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528 results for “Land cover”
Land Use and Land Cover Mapping of Katanino Forest Reserve, Zambia (2019–2023)
<h1><strong>Overview</strong></h1> <p>The land use and land cover maps encompass the Katanino Forest Reserve in the Copperbelt province, Zambia. These maps categorize the area into two classes: forest and non-forest. They were derived from NICFI, Sentinel-2, and Sentinel-1 mosaics, resulting in a spatial resolution of 4.77 meters, covering the period from 2019 to 2023. </p> <h1><strong>Maps Accuracy</strong></h1> <p>The overall accuracy of the final annual maps (2019–2023) ranged from 0.90 to 0.94. The user’s and producer’s accuracies are detailed in Table 1.</p> <p>Table 1: Land use and land cover maps validation, including overall, producer (PA) and user (UA) accuracies values for each class.</p> <table> <tbody> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2019</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2020</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2021</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2022</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2023</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> </tr> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> </tr> <tr> <td> <p><span>Forest</span></p> </td> <td> <p><span>0.87</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.83</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.91</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>1</span></p> </td> </tr> <tr> <td> <p><span>Non Forest</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.86</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.82</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.89</span></p> </td> <td> <p><span>1</span></p> </td> <td> <p><span>0.87</span></p> </td> </tr> <tr> <td> <p><strong><span>Overall Accuracy</span></strong></p> </td> <td> <p><strong><span>0.92</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.90</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.93</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.94</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.93</span></strong></p> </td> <td> <p><span> </span></p> </td> </tr> </tbody> </table> <h1><strong>Files descripion</strong></h1> <ul> <li>KAT_2019.tif: 2019 land use and land cover map</li> <li>KAT_2020.tif: 2020 land use and land cover map</li> <li>KAT_2021.tif: 2021 land use and land cover map</li> <li>KAT_2022.tif: 2022 land use and land cover map</li> <li>KAT_2023.tif: 2023 land use and land cover map</li> <li>qgis_style.qml: QGIS style file</li> <li>KAT_training_samples(.shp, .shx, .dbf, .prj): training samples with class labels</li> <li>KAT_validation_samples(.shp, .shx, .dbf, .prj): validation samples with class labels</li> </ul> <p> </p>
Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"
<h1><strong>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</strong></h1> <p>This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size. </p> <h2><strong>How to use this repository to reproduce results </strong></h2> <p>This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis.</p> <p><strong>What you will need: </strong><br> - Installed R and RStudio for purely spatial cluster and global model analyses<br> - Basic understanding of how to open R and run code </p> <p><strong> You do NOT need:</strong><br> - To download or install R packages on your own; that is taken care of within this environment<br> - To write any code <br> - To set up any working directories in R </p> <h3><strong>Important: Using `renv`</strong></h3> <p>Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages. </p> <p>The R package `renv` was used to create a <strong>project library</strong>, which contains all R packages that are used by the project. The packages in the project library are <strong>the versions used during the original analysis</strong>. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. <strong>You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment</strong>. </p> <h2><strong>## Basic step-by-step guide:</strong></h2> <p>- 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo<br>- 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted)<br>- 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file <br>- 4. Open any of the R scripts in the `analysis/` folder<br>- 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages <br> - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line</p> <h3><strong># `analysis/` Folder</strong></h3> <p>The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there. </p> <p><strong><em>## Files within the `analysis/` folder</em></strong></p> <p>The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run. </p> <p> - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values<br> - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript<br> - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis<br> - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species </p> <h3><strong># `data/` Folder</strong></h3> <p>This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file. </p> <p><strong><em>## Files within the `data/` folder</em></strong></p> <p> - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns. <br> - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'<br> - `sites22.csv` contains the names, site codes, and coordinates of the study sites<br> - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information <br> - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA<br> - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022<br> - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022<br> - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites <br> - `data dictionary.txt` file that defines column names and other data attributes for every dataset </p> <h3><strong># `renv/` Folder</strong></h3> <p>The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder. </p> <p> </p> <h2><strong>References for source data </strong></h2> <p> - Some of the data in this repository were originally obtained from open access sources. </p> <p> - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus</p> <p> - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate</p> <p> - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data</p> <p> - All mosquito collection data was collected directly by the manuscript authors</p>
Dataset from "Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes"
<p>These are the output land cover and land cover change raster maps from the paper, "<a href="https://doi.org/10.3390/rs10020306">Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes</a>," published in Remote Sensing journal.</p> <p>ABSTRACT. Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km<sup>2</sup>; 39% of total area), as well as total forest conversion into agricultural plantations (3,214 km<sup>2</sup>). Gross forest loss (5,133 km<sup>2</sup>) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5,471 km<sup>2</sup>) and rubber (4,025 km<sup>2</sup>) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.</p>
NLCD_INEGI: Harmonized US-Mexico Land Cover Change Dataset, 1992/2001/2011
<p>We have taken the Uso del Suelo y Vegetacion land cover classification products for Mexico (courtesy of Mexico's Instituto Nacional de Estadistica y Geografia, or INEGI) for years 1985, 1993, 2002, 2007, and 2011 (INEGI, 2015); harmonized their classes with the classes of the Multi-Resolution Land Characteristics Consortium (MRLC) National Land Cover Database (NLCD) (Homer et al., 2015), and merged the two datasets to form a single land cover product covering the continental US and Mexico, for years 1992/3, 2001/2, and 2011. Details of processing, along with the processing scripts, are archived in GitHub in the <a href="https://github.com/tbohn/NLCD_INEGI/tree/v1.5">NLCD_INEGI</a> project (Bohn, 2019).</p> <p>Output files are ESRI ascii-format raster files, geographic projection, 0.000350884 degree cellsize. Each file contains a 1x1 degree box or "tile", named nlcd_inegi.<em>lat0</em>_<em>lat1</em>n.<em>lon0</em>_<em>lon1</em>w.asc, where <em>lat0</em> and <em>lat1</em> are the south and north boundaries of the box and <em>lon0</em> and <em>lon1</em> are the west and east boundaries (west = positive).</p> <p>This project contains the following g-zipped tar files:</p> <ul> <li>1992.tgz - land cover from 1992 (NLCD) and 1993 (INEGI)</li> <li>2001.tgz - land cover from 2001 (NLCD) and 2002 (INEGI)</li> <li>2011.tgz - land cover from 2011 (both NLCD and INEGI)</li> <li>stable_2001-2011.tgz - land cover from those pixels that had the same class in 2001 and 2011 ("stable" pixels)</li> </ul> <p>On LINUX, the contents of these files can be extracted via "tar":</p> <p>tar -xvzf 1992.tgz >& log.tar.txt</p> <p>On Windows, applications such as "7-zip" can extract the contents.</p> <p>Each of these .tgz files contain a folder with the same name but without the ".tgz". Within each of these folders is a sub-folder called "asc.clip.1deg". This folder contains the 1x1 tiles.</p>
Murgia Alta: land cover map (2013)
<p>A land cover map in "Murgia Alta" PA, for 2013, obtained by considering a Landsat intra-annual time series of NDVI spectral index computed for 27 images. A Maximum Likelihood classifier was used for a 13 classes problem. The images were preprocessed, atmospherically corrected, masked for clouds and shadow clouds cover and interpolated for the gaps filling.</p> <p>The map was produced at 30 meters spatial resolution and projected in WGS84/UTM33N.</p> <p>The Overall Accuracy (OA) of the map was: OA=83.62%±0.90%.</p> <p>The map has 14 values related as follows in LCCS-FAO taxonomy:</p> <p>Value 0 = Unclassified</p> <p>Value 1 = A12/A1.D1.E2</p> <p>Value 2 = A12/A1.D1.E1</p> <p>Value 3 = A12/A1.D2.E1</p> <p>Value 4 = A12/A2.A6</p> <p>Value 5 = A11/(A1ORA2).A7.A9</p> <p>Value 6 = A11/A1.A7.A10</p> <p>Value 7 = A11/A2.A7.A10</p> <p>Value 8 = A11/A3.A4.S3</p> <p>Value 9 = A11/A3.A4.S7</p> <p>Value 10 = A11/A3.A4.S5(LEGUMES)</p> <p>Value 11 = A11/A3.A4.S5(VEGETABLES)</p> <p>Value 12 = B15/A2.A6</p> <p>Value 13 = BURNED AREAS</p>
Land use and land cover data for Northern Coast of São Paulo State (Brazil) from 1985 to 2015
<p>Authors: Ana Beatriz Pierri Daunt and Thiago Sanna Freire Silva</p> <p>Product: Land use and land cover maps for 1985, 1990, 1995, 2000, 2005, 2010, 2015 in raster format.</p> <p>Study area: Northern Coast of São Paulo State (Brazil)</p> <p>Mapping methods: Land use and land cover were mapped using Landsat images and geographic object-based image analysis (GEOBIA), based on the Random Forests supervised algorithm processing using the “RSGISlib” library, accessible through the Python language. More information at <a href="https://www.rsgislib.org">https://www.rsgislib.org</a>. The automated classification was followed by manual correction of the land cover maps at the 1:25.000 scale. See Metadata.docx for more details and land use/cover description,</p> <p>Financial informations: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, and by the National Council for Scientific and Technological Development (CNPq), fellowship #163870/2018-7, through the Geography Graduate Program, São Paulo State University. T.S.F. Silva acknowledges research productivity grant #310144/2015-9 from CNPq.</p> <p> </p>
Figure 2 in Spatial distribution and effects of land use and cover on cutaneous leishmaniasis vectors in the municipality of Paracambi, Rio de Janeiro, Brazil
Figure 2 Monthly mean relative abundance of medically relevant sand fly species. Please note the scale difference in the Y axis. Paracambi, RJ, Brazil, 1992-1994.
Figure 3 in Effect of land cover on biodiversity and composition of a soil macrofauna community in a reclaimed coastal area at Yancheng, China
Figure 3. The dendrogram of cluster analysis on soil macrofauna from different habitats with Bray–Curtis similarity by paired groups method (A: Uncultivated land; B: Bulrush land; C: Wheat farm; D: Poplar forest; E: Metasequoia forest).
Figure 2 in Effect of land cover on biodiversity and composition of a soil macrofauna community in a reclaimed coastal area at Yancheng, China
Figure 2. One-way ANOVA on taxonomic richness and abundance, Margalef 's richness index (R) and Shannon-Weaver diversity index (H') among different habitats (Mean ± SE). The means with different scripts are significantly different by SNK test, α = 0.05.
US Emission Facilities Land Cover Area Derived At Parcel Scale
<p>This dataset includes US industrial facilities from the Environmental Protection Agency's (EPA) 2017 National Emissions Inventory (NEI), combined with location data from the EPA's Facility Registry Service and land cover classes from the United States Geological Survey's (USGS) National Land Cover Data (NLCD). These land cover classes are measured in square meters at the parcel scale. The matching of facility parcels was done using a tiered approach to enhance spatial accuracy. The parcel data was provided by Homeland Infrastructure Foundation-Level Data (HIFLD) US Parcel Data. Because this parcel data is proprietary, the parcel geometries and fields were removed from the final dataset. However, centroid latitude and longitude coordinates were derived to allow spatial joins with publicly available parcel data.</p> <p>This dataset is organized by unique EPA NEI facilities data fields. Unique facility observations are identified by the field <code>cleaned_name</code> which represents the concatenated address and/or place name for the facility (dependent on data availability). Each row represents a facility matched to parcel scale land cover information and are associated with the unique identifier <code>MatchID</code>. NLCD land cover fields are described as the total area in meters squared of each facility parcel. Please see data README file for more information on individual data fields. </p> <h3>Known Limitations</h3> <ul> <li>Parcels are matched to the facility and in some cases multiple facilities are matched to the same parcel. Data users may want to omit these multiple match parcels and there is a data flag called <code>multi_match</code> that enables this.</li> <li>Approximately 15% of the dataset includes facilities where the latitude/longitude coordinates are over 200 meters from the matched parcel. Spot checking these instances revealed that, in many cases, the facility latitude and longitude locations did not accurately match the street address, city, or postal zip code associated with the facility. These instances are flagged as a potential source of inaccuracy and can be removed at the users discretion.</li> </ul> <p> </p>
ECOCLIMAP-SG-ML: an ensemble land cover map for numerical weather prediction
<p>This dataset contains ensemble land cover maps for numerical weather prediction at 60 m resolution over Europe. As they were<br>generated thanks to machine learning, the weights and the training data are also provided.</p>
Fig. 3. Land use and land cover data for 2014 in Population trends and conservation status of proboscis monkeys (Nasalis larvatus) in the face of habitat change in the Klias Peninsula, Sabah, Borneo, Malaysia
Fig. 3. Land use and land cover data for 2014/2015 within the 1-km buffer distance from surveyed rivers, overlaid with proboscis monkey sightings from the 2004/2005 and 2014 surveys, Protected Areas, and Production Forest Reserve boundaries.
Conservation of woody species in China under future climate and land-cover changes
<ol> <li>Climate and land-cover changes are major threats to biodiversity, and their impacts are expected to intensify in the future. Protected areas (PAs) are crucial for biodiversity conservation. However, their effectiveness under future climate and land-cover changes remains to be evaluated. Moreover, the impacts of climate and land-cover changes on multi-dimensions of biodiversity are rarely considered when expanding PAs.</li> <li>Using distributions of 8732 woody species in China and species distribution models, we identified species that will be threatened by future climate and land-cover changes (i.e. species with significant projected loss of suitable habitats by the 2070s) under different dispersal scenarios. We then estimated the geographical patterns in species richness (SR) and phylogenetic diversity (PD) of these threatened species, evaluated the effectiveness (i.e. the changes in SR and PD) of Chinese PAs, and identified conservation priorities for future PA expansion.</li> <li>Approximately 12-38% of woody species will be threatened under different scenarios. These species tend to be clustered in the tree of life, and their SR and PD show consistent spatial patterns, being highest at low latitudes. PAs currently protect 90% of these threatened species. However, their SR and PD of threatened species within PAs will decrease by 30-40% by the 2070s, which reduces the PA effectiveness, especially for PAs at low elevations and those with low topographic heterogeneity and high natural vegetation loss.</li> <li>The conservation priorities identified from the SR and PD of the threatened species are mainly in mountains in southern China, the Yunnan-Guizhou Plateau, and Taiwan Island. PA expansion and ecological corridors in these regions are needed to conserve these threatened species.</li> <li> <i>Synthesis and applications.</i> We present a systematic study of the impacts of future climate and land-cover changes on the conservation status of woody species and PA effectiveness in China. Our results suggest that future climate and land-cover changes will reduce PA effectiveness, and the spatial prioritization of biodiversity conservation should consider the influences of future global changes on biodiversity. These results shed new light on the conservation priorities for the post-2020 expansion of PAs in China.</li> </ol>
Data for "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover"
<p>Dataset for the "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover". Dataset 01 includes site locations, basin area, dissolved organic carbon (DOC), dissolved lignin concentration and relevant references. Dataset 03 includes mean/discharge-weighted DOC, mean/discharge-weighted dissolved lignin concentrations. Dataset 03 includes geomorphological, climatic, hydrological and land-cover data for the 25 rivers. Dataset 04 includes the reconstructed yield of dissolved lignin and basin area of the 79 rivers.</p>
Data from: Time series of bird abundances, land cover and temperature from standardized breeding bird monitoring schemes (line transects and point count routes) from Norway, Sweden and Finland, for 1975-2016
<p><span>These data on bird species abundance and environmental variables were used in testing and comparing two different species distribution model validation methods that are applied to models which are used to predict the effects of climate change on species' distributions. The aim of the study was to investigate whether different validation methods give different results of the model's predictive performance and to demonstrate that validation methods based on measuring and validating a "static" pattern in distribution can assess model performance over-optimistically compared to methods based on measuring and validating a "change" in the distribution, which can assess the predictive performance more critically. </span></p>
Datasets for Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China
<p>These datasets are the processed and refined data that support and lead to the described results and allow other readers to assess the conclusions in the paper, entitled “<strong>Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China </strong> ”.</p>
Land cover classification data for the first Chinese wetland cities in 2015 and 2020
<p>Land cover classification data for the first Chinese wetland cities in 2015 and 2020</p> <p>A land cover dataset, which had a resolution of 10 m and included four wetland types and five non-wetland types.</p> <p>The first Chinese wetland cities include Yinchuan, Changde, Haikou, Harbin, Dongying and Changshu.</p>
Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago
<p>The use of remote sensing for developing land cover maps in the Arctic has grown considerably in the last two decades, especially for monitoring the effects of climate change. The main challenge is to link information extracted from satellite imagery to ground covers due to the fine-scale spatial heterogeneity of Arctic ecosystems. There is currently no commonly accepted methodological scheme for high-latitude land cover mapping, but the use of remote sensing in Arctic ecosystem mapping would benefit from a coordinated sharing of lessons learned and best practices. Here, we aimed to produce a highly accurate land cover map of the surroundings of the Canadian Forces Station Alert, a polar desert on the northeastern tip of Ellesmere Island (Nunavut, Canada) by testing different predictors and classifiers. To account for the effect of the bare soil background and water limitations that are omnipresent at these latitudes, we included as predictors soil-adjusted vegetation indices and several hydrological predictors related to waterbodies and snowbanks. We compared the results obtained from an ensemble classifier based on a majority voting algorithm to eight commonly used classifiers. The distance to the nearest snowbank and soil-adjusted indices were the top predictors allowing the discrimination of land cover classes in our study area. The overall accuracy of the classifiers ranged between 75 and 88%, with the ensemble classifier also yielding a high accuracy (85%) and producing less bias than the individual classifiers. Some challenges remained, such as shadows created by boulders and snow covered by soil material. We provide recommendations for further improving classification methodology in the High Arctic, which is important for the monitoring of Arctic ecosystems exposed to ongoing polar amplification.</p>
Supplementary Material 7 including Salamandra salamandra occurrence data, topographic, geological and land cover data and node-based resistances
<p>Supplementary material for the article "Habitat connectivity supports the local abundance of fire salamanders (Salamandra salamandra) but also the spread of Batrachochytrium salamandrivorans" by Bolte <em>et al</em>. (2023) published in Landscape Ecology (DOI: 10.1007/s10980-023-01636-8)</p> <p>This folder comprises a .shp file with fire salamander occurrences, topographic and land cover data (GeoTiff) from the northern Eifel region as well as the R Code used for the statistical analysis of salamander habitat suitability and connectivity.</p>
Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets - training datasets
<p>Sample datasets for the <strong>Case Studies</strong> section of the <em> Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets </em>web book (<a href="https://isprs-gis-sdg.readthedocs.io">https://isprs-gis-sdg.readthedocs.io</a>)</p>
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.