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46 results for “surface cover”
Long-term composited and land cover-adjusted Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2020
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Next, we corrected the underestimated ENDISI values of dark impervious surface cover and the overestimated ENDISI values of bright bare soils based on visible Landsat bands and 2020 land cover (Sabu et al. 2023). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 - Sabu, S., Frazier, A., & Rashid, B. (2023). Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020 [Dataset]. Environmental Data Initiative. https://doi.org/10.6073/PASTA/BF18E5856215BD2D4DAB3B024BA87A7E
Cover and frequency of biological soil crust community types, moss species, vascular plants, and abiotic land surface features, on gypsum & non-gypsum soils from the Chihuahuan and Mojave Deserts in 2023
This dataset contains raw and calculated percent cover and frequency data for biological soil crust (hereafter biocrust) functional groups, vascular plant functional groups, and abiotic land surface features on and off gypsum soils in the northern Chihuahuan and eastern Mojave Deserts. Abundance data were obtained from 20 study sites total, 10 located on soils derived from gypsum parent material and 10 located on soils derived from non-gypsum parent materials. Sites were grouped into 10 pairs, in which every gypsum site was partnered with a non-gypsum site located in the same region. Apart from soil type, partnered-site characteristics (topography, climate, elevation, slope, aspect, and presence of biocrusts) were held relatively constant. At each site, cover and frequency assessments were made using the line-point intercept method (LPI) and frequency quadrats (1.0 m^2), respectively. Biocrust functional groups included the following crusts: lichen, moss, incipient algal, light algal, dark algal, unknown photosynthetic crust, and vagrant cyanobacteria. Vascular plant categories included: perennial forbs, perennial graminoids, annual forbs, annual graminoids, subshrub, shrub, Yucca, and cacti. Abiotic land surface features included: woody litter, herbaceous litter, bare soil, rock, bedrock, and animal feces. Moss crusts identified within cover and frequency analyses were sampled, and classified to species level via microscopy. The resulting percent cover and frequency data was used to understand differences in biocrust and moss species abundance and diversity on and off gypsum soils; furthermore, how biocrust and moss species abundance was associated with the measured environmental variables. Soil physical and chemical data from this study can be accessed at knb-lter-jrn.210616002. This study and dataset are complete.
Landsat bands (cloud free), tree cover (2000, 2010), bare-ground and surface water occurrence at 250 m based on GlobalForestWatch and USGS
<p>Landsat bands (cloud free) and tree cover (2000) based on Hansen et al. (2013), global surface water occurrence based on Pekel at al. (2016), and tree cover and bare-ground cover (2010) based the USGS land cover mapping projects (University of Maryland, Department of Geographical Sciences and USGS). All layers resampled to spatial resolution 1/480 d.d. (about 250 m) using gdalwarp with "average" resampling. Antarctica is not included. Original layers are available at 30 m resolution.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>lcv = theme: land cover,</li> <li>bareground = variable: occurrence of bareground,</li> <li>landsat.usgs = determination method: Landsat landcover at 30 m resolution project (https://landcover.usgs.gov/glc/),</li> <li>p = probability or fraction,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2010..2010 = time reference: year 2010,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (CMG)
<p>Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). </p> <p>The 500m global surface blue-sky daily albedo climatology dataset is available at .... After reprojection and aggregation, the global Climate Modeling Grid (CMG) albedo climatology datasets at 0.05° and 0.5° are available here. All of the published datasets include historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached in the CMG files. The International Geosphere-Biosphere Programme (IGBP) and PFT classification results of MCD12Q1 since 2001 were reprojected and aggregated to 0.05° and 0.5° by find mode in each aggregation group. In order to check the heterogeneity of the land cover climatology, the percentage of the dominant type in each aggregation group was also calculated.</p>
High resolution and high cadence time series of land surface categories, land use land cover, and land use land cover changes
<p>A prototype of monthly, 10 m resolution land surface categories, land use land cover (LULC) cover, and LULC change maps derived from Sentinel-2 data over three areas within Belgium, Portugal, and Sicily for the period 2018-2020. The LULC and LULC change maps were independently validated by IIASA. All products were generated within the framework of the RapidAI4EO project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356.</p> <p>The data description can be found below. The validation report of the LULC and LULC change maps can be found in validation_LULC.pdf and validation_change.pdf, respectively, and the validation dataset can be found in Lesiv <em>et al.</em> (2023).</p> <p><strong>Data description</strong></p> <p>Increasing the cadence of the land cover updates from the typical (multi-)annual to monthly cadence poses several challenges. First, several land cover types are difficult to discriminate without any knowledge of temporal dynamics. For instance, croplands are characterized by a dynamic of vegetation growth and a harvest period (i.e. cycles of bare soil, sparsely vegetated and vegetated periods). This contrasts with grasslands that often lack the harvest period resulting in a bare soil cover. Without this temporal information, it is difficult to distinguish a vegetated cropland field from grassland. Second, phenological changes may introduce a large intra-class variability and thus also confusion between classes. For example, the shedding of leaves during autumn or wilting of herbaceous vegetation in dry summer periods introduces spectral variability within land cover classes.</p> <p>To overcome these challenges, we developed a workflow with two main phases. The first phase aims to map land surface categories (LSC) at a monthly resolution. The next phase uses the resulting monthly LSC probability time series to classify land cover.</p> <p><strong><em>Land surface category (LSC)</em></strong></p> <p>These LSC represent basic, observable bio-geophysical properties (categories) of the Earth surface that can be predicted directly from individual monthly composites. LSC classes contain a set of vegetated and non-vegetated surface categories.</p> <p>Discrete LSC classification legend:</p> <table> <tbody> <tr> <td> <p>Map code</p> </td> <td> <p>Land cover class</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>Tree (leaf-on)</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>Shrubland (leaf-on)</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>Woody vegetation (leaf-off)</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>Wilted herbaceous vegetation</p> </td> </tr> <tr> <td> <p>21</p> </td> <td> <p>Bare/sparse vegetation</p> </td> </tr> <tr> <td> <p>22</p> </td> <td> <p>Water</p> </td> </tr> <tr> <td> <p>24</p> </td> <td> <p>Built-up</p> </td> </tr> </tbody> </table> <p>In order to predict the LSC, we trained a CatBoost model (Dorogush et al., 2018) using a DEM, spectral bands and vegetation indices, country, the timing (month) of the spectral data, and the pseudo-probability of a U-Net model trained to segment built-up surfaces as input. Labels were derived by post-processing the land cover labels of the ESA WorldCover product (Zanaga et al., 2021). Please note that the collection of these labels was suboptimal, likely having an impact on the LULC and change maps generated in the prototype.</p> <p><strong><em>Land use land cover</em></strong></p> <p>After predicting LSC over the three AOI’s, we trained a CatBoost model using the LSC probabilities over a window of one year, country, and the timing (month) as independent variable. The use of LSC probabilities over multiple months allows to incorporate information about dynamics, which is necessary to discriminate some classes (e.g. cropland and grassland or cropland and bare). Similar to the LSC labels, the LULC labels were derived from the ESA WorldCover product v100 (year 2020), resulting in a similar legend system.</p> <p>The use of a moving window approach to predict LULC allows to (i) incorporate temporal information that is necessary to discriminate land cover classes and (ii) is expected to lead to more consistent land cover maps. It however has the disadvantage that (i) no land cover predictions are available at the beginning and the end of the time series and (ii) the timing of the predicted land cover change is not always accurate. To resolve these issues, we applied a post-processing step that compares and integrates the LULC predictions and cleaned LSC predictions.</p> <p>Discrete LC classification legend:</p> <table> <tbody> <tr> <td> <p><strong>Map code</strong></p> </td> <td> <p><strong>Land cover class</strong></p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>Tree cover</p> </td> </tr> <tr> <td> <p>20</p> </td> <td> <p>Shrubland</p> </td> </tr> <tr> <td> <p>30</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>40</p> </td> <td> <p>Cropland</p> </td> </tr> <tr> <td> <p>50</p> </td> <td> <p>Built-up</p> </td> </tr> <tr> <td> <p>60</p> </td> <td> <p>Bare/sparse vegetation</p> </td> </tr> <tr> <td> <p>80</p> </td> <td> <p>Permanent water bodies</p> </td> </tr> <tr> <td> <p>90</p> </td> <td> <p>Herbaceous wetland</p> </td> </tr> </tbody> </table> <p><strong><em>Land use land cover change </em></strong></p> <p>Monthly change maps were finally derived from the land cover maps. The pixel values within the change maps represent the percentage of pixels that changed with respect to the previous month over an area of 90x90m. The maps contain values between 0-100, with larger values assigned to larger change patches. A value of 100 indicates that all pixels within an area of 90x90m around the pixel were flagged as change.</p> <p><strong><em>Files</em></strong></p> <p>The zip files contain the following data:</p> <ul> <li>lsc.zip: land surface category maps over the three AOI’s</li> <li>lc.zip: LULC maps over the three AOI’s</li> <li>change.zip: change maps over the three AOI’s</li> </ul> <p>These maps are generated for each month over the period 2018-2020 for each of the tiles (see tiles.gpkg for an overview of all tiles). The files names use the following naming convention: “<em>tile</em>-<em>year</em>-<em>month</em>.tif”.</p> <p><strong><em>References</em></strong></p> <p>Myroslava Lesiv, Halyna Bun, & Martina Duerauer. (2023). Validation data set on land cover changes for RapidAI4EO project [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7825963 </p> <p>Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363.</p> <p><em>Zanaga, D., </em><em>et al.</em><em>., 2021. ESA WorldCover 10 m 2020 v100. </em><a href="https://doi.org/10.5281/zenodo.5571936 "><em>https://doi.org/10.5281/zenodo.5571936 </em></a></p>
Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer
As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land-cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10, 30, 60 and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (about 7 km length, sampled 3-12 times each) spanning a range of impervious and tree canopy cover (0 to 100%, each) in a mid-sized city in the Upper Midwest, USA. Variability in daytime air temperature within the urban landscape averaged 3.5 degreeC (range 1.1 to 5.7 degreeC). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale, and was greatest at the size of a typical city block (60-90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1C (range 1.2 to 3.0 degreeC), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.
Snow depth and land surface cover in Tuolumne basin (California) from Pléiades images
<p>This dataset contains products calculated from Pléiades images.</p> <p>Details about the products are available in https://doi.org/10.5194/tc-2020-15.</p> <p>These products were used in Figure 4.</p> <p>- pleiades_elevation_difference_raw_winter_minus_summer.tif : raw difference of digital elevation models (DEMs) calculated from Pléiades stereo images.</p> <p>- pleiades_snow_depth_winter.tif : difference of DEMs on snow terrain only (where pleiades_land_surface_cover_winter.tif==1 with morphological erosion)</p> <p>- pleiades_land_surface_cover_winter.tif : land cover surface in the winter images (1= snow, 2=forest, 3= stable terrain, 4=water)</p> <p>- pleiades_land_surface_cover_summer.tif : land cover surface in the summer images (1= snow, 2=forest, 3= stable terrain, 4=water) </p> <p>- elevation_difference_style.qml : qgis style used for elevation difference and snow depth.</p> <p>- land_surface_cover_style.qml : qgis style used for land cover surface.</p> <p> </p>
Supplementary data for "Mechanism of surface solar irradiance variability under broken cloud cover"
<p>Open Data for manuscript to be submitted in ACP: "Mechanisms of surface solar irradiance variability under broken clouds". Refer to the README for details. Most (larger) files within the .zip archives are gzipped. Use `gunzip` to decompress.</p>
Imaging the sediment cover offshore central Chile with surface-wave dispersion and P-wave conversion using DAS
<p>This repository contains codes and data used to reproduce the figures in the paper <em>Vernet, C. et al, "Imaging the sediment cover offshore central Chile with surface-wave dispersion and P-wave conversion using distributed acoustic sensing", 2025, (<a href="https://doi.org/10.1029/2024JB030507">https://doi.org/10.1029/2024JB030507</a>).</em></p>
SROADEX: Dataset for binary recognition and semantic segmentation of road surface areas from high resolution Aerial Orthoimages Covering Approximately 8,650 km2 of the Spanish Territory Tagged with Road Information
<p>The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the axes of the different types of roads (urban, interurban and rural). This cartography has been obtained from different Spanish official sources (National Geographic Institute and autonomic cartographic agencies) that we have revised and edited in a meticulous and systematic way to verify that the roads are represented on the cartography according to the orthoimages, available on January 1, 2021 in the download center of the National Center of Geographic Information (CNIG), on 16 rectangular areas (28,5 km * 18,5 km) of the Spanish territory (insular and peninsular).</p> <p>The dataset consists of 777599 images in png format of 256x256 pixels, organized in folders for the different trainings, separating those corresponding to training, testing and validation.</p> <p>The structure of the data is as follows:<br> 1-Road-Ortho and 1-Road-Mask contain the images and ground true for training the semantic segmentation networks.<br> 1-Road-Ortho and 2-NoRoad-Ortho contain aerial images containing or not containing vials, for the training of binary tessellation networks identifying tessellations with vials.<br> Moreover, in each folder the structure is the same: train, test, validation containing 90%, 5% and 5% of the total images and masks of each type.</p> <p>1-Road-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>1-Road-Mask</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>2-NoRoad-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p> </p>
GLOBMAP SWF: a global annual surface water cover frequency dataset since 2000 for change analysis of inland water bodies
<p>The extent of surface water has been changing significantly due to climatic change and human activities. However, it is challenging to capture the interannual changes and trends of inland water bodies due to their high seasonal variation and abrupt change. We generated a global annual surface water cover frequency dataset (GLOBMAP SWF) from the MODIS land surface reflectance products to describe the seasonal and interannual dynamics of surface water. Surface water cover frequency (SWF) was proposed as the percentage of the time period when a pixel is covered by water in a year. Instead of determination of the water observations directly, the SWF was estimated indirectly by identifying land observations among annual clear-sky observations to reduce the influence of clouds and variability of water body and surface background characteristics, which helps to improve the applicability of the algorithm for different regions across the globe. Regional analysis demonstrates that our estimation results show reasonable performances on frozen water, saline lake, bright surface and cloud-frequent regions. This dataset can be used to analyze the interannual variation and change trend of highly dynamic inland water body extent with consideration of its seasonal variation.</p> <p>The GLOBMAP SWF dataset is provided in Version 1.0 (https://zenodo.org/record/6462883#.YxC16HZBw2w). Here we provide the number of MOD09A1 (MODIS 8-day composite land surface reflectance) clear-sky snow/ice-free observations (<em>N<sub>Clear</sub></em>) data as a quality dataset of GLOBMAP SWF product. The clear-sky observation refers to the valid MOD09A1 observation that not covered with clouds and snow/ice. The more available clear-sky observations, the more reliable the estimated SWF.</p> <p>The <em>N<sub>Clear </sub></em>dataset is provided by 296 1200 km × 1200 km tiles at annual temporal and 500 m spatial resolutions in the sinusoidal projection with Geotiff format for each year during 2000-2020. The file is named as "GLOBMAPClearCount. AYYYY001.hHHvVV.V01.tif", where “YYYY” refers to the year of the file, and “HH” and “VV” explains the number of tiles that are the same with MODIS standard tile. The valid range is 0-46, scale factor is 1.0. The <em>N<sub>Clear </sub></em>of permanent water (land obervation count of 46), permanent snow/ice and terrain shadows are set to 50.</p>
Рис. 3. Фотографии Laternula elliptica, сделанные около cтанции «Прогресс», ВосточнаЯ Антарктида. L. elliptica на морском дне с медкими камнЯми или гравием, глубина 27 м (А); несколько сифональных отверстий L. elliptica над поверхностью мЯгких осадков вокруг голотурии Staurocucumis turqueti, глубина 27 м (В); раковина L. elliptica (длина около 110 мм) на снегу около майны сраЗу после иЗвлечениЯ иЗ воды (С); пустые раковины L. elliptica на морском дне, глубина 56 м (D); раковина L. elliptica (вид с дорсального краЯ) на мЯгких осадках с камнЯми, покрытыми иЗвестковыми водорослЯми, глубина 30 м (Е); пара сифональных отверстий L. elliptica на поверхности мЯгких осадков, глубина 27 м (F). Фотографии О. Савинкина (A, B, D–F) и В. Потина (С). Fig. 3. Photographs of Laternula elliptica taken near «Progress» Research Station (East Antarctica). Softshelled clam L. elliptica on sea bottom with small stowns or gravel, depth 27 m (A); several open siphons of L. elliptica above soft bottom sediments around holothurian Staurocucumis turqueti, depth 27 m (B); a shell of L. elliptica (length about 110 mm) on snow near a dive hole just after dragging out of water (C); empty shells of L. elliptica on seafloor, depth 56 m (D); a shell of Laternula elliptica (dorsal view) on soft deposits among stones, covering by Lithothamnion, depth 30 m (E); pair of siphonal opening of L. elliptica on surface of soft sediments, depth 27 m (F). Photographs are taken by O. Savinkin (A, B, D–F) and V. Potin (C). in Species of warm-water origin Laternula elliptica (King, 1832) (Mollusca: Bivalvia: Laternulidae), a widespread mollusk in recent Antarctica
Рис. 3. Фотографии Laternula elliptica, сделанные около cтанции «Прогресс», ВосточнаЯ Антарктида. L. elliptica на морском дне с медкими камнЯми или гравием, глубина 27 м (А); несколько сифональных отверстий L. elliptica над поверхностью мЯгких осадков вокруг голотурии Staurocucumis turqueti, глубина 27 м (В); раковина L. elliptica (длина около 110 мм) на снегу около майны сраЗу после иЗвлечениЯ иЗ воды (С); пустые раковины L. elliptica на морском дне, глубина 56 м (D); раковина L. elliptica (вид с дорсального краЯ) на мЯгких осадках с камнЯми, покрытыми иЗвестковыми водорослЯми, глубина 30 м (Е); пара сифональных отверстий L. elliptica на поверхности мЯгких осадков, глубина 27 м (F). Фотографии О. Савинкина (A, B, D–F) и В. Потина (С). Fig. 3. Photographs of Laternula elliptica taken near «Progress» Research Station (East Antarctica). Softshelled clam L. elliptica on sea bottom with small stowns or gravel, depth 27 m (A); several open siphons of L. elliptica above soft bottom sediments around holothurian Staurocucumis turqueti, depth 27 m (B); a shell of L. elliptica (length about 110 mm) on snow near a dive hole just after dragging out of water (C); empty shells of L. elliptica on seafloor, depth 56 m (D); a shell of Laternula elliptica (dorsal view) on soft deposits among stones, covering by Lithothamnion, depth 30 m (E); pair of siphonal opening of L. elliptica on surface of soft sediments, depth 27 m (F). Photographs are taken by O. Savinkin (A, B, D–F) and V. Potin (C).
Text-fig. 45. Scanning electron microscope (SEM) images of monocolpate pollen of Dinisia portugallica gen. et sp. nov. from a fragmentary stamen; Torres Vedras locality, Portugal. a) Holotype; stamen fragment showing elongated pollen sacs that yielded the pollen in this Text-figure; b) Two pollen grains showing poorly defined distal aperture (arrowhead) and distinctive vermiform reticulum forming luminae of variable shapes and sizes; note especially the irregularly and incomplete reticulum in the grain on the left; c) Reticulum showing smooth, vermiform muri attached to the smooth surface of the foot layer by long columellae; note that columellae often terminate segments of muri that are not closed; d, e) Pollen grains showing proximal surface (d), poorly defined distal aperture (e, arrowhead) and distinctive vermiform reticulum supported by long columellae; note dense covering of small, spherical orbicules on the inner surface of the anther wall. Specimen, TV44-S148216 (holotype). Scale bars 300 Μm (a), 6 Μm (b, d, e), 3 Μm (c). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 45. Scanning electron microscope (SEM) images of monocolpate pollen of Dinisia portugallica gen. et sp. nov. from a fragmentary stamen; Torres Vedras locality, Portugal. a) Holotype; stamen fragment showing elongated pollen sacs that yielded the pollen in this Text-figure; b) Two pollen grains showing poorly defined distal aperture (arrowhead) and distinctive vermiform reticulum forming luminae of variable shapes and sizes; note especially the irregularly and incomplete reticulum in the grain on the left; c) Reticulum showing smooth, vermiform muri attached to the smooth surface of the foot layer by long columellae; note that columellae often terminate segments of muri that are not closed; d, e) Pollen grains showing proximal surface (d), poorly defined distal aperture (e, arrowhead) and distinctive vermiform reticulum supported by long columellae; note dense covering of small, spherical orbicules on the inner surface of the anther wall. Specimen, TV44-S148216 (holotype). Scale bars 300 Μm (a), 6 Μm (b, d, e), 3 Μm (c).
Text-fig. 46. Scanning electron microscope (SEM) images of monocolpate pollen of Teebacia hughesii gen. et sp. nov. pollen from a stamen fragment; Torres Vedras locality, Portugal. a) Holotype; stamen fragment showing elongated pollen sacs that yielded the pollen in this Text-figure (d, e); b) Detail of detached reticulum showing inner surface of muri and scattered columellae; note the finely granular covering of the muri and columellae; c, d) Detail of reticulum showing the outer surface of muri with supratectal ornamentation of narrow ridges; note orbicules attached to the reticulum (d); e, f) Pollen grains showing loose, beaded, reticulum with long columellae; note continuous muri bordering the apertures and densely spaced minute orbicules lining the inner surface of the anther wall (e, arrowheads). Specimens, TV44-S136666 (holotype; a, d, e), TV44-S149207 (b, c, f). Scale bars 300 Μm (a), 6 Μm (e, f), 3 Μm (b), 1.5 Μm (c), 1.2 Μm (d). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 46. Scanning electron microscope (SEM) images of monocolpate pollen of Teebacia hughesii gen. et sp. nov. pollen from a stamen fragment; Torres Vedras locality, Portugal. a) Holotype; stamen fragment showing elongated pollen sacs that yielded the pollen in this Text-figure (d, e); b) Detail of detached reticulum showing inner surface of muri and scattered columellae; note the finely granular covering of the muri and columellae; c, d) Detail of reticulum showing the outer surface of muri with supratectal ornamentation of narrow ridges; note orbicules attached to the reticulum (d); e, f) Pollen grains showing loose, beaded, reticulum with long columellae; note continuous muri bordering the apertures and densely spaced minute orbicules lining the inner surface of the anther wall (e, arrowheads). Specimens, TV44-S136666 (holotype; a, d, e), TV44-S149207 (b, c, f). Scale bars 300 Μm (a), 6 Μm (e, f), 3 Μm (b), 1.5 Μm (c), 1.2 Μm (d).
Text-fig. 5. Scanning electron microscope (SEM) images of megaspores with possible affinities to Isoetales (a–d) and megaspores of uncertain affinity (e–i); Torres Vedras locality, Portugal. a) Paxillitriletes reticulatus megaspore in lateral view showing long appendages on the flanges of the laesurae and the reticulate-spiny distal surface; b) Dijkstraisporites sp. megaspore in oblique lateral view showing long, sometimes dichotomizing, appendages on the equatorial flanges and bordering the laesurae; c, d) Tenellisporites sp. megaspore in proximal view (c) showing equatorial flanges and laesurae with short, broad, flattened and unbranched appendages; note numerous, spiny microspores adhering to the proximal face of the megaspore (d); e) Megaspore type sp. 2 in lateral view showing apical gula and ornamentation of scattered spines; f, g) Megaspore type sp. 3 in lateral (f) and proximal (g) view showing broad, often dichotomously branched appendages covering the megaspore surface; h, i) aff. Flabellisporites sp. megaspores in proximal (h) and lateral (i) view showing long, narrow appendages covering the megaspore surface. Specimens, TV39-S174619 (a), TV38-S170220 (b), TV38-S170221 (c, d), TV44-S174574 (e), TV44-S174575 (f), TV44-S174577 (g), TV38-S170223 (h), TV38-S170222 (i). Scale bars 100 Μm (a–c, e–i), 25 Μm (d). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 5. Scanning electron microscope (SEM) images of megaspores with possible affinities to Isoetales (a–d) and megaspores of uncertain affinity (e–i); Torres Vedras locality, Portugal. a) Paxillitriletes reticulatus megaspore in lateral view showing long appendages on the flanges of the laesurae and the reticulate-spiny distal surface; b) Dijkstraisporites sp. megaspore in oblique lateral view showing long, sometimes dichotomizing, appendages on the equatorial flanges and bordering the laesurae; c, d) Tenellisporites sp. megaspore in proximal view (c) showing equatorial flanges and laesurae with short, broad, flattened and unbranched appendages; note numerous, spiny microspores adhering to the proximal face of the megaspore (d); e) Megaspore type sp. 2 in lateral view showing apical gula and ornamentation of scattered spines; f, g) Megaspore type sp. 3 in lateral (f) and proximal (g) view showing broad, often dichotomously branched appendages covering the megaspore surface; h, i) aff. Flabellisporites sp. megaspores in proximal (h) and lateral (i) view showing long, narrow appendages covering the megaspore surface. Specimens, TV39-S174619 (a), TV38-S170220 (b), TV38-S170221 (c, d), TV44-S174574 (e), TV44-S174575 (f), TV44-S174577 (g), TV38-S170223 (h), TV38-S170222 (i). Scale bars 100 Μm (a–c, e–i), 25 Μm (d).
Text-fig. 23. Scanning electron microscope (SEM) images of Clavatipollenites sp. 2 (a–c) from a fragmentary stamen, and stamen fragments with in situ pollen of Clavatipollenites sp. 3 (d–j); Torres Vedras locality, Portugal. a, b) Distal (a) and proximal (b) view of pollen showing simple, elongate colpus on distal surface and semitectate-reticulate pollen wall; c) Detail of pollen wall showing muri with finely verrucate supratectal ornamentation and long scattered columellae; d) Fragment of tetrasporangiate stamen; e, f) Distal views of pollen from stamen fragment showing poorly defined aperture, coarse reticulum and long scattered columellae; g) Fragment of stamen; h) Distal view of pollen showing poorly defined aperture covered by irregular verrucae; i, j) Pollen wall showing rounded orbicules (i) and fractured pollen wall showing long scattered columellae (j). Specimens, TV43-S136728 (a–c), TV44-S149201 (d–f), TV44-S149220 (g–j). Scale bars 300 Μm (d, g), 6 Μm (a, b, e, f, h), 3 Μm (c), 1.5 Μm (i, j). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 23. Scanning electron microscope (SEM) images of Clavatipollenites sp. 2 (a–c) from a fragmentary stamen, and stamen fragments with in situ pollen of Clavatipollenites sp. 3 (d–j); Torres Vedras locality, Portugal. a, b) Distal (a) and proximal (b) view of pollen showing simple, elongate colpus on distal surface and semitectate-reticulate pollen wall; c) Detail of pollen wall showing muri with finely verrucate supratectal ornamentation and long scattered columellae; d) Fragment of tetrasporangiate stamen; e, f) Distal views of pollen from stamen fragment showing poorly defined aperture, coarse reticulum and long scattered columellae; g) Fragment of stamen; h) Distal view of pollen showing poorly defined aperture covered by irregular verrucae; i, j) Pollen wall showing rounded orbicules (i) and fractured pollen wall showing long scattered columellae (j). Specimens, TV43-S136728 (a–c), TV44-S149201 (d–f), TV44-S149220 (g–j). Scale bars 300 Μm (d, g), 6 Μm (a, b, e, f, h), 3 Μm (c), 1.5 Μm (i, j).
Text-fig. 11. Scanning electron microscope (SEM) images of isolated "Stamen fragment with Clavatipollenites-type pollen sp. 2"; Catefica locality, Portugal. a) Fragment of tetrasporangiate stamen with pollen in situ; b) Detail from stamen fragment showing distal and proximal surfaces of in situ pollen grains; c) Pollen grain in distal view showing short colpus with irregular margin and aperture membrane covered by irregular verrucae; d) Detail of pollen wall showing tiny spherical orbicules; e) Detail of pollen wall showing the semitectate-reticulate tectum and long, scattered columellae supporting the narrow muri with finely verrucate supratectal ornamentation. Specimen, Catefica 50-S170389 (a–e). Scale bars = 600 Μm (a), 20 Μm (b), 6 Μm (c), 3 Μm (d), 1.5 Μm (e). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 11. Scanning electron microscope (SEM) images of isolated "Stamen fragment with Clavatipollenites-type pollen sp. 2"; Catefica locality, Portugal. a) Fragment of tetrasporangiate stamen with pollen in situ; b) Detail from stamen fragment showing distal and proximal surfaces of in situ pollen grains; c) Pollen grain in distal view showing short colpus with irregular margin and aperture membrane covered by irregular verrucae; d) Detail of pollen wall showing tiny spherical orbicules; e) Detail of pollen wall showing the semitectate-reticulate tectum and long, scattered columellae supporting the narrow muri with finely verrucate supratectal ornamentation. Specimen, Catefica 50-S170389 (a–e). Scale bars = 600 Μm (a), 20 Μm (b), 6 Μm (c), 3 Μm (d), 1.5 Μm (e).
Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (500m)
<p>Only the first days of each month were uploaded to Zenodo due to the data storage limitation, and the full dataset is available at http://glass.umd.edu/albedo_clim/.</p> <p>Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). </p> <p>The 500m global surface blue-sky daily albedo climatology dataset follows the basic MODIS product format and employed the sinusoidal projection. It includes historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached. The International Geosphere-Biosphere Programme (IGBP) and PFT classification climatology of MCD12Q1 since 2001 were also generated.</p>
Impervious surface cover and number of restaurants shape diet variation in an urban carnivore
Open the record for dataset details and reuse information.
FIGURE. Seedlings, seeds, embryos, anthers, and pollen in Dicorynia. A–D. Different stages of development in seedlings of D. paraensis, First eophiles unifoliolate and opposite; E–G. Seed of D. guianensis: E. External surface; F. Endosperm of the longitudinally sectioned seed, note the slightly gelatinous upper region; G. Cotyledon and embryo of longitudinally sectioned seed; H. SEM of seed's testa in D. paraensis; I. SEM of endosperm's surface in D. guianensis (notice the presence of circular perforations); J–K. SEM of the hypocotyl-radicular axis of the seed in D. guianensis and D. paraensis; L. SEM of seed's testa in D. guianensis; M–N. SEM of plumule region in embryo of D. guianensis and D. paraensis (note the developed leaf primordia); O. Apex of anther in longer stamen of D. paraensis, showing 4 sporangia and two pores covered by an apicle; P. Apex of anther in shorter stamen of D. guianensis, at least 9 sporangia; Q. Apex of anther in longer stamen of D. guianensis, 8 sporangia; R. Pollen grains in D. paraensis. A–D: Falcão, M.J. 91; E–G, I–J, L–M: Gentry 63030; H, K, N: Berry, P.E. 7460; O: Amaral, E. 618; P, Q: Unknown collector MO1576407; Scale bar. A–D: 2cm; E–G: 3mm; H–L: 1mm; M–N: 100 μm; O-Q: 200μm; R: 5 μm. in A Taxonomic Revision of the Amazonian Genus Dicorynia (Fabaceae: Dialioideae)
FIGURE. Seedlings, seeds, embryos, anthers, and pollen in Dicorynia. A–D. Different stages of development in seedlings of D. paraensis, First eophiles unifoliolate and opposite; E–G. Seed of D. guianensis: E. External surface; F. Endosperm of the longitudinally sectioned seed, note the slightly gelatinous upper region; G. Cotyledon and embryo of longitudinally sectioned seed; H. SEM of seed's testa in D. paraensis; I. SEM of endosperm's surface in D. guianensis (notice the presence of circular perforations); J–K. SEM of the hypocotyl-radicular axis of the seed in D. guianensis and D. paraensis; L. SEM of seed's testa in D. guianensis; M–N. SEM of plumule region in embryo of D. guianensis and D. paraensis (note the developed leaf primordia); O. Apex of anther in longer stamen of D. paraensis, showing 4 sporangia and two pores covered by an apicle; P. Apex of anther in shorter stamen of D. guianensis, at least 9 sporangia; Q. Apex of anther in longer stamen of D. guianensis, 8 sporangia; R. Pollen grains in D. paraensis. A–D: Falcão, M.J. 91; E–G, I–J, L–M: Gentry 63030; H, K, N: Berry, P.E. 7460; O: Amaral, E. 618; P, Q: Unknown collector MO1576407; Scale bar. A–D: 2cm; E–G: 3mm; H–L: 1mm; M–N: 100 μm; O-Q: 200μm; R: 5 μm.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.