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5,023 results for “landing”
Climate and land-use effects on dung beetle assemblages
<p>This dataset contains data from a field study conducted in 2019 and described in the paper "Dung beetle diversity is mainly affected by land use, while community specialization is driven by climate" by Englmeier et al.</p> <p>To test the effects of land use and climate on α-diversity, local community specialization (H<sub>2</sub>'), and γ-diversity of dung beetles, we used pitfall traps baited with four different dung types at 114 study sites, distributed over a spatial extent of 300 km x 300 km and 1000 m in elevation. Study sites were established in four local land-use types: forests, grasslands, arable sites, and settlements, embedded in near-natural, agricultural, or urban landscapes.</p> <p>We used negative-binomial generalized linear models for alpha-diversity, calculated community specialization on dung resources by using the standardized two-dimensional Shannon entropy (H<sub>2</sub>'), and assessed γ-diversity by Hill-numbers.</p> <p>Our results show that intensive land use i.e., agriculture and urban areas, strongly reduced dung beetle α- and γ-diversity, respectively. Dung beetle abundance and species density were strongly affected by agricultural land use on both spatial scales, whereas γ-diversity on a local scale was mainly affected by settlements and on a landscape scale equally by agricultural and urban land use. Increasing precipitation diminished dung beetle abundance, and higher temperatures reduced community specialization and γ-diversity. These results indicate that intensive land use and higher temperatures may cause a loss in dung beetle diversity and alter community networks of dung beetle assemblages. A decrease in dung beetle diversity may disturb decomposition processes at both local and regional scales and alter ecosystem functioning, which leads to drastic ecological and economic damage.</p>
CS3 Land-based abalone IMTA - bioeconomic model South Africa 2022
<p>Data set containing bio-economic modelling results from the estimated growth of land-based abalone fed with IMTA produced macroalgae in South Africa. The dataset contains calculations and modelling of a baseline scenario and four alternative scenarios according to the geographical location of the abalone farm and the feed strategy with or without IMTA macroalgae. Sensitivity analysis for productivity and feed price/FCR is provided.</p>
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (1981–2000)
<ul> <li>Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021.</li> <li>Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K.</li> <li>More details of this dataset can be seen in <em>readme.pdf.</em></li> <li>This dataset provides GT-LST product from 1981 to 2000.</li> </ul>
A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (2001–2005)
<ul> <li>Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021.</li> <li>Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K.</li> <li>More details of this dataset can be seen in <em>readme.pdf.</em></li> <li>This dataset provides GT-LST product from 2001 to 2005.</li> </ul>
Combined effects of stream hydrology and land use on basin-scale hyporheic zone denitrification in the Columbia River Basin
<p><strong>This data includes inputs and outputs of a coupled carbon-nitrogen river corridor model (RCM) for the Columbia River Basin and sensitivity results with varying substrate concentrations, and random forest model results with key model inputs/watershed/stream variables. The detailed descriptions of the individual files are included in the readme.txt file. </strong></p>
Temperature and land-use rates of change for populations of fast and slow species in the LPD
<p>Human-induced environmental changes have a direct impact on species populations, with some species experiencing declines while others display population growth. Understanding why and how species populations respond differently to environmental changes is fundamental to mitigate and predict future biodiversity changes. Theoretically, species life-history strategies are key determinants shaping the response of populations to environmental impacts. Despite this, the association between species' life-histories and the response of populations to environmental changes has not been tested. In this study, we analysed the effects of recent land-cover and temperature changes on rates of population change of 1,072 populations recorded in the Living Planet Database. We selected populations with at least 5 yearly consecutive records (after imputation of missing population estimates) between 1992 and 2016, and for which we achieved high population imputation accuracy (in the cases where missing values had to be imputed). These populations were distributed across 553 different locations and included 461 terrestrial amniote vertebrate species (273 birds, 137 mammals, and 51 reptiles) with different life-history strategies. We showed that populations of fast-lived species inhabiting areas that have experienced recent expansion of cropland or bare soil present positive population trends on average, whereas slow-lived species display negative population trends. Although these findings support previous hypotheses that fast-lived species are better adapted to recover their populations after an environmental perturbation, the sensitivity analysis revealed that model outcomes are strongly influenced by the addition or exclusion of populations with extreme rates of change. Therefore, the results should be interpreted with caution. With climate and land-use changes likely to increase in the future, establishing clear links between species characteristics and responses to these threats is fundamental for designing and conducting conservation actions. The results of this study can aid in evaluating population sensitivity, assessing the likely conservation status of species with poor data coverage, and predicting future scenarios of biodiversity change.</p>
Data from: In both directions: Expansions of European land snails to the north and south from glacial refugia
<p><strong>Aim</strong>: The location of glacial refugia and the timing of postglacial colonisations remain of interest because together they inform about the ability of species to track shifting climates. In the associated paper, we tested a hypothesis that a central European forest land snail species survived in a northern refugium, which was proposed based on fossil finds indirectly dated to the Last Glacial Maximum and Late Glacial. We assessed the credibility of our results by comparing them to those obtained from three other, broadly sympatric land snail species.</p> <p><strong>Location</strong>: Central Europe and the Balkans.</p> <p><strong>Taxon</strong>: Pulmonate land snails: <em>Monachoides incarnatus</em> (Hygromiidae) and <em>Helix pomatia</em>, <em>Helix thessalica,</em> and <em>Caucasotachea vindobonensis</em> (Helicidae).</p> <p><strong>Methods</strong>: We used continuous phylogeographic analysis of mitochondrial data to trace the origin of postglacially expanding lineages and direct radiocarbon dating to verify the presumed glacial presence of <em>M. incarnatus</em> on the territories of Czechia and Slovakia. Geolocated concatenated sequences of partial 16S rRNA and cytochrome c oxidase subunit I were analysed using BEAST 1.10.4. and conclusions were drawn from the reconstructed geographic positions of internal nodes in the resulting phylogenetic trees.</p> <p><strong>Data</strong>: Input files for the phylogeograpic analyses, containing the sequence alignments and analysis settings, and the trees summarizing the results of the analyses.</p>
Korean peninsula's Land use and land cover data, Landsat NDVI data, and CO2 data
<p><strong>Paper: "Greening rate in North Korea doubles South Korea"</strong></p> <p>Data in this repository include Landsat NDVI data, Land use, land cover data, and CO2 data of the Korean Peninsula.</p> <p>Korean Peninsula's <strong>NDVI data</strong> 1986-2017: <a href="https://zenodo.org/record/7221229#.Y07A2HZBxdg">https://zenodo.org/record/7221229#.Y07A2HZBxdg</a></p> <p>Korean Peninsula's <strong>Landuse Data</strong> 1986-2017: <a href="https://zenodo.org/record/7214788#.Y07BD3ZBxdg">https://zenodo.org/record/7214788#.Y07BD3ZBxdg</a></p> <p>Korean Peninsula's <strong>CO2 Data </strong>1986-2017: https://zenodo.org/record/7260091</p> <p>All three types of data are combined and displayed in this repository link. You can view individual data based on the separate link behind the data.</p>
Land-use areas
<p>Data used in this study were from the Office of Agricultural Economics Thailand. Land-use areas data from Agricultural statistics of Thailand are publicly available. </p> <p><strong>File descriptions</strong>: Types of land use areas for agricultural consist of rice field area, field crops (sugarcane, cassava, peanut, common tobacco, corn, and potato), fruit trees and perennial (including rubber tree, eucalyptus tree, oil palm tree), vegetables and flowers, and other agriculture that averaged in years 2018-2020, Thailand :<br> cwt : post code<br> cwt_n : province names<br> Land : total land area each of provinces in years 2018-2020<br> LandUse : total land use area each of provinces in years 2018-2020<br> pLand : proportion of land area in years 2018-2020<br> Rice : rice field area in years 2018-2020<br> pRice : proportion of rice field area in years 2018-2020<br> Field crops : field crops area in years 2018-2020<br> pField : proportion of field crops area in years 2018-2020<br> Fruit trees / perennial : fruit trees and perennial area in years 2018-2020<br> pFruit : proportion of fruit trees and perennial area in years 2018-2020<br> Vegetables / Flowers : vegetables and flowers area in years 2018-2020<br> pVege : proportion of vegetables and flowers area in years 2018-2020<br> Other agriculture : other agriculture area in years 2018-2020<br> pOther: proportion of other agriculture area in years 2018-2020</p>
Effects of land clearing for agriculture on soil organic carbon stocks in drylands: A meta-analysis
<p><span>To improve our understanding of clearing natural ecosystems for cropland on soil organic carbon stocks in drylands, we searched for related peer-reviewed research papers published from 1980 to 2022 on the Web of Science (<a href="https://www.webofscience.com">https://www.webofscience.com</a>) and the Scopus Database (<a href="https://www.scopus.com">https://www.scopus.com</a>) (accessed on 30th April 2022). Then, we screened papers for </span><span>integrity, relevance, and scientific merit under the following criteria: (1) We made sure all studies were independent and based on field-measured data; (2) Each study had to report paired SOC stocks of cropland and adjacent natural ecosystems with the same or a similar suite of environmental factors; (3) Studies need to explicitly present results on SOC stocks or concentrations for certain depths and areas; (4) Studies have specified the types of natural ecosystems that were converted to cropland, which are used as criteria for defining CNEC types. Finally, we winnowed results to a total of 159 scientific journal articles, comprising 242 sites with 1379 paired soil layer observations from 601 paired soil profiles.</span></p>
Room to Breathe - Landing Area
The [Migration Museum ](https://www.migrationmuseum.org/) is currently based in Lambeth, where it is possible to visit their incredible immersive exhibition _Room to Breathe_. This scene is a re-interpretation of the exhibition's reception space, lit and styled to emphasise the unsettling sensation of arriving in a new and strange place and being faced with a wall of bureaucracy and red tape. King's College London is engaged with the museum in a project to preserve elements of this important installation, and to transmit the stories and experiences it documents to wider audience. Source: Objaverse 1.0 / Sketchfab
Torso. The Bible Lands Museum Jerusalem
Torso. The Bible Lands Museum Jerusalem. Height: Aprox. 80 cm. Created with **TRNIO** iPhone app. Source: 70 images taken on Nikon D90. Compare with same object processed 5 years ago with Autodesk Recap. https://skfb.ly/CYLP Source: Objaverse 1.0 / Sketchfab
Mars Perseverence Landing Scan
A test scan of the NASA Perseverence rover landing location. Based on the landing footage. Source: Objaverse 1.0 / Sketchfab
WW1 land front shelter, Helsinki, Finland
Remains of First World War wooden shelter room in [Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) land front defense line from base XXI:6 Oulunkylä, Helsinki, Finland. Wooden Shelter room built in 1915 - 1918 is completely decomposed but the place and part of the trench is clearly visible. More info: * [Location in Google Maps](https://goo.gl/maps/FvddG7TCM5Ut97qw9) * [Wikipedia: Krepost Sveaborg](https://en.wikipedia.org/wiki/Krepost_Sveaborg) * [John Lagerstedt , Markku Saari: Krepost Sveaborg](http://www.novision.fi/viapori/eavaus.htm) * [Finnish Heritage Agency site info](https://www.kyppi.fi/to.aspx?id=112.1000013764) (only in finnish) Source: Objaverse 1.0 / Sketchfab
Wellington bomber landing gear
Remains of the starboard landing gear (lying turtle) of a RAF Wellington bomber crashed in the North Sea during WO2 just above the Dutch island Ameland. See also our YouTube report/documentary about this wreck https://www.youtube.com/watch?v=81jExNR-QC8 Source: Objaverse 1.0 / Sketchfab
Land use following deforestation for Africa
<p><strong>Data Description</strong></p> <p>These datasets were generated from the research article "<strong>Mapping the diversity of land uses following deforestation across Africa</strong>".</p> <p>Here we publish the yearly time series maps showing land use following deforestation across Africa at 30 m resolution covering the year 2001 to 2020 (20 years). The maps are derived from Planet-NICFI images using U-Net deep neural network architecture enhanced with attention.</p> <p>The original landuse maps are derived from 5 m resolution Planet-NICFI imagery (research license with Planet Labs Inc) of 2022. The Planet-NICFI imagery are accessible upon sign up at https://www.planet.com/nicfi/ or https://developers.planet.com/docs/integrations/gee/nicfi/</p> <p>The landuse dataset has fifteen classes with values ranging from 1 -15, where 1: Larger-scale cropland, 2: Pasture, 3: Mining, 4: Small-scale cropland, 5: Roads, 6: Other land with tree cover, 7: Plantation forest, 8: Coffee, 9: Settlement, 10: Tea plantation, 11: Water, 12: Oil palm, 13: Rubber, 14: Cashew, 15: Cacao</p> <p>The data is accompanied by the sld and qml file (Visualisation_layer_descriptor_sld_and_qml) to aid in visualisation or legend creation.</p> <p> </p> <p><strong>Data Visualisation at 5 m resolution:</strong> <a href="https://robertnag82.users.earthengine.app/view/africalu" target="_blank" rel="noopener">https://robertnag82.users.earthengine.app/view/africalu</a></p> <p><strong>File Naming:</strong> LandUseFollowingDeforestation_dl_30m_20010101_20201231_af_epsg.4326_v1.tif</p> <ul> <li><u>Generic variable name</u>: <strong>LandUseFollowingDeforestation</strong></li> <li><u>Method of data production</u>: <strong>dl</strong> (Deep learning)</li> <li><u>Spatial support in m</u>: <strong><em>30m</em></strong></li> <li><u>Time reference begin time (YYYYMMDD)</u>: i.e. <strong><em>20010101</em></strong></li> <li><u>Time reference end time</u>: <strong><em>20201230</em></strong></li> <li><u>Bounding box (2 letters max)</u>: <strong>af</strong> (Refering to Africa)</li> <li><u>EPSG code</u>: <strong><em>epsg.4326</em></strong></li> <li><u>Version code</u>: <strong><em>v1</em></strong></li> </ul> <p><strong>JSON field representing the legend</strong>. This is important for the visualization of the legend. </p> <p>[ { "Large-scale cropland": "1", "color": "#FFFF00" }, { "Pasture": "2", "color": "#808080" }, { "Mining": "3", "color": "#FFC0CB" }, { "Small-scale cropland": "4", "color": "#F39C12" }, { "Roads": "5", "color": "#800000" }, { "Other land with tree cover/Regrowth ": "6", "color": "#008000" }, { "Plantation forest": "7", "color": "#808000" }, { "Coffee": "8", "color": "#008080" }, { "Settlement": "9", "color": "#FF0000" }, { "Tea plantation": "10", "color": "#3CB371" }, { "Water": "11", "color": "#0000FF" }, { "Oil palm": "12", "color": "#FF00FF" }, { "Rubber": "13", "color": "#4B0082" }, { "Cashew": "14", "color": "#00FF00" }, { "Cocoa": "15", "color": "#00FFFF" } ]</p> <p><strong>Data Use Agreement</strong></p> <p>The datasets are restricted to non-commercial purposes or purposes indicated in the licence document. This include the rights to: (i) use, access, and view Content through the Platform; (ii) download, reproduce, store, display, and print Content; and (iii) create Derivative Products; all for Licensee's own internal, non-commercial business purposes and those are the only rights that are extended. More information on the licence can be accessed here: <strong><em>https://assets.planet.com/docs/ToS_NICFI_GeneralPartners_level2.pdf</em></strong></p> <p>The user agrees to assume all risks and liabilities associated with the interpretation and use of any data or results obtained from the datasets. Wageningen University, Planet-NICFI, Planet Labs Inc, and any affiliated parties shall not be held liable for any consequences arising from the misuse of the data. </p>
Land use following deforestation in Ethiopia
<p>These datasets were generated from the research article "Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia".</p> <p>Here we publish the yearly time series maps showing land use following deforestation across Ethiopia produced at 10m resolution covering the year 2001 to 2015 (15 years). The land use maps are derived from sentinel-2 images using U-Net deep neural network architecture enhanced with attention.</p> <p>The dataset has eleven land use classes with values ranging from 1 -11, where 1: Larger-scale cropland, 2: Pasture, 3: Mining, 4: Small-scale cropland, 5: Roads, 6: Other land with tree cover, 7: Plantation forest, 8: Coffee, 9: Settlement, 10: Tea plantation, and 11: Water.</p> <p>The data is accompanied by the sld and qml file in a zip folder (Visualisation_layer_descriptor_sld_and_qml) to aid in visualisation or legend creation.</p> <p> </p> <p><strong>Data Visualisation at 10m resolution:</strong> <a href="https://robertnag82.users.earthengine.app/view/deforestationdriverethiopia" target="_blank" rel="noopener noreferrer">https://robertnag82.users.earthengine.app/view/deforestationdriverethiopia</a></p> <p><span>Zoom to this location (<strong>Longitude:35.27458, Latitude: 7.3291</strong>) to visualize expansion of coffee plantation, small and large scale croplands in a degraded forest.</span></p> <p><span>Zoom to this location <span> </span>(<strong>Longitude:35.48129, Latitude: 7.84093</strong>) to visualize expansion of Tea plantation after deforestation.</span></p> <p><strong>File Naming:</strong> Landuse_dl_10m_s_20010101_20011230_af_epsg.4326_v20240201.tif</p> <ul> <li><u>Generic variable name</u>: <strong>Landuse</strong></li> <li><u>Method of data production</u>: <strong>dl</strong> (Deep learning)</li> <li><u>Position in the probability distribution / variable type</u>: <em><strong>c</strong></em></li> <li><u>Spatial support in m</u>: <em><strong>10m</strong></em></li> <li><u>Depth reference at surface ("s")</u>: <em><strong>s</strong></em></li> <li><u>Time reference begin time (YYYYMMDD)</u>: i.e. <em><strong>20010101</strong></em></li> <li><u>Time reference end time</u>: <em><strong>20011230</strong></em></li> <li><u>Bounding box (2 letters max)</u>: <strong>af</strong> (Refering to Africa)</li> <li><u>EPSG code</u>: <em><strong>epsg.3035</strong></em></li> <li><u>Version code i.e. creation date</u>: <em><strong>v20240201</strong></em></li> </ul> <p> </p> <p><strong>JSON field representing the legend</strong>. This is important for the visualization of the legend. </p> <p>[ { "Large-scale cropland": "1", "color": "#FFFF00" }, { "Pasture": "0", "color": "#808080" }, { "Mining": "1", "color": "#FFC0CB" }, { "Small-scale cropland": "120", "color": "#F39C12" }, { "Roads": "1", "color": "#800000" }, { "Other land with tree cover/Regrowth ": "0", "color": "#008000" }, { "Plantation forest": "1", "color": "#808000" }, { "Coffee": "120", "color": "#008080" }, { "Settlement": "1", "color": "#FF0000" }, { "Tea plantation": "0", "color": "#3CB371" }, { "Water": "1", "color": "#0000FF" } ]</p>
Baltic Sea Region Land Cover Plus - Training and Validation data
<p>Training and validation data used in creating Baltic Sea Region Land Cover Plus (BSRLC+) maps: <a href="https://doi.org/10.5281/zenodo.10653871" target="_blank" rel="noopener">Dataset link</a></p> <ul> <li><strong>landcover_training_data_2006_2018.gpkg</strong>: Points data of consistent land cover from 2006 to 2018</li> <li><strong>crop_training_data_{year}.gpkg</strong>: Points data of crop types derived from <a href="https://doi.org/10.1038/s41597-023-02517-0">EuroCrop dataset </a>in particular year (2019, 2021, 2023)</li> <li><strong>landcover_validation_{year}.gpkg</strong>: Points data of validation data derived from <a href="https://doi.org/10.1038/s41597-020-00675-z">LUCAS points </a>in particular year (2009, 2012, 2015, 2018)</li> <li><strong>Metadata.pdf</strong>: Information of land cover code in each dataset</li> </ul> <p>Version notes:</p> <p>Version 2: Correcting the validation data 2018 and Metadata file</p> <p>Version 1: Original upload</p>
European CFP EGMS Vertical Land Motion
<p>This dataset contains the estimates of EGMS-derived vertical land motion in European Coastal Flood Plain.</p>
Supplementary File 8; The full data set derived from formal quantitative surveys of land cover types and activities of humans, livestock and wildlife (Section 2.3) that were used for the analyses described in sections 2.4, 2.5 and 2.7
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