% cover, indicating that the models can be used as an operational tool, considering the subjective nature of existing vegetation survey results. These predicted coverage images are the first quantitative landscape scale monitoring results to be produced OPEN ACCESSRemote Sens. 2014, 6 717 for the site. High resolution hyperspectral mapping of PFTs has the potential to assess recovery of peatland systems at landscape scale for the first time.

This paper describes and illustrates methods for quantifying regional differences in land use/land cover changes. A series of approaches are used to analyse differences in land cover change from data held in change matrices. These are contingency tables and are commonly used in remote sensing to describe the spatial coincidence of land cover recorded over two time periods. Comparative analyses of regional change are developed using odds ratios to analyse data in two regions. These approaches are extended using generalised linear models to analyse data for three or more regions. A generalised Poisson regression model is used to generate a comparative index of change based on differences in change likelihoods. Mosaic plots are used to provide a visual representation of statistically surprising land use losses and gains. The methods are explored using a hypothetical but tractable dataset and then applied to a national case study of coastal land use changes over 50 years conducted for the National Trust. The suitability of the different approaches to different types of problem and the potential for their application to land cover accuracy measures are briefly discussed.