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North Temperate Lakes LTER Yahara Lakes District Land Use - Land Cover 1990s
Land use/land cover was interpreted from historical aerial photographs for selected watersheds in Dane County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.
Land cover for the barrier islands of the Delmarva Peninsula in Virginia, 1984-2016
We quantified state change from sand, grassland, and woody vegetation in seven undeveloped barrier islands over three time intervals because the ecological and economic value of upland barrier systems is significant, yet often overlooked. We focused on seven islands with woody land cover: Cedar, Parramore, Hog, Cobb, Wreck, Smith, and Fishermans. Landsat TM5 satellite images were obtained from the USGS Global Visualization Viewer for the following dates: September 21, 1984, September 12, 1998, August 15, 2011 and September 12, 2016. Images were chosen from available dates within the growing season and were cloud-free in order to minimize uncertainties due to heterogeneous atmospheric conditions. Barrier islands provide the first line of defense against storms for millions of people living in coastal areas. Upland vegetation (that is, grassland, shrubland, and maritime forest) has received little attention, even though this land surface is most strongly affected by development pressures. We use remote sensing analysis to assess state change on seven undeveloped Virginia barrier islands over 32 years (1984-2016) that are free from direct human influence. Our analysis highlights the spatial-temporally dynamic nature of barrier island upland land area and vegetation, with rapidly changing ecosystem states. Between 1984 and 2011, upland vegetation was dramatically reduced by 29% whereas woody vegetation cover increased 40% across all islands. Although conversions between sand, grassland, and woody vegetation were variable within each island, three major patterns of vegetative land cover change were apparent: overall loss of vegetative cover,frequent transitions between grass and woody cover, and gain in woody cover. These patterns are valuable for understanding natural evolution of barrier islands in response to sea-level rise. Evaluation of temporal dynamics in barrier upland is needed to characterize underlying processes including island resilience or chronic stress, a
GlobalHighPM₂.₅: Global Daily Seamless 1 km Ground-Level PM₂.₅ Dataset over Land (2017–Present)
<p>GlobalHighPM<sub>2.5</sub> is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>This dataset contains input data, analysis codes, and generated dataset used for the following article. If you use the GlobalHighPM<sub>2.5</sub> dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. <a href="https://weijing-rs.github.io/publications/Wei_et_al-NC-2023.pdf" target="_blank" rel="noopener">First close insight into global daily gapless 1 km PM<sub>2.5</sub> pollution, variability, and health impact</a>. <em>Nature Communications</em>, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3</p> </li> </ul> <p><strong>Input Data</strong></p> <p>Relevant raw data for each figure (compiled into a single sheet within an Excel document) in the manuscript.</p> <p><strong>Code</strong></p> <p>Relevant Python scripts for replicating and ploting the analysis results in the manuscript, as well as codes for converting data formats.</p> <p><strong>Generated Dataset</strong></p> <p>Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM<sub>2.5</sub> dataset over land from 2017 to the present. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R<sup>2</sup>) of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 µg m<sup>-3</sup> on the daily, monthly, and annual bases, respectively.</p> <p><strong>Due to data volume limitations, </strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2022 </strong>is accessible at: <strong><a href="../records/10795661">GlobalHighPM2.5 (2022)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2021 </strong>is accessible at: <strong><a href="../records/10398385">GlobalHighPM2.5 (2021)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2020 </strong>is accessible at: <strong><a href="../records/10402639">GlobalHighPM2.5 (2020)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2019 </strong>is accessible at: <strong><a href="../records/10402723">GlobalHighPM2.5 (2019)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2018 </strong>is accessible at: <strong><a href="../records/10402824">GlobalHighPM2.5 (2018)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2017 </strong>is accessible at: <strong><a href="../records/10403497">GlobalHighPM2.5 (2017)</a></strong></p> <p> continuously updated...</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
Land cover in the Purapel fluvial catchment
<p>The dataset contains 6 Land Cover maps at a 30m/pixel spatial resolution for the Purapel river catchment located in South-Central Chile. They were generated for the summer periods of 1986, 2000, 2005, 2010, 2015 and 2017.</p> <p>Maps of 1986-2015 were generated using atmospherically corrected Landsat CDR Scenes (<em>images courtesy of the U.S. Geological Survey</em>) including VNIR and SWIR bands from the TM5, ETM+ and OLI sensors and vegetation indices as auxiliary bands to highlight phenological differences among covers. Specifically the Normalized Difference Vegetation Index (NDVI) (Rouse et al,. 1974), the Green NDVI (Gitelson et al., 1996) and NDVI winter-summer Difference Index (ΔNDVI).</p> <p>Training and validation points were defined from field trips to the area in 2014-2015, various mid resolution satellite imagery sources and high-resolution Google Earth imagery (Map data ©2015 Google) when available. A topographic correction was applied using the C-Correction method (Teillet et al 1982), as proposed by Hantson and Chuvieco (2011), and the SRTM v3 DEM to account for the effect of local relief in the scene’s lighting.</p> <p>Accuracy assessment resulted in Overall Accuracy (OA), ranging from 82% to 92% (table 1).</p> <p>Table 1. Overall Accuracies for Land Cover maps from 1986 to 2017</p> <table> <tbody> <tr> <td> <p>Year</p> </td> <td> <p>OA</p> </td> </tr> <tr> <td> <p>1986</p> </td> <td> <p>89.7</p> </td> </tr> <tr> <td> <p>2000</p> </td> <td> <p>92.2</p> </td> </tr> <tr> <td> <p>2005</p> </td> <td> <p>91.5</p> </td> </tr> <tr> <td> <p>2010</p> </td> <td> <p>89.8</p> </td> </tr> <tr> <td> <p>2015</p> </td> <td> <p>82.7</p> </td> </tr> <tr> <td> <p>2017</p> </td> <td> <p>0.98</p> </td> </tr> </tbody> </table> <p> </p> <p>The 2017 map was generated using Random Forest classifier using several SI from Sentinel 2, Sentinel 1 C-band radar data (imagery from European Space Agency courtesy of the U.S. Geological Survey) and hydro-geomorphic indices obtained from 2009 LiDAR DTM data (Tolorza et al., 2022). Ninety polygons were used for training and thirty polygons and the classification of Zhao et al. (2016) were used for validation, obtaining an overall accuracy 0.98 (table 1).</p> <p> </p> <p>The 7 land cover classes defined following these codes and land use / covers:</p> <ul> <li>0 = Unclassified</li> <li>1 = Others (mainly crops and natural prairies in riverbeds)</li> <li>2 = Native Forest (mainly secondary-growth deciduous Nothofagus sp. Stands)</li> <li>3 = Shrubland (highly degraded formation of xerophytic and sclerophyllous shrubs such as <em>Acacia</em> <em>caven</em>, <em>Quillaja</em> <em>saponaria</em> and <em>Lithraea</em> <em>caustica</em>, among others).</li> <li>4 =Tree Plantations (industrial monocultures of <em>Pinus</em> <em>radiata</em> and <em>Eucalyptus</em> spp. of various age and development)</li> <li>5 = Seasonal grassland (annual pastures which wither in summer and urban areas)</li> <li>6 = Clear cuts (bare lands within industrial forestry surface)</li> </ul> <p>Codes 7 to 9 are specific to 2015 y 2017 because of the occurrence of two large (>5,000 hectares) fire events, and represent different Fire Severity levels based on the dNBR index (López and Caselles, 1991) according to Key and Benson (2006). They represent the following cases:</p> <ul> <li>7= Low Severity fire</li> <li>8 = Moderate severity fire</li> <li>9 = High severity fire</li> </ul> <p> </p> <p>Sources:</p> <p>Hantson, S. Chuvieco, E. 2011. Evaluation of different topographic correction methods for Landsat imagery. International Journal of Applied Earth Observation and Geoinformation 13:691-700.</p> <p>Rouse, J., R. Haas, J. Schell, and D. Deering. 1974. Monitoring vegetation systems in the Great Plains with erts. Third Earth Resources Technology Satellite-1 Symposium Volume I: Technical Presentations. NASA SP-351, compiled and edited by S.C. Freden, E.P. Mercanti, and M.A. Becker. Washington, DC: National Aeronautics and Space Administration</p> <p>Gitelson, A., Y. Kaufman, and M. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58(3):289-298.</p> <p>Teillet, P., B. Guindon, and D. Goodenough. 1982. On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing 8:84–106.</p> <p>Key, C. Benson, N. 2006. Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio. FIREMON: Fire Effects Monitoring and Inventory System. Pp: 1-51.</p> <p>López, MJ. Caselles, V. 1991. Mapping burns and natural reforestation using Thematic Mapper data. Geocarto International (1) 1991: 31- 37.</p> <p>Tolorza, V. Poblete-Caballero, D. Banda, D. Little, C. Galleguillos, M. 2022. An operational method for mapping the composition of post-fire litter. Remote Sensing letters (13) 2022: 511-521. 10.1080/2150704X.2022.2040752</p> <p> Zhao, Y. D. Feng, L. Yu, X. Wang, Y. Chen, Y. Bai, H. Hernández, et al. 2016. Detailed Dynamic Land Cover Mapping of Chile: Accuracy Improvement by Integrating Multi-temporal Data. Remote Sensing of Environment 183: 170–185. 10.1016/j.rse.2016.05.016.</p>
Land transformation on multi-decadal timescales reveals expanding croplands and settlements at the expense of tree-covered areas and mangroves in Nigeria
<p>A comparative assessment of the change patterns was conducted for seven categories using multi-decadal timescales in seven agroecological zones during three time-intervals (i.e., 1986 – 2000, 2000 – 2013, and 2013 – 2022). These selected periods cover important epochs in Nigeria’s recent history. To examine how much humans have appropriated natural cover (HANLC) in Nigeria over the last four decades, we differentiated natural covers (e.g., tree-covered areas, grasslands, wetlands, and waterbodies) from human activity-related uses (e.g., cropland, artificial surfaces and otherland). To identify trajectories of changes signifying human appropriation of land cover, we evaluated the drivers and processes underlying these major transitions, 1) Natural regeneration and afforestation, 2) Cropland expansion, and 3) Settlement and infrastructure development. Cropland expansion is Nigeria’s most widespread change process with much loss of croplands related to natural regeneration and settlement expansion. The transition matrix is provided showing the extent of land-cover changes in Nigeria over almost four decades (1986 - 2022). Major land cover transitions in each agroecological zone is presented. Analysis of land cover change in each agroecological zone is over 100% when areas of persistence (i.e., areas of no change) are not considered in the analysis.</p>
Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany
<p>The dataset consists of particulate matter pollution concentration, measured in three localities - Hermsdorf, Charlottenburg and Adlershof, in Berlin, Germany.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer_rd_30s.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer_rd_30s.geojson</a> shows the observed PM2.5 concentration in a 30 second interval.</p> <p><a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> shows the concentrations shown is the local concentration (observed concentration - background concentration) in a 30 second interval. The background concentration is calculated as the lowest 5 percentile of the measured concentration for each measurement round. </p> <p><a href="../api/records/10076056/draft/files/PM2.5_lc_max.geojson/content" target="_blank" rel="noopener noreferrer">PM2.5_lc_max.geojson</a> contains the information from <a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> in a 25m resolution. Additionally, it contains the land use information for each coordinate.</p> <p>The original publication providing all necessary background information on study sites, methodology and data processing is the following: Venkatraman Jagatha, J., T. Sauter, C. Schneider (2024): Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany. MDPI Sensors, 24(13), 4193, DOI: 10.3390/s24134193. The paper is fully open access and can be downloaded at <a href="https://doi.org/10.3390/s24134193">https://doi.org/10.3390/s24134193</a>.</p> <p>Information on working with geojson file can be found under <a href="https://geojson.readthedocs.io/en/latest/">GeoJSON</a> .</p>
3-D velocity field of the Tibetan Plateau due to land water loading
<h3>Basic information:</h3> <p>This dataset includes a series of 3-D loading deformation velocity fields, which are supplements to the GRL paper entitled "Present-Day Three-Dimensional Crustal Deformation Velocity of the Tibetan Plateau Due to Multi-Component Land Water Loading" [<a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a>]. The deformation velocities are fitted using long time span data during 2000-2020, and the detailed description of the data processing and calculation methods can be found through the GRL paper. There are results of three different grid resolutions (0.5x0.5, 0.25x0.25, 0.1x0.1), and for distinction, different file naming suffixes are used. For example, '0point5grids' indicates the results are in 0.5-degree grid resolutions (0.5x0.5), and so forth.</p> <h3>Application scenario:</h3> <p>The velocity fields here can be directly used for the analysis of crustal deformation or used for the separation of land water-induced loading deformation within geodetic deformation velocity fields over Tibetan Plateau. There are results of all the six main land water components, including soil moisture (SM) [Table S1], snow water equivalent (SWE) [Table S2], glacier [Table S3], lake [Table S4], permafrost (PM) [Table S5] and groundwater storage (GWS) [Table S6], thus users can choose one or some they focus on, or directly choose the sum of all the six main components (i.e., GRACE-inferred total terrestrial water storage [Table S7]).</p> <h3>Citation: </h3> <p>When using this dataset, please cite the GRL paper: Jiao, J., Pan, Y., Ren, D., & Zhang, X. (2024). Present-day three-dimensional crustal deformation velocity of the Tibetan Plateau due to multi-component land water loading. <em>Geophysical Research Letters</em>, 51, e2024GL108684. <a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a></p> <h3>Contents:</h3> <p>Table S1. 3-D velocity field of the Tibetan Plateau due to the loading of soil moisture (SM).</p> <p>Table S2. 3-D velocity field of the Tibetan Plateau due to the loading of snow water equivalent (SWE).</p> <p>Table S3. 3-D velocity field of the Tibetan Plateau due to the loading of glacier.</p> <p>Table S4. 3-D velocity field of the Tibetan Plateau due to the loading of lake.</p> <p>Table S5. 3-D velocity field of the Tibetan Plateau due to the loading of permafrost (PM).</p> <p>Table S6. 3-D velocity field of the Tibetan Plateau due to the loading of groundwater storage (GWS).<br>Table S7. 3-D velocity field of the Tibetan Plateau due to the loading of GRACE-inferred total terrestrial water storage (TWS).</p>
Antarctic Ecosystem Inventory: Spatial data for Ice-free lands v1.0
<p>This is Antarctica’s first comprehensive ecosystem map of ice-free lands. The data comprise a spatially explicit 3-tiered hierarchical ecosystem classification with nine Major Environment Types (tier 1), 33 Habitat Complexes (tier 2) and 269 Bioregional Ecosystem Types (tier 3). These Bioregional Ecosystem Types are aligned with ‘level 4’ of the IUCN Global Ecosystem Typology (Keith et al. 2022). </p> <p><br>The spatial data are available in raster format (TIF) at 100 m resolution in the Polar Stereographic Projected Coordinate System (GCS_WGS_1984) for all known ice-free areas south from latitude -57.330551 decimal degrees South (pdf map shows extent of ice-free areas in relation to terrestrial ice and ice shelves). A value attribute table (VAT) provides text fields containing codes and full names for each unit in each level of the classification hierarchy and the spatial extent of tier 3 units in hectares.</p> <p><br>Methods of development, source data and uses of the inventory are detailed by Tóth et al. (2025a). Descriptive profiles for tier 1 and 2 units are available in Tóth et al. (2025b).</p> <p><br>References<br>Keith, D.A., Ferrer-Paris, J.R., Nicholson, E., Bishop, M.J., Polidoro, B.A., Ramirez-Llodra, E., Tozer, M.G., Nel, J.L., Nally, R. Mac, Gregr, E.J., Watermeyer, K.E., Essl, F., Faber-Langendoen, D., Franklin, J., Lehmann, C.E.R., Etter, A., Roux, D.J., Stark, J.S., Rowland, J.A., Brummitt, N.A., Fernandez-Arcaya, U.C., Suthers, I.M., Wiser, S.K., Donohue, I., Jackson, L.J., Pennington, R.T., Iliffe, T.M., Gerovasileiou, V., Giller, P., Robson, B.J., Pettorelli, N., Andrade, A., Lindgaard, A., Tahvanainen, T., Terauds, A., Chadwick, M.A., Murray, N.J., Moat, J., Pliscoff, P., Zager, I. & Kingsford, R.T. (2022) A function-based typology for Earth’s ecosystems. Nature 610, 513–518. [doi: 10.1038/s41586-022-05318-4].<br>Tóth, A.B., Terauds, A., Chown, S.L., Hughes, K.A., Convey, P., Hodgson, D.A., Cowan, D.A., Gibson, J., Leihy, R.I., Murray, N.J., Robinson, S.A., Shaw, J.D., Stark, J.S., Stevens, M.I., van den Hoff, J., Wasley, J. and Keith D.A. (2025a). A dataset of Antarctic ecosystems in ice-free lands: classification, descriptions, and maps. Scientific Data 12, 133. [https://doi.org/10.1038/s41597-025-04424-y] <br>Tóth, A.B., Terauds, A., Chown, S.L., Hughes, K.A., Convey, P., Hodgson, D.A., Cowan, D.A., Gibson, J., Leihy, R.I., Murray, N.J., Robinson, S.A., Shaw, J.D., Stark, J.S., Stevens, M.I., van den Hoff, J., Wasley, J. & Keith D.A. (2025b). Antarctic Ecosystem Inventory: Descriptive profiles for ice-free lands v1.0. DOI: 110.5281/zenodo.14625890. Australian Antarctic Data Centre.</p>
Bulk, biomarker and mineralogy data of grain size fractions along a land-sea transect offshore the Atchafalaya river, northern Gulf of Mexico
<p>This dataset comprises the bulk, biomarker and mineralogy data of partitioned surface sediments along a land-sea transect offshore the Atchafalaya River, northern Gulf of Mexico. It includes the total concentrations of the biomarkers and proxies as presented in the accompanied publication, as well as concentrations of single isomers. Supplement to: Yedema et al., (2024); Influence of Organo-mineral Associations on Terrestrial Particulate Organic Matter Dispersal in the northern Gulf of Mexico (doi.)</p> <p> </p> <p><strong>This research has been supported by the Netherlands Earth System Science Centre (grant no. 024.002.001)</strong></p> <p> </p>
Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023)
<h3>Background</h3> <p>Human-induced land use change (LUC), driven by activities such as forestry, logging, and the production of agricultural commodities (e.g. fruits, nuts, and meat) significantly impacts the Global Commons, encompassing the climate system, ice sheets, land biosphere, oceans, and the ozone layer. The convertion of natural forests into areas dedicated to these activities lead to disrupted ecosystems (Foley et al. 2005), severely degraded biodiversity (Newbold et al. 2015), and the release of substantial amounts of greenhouse gases (GHGs) into the atmosphere (Hong et al. 2021), further exacerbating climate change and ocean acidification (Doney et al. 2009). The expansion of the agricultural frontier is identified as the predominant direct cause of deforestation globally, with other industries like timber and mining also playing significant roles (Curtis et al. 2018). To achieve global climate targets, forestry, and other land use GHG emissions must decrease along a nonlinear trajectory and reach carbon neutrality by 2050 (Rockström et al. 2017). However, to successfully address this road map, improving our understanding of deforestation drivers is urgently needed.</p> <h3>Summary</h3> <p>This dataset is the result of data processing performed to estimate the extent to which commodities and other agricultural products have replaced forests, while mapping the CO2 emission impact making use of the best available spatially explicit data. Results are reported globally for 52 products at national level, as well as agroecological and thermal zones (FAO & IIASA) and a 50km cell vector grid.</p> <p>In order to detect spatially-explicit deforestation drivers, the current extent of commodities and agricultural products was overlapped with global annual tree cover loss in the 10-year period from 2014 to 2023. Carbon stocks in the deforested areas were then assumed to have been emmited into the atmosphere. Recent, detailed crop and pasture maps for relevant commodities were used whenever available, and coarser resolution datasets were used as supplements when needed. Operations were performed in Google Earth Engine.</p> <h3>Datasets used</h3> <p><em>Forest and biomass carbon distribution</em></p> <p>The <a href="https://earthenginepartners.appspot.com/science-2013-global-forest">Global Forest Change</a> dataset (Hansen et al., 2013) is used to estimate deforestation between 2014 and 2023. This tree cover loss dataset measures the first instance of complete removal of tree cover canopy at a 30-meter resolution for all woody vegetation over 5 meters in height.</p> <p>The <a href="https://data-gis.unep-wcmc.org/portal/home/item.html?id=374a99fc76574f72bb8c71af7b428d0a">WCMC Above and Below Ground Biomass Carbon Density </a>(Soto-Navarro et al., 2020), for reference year 2010 at 300m pixel, is overlapped with resulting deforested areas pixels to dermine the biomass carbon present in the areas before deforestation.</p> <p><em>Generalized deforestation drivers</em></p> <p><a href="https://data.globalforestwatch.org/documents/ff304784a9f04ac4a45a40f60bae5b26/about">Tree cover loss by dominant driver</a> (Curtis et al., 2022) in 2023 is used to determine wide categories of deforestation drivers (commodities, shifting agriculture, forestry, wildfire and urbanization). Pixels indicating deforestation in the Global Forest Change dataset (Hansen et al., 2013) that overlap the commodities and shifting agriculture pixels from this dataset (Curtis et al., 2022) have their drivers further detailed with the data sources listed in the below.</p> <p><a href="http://www.earthstat.org/">EarthStat</a> pasture areas layer (Ramankutty et al., 2008) is used to identify areas for which specific livestock categories are to be defined. The project provides pasture areas for reference year 2000 at ~10km resolution.</p> <p><em>Detailed deforestation drivers</em></p> <p>The <a href="https://earthobservations.org/geoglam.php">Group on Earth Observations Global Agricultural Monitoring</a> (GEOGLAM) commodity distibution layer (Becker-Reshef et al., 2023) is used to identify specific commodities (winter wheat, spring wheat, maize, rice and soybean) to deforestation pixels pertaining to the "commodities" class. The ressource provides commodity distribution mapping at 5km pixel resolution. Values are provided as percentage of pixel area occupied by given crop.</p> <p>The <a href="https://mapspam.info/">Spatial Production Allocation Model (SPAM)</a> physical area layer (You et al., 2014) for reference year 2020 is used to detail drivers pertaining to the "shifting agriculture" class. The dataset covers 46 crops and crop groups at ~9km pixel resolution. Values are provided as percentage of pixel area occupied by given crop or crop group.</p> <p>The <a href="https://www.fao.org/livestock-systems/global-distributions/en/">Gridded Livestock of the World (GLW3)</a> (Gilbert et al., 2022) is used to determine which species (cattle, goat, sheep or horse) of livestock is raised in areas identified as pasture in the EarthStat layer and pertaining to the "commodities" class. The project provides livestock distribution for reference year 2015 at ~9km resolution. Values are provided as number of individuals located within the pixel. Values were converted into percentage of pixel area covered by grazing field for given species based on species density thresholds.</p> <h3>Data processing</h3> <p>Most of data processing takes place in Google Earth Engine, with scripts redacted in javascript. In summary, two strategies were implemented:</p> <p><strong>Proportional driver distribution strategy</strong>: When deforestation pixels (Hansen et al., 2013) overlapped with pixels from at least one of the detailed deforestation drivers data sources, the driver describe in the latter were associated with that deforested area. Whenever more than one of these data sources had non-null pixels overlapping the area, a proportional distribution was assumed (i.e. if SPAM indicated 100% of the area to be covered by cowpea crops, GEOGLAM 100% by maize, and GLW3 100% by cattle grazing fields, the pixel is assumed to have 33.3% of its deforested area associated with each of these drivers).</p> <p><strong>Main driver strategy</strong>: When deforestation pixels did not overlap with any non-null pixels from any of the detailed drivers sources, the pixel is assumed to have the entirety of its deforested area associated with one single main driver resulting from a crop-livestock mosaic. The mosaic is created by taking the highest value from each of the crop or livestock distribution rasters, and then assigning the raster category to be the new pixel value, ultimately creating a category raster layer containing the main crop, crop group or livestock species occupying that pixel area. Null or zero values in this mosaic are filled-in by nearest neighbour analysis, to a limit of 20 pixels expansion. This was enough to ensure that all deforestation pixels had at least one detailed driver with which it could be associated. The logic behind this operation resides in the fact that the deforestation layer (Hansen et al., 2013) has a larger temporal coverage (with the more recent data point being the reference year 2023), while the detailed driver layers can be as old as reference year 2015. This means we're assuming the main deforestation drivers continued to expand their limits to neighbouring areas during the years for which no data is available.</p> <p>Resulting rasters from both strategies are put together and a zonal statistics operation is performed in order to populate the vector grid cells.</p> <h3><strong>Files</strong></h3> <p>This repository contains the following files:</p> <ul> <li><em>deforested_area_by_LUC_driver_2014_2023</em>.CSV contains the deforested area (hectares) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>carbon_emissions_by_LUC_driver_2014_2023</em>.CSV contains the carbon emitted (Mg CO2 eq.) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>spatial_grid</em>.gpkg contains the raw 50km cell grid, with identification of country (iso3 and name fields), region, and FAO agroecological zone (zone field) and thermal zone (thermal field), in Geopackage format. In order to visualize the data in a map, the user will need to join one of the csv files to this geopackage file by basing the join on the 'id' field.</li> <li><em>summary_showcase</em>.png is an image showcasing maps created using the database, as well as a diagram showing the datasets used to create the final dataset.</li> </ul> <h3><strong>How to cite</strong></h3> <p>Iablonovski, G.; Berthet, E. C.; Roberts, S. (2024). Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023) [Data set]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.13308514</p> <h3>Authors and contact</h3> <p>Authors: Guilherme Iablonovski*, Etienne Charles Berthet, Sophie Roberts</p> <p>*Corresponding author: Guilherme Iablonovski (guilherme.iablonovski@unsdsn.org)</p>
Methane concentrations and oxidation rates in land-terminating glacial runoff: measurements from three glacial rivers and a paraglacial lake in Iceland and a literature review
<div> <p>This dataset contains methane measurements from Icelandic lakes and rivers during the summer of 2018 and 2019. This includes data from net methane oxidation assays with sediment and overlying water from one paraglacial lake and one glacial river, and surface methane concentration data from grab samples in 3 glacial streams and 15 Icelandic lakes (1 of which is paraglacial). The dataset also contains methane concentration data from a synthesis of relevant aquatic ecosystems, used to compare against the original measurements collected. </p> </div> <div> <p>Data and Literature Review Synthesis is supplement to Strock et al. 2024 <em>Oxidation is a potentially significant methane sink in land-terminating glacial runoff</em> published in Nature Scientific Reports. </p> <div> <p>This study was funded by: National Geographic Society Changing Polar Systems grant (CP4-162R-18); In-kind support from the U.S. Geological Survey; Dickinson College Research and Development; Churchill Exploration Fund at Dickinson College </p> </div> </div>
MODIS MCD12Q1 Land Cover and Land Use Time Series Global Mosaics 2001-2022 (500 m)
<p><strong>General Description</strong></p> <p>The yearly land use and land cover dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD12Q1"><abbr title="MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m">MCD12Q1 v061</abbr></a>. This data provides an yearly mosaics of land use and land cover data from 2001 to 2022 in cloud optimized Geotiff (COG) format. This dataset includes layers of land cover type 1 (t1), 2 (t2), and 5 (t5), land cover property 1 (p1) and 2 (p2), land cover property assessment 1 (p1a) and 2 (p2a), and land cover quality control (qc). </p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> 2001–2022</li> <li><strong>Type of data:</strong> Land cover and land use</li> <li><strong>How the data was collected or derived:</strong> Derived from MCD12Q1 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>.</li> <li><strong>Statistical methods used:</strong> None</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.00081, 179.99994, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/120 d.d. = 0.008333333 (1km)</li> <li><strong>Image size:</strong> 86,400 x 35,849</li> <li><strong>File format:</strong> Cloud optimized Geotiff.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li> </ul> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: </p> <ol> <li>generic variable name: lc = Land cover</li> <li>variable procedure combination: mcd12q1v061.t1 = MCD12Q1 v061 LC Type1 band</li> <li>Position in the probability distribution / variable type: c = class | p = probability</li> <li>Spatial support: 500m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20010101 = 2001-01-01</li> <li>Time reference end time: 20011231 = 2001-12-31</li> <li>Bounding box: go = global (without Antarctica)</li> <li>EPSG code: epsg.4326 = EPSG:4326</li> <li>Version code: v20230818 = creation date</li> </ol>
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (No Long-term Weather Trend) Control Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations without a long term weather trend.
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (With a Long-term Trend) on Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations with a long-term weather trend.
Composited land surface temperature of the greater Phoenix, Arizona, USA metropolitan area and surrounding Sonoran desert derived from cloud-free, summer (June, July, and August) Landsat imagery: 1985-2020
This project calculates land surface temperature (LST) from remotely sensed imagery. The intent is to extend the previous version of the LST data for the CAP LTER study area in central Arizona, USA to include 2020 and update the products so that they are based on a composite of images from each year (all available cloud-free acquisitions from June, July, and August) in the analysis to reduce the potential for outlier images or pixels to impact analyses. The aim is to make updated LST data accessible to stakeholders and researchers studying the greater Phoenix, Arizona, USA metropolitan area. LST is calculated from cloud-free Landsat 5 and 8 imagery (30m resolution) from summer months (June, July, and August) in 1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. All images are cropped to the CAP LTER study area boundary.
Land-cover mapping of the central Arizona region based on 2015 National Agriculture Imagery Program (NAIP) imagery
Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science, especially for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) program. This project provides a 1-meter land-cover mapping of the CAP LTER study area (greater Phoenix metropolitan area and surrounding Sonoran desert). The mapping is generated primarily using 2015 National Agriculture Imagery Program (NAIP) four-band data, with auxiliary GIS data used to improve accuracy. Auxiliary data include the 2015 cadastral parcel data, the 2014 USGS LiDAR data (1-meter), the 2014 Microsoft/OpenStreetMap Building Footprint data, the 2015 Street TIGER/Line, and a previous (2010) NAIP-based land-cover map of the study area (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=623). Among auxiliary data, building footprints and LiDAR data significantly improved the boundary detection of above-ground objects. Post-classification, manual editing was applied to minimize classification errors. As a result, the land-cover map achieves an overall accuracy of 94 per cent. The map contains eight land cover classes, including: (1) building, (2) asphalt, (3) bare soil and concrete, (4) tree and shrub, (5) grass, (6) water, (7) active cropland, and (8) fallow. When compared to the aforementioned, previous (2010) NAIP-based land-cover map for the study area, buildings and tree canopies are classified more accurately in this 2015 land-cover map.
Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020
## overview The project extends the long-term, LULC datasets to facilitate environmental change monitoring and social-ecological studies regarding urban sprawl and dynamics, urban heat islands, and outdoor water consumption, among others. Six land-use/land-cover (LULC) maps at 30 m resolution were previously created from 1985 to 2010 at five-year intervals (Zhang and Li 2017). This project updates that suite with maps for 2015 and 2020. As with the prior set, systematic object-based classification was utilized to ensure map consistency and direct comparison capability over time. The maps comprise 11 land-use/land-cover classes with an overall accuracy of 89.1% for 2015 and 89.6% for 2020. ## literature cited - Zhang, Y. and X. Li. 2017. Land cover classification of the CAP LTER study area at five-year intervals from 1985 to 2010 using Landsat imagery ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/dab4db27974f6c8d5b91a91d30c7781d (Accessed 2022-07-13).
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
FCE Redlands 1994 Land Use, Miami-Dade County, South Florida
Urban growth models have increasingly been used by planners and policy makers to visualize, organize, understand, and predict urban growth. However, these models reveal a wide disparity in their attention to policy factors. Some urban growth models capture few if any specific policy effects (e.g.,as model variables), while others integrate certain policies but not others. Since zoning policies are the most widely used form of land use control in the United States, their conspicuous absence from so many urban growth models is surprising. This research investigated the impacts of zoning on urban growth by calibrating and simulating a cellular automaton urban growth model, SLEUTH, under two conditions in a South Florida location. The first condition integrated restrictive agricultural zoning into SLEUTH, while the other ignored zoning data. Goodness of fit metrics indicate that including the agricultural zoning data improved model performance. The results further suggest that agricultural zoning has been somewhat successful in retarding urban growth in South Florida. Ignoring zoning information is detrimental to SLEUTH performance in particular, and urban growth modeling in general.
FCE Redlands 1998 Land Use, Miami-Dade County, South Florida
Urban growth models have increasingly been used by planners and policy makers to visualize, organize, understand, and predict urban growth. However, these models reveal a wide disparity in their attention to policy factors. Some urban growth models capture few if any specific policy effects (e.g.,as model variables), while others integrate certain policies but not others. Since zoning policies are the most widely used form of land use control in the United States, their conspicuous absence from so many urban growth models is surprising. This research investigated the impacts of zoning on urban growth by calibrating and simulating a cellular automaton urban growth model, SLEUTH, under two conditions in a South Florida location. The first condition integrated restrictive agricultural zoning into SLEUTH, while the other ignored zoning data. Goodness of fit metrics indicate that including the agricultural zoning data improved model performance. The results further suggest that agricultural zoning has been somewhat successful in retarding urban growth in South Florida. Ignoring zoning information is detrimental to SLEUTH performance in particular, and urban growth modeling in general.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.