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657 results for “July”
Marsh water table height, logging data from the Railroad site on the Parker River for July-November 2004.
Measurements of water table height in the Parker River marsh located downstream of the railroad bridge. Measurements were taken every 10 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the railroad site, MAR-PR-Wtable-RR, for July-November 2004.
Biomass and shoot densities of marsh grasses at hayed and reference sites for samples collected in June and early July 2000
Biomass and shoot densities of dominant marsh grasses at hayed and reference sites for samples collected in June and early July 2000 at hayed and reference marsh sites near Stackyard Rd. and Patmos Rd, Rowley, Massachusetts
Counts of tagged striped bass at forty sites throughout Plum Island estuary conducted July-October 2009 using acoustic telemetry.
Manual survey data was collected to measure striped bass distribution in Plum Island Estuary during the time period that they are in New England during their summer foraging migration. Acoustic telemetry was used to tag and track individual fish and provide measures of abundance at sample sites distributed throughout the estuary.
PIE LTER nutrient grab samples collected between December 1998 and July 2017 in the mainstem, tributaries, and headwater streams in the Ipswich and Parker River watersheds, Massachusetts.
Data set of Ipswich and Parker Rivers and tributaries in Massachusetts collected between December 1998 and July 2017 by MBL, UNH and/or members of the Ipswich and Parker River watershed associations. The data is primarily nutrient content of the streams, but there are some physical descriptors (i.e. discharge, temperature, dissolved oxygen) for some dates and sites. Site description file for all stations sampled in WAT-UNH-IPPR-Synoptic file can be found in WAT-UNH-IPPR-Synoptic-Sites file.
Gap Fire Perimeter (Santa Barbara County, CA), July 9, 2008 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Gap Fire burned from 2008-07-01 to 2008-07-28, Lizard's Mouth area of Los Padres National Forest, Santa Barbara County. Approximately 9544 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-07-09, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Model outputs for occurrence and hunting data‐based models of wild boar distribution and abundance, July 2019 update
<p>These maps are wild boar habitat suitability outputs based on newly available data of wild boar, and models for predicting wild boar relative abundance using hunting yields.</p> <p><strong>Objectives</strong>:</p> <p>- Validation of previously produced hunting yield maps and new ones<br> - Downscaling to 10x10 km grid<br> - Downscaling to 2x2 km grid</p> <p><strong>Model settings and predictors: </strong> <br> - Model from ENETWILD report August 2019<br> - Assuming cells as municipality in 10x10 km grid downscaling<br> - Assuming cells as hunting grounds in 2x2 km grid downscaling </p> <p><strong>Conclusions guiding future methodological steps</strong><br> - To update wild boar hunting yield data for some specific regions;<br> - To increase hunting yield data resolution;<br> - To explore model independent parametrization for each bioregion.</p> <p><strong>Files:</strong></p> <p>August_2019_HY_nut00_10x10 >> Model outputs based on hunting yield GLM analyses<br> August_2019_occurrences_bioclim >> Model outputs based on Bioclim analyses<br> August_2019_occurrences_glm >> Model outputs based on Generalised linear model<br> August_2019_occurrences_ksvm >> Model outputs based on Support vector Machine analyses<br> August_2019_occurrences_maxent >> Model outputs based on Maxent analyses<br> August_2019_occurrences_randomForest>> Model outputs based on Random Forest analyses</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>These maps are models obtained in intermediate phases of the ENETWILD project based on available information. <br> There are frequent updates in order to improve the results. For methodological approach and details check the paper: </p> <p>ENETWILD‐consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A. Cohen, R. Soriguer, J. Vicente (2019). ENETwild modelling of wild boar distribution and abundance: update of occurrence and hunting data‐based models. EFSA Supporting Publications, 16(8), 1674E.<br> <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fefsa.onlinelibrary.wiley.com%2Fdoi%2Fabs%2F10.2903%2Fsp.efsa.2019.EN-1674&data=02%7C01%7C%7Ca8ad922eefde42f5cb5208d7c5054851%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637194498792136402&sdata=fqdiYEOqYIlHaDbp5a7kVdGQ6FWuFEydJNhSWOghH%2FQ%3D&reserved=0">https://efsa.onlinelibrary.wiley.com/doi/abs/10.2903/sp.efsa.2019.EN-1674</a></p> <p>.</p> <p>Permission for reuse occurrence outputs records is granted under the terms of a CC-BY-NC license.<br> Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.</p>
Drifter deployed on Inhambane Bay (Mozambique) in July 21, 2017.
<p>This is the data set of the track for the drifter deployed on Inhambane Bay (Mozambique) in July 21, 2017.</p> <p>This data is reported in the manuscript:</p> <p>Solana G.; Grifoll, M.; and Espino, M; 2020. Hydrographic variability and estuarine classification of Inhambane Bay (Mozambique) In: Malvárez, G. and Navas, F. (eds.), Proceedings from the International Coastal Symposium (ICS) 2020 (Seville, Spain). Journal of Coastal Research, Special Issue No. 95, pp. 50-54. Coconut Creek (Florida), ISSN 0749-0208. DOI: 10.2112/SI95-126.1</p> <p>The instrument used was a Arduino + GPS Fona Shield</p> <p>METADATA</p> <p>File is in csv format.</p> <p>Time is given in UTM+2 format.</p> <p>Period:'2017-07-21 09:43:00 - '2017-07-21 14:30:00</p> <p>Data headers:</p> <p>Valid,Time,Latitude,Longitude,Altitude,Speed (Km/h),Address,Attributes</p> <p>1,2017-07-21 09:43:28,-23.792458,35.386703,0 m,0.6 ,Unnamed Road. Inhambane. MZ,battery=87 hdop=355.7 ip=197.218.83.149 distance=1.76 totalDistance=1.0659886039E8</p>
Conclusions of the online assessment debate held on July 2, 2020, at the University of Salamanca (Spain)
<p>Conclusions of the online assessment debate held on July 2, 2020, at the University of Salamanca (Spain)</p>
Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil
<p>Title:</p> <p>Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170711</p> <p>Time of day (BRT = -3)</p> <p>10h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p>Breunig, Fábio Marcelo (2017, July 7). Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4327943</p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil
<p>Title:Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170707</p> <p>Time of day (BRT = -3)</p> <p>14h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High-speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p>Title:</p> <p> </p> <p>Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170707</p> <p>Time of day (BRT = -3)</p> <p>14h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p>
Measurement and model data comparisons for the HALO-FAAM formation flight during EMeRGe on 17 July 2017
<p>Within the project “Effect of Megacities on the transport and transformation of pollutants on the Regional and Global scales” (EMeRGe), the measurement flight of 13 July 2017 was performed for comparison of the instrumentation onboard of the research aircraft HALO and FAAM. The aircraft flew for 1.6 h in close formation along a racetrack pattern at three flight levels in Southern Germany. The flight started in a rather dry and clean troposphere and ended in a more polluted convective boundary layer. 28 measurement pairs sampled on both aircraft were found suitable for comparison. 17 further pairs of data are available from sampling on either HALO or FAAM. In addition, observations obtained at the DWD Hohenpeissenberg and results from 6 models are included in the comparisons. Overall, about 30% of the measured data pairs show deviations within the combined error estimates. Some measurements deviate considerably from model results.</p> <p>This dataset contains a pdf of the report and a zip file of the comparison data as described in that report.</p>
Cumulative PLOS ALM Report - July 2015
Article-Level Metrics (ALM) measure the reach and online engagement of scholarly works. This PLOS ALM report contains the cumulative stats collected for all works through July 13, 2015. Data are generated by the Lagotto open source software. Go to the Lagotto forum for questions or comments.
Wordpress blog export, posts from 2006--18 July, 2015.
<p>An XML Wordpress export of the 440 blog posts and associated comments by Henry Rzepa up to July 18, 2015.</p>
Satellite images of the 17 July 2016 Aru Co glacier collapse
<p>These satellite images were made to visualize the Aru Co glacier avalanche. Some of them were used in these blog posts:</p> <ul> <li>Séries Temporelles (2016, August 25) Sentinel-2A captures a giant ice avalanche in Tibet. http://www.cesbio.ups-tlse.fr/multitemp/?p=8294</li> <li>Séries Temporelles (2016, August 25) Sentinel-2A (and Landsat-8) capture a giant ice avalanche in Tibet http://www.cesbio.ups-tlse.fr/multitemp/?p=8327</li> </ul> <p>Files description:</p> <ul> <li>File 2016-07-21_S2.tif: Sentinel-2A image of the Aru Co glacier avalanche acquired on 21-Jul-2016 (4 days after the event). RGB composite of bands B4,B3,B2 scaled to bytes between 0 and 0.5 from level 1C product (orthorectified top-of-atmosphere reflectances). Format: Geotiff, WGS 84 / UTM zone 44N.</li> <li>File 2016-06-24_L8mos.tif: Landsat-8 image of the Aru Co area acquired on 24-Jun-2016 (23 days before the event). RGB composite of bands B4,B3,B2 scaled to bytes between 0 and 0.5 from level 1C product (orthorectified top-of-atmosphere reflectances). Format: Geotiff, WGS 84 / UTM zone 44N.</li> <li>File anim.gif: animated sequence of both images using the lowest resolution image (Landsat-8)</li> <li>File diff_S2minusL8_band3.tif: difference between the band 3 of the 2016-07-21 Sentinel-2A image and the 2016-06-24 Landsat-8 image after a nearest neighbour resampling of the Sentinel-2 image to the same resolution as the Landsat-8 image (30 m).</li> <li>2016-07-25_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 25-Jul-2016 (8 days after the event). VV co-polar band, ascending orbit. The images was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-25_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 25-Jul-2016 (8 days after the event). VV co-polar band, ascending orbit. The image was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-01_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 07-Jul-2016 (10 days before the event). VV co-polar band, ascending orbit. The image was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-21-01_S1_diff_smoothed_Lee.tif : difference between both Sentinel-1 images after applying a refined Lee filter on the radar intensities</li> </ul> <p>Spatial extent of all the images in WGS 84 UTM 44N and lon/lat coordinates :</p> <p>Upper Left ( 602260.000, 3777670.000) ( 82d 6'32.64"E, 34d 8' 5.67"N)<br> Lower Left ( 602260.000, 3755030.000) ( 82d 6'23.08"E, 33d55'50.74"N)<br> Upper Right ( 640720.000, 3777670.000) ( 82d31'33.86"E, 34d 7'49.56"N)<br> Lower Right ( 640720.000, 3755030.000) ( 82d31'20.72"E, 33d55'34.75"N)</p>
Figure 18b. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 18b. - PteristripartitaFigure 18a.General aspect of the frondFigure 18b.Lower face of the lamina showing sori <br> Lower face of the lamina showing sori
Figure 17c. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 17c. - PteristogoensisFigure 17a.General aspect of the fern (upper face)Figure 17b.General aspect of the fern (lower face)Figure 17c.Lower face of a pinnae showing linear soriFigure 17d.Sori <br> Lower face of a pinnae showing linear sori
Figure 17d. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 17d. - PteristogoensisFigure 17a.General aspect of the fern (upper face)Figure 17b.General aspect of the fern (lower face)Figure 17c.Lower face of a pinnae showing linear soriFigure 17d.Sori <br> Sori
Figure 18a. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 18a. - PteristripartitaFigure 18a.General aspect of the frondFigure 18b.Lower face of the lamina showing sori <br> General aspect of the frond
Figure 17b. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 17b. - PteristogoensisFigure 17a.General aspect of the fern (upper face)Figure 17b.General aspect of the fern (lower face)Figure 17c.Lower face of a pinnae showing linear soriFigure 17d.Sori <br> General aspect of the fern (lower face)
Figure 17a. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 17a. - PteristogoensisFigure 17a.General aspect of the fern (upper face)Figure 17b.General aspect of the fern (lower face)Figure 17c.Lower face of a pinnae showing linear soriFigure 17d.Sori <br> General aspect of the fern (upper face)
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