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528 results for “Land cover”
Long-term composited and land cover-adjusted Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2020
This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Next, we corrected the underestimated ENDISI values of dark impervious surface cover and the overestimated ENDISI values of bright bare soils based on visible Landsat bands and 2020 land cover (Sabu et al. 2023). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). 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 the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 - Sabu, S., Frazier, A., & Rashid, B. (2023). Land use and land cover (LULC) classification of the CAP LTER study area (central Arizona, USA) using Landsat imagery: 2015 and 2020 [Dataset]. Environmental Data Initiative. https://doi.org/10.6073/PASTA/BF18E5856215BD2D4DAB3B024BA87A7E
Harmonized National Land Cover Dataset Values for HydroBASINS Basins
Water quality is largely reflective of processes occurring on the surrounding landscape. While national landcover data are widely available via remotely sensed products, they are usually not aggregated in a manner that is expeditiously merged with basin-level data. To facilitate national-scale analyses of basin-level landcover with co-located water quality data, we present aggregated land cover data for the Contiguous United States. Data are aggregated using the HydroBASINS basin shapefiles. HYBAS_ID is retained to enable merging with HydroBASINS parent datasets.
1830 Map of Land Cover and Cultural Features in Massachusetts
Background and Data Limitations The Massachusetts 1830 map series represents a unique data source that depicts land cover and cultural features during the historical period of widespread land clearing for agricultural. To our knowledge, Massachusetts is the only state in the US where detailed land cover information was comprehensively mapped at such an early date. As a result, these maps provide unusual insight into land cover and cultural patterns in 19th century New England. However, as with any historical data, the limitations and appropriate uses of these data must be recognized: (1) These maps were originally developed by many different surveyors across the state, with varying levels of effort and accuracy. (2) It is apparent that original mapping did not follow consistent surveying or drafting protocols; for instance, no consistent minimum mapping unit was identified or used by different surveyors; as a result, whereas some maps depict only large forest blocks, others also depict small wooded areas, suggesting that numerous smaller woodlands may have gone unmapped in many towns. Surveyors also were apparently not consistent in what they mapped as ‘woodlands’: comparison with independently collected tax valuation data from the same time period indicates substantial lack of consistency among towns in the relative amounts of ‘woodlands’, ‘unimproved’ lands, and ‘unimproveable’ lands that were mapped as ‘woodlands’ on the 1830 maps. In some instances, the lack of consistent mapping protocols resulted in substantially different patterns of forest cover being depicted on maps from adjoining towns that may in fact have had relatively similar forest patterns or in woodlands that ‘end’ at a town boundary. (3) The degree to which these maps represent approximations of ‘primary’ woodlands (i.e., areas that were never cleared for agriculture during the historical period, but were generally logged for wood products) varies considerably from town to town, depending on wheth
Land Cover on the Elizabeth Islands, Martha's Vineyard, and Nantucket 1938
The widespread influence of land use and natural disturbance on population, community, and landscape dynamics and the long-term legacy of disturbance on modern ecosystems requires that a historical, broad-scale perspective become an integral part of modern ecological studies and conservation assessment and planning. In previous studies, the Harvard Forest Long Term Ecological Research (LTER) program has developed an integrated approach of paleoecological and historical reconstruction, meteorological modeling, air photo interpretation, GIS analyses, and field studies of vegetation and soils, to address fundamental ecological questions concerning the rates, direction, and causes of vegetation change, to evaluate controls over modern species and community distributions and landscape patterns, and to provide critical background for conservation and restoration planning. In the current study, we extend this approach to investigate the link between landscape history and the abundance, distribution, and dynamics of species, communities and landscapes of the Cape Cod to Long Island coastal region, including the islands of Martha's Vineyard, Nantucket, and Block Island. The study region includes many areas of high conservation priority that are linked geographically, historically, and ecologically. This dataset includes a land cover GIS layer created from aerial photographs from 1938. Janice Stone interpreted the photos onto acetates which were then redrawn onto USGS topographic maps using a zoom transfer scope to reduce edge distortion from the photographs. The landcover polygons were then digitized into a GIS. As 1938 is near the midpoint between the peak of 19th century agricultural land clearance and the modern plant communities of the region, this data provides valuable information on changing landscape characteristics and vegetation successional patterns which shape the modern landscape.
Historical and future land use and land cover data for the STARS4Water river basins
<p>Dataset contains data on historical and future land use and land cover for seven European river basins (Danube, Drammen, Duero, East Anglia, Messara, Rhine and Seine) being case study basin in the STARS4Water, and a shapefile with river basin boundaries. The average area fraction of five general land use classes (crop, forest, grass, urban and other) within the project river basins was calculated at five-year intervals starting in 2016 and ending in 2051. This dataset was prepared based on the data available in the "LUCAS LUC future land use and land cover change dataset for Europe (Version 1.1)" repository (Hoffmann et al., 2022, DOI: 10.26050/WDCC/LUC_future_EU_v1.1).</p>
Land-Cover Change Scenarios for Massachusetts 2010-2060
Working with a panel of practitioners and regional experts, we developed and analyzed four plausible but divergent land-use scenarios that depict the future of Massachusetts from 2010 to 2060. We simulated the land-use scenarios and their interactions with anticipated climate change by coupling statistical models of land use to the LANDIS-II landscape model and then evaluated the outcomes in terms of the magnitude and spatial distribution of (1) direct human uses of the landscape (residential and commercial development, agricultural, timber harvest), (2) ecosystem services (carbon storage, flood regulation, nutrient retention), and (3) habitat quality (forest tree species composition, interior forest habitat). Across all scenarios, conflicts occurred between dispersed residential development and the supply of ecosystem services and habitat quality. In all but the scenario that envisioned a significant agricultural expansion, forest growth resulted in net increases in aboveground carbon storage, despite the concomitant forest clearing and harvesting. One scenario, called Forests as Infrastructure, showed the potential for synergies between increased forest harvest volume through the sustainable practices that encouraged the maintenance of economically and ecologically important tree species, and carbon storage. This scenario also showed trade-offs between development density and water quantity and quality at the watershed scale. The process of integrated scenario analysis led to important insights for land managers and policymakers in a populated forested region where there are tensions among development, forest harvesting, and land conservation. More broadly, the results emphasize the need to consider the consequences of contrasting land-use regimes that result from the interactions between human decisions and spatially heterogeneous landscape dynamics.
North Temperate Lakes LTER Northern Highland Lake District Land Use - Land Cover 1930s
Land use/land cover was interpreted from historical aerial photographs for riparian buffer zones surrounding 50 lakes in Vilas County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.
North Temperate Lakes LTER Northern Highland Lake District Land Use - Land Cover 1960s
Land use/land cover was interpreted from historical aerial photographs for riparian buffer zones surrounding 50 lakes in Vilas County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.
North Temperate Lakes LTER Northern Highland Lake District Land Use - Land Cover 1990s
Land use/land cover was interpreted from historical aerial photographs for riparian buffer zones surrounding 50 lakes in Vilas County, Wisconsin. Photography from the 1930s, 1960s, and 1990s were interpreted, resulting in land use/land cover data for three time periods.
North Temperate Lakes LTER Yahara Lakes District Land Use - Land Cover 1930s
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.
North Temperate Lakes LTER Yahara Lakes District Land Use - Land Cover 1960s
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.
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
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>
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>
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).
Cover and frequency of biological soil crust community types, moss species, vascular plants, and abiotic land surface features, on gypsum & non-gypsum soils from the Chihuahuan and Mojave Deserts in 2023
This dataset contains raw and calculated percent cover and frequency data for biological soil crust (hereafter biocrust) functional groups, vascular plant functional groups, and abiotic land surface features on and off gypsum soils in the northern Chihuahuan and eastern Mojave Deserts. Abundance data were obtained from 20 study sites total, 10 located on soils derived from gypsum parent material and 10 located on soils derived from non-gypsum parent materials. Sites were grouped into 10 pairs, in which every gypsum site was partnered with a non-gypsum site located in the same region. Apart from soil type, partnered-site characteristics (topography, climate, elevation, slope, aspect, and presence of biocrusts) were held relatively constant. At each site, cover and frequency assessments were made using the line-point intercept method (LPI) and frequency quadrats (1.0 m^2), respectively. Biocrust functional groups included the following crusts: lichen, moss, incipient algal, light algal, dark algal, unknown photosynthetic crust, and vagrant cyanobacteria. Vascular plant categories included: perennial forbs, perennial graminoids, annual forbs, annual graminoids, subshrub, shrub, Yucca, and cacti. Abiotic land surface features included: woody litter, herbaceous litter, bare soil, rock, bedrock, and animal feces. Moss crusts identified within cover and frequency analyses were sampled, and classified to species level via microscopy. The resulting percent cover and frequency data was used to understand differences in biocrust and moss species abundance and diversity on and off gypsum soils; furthermore, how biocrust and moss species abundance was associated with the measured environmental variables. Soil physical and chemical data from this study can be accessed at knb-lter-jrn.210616002. This study and dataset are complete.
US Atlantic and Gulf Coast Annual Wetland Land Cover and Change Maps, 1985 to 2022
<h3>This dataset is associated with the following article published in Remote Sensing Applications: Society and Environment, which can be accessed here: https://doi.org/10.1016/j.rsase.2024.101392</h3> <p>Shortly after publishing version 2, errors in the map projections were identified and corrected. Please use version 3 instead of version 2.</p> <p>Updates to version 2 were as follows:</p> <ul> <li>Includes watersheds in Texas that were not included in Version 1.</li> <li>A color map (using ArcPro) was added for improved interpretation.</li> <li>A sub-pixel scale offset in the change type map, related to map projection errors, was corrected.</li> </ul> <h2><strong>Mapping Coastal Wetland Changes from 1985 to 2022 in the US Atlantic and Gulf Coasts using Landsat Time Series and National Wetland Inventories</strong></h2> <p>Courtney A. Di Vittorio<sup>1</sup>, Melita Wiles<sup>2</sup>, Yasin W. Rabby<sup>2</sup>, Saeed Movahedi<sup>2</sup>, Jacob Louie<sup>1</sup>, Lily Hezrony<sup>1</sup>, Esteban Coyoy Cifuentes<sup>1</sup>, Wes Hinchman<sup>1</sup>, Alex Schluter<sup>1</sup></p> <p><sup>1</sup>Department of Engineering, Wake Forest University, Winston-Salem, North Carolina, USA.</p> <p><sup>2</sup>Department of Statistics, Wake Forest University, Winston-Salem, North Carolina, USA.</p> <h3>Abstract</h3> <p>The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), the widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution, and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses Google Earth Engine and the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing imagery with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The average overall accuracy of the algorithm is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsat-derived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the magnitude of severe changes matches that of the existing inventory and the magnitude of the moderate changes matches that of the more comparable product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.</p>
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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.