Find research datasets worth reusing

opencc-by-4.0Dec 2021View details →

Code for "New land-use-change emissions indicate a declining CO2 airborne fraction"

Data and programming scripts for reproducing the results from the Nature publication titled: "New land-use-change emissions indicate a declining CO2 airborne fraction". Authors: Margreet J. E. van Marle*, Dave van Wees*, Richard A. Houghton, Robert D. Field, Jan Verbesselt, and Guido R. van der Werf * These authors contributed equally. DOI: https://doi.org/10.1038/s41586-021-04376-4   This dataset includes the following (All files are preceded by "Marle_et_al_Nature_AirborneFraction_"): - "Datasheet.xlsx": Excel dataset containing all annual and monthly emissions and CO2 time series used for the analysis, and the resulting airborne fraction time series. - "Script.py": BEFORE RUNNING THE SCRIPT: change the 'wdir' variable to the directory containing the provided script and files. NOTE: This script requires the Python module: 'pymannkendall' Python script used for reproducing the results and figures from the paper. The provided Datasheet.xlsx file and the .zip and .npz files are required for this program. In case all these files are found by the script, it should run within several seconds. Successful execution of the script will save Figures 1-4 from the main text and print the data from Table 1. In case script execution takes longer, please check if the .xlsx, .zip and .npz files are correctly present in the assigned 'wdir' directory. Otherwise the script will start recalculating these files, which might take a while (see notes below). - "MC10000_MK_ts_TRENDabs.zip": .zip file containing all results from the Monte-Carlo simulation for trend estimation for Figure 3 (calculated using Python function 'calc_AF_MonteCarlo()'). This .zip file contains multiple .npz files for different emission scenarios and data treatments. This .zip file is managed by the Python script function 'calc_AF_MonteCarlo_filemanager()', there is no need to unzip the file manually. In case the .zip file is not found by the Python script (e.g. because the .zip file was unpacked manually and deleted), the program will start recalculating and save a new .zip file. This can take several minutes dependent on the computer used. Recalculated results could differ very slightly due to the random factor in the Monte-Carlo approach, even though the 10,000 iterations bring this variation to a minimum. - "MC1000_MK_run50x50_TRENDabs.npz": .npz file containing the Monte-Carlo results used for producing Figure 4 (calculated using Python function 'calc_AF_MonteCarlo_ARR()'). In case the .npz file is not found by the Python script (e.g. because it was deleted or not downloaded), the program will start recalculating and save a new file. This can take around 30 hours(!) dependent on the computer used. Recalculated results could differ slightly due to the random factor in the Monte-Carlo approach. - "tol_colors.py": Additional Python module used in script.py, required for producing the colors used in the Main text figures. Source: https://personal.sron.nl/~pault/ - Figure files: Figures 1-4 from the Main text saved as .pdf files. Figure 3 is saved as three independent panels. The Figures are also reproduced by script.py if executed successfully.

opencc-by-4.0Jun 2022View details →

Data from: Land use, season, and parasitism predict metal concentrations in Australian flying fox fur

There are two .csv files in this upload. The "Pteropus_metal_data_wide.csv" file contains metal concentrations (reported in ng/g) measured in fur samples collected from Pteropus flying foxes (P. alecto, P. conspicillatus, P. poliocephalus). Flying foxes were captured from 2015-2018 at multiple sites across Australia. The file also contains capture information (e.g. date, location) and biological information (e.g. species, sex, age class, parasitism) for each flying fox. The "Pteropus_metadata.csv" file provides further details on all column names in the primary data file, including the specific metals that were quantified. Detailed information on the study methods and results can be found in the associated Science of the Total Environment publication, "Land use, season, and parasitism predict metal concentrations in Australian flying fox fur" by Sánchez et al.

opencc-by-4.0May 2024View details →

A Map of Land Use and Land Cover in Southern Malawi Derived from Sentinel-2 Data (2023)

<h3>Overview</h3> The land use and land cover map comprises the Mulanje and Phalombe districts, in Southern Malawi. It includes five classes: forest, natural vegetation, cropland, wetland, and other lands. The map is derived from Sentinel-2 mosaics, resulting in a spatial resolution of 10 meters, for 2023.    <h3>Map Accuracy</h3> The land use and land cover map achieves an overall accuracy of 89%. Details of user and producer accuracies are provided in Table 1. Table 1:  Land use and land cover classification validation,including overall, producer (PA) and user (UA) accuracies values for each class. <div> <table> <tbody> <tr> <td> Class  </td> <td> Producer Accuracy </td> <td> User Accuracy </td> </tr> <tr> <td> Cropland </td> <td>             93% </td> <td>        85% </td> </tr> <tr> <td> Wetland </td> <td>          100% </td> <td>       100% </td> </tr> <tr> <td> Other Lands </td> <td>            90% </td> <td>        95% </td> </tr> <tr> <td> Forest </td> <td>            79% </td> <td>       90% </td> </tr> <tr> <td> Natural Vegetation </td> <td>           90% </td> <td>       90% </td> </tr> <tr> <td> Overall Accuracy </td> <td>            89% </td> </tr> </tbody> </table> </div> <h3> </h3> <h3>Files descripion</h3> <ul> <li>MLW_Sentinel_LULC_2023.tif / .qml: land use and land cover map and QGIS style file</li> <li>training_samples.gpkg: training samples with class labels</li> <li>validation_samples.gpkg: validation samples with class labels</li> </ul>

opencc-by-4.0Jul 2024View details →

Data from Davison et al. (2024) Changes in Danish bird communities over four decades of climate and land-use change

Environmental and biodiversity data associated with the article: Davison, C. W., Rahbek, C., & Morueta-Holme, N. (2024) Changes in Danish bird communities over four decades of climate and land-use change. Oikos. https://doi.org/10.1111/oik.10697 Data on local bird species richness, functional diversity, temporal and spatial turnover (beta diversity), abundance, and biomass at volunteer led survey routes across Denmark. Matched habitat data (from volunteers) and historical climate data (E-OBS). Bird observations are a subset of the Common Bird Monitoring programme (DOF – BirdLife Denmark) that include routes surveyed in the summer season, spanning ≥10 years, and with full GPS coordinates. This excel document contains all of the derived (and anomysied) data used in the final analyses and includes metadata describing the variables. Climate and trait data were obtained from open-access databases (see references). Metadata is included in the excel file. <ul> <li>Danish Common Bird Monitoring programme – Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. Dansk Ornitologisk Forening.</li> <li>E-OBS European gridded climate data – Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., & New, M. (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. Journal of Geophysical Research Atmospheres, 113(20). https://doi.org/10.1029/2008JD010201</li> <li>AVONET bird traits data – Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898</li> </ul>

opencc-by-4.0Oct 2024View details →

Navigating deep learning strategies for large-area land cover mapping using very-high-resolution imagery in Senegal: Validation Data

Rapid advances in deep learning for land cover classification of trees, shrubs and very small agricultural fields using very high-resolution satellite data (< 2 m), has tremendous potential for resolving current challenges in quantifying land cover change in sub-Saharan African (SSA), due to growing demand for food resources. We conducted experiments with different training strategies for scaling up UNet convolutional neural network models for regional land cover mapping with multispectral WorldView (WV)-2 and –3, imagery in three distinct regions of Senegal which has complex seasonal wet/dry conditions and cropland-savanna mosaics.  The validation exercise of this research consisted in validating more than 70,000 km2 across Senegal. The infrastructure was setup in the NASA SMCE system with a total of twelve George Mason University (GMU) students participating as operators. These operators validated more than 59 WV-2 and -3 images, each consisting of 200 stratified points in 5,000 x 5,000-pixel images. This effort resulted in a total of ~35,000 aggregated observations that are available through the eo-validation API for public consumption. Each validation point from this dataset has three individual observations.

opencc-by-4.0Oct 2024View details →

Review of existing modelling studies focusing on specific soil-based ecosystem services (SES) and threats (ST) including climate change, management and land use change scenarios.

We reviewed existing modelling studies focusing on soil ecosystem services (SES) and soil threats (ST) including climate change, land use change and management scenarios. A publication has been submitted and is currently being reviewed. The title of the manuscript is: Assessing and mapping soil ecosystem services and soil threats changes in agroecosystems through scenario-based approaches – a systematic review. Work was split between various authors. All Co-authors were working on either one or more SES or one ST. Excel sheets were prepared by INRA and BFW to ensure the comparability of results that members extracted from the papers found. Literature search was done in Scopus and Web of Science. The final list of related publications is reported here.

opencc-by-4.0Sep 2021View details →

Data supplement for "Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century"

This data supplements the publication "Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century" by Thomas Kastner, Sarah Matej, Matthew Forrest, Simone Gingrich, Helmut Haberl, Thomas Hickler, Fridolin Krausmann, Gitta Lasslop, Maria Niedertscheider, Christoph Plutzar, Florian Schwarzmüller, Jörg Steinkamp, Karl-Heinz Erb. For details, please refer to the included readme file and to the publication (<a href="https://doi.org/10.1111/gcb.15932">https://doi.org/10.1111/gcb.15932</a>) In this new Version 1.01, we changed the file structure to make the data more accessible, we added data on means across modulations as used in the paper, and we include csv files with national totals for the different HANPP components.

openCC (other)Dec 2023View details →

Supervised land cover classification using Google Earth Engine in Córdoba, Argentina, 2018-2020

Land cover information is critical to scientific, economic, and public policy-making. There is a high demand for accurate and timely land cover information that affects the accuracy of all subsequent applications. The availability of Google Earth Engine (GEE), which derives temporal aggregation methods from time-series images (i.e., the use of metrics such as mean or median), has also enabled optimization of computation time, such as managing large amounts of data to obtain more accurate results. Our objective was to obtain a land cover map for the northwest of the province of Córdoba, Argentina. The study was carried out in rural communities that belong to the departments of Cruz del Eje and Ischilín, northwest of Córdoba, and have different degrees of intervention in the land cover. Sentinel 2 Level 2A images were acquired for the study area. Images available from January 1, 2018, to December 31, 2020, were sampled. To create a thematic map, the median value was calculated for the sample of images from the selected time interval. Finally, the Normalized Difference Vegetation Index (NDVI) was calculated and added to the total bands of the median image. Training polygons were placed there considering the visual features in the median image. The Random Forest algorithm was used as the classification method. To verify the quality of the classified map, a list of 97,753 verification pixels was obtained. In addition, a confusion matrix was created to collect the conflicts that arise between categories, and the precision and kappa coefficient was calculated to define the quality of the map obtained. Image acquisition, preprocessing, and analysis were performed on the Google Earth Engine platform. Thematic maps with eight classes were obtained, with a total area of 719880 ha. The confusion matrix showed an overall precision of 99.26% and a corrected kappa index of 0.99, the classes were correctly classified by the algorithm.

openCC (other)Oct 2025View details →

Land use and management effects on soil carbon in the Lake States, Northern United States - Nave, et al 2022

There is growing need to quantify and communicate how land use and management activities influence soil organic carbon (SOC) at scales relevant to, and in the tangible control of landowners and forest managers. The continued proliferation of publications and growth of datasets, data synthesis and meta-analysis approaches allows the application of powerful tools to such questions at ever finer scales. In this analysis, we combined a literature review and effect-size meta-analysis with two large, independent, observational databases to assess how land use and management impact SOC stocks, primarily with regards to forest land uses. We performed this work for the (Great Lakes) Lake States, which comprise 6% of the land area, but 7% of the forest and 9% of the forest SOC in the U.S., as the second in a series of ecoregional SOC assessments. Most importantly, our analysis indicates that natural factors, such as soil texture and parent material, exert more control over SOC stocks than land use or management. With that for context, our analysis also indicates which natural factors most influence management impacts on SOC storage. We report an overall trend of significantly diminished topsoil SOC stocks with harvesting, consistent across all three datasets, while also demonstrating how certain sites and soils diverge from this pattern, including some that show opposite trends. Impacts of fire grossly mirror those of harvesting, with declines near the top of the profile, but potential gains at depth and no net change when considering the whole profile. Land use changes showing significant SOC impacts are limited to reforestation on barren mining substrates (large and variable gains) and conversion of native forest to cultivation (losses). We describe patterns within the observational data that reveal the physical basis for preferential land use, e.g., cultivation of soils with the most favorable physical properties, and forest plantation establishment on the most marginal so

openCC (other)Apr 2022View details →

A Land-use/Land Cover Classification of Baltimore City in 1953

Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 113 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 700 km2 (270 mi2) including all of Baltimore City, and a portion of Baltimore County immediately surrounding the city. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures an ecologically and socially important moment in the post-war history of the city. It can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region.

openCC0Feb 2023View details →

Lots for greening: Identification of metropolitan vacant land and its potential use for cooling and agriculture in Phoenix, Arizona, USA

This project provides the first systematic assessment of non-governmental vacant parcels for potential greening (VPPG) the Phoenix metropolitan area—land parcels that are or can be privately owned but which contain no buildings, are unpaved, have no apparent use, and are potential candidates for urban greening. To achieve the data, a new method for the identification of vacant lands was employed that combines remote sensing techniques and cadastral data and trains the computer to distinguish different forms of vacant land. The classification result proved to be an effective approach for open land identification and identified approximately 19500 ha of open land in the metro area. The model achieved an average accuracy of 90.67%. This dataset only includes VPPG and does not include other vacant land determined to be inappropriate for potential greening (developed/abandoned or impervious surface). (Overall accuracy for all classes was 87.20%).

openCC0May 2023View details →

AGW02 Measurement of groundwater physical and chemical properties from wells in contrasting land uses near Kings Creek, Konza Prairie

Wells were drilled in two sites on Konza Prairie Biological Station in April, 1993 approximately 100 m from Kings Creek. The two sites are located in a grassland and an agricultural area. The grassland site (K01A) is an old field that was planted with brome sometime prior to 1976. It has not been grazed for 25 years and is burned in spring every 1-2 years. The agricultural site is currently under a mix of cultivation and restoration plots. Historically, it was cultivated from sometime between 1939 and 1950 to the present. It is approximately 1 km downstream in an area geologically similar to K01A. The soil at both sites is mapped as Reading silt loam (fine, mixed, mesic Typic Arguidolls). Samples are taken monthly by PVC bailer following removal of 2 times the well volume. Samples are analyzed with same methods as used for LTER stream water chemistry.

openCC (other)Jul 2021View details →

PIE LTER nutrient samples collected by Sigma Autosampler between 2001 and 2017 in three headwater sites of contrasting land use, and at the Parker and Ipswich River Dams as they enter into the Plum Island Sound estuary, Massachusetts.

Total organic nitrogen, total organic phosphorus, and nitrate concentrations collected frequently by Sigma autosampler (or volunteers in winter) from 5 sites. Sites include three headwater sites of contrasting land use (CC= Forest, SB = suburban, CS = wetland) and at the mouth of the Ipswich and Parker Rivers where they flow into the estuary.

openCustomMay 2024View details →

Marsh migration land use inferred from historical T-Sheets of the Chesapeake Bay

Detailed methods are listed in the associated publication (Schieder et al., 2018 https://doi.org/10.1007/s12237-017-0336-9). Briefly, we compared the spatial distribution of marshes in nineteenth-century maps to modern aerial photographs for the areas included in 40 NOS topographic sheets ("T-sheets") that included information on simple land types (e.g., marsh, farmland, forests) from the tidal portions of the Chesapeake Bay. Tidal marsh extent was digitized by hand by tracing the boundary between marsh and open water and the boundary between marsh and upland. The marsh-forest boundary was identified as the line between the dense tree canopy and marsh, the marsh-agriculture boundary was identified as the line between agriculture and marsh, and the marsh-water boundary was identified as the line between open water and adjacent land excluding beaches. The areas of agricultural land converted to marsh, forestland converted to marsh, and total upland conversion to marsh were summarized for each T-Sheet.

opencc-by-4.0Mar 2020View details →

Capturing land cover and land use with street level imagery

This dataset, collected in September 2018, contains street-level photographs captured by three cameras fixed on the roof of car. A field survey was focused in the Vojvodina, Serbia to more closely examine land cover/land use within croplands monitored by LandSense citizen scientists (March-September 2018). The dataset has the following characteristics: <ul> <li>Time period of data collection: Sep 2018</li> <li>Total number of photographs: 26759</li> <li>Region of interest: Vojvodina - Ruma municipality (Serbia)</li> </ul> Associated files: Serbia Streetlevelimagery2018 – Attributes.txt, Serbia Streetlevelimagery2018.csv, Serbia Streetlevelimagery2018.zip This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://ec.europa.eu/jrc/en">Joint Research Centre</a> and <a href="https://inosens.rs/">InoSens</a>. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.

opencc-by-4.0Aug 2020View details →

GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery

A  novel global 30-m land-cover product with a fine classification system for the year 2015 (GLC_FCS30-2015). The product was produced by combining time-series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the MCD43A4 NBAR and CCI_LC land-cover products. Secondly, a local adaptive random forest model was built for each 5°×5° geographical tile by using the multi-temporal Landsat spectral and textures features of the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile.

opencc-by-4.0Dec 2020View details →

Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model

Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.

opencc-by-4.0Oct 2015View details →

1988-2009 time-series of land-use/land-cover maps for the Mar Menor / Campo de Cartagena watershed by means of supervised classification of Landsat images.

Serie de mapas de usos y coberturas de la cuenca del Mar Menor (SE España): 2009, 2000, 1997 y 1998. Así como el documento completo de tesis en las que se generaron y analizaron. Time-series of land-use / land-cover maps of Mar Menor watershed (SE Spain): 2009, 2000, 1997 y 1998. As well as the complete thesis document in which they were generated and analyzed.

opencc-by-4.0May 2017View details →

Social sensing of urban land use based on analysis of Twitter users' mobility patterns

A companion dataset for the paper "Social sensing of urban land use based on analysis of Twitter users' mobility patterns". This dataset contains five files and one dictionary depicting the preferential return of Twitter users to their key locations and the urban land use types at these locations. More details can be found in the README file.