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193 results for “Liberia”
National Checklists 2017: Liberia Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Liberia collected using effechecka and geonames polygons
National Checklists 2019: Liberia Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Liberia collected using effechecka and geonames polygons
Fig. 4 in Eugenia sapoensis Jongkind from Liberia and Eugenia breteleri Jongkind from Gabon, two new forest species (Myrtales: Myrtaceae)
Fig. 4. Eugenia breteleri sp. nov. A. Branch with leaves. B. Detail of leaf from below showing secretory cavities. C. Fruit (from pickled specimen) with style and part of the calyx still present. D. Cross section of fruit and single seed. A & B from Breteler 11457, C & D from Breteler 11546. Drawing by Hans de Vries.
Fig. 2. A–C in Eugenia sapoensis Jongkind from Liberia and Eugenia breteleri Jongkind from Gabon, two new forest species (Myrtales: Myrtaceae)
Fig. 2. A–C. Eugenia sapoensis sp. nov. A. Branchlet with fruit. B. Cross section of the fruit and single large seed. C. Lateral view of embryo (r = radicular protuberance). D–E. Morphology of the seed of Eugenia "Group X" as described by van Wyk (1980). D. Seed with part of the thin testa removed showing the surface of the embryo beneath (h = hilum). E. Lateral view of embryo (r = radicular protuberance). A–C from Jongkind 9351; D & E from van Wyk & Botha (1984: 66). Photographed by the author.
Fig. 1 in Eugenia sapoensis Jongkind from Liberia and Eugenia breteleri Jongkind from Gabon, two new forest species (Myrtales: Myrtaceae)
Fig. 1. Eugenia calophylloides DC. Branchlet with leaves and fruits. From Jongkind 8311. Photographed by the author.
Fig. 4 in Description of Gaertnera luteocarpa (Gentianales: Rubiaceae), with two subspecies, a new forest shrub species from Liberia, Ivory Coast and Ghana
Fig. 4. Distribution map. Gaertnera luteocarpa sp. nov. subsp. luteocarpa (stars) and G. luteocarpa subsp. sinoensis subsp. nov. (dots).
Fig. 3. A. Gaertnera spicata fruits. B. Gaertnera cooperi fruits. A from Lachenaud & Walters 1163. Photo O.L.S. Lachenaud. B in Description of Gaertnera luteocarpa (Gentianales: Rubiaceae), with two subspecies, a new forest shrub species from Liberia, Ivory Coast and Ghana
Fig. 3. A. Gaertnera spicata fruits. B. Gaertnera cooperi fruits. A from Lachenaud & Walters 1163. Photo O.L.S. Lachenaud. B from Jongkind, de Wet & Sambolah 12104. Photo C.C.H. Jongkind.
Fig. 2 in Description of Gaertnera luteocarpa (Gentianales: Rubiaceae), with two subspecies, a new forest shrub species from Liberia, Ivory Coast and Ghana
Fig. 2. Gaertnera luteocarpa sp. nov. subsp. sinoensis subsp. nov. A. Fruits and leaves. B. Close up of fruits. From Jongkind, Bilivogui & Daniels 9832. Photos C.C.H. Jongkind.
Fig. 1 in Description of Gaertnera luteocarpa (Gentianales: Rubiaceae), with two subspecies, a new forest shrub species from Liberia, Ivory Coast and Ghana
Fig. 1. Gaertnera luteocarpa sp. nov. subsp. luteocarpa. A. Habit in fruit. B. Fruits. C. Stipules. D. Twig showing ridges around base of petiole. A, B & D from Hawthorne & Gyakari 205a063; C from Hawthorne & Gyakari 201a223. Photos W.D. Hawthorne.
Fig. 2 in Rhaphiostylis minima Jongkind (Icacinaceae), a new liana species from Ivory Coast & Liberia
Fig. 2. Rhaphiostylis preussii Engl. showing flowers with the flattened filaments closing around the ovary while the petals are bending down. Photograph by Ehoarn Bidault (Missouri Botanical Garden) from Bidault 786 (MO) from Gabon.
Fig. 1. A–C. Rhaphiostylis elegans Engl. A. Flower B. Flower without petals and stamens. C. Leaf. D–G in Rhaphiostylis minima Jongkind (Icacinaceae), a new liana species from Ivory Coast & Liberia
Fig. 1. A–C. Rhaphiostylis elegans Engl. A. Flower B. Flower without petals and stamens. C. Leaf. D–G. Rhaphiostylis minima sp. nov. D. Branch with flowers. E. Flower. F. Flower without petals and stamens. G. Leaf. H–M. Rhaphiostylis preussii Engl. H. Flower. I. Flower without petals and stamens. J. Leaf. K. Flower. L. Flower without petals and stamens. M. Leaf. All flowers are on the same scale (scale bar with B) and all single leaves too (scale bar with G). A–C from Tchouto & Elad 3310 (WAG), D–G from W.de Wilde 1061 (WAG), H–J from Beentje 1353 B from Ivory Coast (WAG), K–M from Breteler 14204 from Gabon (WAG). Drawn by Hans de Vries.
qdgc Liberia
<p>QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> The attributes for each table are:<br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it.<br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 20th of January, 2021<br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Liberia
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2025)</li> <li>railways (OpenStreetMap, 2025)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2025) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Transport Starter Data Kit: Historical socio-transport data for Liberia
<p>This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.</p>
National Checklists: Liberia Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Fig. 1. – Tragia liberica Jongkind. A in Tragia liberica Jongkind (Euphorbiaceae), a new forest species from Liberia
Fig. 1. – Tragia liberica Jongkind. A. Habit with inflorescences; B. Leaf margin from below; C-D. Leafbase and stipules; E. Female flower with bracts; F. Male flower with bract; G. Male flower in cross-section; H. Lower part of pedicel and bracts after male flower dropped; I. Developing fruit; J. Female sepal; K. Large bract from I; L. Columella and sepals after dehiscence of the fruit; M-O. Mericarp of split capsule with enclosed seed from three different angels. [A-C, E-H, L-O: Jongkind 12145, BR; D, I-K: Baldwin 10353, WAG] [Drawing: Hans de Vries]
Fig. 4. – Maesobotrya liberica Jongkind. A in Maesobotrya liberica Jongkind (Phyllanthaceae), a new forest species from Liberia
Fig. 4. – Maesobotrya liberica Jongkind. A. Habit with male inflorescence; B. Leaf margin from below; C. Male inflorescence; D. Male flower; E. Same as D but with sepal removed, showing the hairy lobes of the disk; F. Female flower; G. Female inflorescence and stipules; H. Same as G but with sepal removed, showing hairy disk; I. Fruit. [A-E: Jongkind 12333B, BR; F-H: de Gier & Goll 48, WAG; I: Jongkind 12333A, BR] [Drawing: H. de Vries]
Fig. 3. – Maesobotrya liberica Jongkind. A in Maesobotrya liberica Jongkind (Phyllanthaceae), a new forest species from Liberia
Fig. 3. – Maesobotrya liberica Jongkind. A. Leaf from below and male infloresences; B. Stem c. 4 cm in diam. [Jongkind et al. 12943] [Photo: C. Jongkind]
Fig. 2 in Maesobotrya liberica Jongkind (Phyllanthaceae), a new forest species from Liberia
Fig. 2. – Distribution map with to the right Maesobotrya pauciflora Pax (surrounded by a black line) overlapping M. oligantha O. Lachenaud & Breteler (squares), and to the left M. liberica Jongkind (circles).
Fig. 1 in Maesobotrya liberica Jongkind (Phyllanthaceae), a new forest species from Liberia
Fig. 1. – Maesobotrya barteri (Baill.) Hutch. from Guinea: stem with immature green fruits showing the woody bumps were the fruits grow from every fruiting season. [Photo: C.Jongkind]
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