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120 results for “land cover data”
Data from: Cover crops in arable lands increase functional complementarity and redundancy of bacterial communities
1. Reducing the deleterious effects of intensive tillage and fertilisation on ecosystem integrity and human health is challenging for sustainable agriculture. The use of cover crops has been advocated as a suitable technique for this purpose, but scientific evidence to support this has been scarce. 2. After four years and a complete rotation; including wheat, maize and green pea as main crops in a ploughing system, we investigated the respective and combined effects of cover crops and nitrogen fertilisation on soil chemical and biological properties using a controlled experiment combining soil chemical analyses, high-throughput sequencing and community level physiological profiles. 3. Cover crops impeded the soil carbon and nitrogen depletion induced by intensive tillage, not only in the topsoil but also within deeper soil horizons, where more specialized bacterial communities established. 4. Cover crops induced a significant shift in soil bacterial community diversity and composition, which was associated with changes in soil chemical features and bacterial metabolic activities along the entire soil profile. 5. Cover crops enhanced soil resilience to nitrogen fertilisation by increasing functional redundancy and complementarity within soil bacterial communities and across soil horizons. 6. Synthesis and applications. In the ploughing systems commonly used for intensive agriculture in Western Europe, the use of cover crops fosters a high functional diversity among soil bacteria and thus can help to achieve a more sustainable agriculture by reducing nitrogen fertilization while maintaining yields.
Data from: Quantifying and modelling decay in forecast proficiency indicates the limits of transferability in land-cover classification
1. The ability to provide reliable projections for the current and future distribution patterns of land-covers is fundamental if we wish to protect and manage our diminishing natural resources. Two inter-related revolutions made map productions feasible at unprecedented resolutions- the availability of high-resolution remotely-sensed data and the development of machine-learning algorithms. However, the ground-truth data needed for training models is in most cases spatially and temporally clustered. Therefore, map production requires extrapolation of models from one place to another and the uncertainty cost of such extrapolation is rarely explored. In other words, we focus mainly on projections, and less on quantifying how reliable they are. 2. Following the concept of 'forecast horizon', we suggest that the predictability of land-cover classification models should be methodologically explored with quantitative tools as a continuum against distances measured along multiple dimensions. Focusing on ten agricultural sites from England and using models specifically designed to predict multivariate decay-curves we ask: how does a model's predictive performance decay with distance? More specifically, we explored if we could predict the proficiency (kappa statistics) of a model trained in one site when making predictions in another site based on the spatial, temporal, spectral and environmental distances between sites. 3. We found that model proficiency decays with spatial, temporal, spectral and environmental distance between sites. More importantly, we found for the first time that it is possible to predict the performance a model transferred to or from a novel site will have, based on its distances from known sites. The spatial distance variables where the most important when predicting model transferability. 4. Exploring model transferability as a continuum may have multiple usages including predicting uncertainty values in space and time, prioritization of strategies for ground-truth data collection, and optimizing model characteristics for defined tasks.
Data and calculations associated with "Tracking cropland transitions: a comparative analysis of U.S. land cover change data"
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Data from: Landscape-specific thresholds in the relationship between species richness and natural land cover
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Data from: Cover crops in arable lands increase functional complementarity and redundancy of bacterial communities
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Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data
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Data from: Quantifying and modelling decay in forecast proficiency indicates the limits of transferability in land-cover classification
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Land cover classification of Central Arizona-Phoenix using Landsat Thematic Mapper (TM) data - year 1985
Land cover classification for the CAP LTER study region using Landsat Thematic Mapper (TM) data - year 1985
SAFARI 2000 MODIS L3 Albedo and Land Cover Data, Southern Africa, Dry Season 2000
The Filled Land Surface Albedo Product for Southern Africa, which is generated from MOD43B3 Product (the official Terra/MODIS-derived Land Surface Albedo - http://geography.bu.edu/brdf/userguide/albedo.html ), is a subset of the global data set of spatially complete albedo maps computed for both white-sky and black-sky at 10 wavelengths. The data spatial extent is from approximately 5 degrees N to -30 degrees S latitude and 5 minutes E to 60 degrees E longitude and covers 7 sixteen day periods starting on July 11 through October 15, 2000.Map Products, containing spatially complete land surface albedo data, are generated at 1-minute resolution on an equal-angle grid. The maps are stored in separate HDF files for each wavelength, each 16-day period and each albedo type (white- and black-sky). Data belonging to black sky and white sky albedo have been zipped separately. This format allows the user to have flexibility to download and store only the data absolutely needed.The One-Minute Land Ecosystem Classification Product is a global (static map) data set of the International Geosphere-Biosphere Programme (IGBP) classification scheme stored on an equal-angle rectangular grid at 1-minute resolution. The dataset is generated from the official MODIS land ecosystem classification dataset, MOD12Q1 for year 2000, day 289 data (October 15, 2000). This dataset is used in generating the spatially complete albedo maps, but is also a stand-alone product designed for use by the user community. The Land Ecosystem Classification Map File product file is stored in Hierarchical Data Format (HDF).
NASA Web-Enabled Landsat Data 5 year Land Cover Land Use Change Product V001
WELDLCLUC.015 was decommissioned on December 2, 2019. The Web-Enabled Landsat Data (WELD) 5-year Land Cover Land Use Change (LCLUC) is a composite of 30 meter (m) land use land change product for the contiguous United States (CONUS). The data were generated from five years of consecutive growing season WELD weekly composite inputs from April 15, 2006, to November 17, 2010. WELD data are created using Landsat Thematic Mapper Plus (ETM+) Terrain Corrected data. This product includes data about tree cover loss and bare ground gain, which are composited over the five year period. WELD LCLUC is distributed in Hierarchical Data Format 4 (HDF4).The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues* WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.Improvements/Changes from Previous Version* Version 1.5 is the original version.
EASE-Grid Land-Ocean-Coastline-Ice Masks Derived from Boston University MODIS/Terra Land Cover Data, Version 1
These Land-Ocean-Coastline-Ice (LOCI) files provide land classification masks derived from the Boston University MOD12Q1 V004 MODIS/Terra 1 km Land Cover Product (Friedl et al. 2002). The masks are available in various EASE-Grid azimuthal and global projections, at 12.5 km and 25 km spatial resolutions. The masks are in flat binary, 1 byte files stored by row. Quick-look browse images of the masks are also available in PNG (.png) format.
EASE-Grid 2.0 Land Cover Classifications Derived from Boston University MODIS/Terra Land Cover Data, Version 1
These data provide land cover classifications derived from the Boston University MOD12Q1 V004 MODIS/Terra 1 km Land Cover Product (Friedl et al. 2002). The data are available in various EASE-Grid 2.0 azimuthal and global projections, in multiple spatial resolutions ranging from 3 km to 100 km. The data are in flat binary, 1 byte files that are stored by row.
EASE-Grid Land Cover Classifications Derived from Boston University MODIS/Terra Land Cover Data, Version 1
These data provide land cover classifications derived from the Boston University MOD12Q1 V004 MODIS/Terra 1 km Land Cover Product (Friedl et al. 2002). The data are available in various EASE-Grid azimuthal and global projections, in 12.5 km and 25 km spatial resolutions. The data are in flat binary, 1 byte files that are stored by row.
EASE-Grid 2.0 Land-Ocean-Coastline-Ice Masks Derived from Boston University MODIS/Terra Land Cover Data, Version 1
These Land-Ocean-Coastline-Ice (LOCI) files provide land classification masks derived from the Boston University MOD12Q1 V004 MODIS/Terra 1 km Land Cover Product (Friedl et al. 2002). The masks are available in various EASE-Grid 2.0 azimuthal and global projections, at various spatial resolutions ranging from 3 km to 100 km. The masks are in flat binary, 1 byte files stored by row. Quick-look browse images of the masks are also available in PNG (.png) format.
Data and script:Interactive persistent effects of past land-cover and its trajectory on tropical freshwater biodiversity
<p>This is the unique dataset used to run the analysis of the manuscript: "Interactive persistent effects of past land-cover and its trajectory on tropical freshwater biodiversity". You'll find in the "corumbatai_rb.txt" the catchment's and stream's ID, forest cover (%) within reach contribution areas in 1962, 1972, 1978, 2003 and 2011. The document "aquatic_insects.txt" contains the community data (columns= taxa, rows=sites). We also provide and R code used to analyze the relationship between biodiversity and change in forest cover that occurred across five decades, including landscape trajectories of forest gain and loss.<br> <br> </p>
MONET cost, land cover and CO2 storage capacity data.
<p>This dataset contains cost, land cover and CO<sub>2 </sub>storage capacity data used in the MONET (Modelling and Optimisation of Negative Emissions Technologies) framework. For full MONET model description and dataset see Fajardy (2019) available online at <a href="https://doi.org/10.25560/80691">https://doi.org/10.25560/80691</a>. </p>
Code and data used for findings and figures in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region"
<p><span>This is the code and data used in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region" to generate all findings and figures.</span></p>
Data for "LAND COVER CLASSIFICATION FROM A MAPPING PERSPECTIVE: PIXELWISE SUPERVISION IN THE DEEP LEARNING ERA"
<p><strong>Contents</strong></p> <ul> <li>clc_maps.zip contains the dataset.</li> <li>LICENSE.txt describes the usage terms of the maps.</li> </ul> <p>The maps contained in clc_maps.zip follow the naming convention of BigEarthNet [1], i.e. each sample of BigEarthNet has a corresponding pixel-level label map in the dataset.</p> <p>[1] G. Sumbul, M. Charfuelan, B. Demir, and V. Markl, “Bigearthnet: A large-scale benchmark archive for remote sensing image understanding,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019, pp. 5901–5904.</p> <p><strong>Description</strong></p> <p>The original shape file (<a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download">link</a>) was altered by reprojecting the shape file onto the coordinate reference system (CRS) of the respective BigEarthNet sample images to ensure pixel synchronicity. Afterwards, the shapes present in the sample CRS are rasterized by burning a linearly increasing class index which replaces the textual CLC nomenclature. The class IDs and their corresponding class names are presented in the following section. </p> <p><strong>Classes</strong></p> <p>Class ID - Corine Land Cover 2018 class name<br> 1 - Continuous urban fabric<br> 2 - Discontinuous urban fabric<br> 3 - Industrial or commercial units<br> 4 - Road and rail networks and associated land<br> 5 - Port areas<br> 6 - Airports<br> 7 - Mineral extraction sites<br> 8 - Dump sites<br> 9 - Construction sites<br> 10 - Green urban areas<br> 11 - Sport and leisure facilities<br> 12 - Non-irrigated arable land<br> 13 - Permanently irrigated land<br> 14 - Rice fields<br> 15 - Vineyards<br> 16 - Fruit trees and berry plantations<br> 17 - Olive groves<br> 18 - Pastures<br> 19 - Annual crops associated with permanent crops<br> 20 - Complex cultivation patterns<br> 21 - Land principally occupied by agriculture, with significant areas of natural vegetation<br> 22 - Agro-forestry areas<br> 23 - Broad-leaved forest<br> 24 - Coniferous forest<br> 25 - Mixed forest<br> 26 - Natural grasslands<br> 27 - Moors and heathland<br> 28 - Sclerophyllous vegetation<br> 29 - Transitional woodland-shrub<br> 30 - Beaches, dunes, sands<br> 31 - Bare rocks<br> 32 - Sparsely vegetated areas<br> 33 - Burnt areas<br> 34 - Glaciers and perpetual snow<br> 35 - Inland marshes<br> 36 - Peat bogs<br> 37 - Salt marshes<br> 38 - Salines<br> 39 - Intertidal flats<br> 40 - Water courses<br> 41- Water bodies<br> 42 - Coastal lagoons<br> 43 - Estuaries<br> 44 - Sea and ocean<br> 48 - NODATA<br> 49 - UNCLASSIFIED LAND SURFACE<br> 50 - UNCLASSIFIED WATER BODIES </p> <p>More details about the CLC classes and conventions can be found in the CLC nomenclature guide (<a href="https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html">Link</a>).</p> <p><strong>Attribution</strong></p> <p>If you find this work useful please consider citing:</p> <p>Wilhelm, T.; Koßmann, D. LAND COVER CLASSIFICATION FROM A MAPPING PERSPECTIVE: PIXELWISE SUPERVISION IN THE DEEP LEARNING ERA. In Proceedings of the IGARSS 2021—2021 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 12 – 16 July 2021; to appear.</p> <p><strong>License</strong></p> <p>The generated maps are based on data from the Copernicus program, which are subject to the terms described here:<br> <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=metadata">https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=metadata</a></p>
National Land Cover Data set 1992 (NLCD1992)
National Land Cover Dataset 1992 (NLCD1992) is a 21-class land cover classification scheme that has been applied consistently across the lower 48 United States at a spatial resolution of 30 meters. NLCD92 is based primarily on the unsupervised classification of Landsat Thematic Mapper (TM) circa 1990's satellite data. Other ancillary data sources used to generate these data included topography, census, and agricultural statistics, soil characteristics, and other types of land cover and wetland maps. NLCD1992 is the only NLCD dataset that can be downloaded by state and by user defined area from the MRLC Consortium Viewer.
Original data for resident and Delphi surveys and green land cover assessment
<p>This repository contains raw survey data, Delphi analysis responses, and green space quantification GIS data for the Friendly Area Neighborhood in Eugene, Oregon.</p>
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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.