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115 results for “land use and land cover”
Data from: multi-level determinants of land use land cover change in Tigray, Ethiopia: a mixed-effects approach using socioeconomic panel and satellite data
<p>The dataset contains six files from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on land use change conducted in August 2019 in Tigray; and (3) land cover transition data derived from LandSat satellite imagery for years 1986 and 2016. The files include data on household and plot features, prices of land use outputs, a diagonal block matrix of variables for mixed effects analysis, beliefs and opinions on land use change, and land cover transitions. The dataset covers 34 Enumeration Areas (EA) of the ERSS/LSMS-ISA and is representative of the region. It can be useful for studies on land use policies, environmental protection, and the drivers and impacts of land use land cover change in Tigray, Ethiopia. The data were processed using user-written codes in STATA v.17.</p>
GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change
<p>We complied the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change (GSOCS-LULCC) from 632 papers documented in Web of Science till the June 2024. This database comprises 1,187 sites with 5,805 records at multiple sample depths.<br>This dataset (in csv formats) is associated to the "GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change" by Chen et al. (2025). The README file includes the full explanation of all the columns.<br>Manuscript citation: Chen, S., Shuai, Q., Arrouays, D., Chen, Z., Dai, L., Hong, Y., Hu, B., Huang, Y., Ji, W., Li, S., Liang, Z., Ma, Y., Richer-de-Forges, A.C., Schillaci, C., Su, Y., Teng, H., Wang, N., Wang, X., Wang, Y., Wang, Z., Wang, Z., Xu, D., Xue, J., Ye, S., Zhang, X., Zhou, Y., Zhu, P., Shi, Z. , 2025. GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change. In preparation.<br>When using the data, please cite repositories as well as the original manuscript.<br>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
Deepened snow cover mitigates soil carbon loss from intensive land use in a semi-arid temperate grassland
<p>Carbon (C) loss due to soil erosion is a major issue in semi-arid grasslands. The extent of soil erosion is determined by soil properties and vegetation structure, especially during the non-growing season. In many Inner Mongolian grasslands, intensive land use, such as overgrazing and mowing, has severely reduced plant cover and damaged soil structure, which has exacerbated soil C loss by erosion. At the same time, increasing winter snowfall due to climate change is stimulating plant growth and altering plant composition. However, we do not know how changes in winter snow cover interact with land-use practices to regulate soil C loss due to erosion.</p> <p>Here, we conducted a six-year snow manipulation experiment under different land-use practices (control; moderately mowed, MM; heavily mowed, HM) to measure net changes in soil depth, soil C, plant biomass, and vegetation structure.</p> <p>After six years, soil C loss under ambient snow was three times greater in the MM and four times greater in the HM treatment compared with controls during non-growing season. However, deepened winter snow alleviated erosion-induced soil C loss by 14%, 47%, 16% in the controls, MM and HM treatments, respectively.</p> <p>The severity of soil C loss declined with increasing aboveground biomass (AGB), surface root biomass and vegetation structure. Vegetation structure and AGB explained more of the variation in soil C loss than surface root biomass, possibly because a complex canopy and plant cover increases overall surface roughness, thereby reducing soil C loss. Intensified land use reduced AGB, surface root biomass and vegetation structure, but deepened snow increased overall surface roughness by promoting AGB. Hence, our study demonstrates that deepened snow can alleviate soil C loss due to land use practices by promoting AGB.</p>
Linking land-use and land-cover transitions to their ecological impact in the Amazon
<p>Authors: Cássio Alencar Nunes, Erika Berenguer, Filipe França, Joice Ferreira, Alexander C. Lees, Julio Louzada, Emma J. Sayer, Ricardo Solar, Charlotte C. Smith, Luiz E. O. C. Aragão, Danielle de Lima Braga, Plinio Camargo, Carlos Eduardo Pellegrino Cerri, Raimundo Cosme, Mariana Durigan, Nárgila Moura, Victor Hugo Fonseca Oliveira, Carla Ribas, Fernando Vaz-de-Mello, Ima Vieira, Ronald Zanetti, Jos Barlow</p> <p>Code repository for the paper: Nunes et al. Linking land-use and land-cover transitions to their ecological in the Amazon. Proceedings of the National Academy of Sciences. 2022. In this repository we included codes and data that we used to run the all the analyses presented in the paper.</p>
Land cover maps: Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Land cover maps obtained with Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models for the year 2018 with Sentinel-2 acquisitions.</p> <p>For further details see section VII-A-2 (Results-Performance results in the Southfrance area-Qualitative results) of the article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p>
Classification Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Classification data set (train, validation, test) from the study area based on 27 tiles on the south of the France. Data set are provided for each eco-climatic region. The size corresponds to the data set DS-A. Only one random pixel sampling is provided: seed 0. This data set was used to train Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models.</p> <p>For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Korean peninsula's Land use and land cover data, Landsat NDVI data, and CO2 data
<p><strong>Paper: "Greening rate in North Korea doubles South Korea"</strong></p> <p>Data in this repository include Landsat NDVI data, Land use, land cover data, and CO2 data of the Korean Peninsula.</p> <p>Korean Peninsula's <strong>NDVI data</strong> 1986-2017: <a href="https://zenodo.org/record/7221229#.Y07A2HZBxdg">https://zenodo.org/record/7221229#.Y07A2HZBxdg</a></p> <p>Korean Peninsula's <strong>Landuse Data</strong> 1986-2017: <a href="https://zenodo.org/record/7214788#.Y07BD3ZBxdg">https://zenodo.org/record/7214788#.Y07BD3ZBxdg</a></p> <p>Korean Peninsula's <strong>CO2 Data </strong>1986-2017: https://zenodo.org/record/7260091</p> <p>All three types of data are combined and displayed in this repository link. You can view individual data based on the separate link behind the data.</p>
Supplementary File 8; The full data set derived from formal quantitative surveys of land cover types and activities of humans, livestock and wildlife (Section 2.3) that were used for the analyses described in sections 2.4, 2.5 and 2.7
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Data and R code associated to the publication: "Effects of land use, cover and protection on stream and riparian ecosystem services and biodiversity"
<p>This R code and dataset accompany Hanna et al's 2019 publication in Conservation Biology titled "Effects of land use, cover and protection on stream and riparian ecosystem services and biodiversity". Read the "Metadata" tab of the data file and code annotations for more information. </p>
Integrated Approach to Global Land Use and Land Cover Reference Data Harmonization
<h2><strong>INTRODUCTION</strong></h2> <p>This document outlines the creation of a global inventory of reference samples and Earth Observation (EO) / gridded datasets for the Global Pasture Watch (GPW) initiative. This inventory supports the training and validation of machine-learning models for GPW grassland mapping. This documentation outlines methodology, data sources, workflow, and results.</p> <p><strong>Keywords:</strong> Grassland, Land Use, Land Cover, Gridded Datasets, Harmonization</p> <p> </p> <h2><strong>OBJECTIVES</strong></h2> <ul> <li> <p>Create a global inventory of existing reference samples for land use and land cover (LULC);</p> </li> <li> <p>Compile global EO / gridded datasets that capture LULC classes and harmonize them to match the GPW classes;</p> </li> <li> <p>Develop automated scripts for data harmonization and integration.</p> </li> </ul> <p> </p> <h2><strong>DATA COLLECTION </strong></h2> <p>Datasets incorporated:</p> <table> <tbody> <tr> <td><strong>Datasets</strong></td> <td> <p><strong>Spatial distribution</strong></p> </td> <td><strong>Time period</strong></td> <td><strong>Number of individual samples</strong></td> </tr> <tr> <td>WorldCereal</td> <td>Global</td> <td>2016-2021</td> <td>38,267,911</td> </tr> <tr> <td>Global Land Cover Mapping and Estimation (GLanCE)</td> <td>Global</td> <td>1985-2021</td> <td>31,061,694</td> </tr> <tr> <td>EuroCrops</td> <td>Europe</td> <td>2015-2022</td> <td>14,742,648</td> </tr> <tr> <td>GeoWiki G-GLOPS training dataset</td> <td>Global</td> <td>2021</td> <td>11,394,623</td> </tr> <tr> <td>MapBiomas Brazil</td> <td>Brazil</td> <td>1985-2018</td> <td>3,234,370</td> </tr> <tr> <td>Land Use/Land Cover<br>Area Frame Survey (LUCAS)</td> <td>Europe</td> <td>2006-2018</td> <td>1,351,293</td> </tr> <tr> <td>Dynamic World</td> <td>Global</td> <td>2019-2020</td> <td>1,249,983</td> </tr> <tr> <td>Land Change Monitoring,<br>Assessment, and Projection (LCMap)</td> <td>U.S. (CONUS)</td> <td>1984-2018</td> <td>874,836</td> </tr> <tr> <td>GeoWiki 2012</td> <td>Global</td> <td>2011-2012</td> <td>151,942</td> </tr> <tr> <td>PREDICTS</td> <td>Global</td> <td>1984-2013</td> <td>16,627</td> </tr> <tr> <td>CropHarvest</td> <td>Global</td> <td>2018-2021</td> <td>9,714</td> </tr> </tbody> </table> <p><strong>Total:</strong> 102,355,642 samples</p> <p> </p> <h2><strong>WORKFLOW</strong></h2> <h3><strong>Harmonization Process</strong></h3> <p>We harmonized global reference samples and EO/gridded datasets to align with GPW classes, optimizing their integration into the GPW machine-learning workflow.</p> <p>We considered reference samples derived by visual interpretation with spatial support of at least 30 m (Landsat and Sentinel), that could represent LULC classes for a point or region.</p> <p>Each dataset was processed using automated Python scripts to download vector files and convert the original LULC classes into the following GPW classes:</p> <p> 0. Other land cover</p> <p> 1. Natural and Semi-natural grassland</p> <p> 2. Cultivated grassland</p> <p> 3. Crops and other related agricultural practices</p> <p>We empirically assigned a weight to each sample based on the original dataset's class description, reflecting the level of mixture within the class. The weights range from 1 (Low) to 3 (High), with higher weights indicating greater mixture. Samples with low mixture levels are more accurate and effective for differentiating typologies and for validation purposes.</p> <p>The harmonized dataset includes these columns:</p> <table> <tbody> <tr> <td><strong>Attribute Name</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td>dataset_name</td> <td>Original dataset name</td> </tr> <tr> <td>reference_year</td> <td>Reference year of samples from the original dataset</td> </tr> <tr> <td>original_lulc_class</td> <td>LULC class from the original dataset</td> </tr> <tr> <td>gpw_lulc_class</td> <td>Global Pasture Watch LULC class</td> </tr> <tr> <td>sample_weight</td> <td>Sample's weight based on the mixture level within the original LULC class</td> </tr> </tbody> </table> <p> </p> <h2><strong>ACKNOWLEDGMENTS</strong></h2> <p>The development of this global inventory of reference samples and EO/gridded datasets relied on valuable contributions from various sources. We would like to express our sincere gratitude to the creators and maintainers of all datasets used in this project.</p> <p> </p> <h2><strong>REFERENCES</strong></h2> <ul> <li> <p>Brown, C.F., Brumby, S.P., Guzder-Williams, B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Sci Data 9, 251 (2022). https://doi.org/10.1038/s41597-022-01307-4Van Tricht, K. et al. Worldcereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping. Earth Syst. Sci. Data 15, 5491–5515, 10.5194/essd-15-5491-2023 (2023)</p> </li> <li> <p>Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963</p> </li> <li> <p>d’Andrimont, R. et al. Harmonised lucas in-situ land cover and use database for field surveys from 2006 to 2018 in the european union. Sci. data 7, 352, 10.1038/s41597-019-0340-y (2020)</p> </li> <li> <p>Fritz, S. et al. Geo-Wiki: An online platform for improving global land cover, Environmental Modelling & Software, 31, https://doi.org/10.1016/j.envsoft.2011.11.015 (2012)</p> </li> <li> <p>Fritz, S., See, L., Perger, C. et al. A global dataset of crowdsourced land cover and land use reference data. Sci Data 4, 170075 https://doi.org/10.1038/sdata.2017.75 (2017)</p> </li> <li> <p>Schneider, M., Schelte, T., Schmitz, F. & Körner, M. Eurocrops: The largest harmonized open crop dataset across the european union. Sci. Data 10, 612, 10.1038/s41597-023-02517-0 (2023)</p> </li> <li> <p>Souza, C. M. et al. Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote. Sens. 12, 2735, 10.3390/rs12172735 (2020)</p> </li> <li> <p>Stanimirova, R. et al. A global land cover training dataset from 1984 to 2020. Sci. Data 10, 879 (2023) </p> </li> <li>Stehman, S. V., Pengra, B. W., Horton, J. A. & Wellington, D. F. Validation of the us geological survey’s land change monitoring, assessment and projection (lcmap) collection 1.0 annual land cover products 1985–2017. Remot Sensing environment 265, 112646, 10.1016/j.rse.2021.112646 (2021).</li> <li> <p>Tsendbazar, N. et al. Product validation report (d12-pvr) v 1.1 (2021).</p> </li> <li>Tseng, G., Zvonkov, I., Nakalembe, C. L., & Kerner, H. (2021). CropHarvest: A global dataset for crop-type classification. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.</li> </ul>
R scripts for the practical exercises with QGIS in the book "Land Use Cover Datasets and Validation Tools"
<p>This dataset includes a series of R scripts required to carry out some of the practical exercises in the book “Land Use Cover Datasets and Validation Tools”, available in open access.</p> <p>The scripts have been designed within the context of the R Processing Provider, a plugin that integrates the R processing environment into QGIS. For all the information about how to use these scripts in QGIS, please refer to Chapter 1 of the book referred to above.</p> <p>The dataset includes 15 different scripts, which can implement the calculation of different metrics in QGIS:</p> <ul> <li>Change statistics such as absolute change, relative change and annual rate of change (Change_Statistics.rsx)</li> <li>Areal and spatial agreement metrics, either overall (Overall Areal Inconsistency.rsx, Overall Spatial Agreement.rsx, Overall Spatial Inconsistency.rsx) or per category (Individual Areal Inconsistency.rsx, Individual Spatial Agreement.rsx)</li> <li>The four components of change (gross gains, gross losses, net change and swap) proposed by Pontius Jr. (2004) (LUCCBudget.rsx)</li> <li>The intensity analysis proposed by Aldwaik and Pontius (2012) (Intensity_analysis.rsx)</li> <li>The Flow matrix proposed by Runfola and Pontius (2013) (Stable_change_flow_matrix.rsx, Flow_matrix_graf.rsx)</li> <li>Pearson and Spearman correlations (Correlation.rsx)</li> <li>The Receiver Operating Characteristic (ROC) (ROCAnalysis.rsx)</li> <li>The Goodness of Fit (GOF) calculated using the MapCurves method proposed by Hargrove et al. (2006) (MapCurves_raster.rsx, MapCurves_vector.rsx)</li> <li>The spatial distribution of overall, user and producer’s accuracies, obtained through Geographical Weighted Regression methods (Local accuracy assessment statistics.rsx).</li> </ul> <p>Descriptions of all these methods can be found in different chapters of the aforementioned book.</p> <p>The dataset also includes a readme file listing all the scripts provided, detailing their authors and the references on which their methods are based.</p>
Data for the practical exercises in the book "Land Use Cover Datasets and Validation Tools"
<p>This dataset contains all the data that is required to carry out the practical exercises in the book “Land Use Cover Datasets and Validation Tools”, available in open access.</p> <p>The dataset includes data for three different case studies: The Asturias Central Area (Spain), the Ariège Valley (France) and Marqués de Comillas (Mexico). For the Asturias Central Area and the Ariège Valley, the dataset includes Land Use Cover (LUC) maps for several years of reference as well as data (simulation outputs, model drivers) for different modelling exercises. For Marqués de Comillas, the dataset includes a LUC map and a set of reference points used to validate it.</p> <p>The dataset includes a readme file listing all the files it contains and auxiliary files describing the data. For further information on the study area and the files used in the practical exercises, users are referred to Chapter 1 of the book.</p>
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
<p>EuroSAT is a land use and land cover classification dataset. The dataset is based on Sentinel-2 satellite imagery covering 13 spectral bands and consists of 10 LULC classes with a total of 27,000 labeled and geo-referenced images. The dataset is associated with the publications "<a href="https://ieeexplore.ieee.org/abstract/document/8519248">Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification</a>" and "<a href="https://ieeexplore.ieee.org/abstract/document/8736785">EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification</a>".</p> <p>EuroSAT_RGB.zip contains the RGB version of the dataset, which includes the optical R, G and B frequency bands encoded as JPEG images.</p> <p>EuroSAT_MS.zip contains the multi-spectral version of the EuroSAT dataset, which includes all 13 Sentinel-2 bands in the original value range.</p>
Land cover maps: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Land cover maps obtained with mTAN-GP, mTAN-MLP, mTAN-LTAE and raw-LTAE models for the year 2018 with Sentinel-2 acquisitions.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p>
Mapping past land cover on Poitiers in 1993 at Very High Resolution using GEOBIA approach and open data
<p>This dataset contains a land cover map of Poitiers in 1993 over an area of 225km².</p> <p>The land cover map was achieved using aerial images of the French National Geographic Institute (IGN) and Landsat-5 TM images combined with remote sensing methods. Geographic Object-Based Image Analysis (GEOBIA) and Random Forest classifications produced a reliable land cover map at a 1m of spatial resolution.</p> <p>Orthophotos produced as well as training and validating polygons to achieve the classifications were added into this dataset.</p> <p>As land cover changes is crucial to land management, this map will help to understand changes from 1993 to now for urban, agricultural issues but also their impact on ecological processes. Data will be easily used in GIS applications for any users.</p> <p>This work is part of the thesis of Elie Morin which was funded by la région Nouvelle-Aquitaine and Grand Poitiers Communauté urbaine, among others.</p>
Data from: Land use type, forest cover, and forest edges modulate avian cross-habitat spillover
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Land use and cover changes and sand fly (Diptera: Psychodidae) assemblages in an emerging focus of leishmaniasis
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Deepened snow cover mitigates soil carbon loss from intensive land use in a semi-arid temperate grassland
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Data from: multi-level determinants of land use land cover change in Tigray, Ethiopia: a mixed-effects approach using socioeconomic panel and satellite data
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Land cover classification using ASTER data - year 2000
Land cover classification for the CAP LTER study region using ASTER imagery acquired September 19, 2000. Current classification is broadly similar to previous classifications using Landsat TM by Stefanov et al (2001). Three visible bands (15m/pixel) of ASTER were used to perfom a multistep classification of the area. The fifteen-class classification is produced by applying the expert system approach and using the initially derived 16-class minimum distance to means (MDM) supervised classification, Normalized Difference Vegetation Index (NDVI), spatial variance texture image, and land use vector coverage. The overall classification accuracy is 88.06%. Although it does not cover the entire CAP LATER, the dataset can be used as higher spatial resolution alternative to Landsat-derived land cover.
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