We present an algorithm discriminating ice from open water on icy lakes using multispectral data from Landsat 8. Lakes are first extracted from the images using a new radiometric index coined ILI (Icy Lakes Index) which uses the stability of the reflectance of water and ice in the shortwave infrared bands. Quantitative comparisons to state-of-the-art indexes such as the Modified Normalised Difference Water Index (MNDWI) or the Water Ratio Index (WRI) show the ILI separates mixed ice/water bodies from land better than the current indexes, consistently achieving kappa coefficients ( ) above 0.93 with manually labelled reference data. Additionally, these results suggest that the ILI has a very high optimal threshold stability, compared to other indexes. In the delineated lake area, we perform a supervised classification with a decision tree using radiometric properties, but also texture properties such as the local standard deviations and average gradients in each band. A key feature of this classification is the Water-Ice Classification Index (WICI), also texture-based, which discriminates shallow water from ice efficiently. We prove the robustness of our classification algorithm by comparing its results to manually digitised reference data, but also to concurrent Sentinel-1 Synthetic Aperture Radar (SAR) data in the case of Lake Ladoga. In both cases, the comparison leads to ranging from 0.84 to 0.97.

Hyperspectral imagery has proven its potential in many research applications, especially in the field of environmental sciences. Currently, hyperspectral imaging is generally performed by satellite or aircraft platforms, but mini-UAV (Unmanned Aerial Vehicle) platforms (<20 kg) are now emerging. On such platforms, payload restrictions are critical, so sensors must be selected according to stringent specifications. This article presents the integration of a light pushbroom hyperspectral sensor onboard a multirotor UAV, which we have called Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations). This article depicts the system design: the UAV platform, the imaging module, the navigation module, and the interfacing between the different elements. Pushbroom sensors offer a better combination of spatial and spectral resolution than full-frame cameras. Nevertheless, data georectification has to be performed line by line, the quality of direct georeferencing being limited by mechanical stability, good timing accuracy, and the resolution and accuracy of the proprioceptive sensors. A georegistration procedure is proposed for geometrical pre-processing of hyperspectral data. The specifications of Hyper-DRELIO surveys are described through two examples of surveys above coastal or inland waters, with different flight altitudes. This system can collect hyperspectral data in VNIR (Visible and Near InfraRed) domain above small study sites (up to about 4 ha) with both high spatial resolution (<10 cm) and high spectral resolution (1.85 nm) and with georectification accuracy on the order of 1 to 2 m.

Asymmetric division is crucial for embryonic development and stem cell lineages. In the one-cell C. elegans embryo, a contractile cortical actomyosin network contributes to asymmetric division by segregating PAR proteins to discrete cortical domains. Here, we discovered that the plasma membrane lipid phosphatidylinositol 4,5-bisphosphate (PIP₂) localizes to polarized dynamic structures in C. elegans zygotes, distributing in a PAR-dependent manner along the anterior-posterior (A-P) embryonic axis. PIP₂ cortical structures overlap with F-actin, and coincide with the actin regulators RHO-1, CDC-42 as well as ECT-2. Particle image velocimetry analysis revealed that PIP₂ and F-actin cortical movements are coupled, with PIP₂ structures moving slightly ahead. Importantly, we established that PIP₂ cortical structure formation and movement is actin-dependent. Conversely, we found that decreasing or increasing the level of PIP₂ results in severe F-actin disorganization, revealing interdependence between these components. Furthermore, we uncovered that PIP₂ and F-actin regulate the sizing of PAR cortical domains, including during the maintenance phase of polarization. Overall, our work establishes that a lipid membrane component, PIP₂, modulates actin organization and cell polarity in C. elegans embryos.

EUMETSAT, the European Organization for the Exploitation of Meteorological Satellites, is one of the key contributors to global atmospheric motion vector (AMV) production around the world. Its current contribution includes geostationary satellites at 0.0 and 41.5 degrees east, and several products extracted from the Metop low-orbit satellites. These last ones mainly cover high-latitude regions completing the observations from the geostationary ring. In the upcoming years, EUMETSAT will launch a new generation of geostationary and low-orbit satellites. The imager instruments Flexible Combined Imager (FCI) and METImage will take over the nominal AMV production at EUMETSAT around 2022 and 2024. The enhanced characteristics of these new-generation instruments are expected to increase AMV production and to improve the quality of the products. This paper presents an overview of the current EUMETSAT AMV operational production, together with a roadmap of the preparation activities for the new generation of satellites. The characteristics of the upcoming AMV products are described and compared to the current operational AMV products. This paper also presents a recent investigation into AMV extraction using the Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) instrument, as well as the retrieval of wind profiles from infrared sounders.

Direct georeferencing of airborne pushbroom scanner data usually suffers from the limited precision of navigation sensors onboard of the aircraft. The bundle adjustment of images and orientation parameters, used to perform geocorrection of frame images during the post-processing phase, cannot be used for pushbroom cameras without difficulties—it relies on matching corresponding points between scan lines, which is not feasible in the absence of sufficient overlap and texture information. We address this georeferencing problem by equipping our aircraft with both a frame camera and a pushbroom scanner: the frame images and the navigation parameters measured by a couple GPS/Inertial Measurement Unit (IMU) are input to a bundle adjustment algorithm; the output orientation parameters are used to project the scan lines on a Digital Elevation Model (DEM) and on an orthophoto generated during the bundle adjustment step; using the image feature matching algorithm Speeded Up Robust Features (SURF), corresponding points between the image formed by the projected scan lines and the orthophoto are matched, and through a least-squares method, the boresight between the two cameras is estimated and included in the calculation of the projection. Finally, using Particle Image Velocimetry (PIV) on the gradient image, the projection is deformed into a final image that fits the geometry of the orthophoto. We apply this algorithm to five test acquisitions over Lake Geneva region (Switzerland) and Lake Baikal region (Russia). The results are quantified in terms of Root Mean Square Error (RMSE) between matching points of the RGB orthophoto and the pushbroom projection. From a first projection where the Interior Orientation Parameters (IOP) are known with limited precision and the RMSE goes up to 41 pixels, our geocorrection estimates IOP, boresight and Exterior Orientation Parameters (EOP) and produces a new projection with an RMSE, with the reference orthophoto, around two pixels.

There was an error in Development (2018) 145, [dev164988][1] ([doi: 10.1242/dev.164988][2]). It was brought to the authors’ attention that cortical structures bearing GFP::PHPLC1δ1 have been reported in Nakayama et al., 2009 (Figure S7). The authors apologise for not having been aware of this