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528 results for “Land cover”
Land use and land cover scenarios for the Maurienne valley (French Alps) at 2085 horizon produced using CLUMPY model
<p>We built three contrasted future LULC scenarios from 2020 to 2085 with the CLUMPY model (Mazy and Longaretti, 2022). The CLUMPY model is an innovative model of land use and land cover change comprising a calibration-estimation module separate from a non-biased allocation module. It is calibrated by using time series of past LULC maps (Mazy and Longaretti, 2022). The model then calculates transition probabilities for each LULC class according to relevant spatial explanatory variables. Next, the model can produce maps of future LULC distributions according to information it learned during the calibration-estimation phase. This model has the benefits of being easy to use, proposing nonbiased allocation methods and producing scenarios of future LULC change either by adjusting manually the matrix of LULC transitions probabilities (used for the Conservation and Tourism scenarios) or by training the model on specific areas of the past time series (only used for the Conservation scenario).</p>
Taiwan land cover data LUH2 SSPs
<p>This new version updates the LU map of Taiwan, downscaled from the LUH2 SSPs dataset, for the years 2016 to 2099 at a spatail resolution of 500 m by 500 m. Taiwan is defined within the LUH2 dataset boundaries, ranging from 120° to 122°E and 21.5° to 25.5°N. The dataset includes five scenarios: SSP1 with low challenges from adaptation and mitigation; SSP2 with intermediate challenges; SSP3 with high challenges; SSP4 where adaptation challenges dominate; and SSP5 where mitigation challenges dominate.</p> <p>The future land-use share was directly extracted from the LUH2 dataset at a spatial resolution of 25 km x 25 km. To downscale this information for Taiwan, a long-term land-use change/transition probability map is required as a reference to allocate future land types from coarse to fine spatial resolution. In this study, we applied the long-term land-use change/transition probability map derived from the (potential land-use change, PLC) method. The spatial allocation algorithm integrates PLC maps from historical LULCC reconstructions with gross change information from future land-use maps based on the LUH2 dataset.</p> <p>For a detailed description of the downscaling approach, please refer to the study "Navigating Land-Use Trends: Bridging Present Realities and Future Projections in Taiwan" by Chen et al. (in submission).</p> <p>If you have any questions, please contact Dr. Yi-Ying Chen at Academia Sinica, Taiwan, via email: yiyingchen@gate.sinica.edu.tw.</p>
LAND USE AND LAND COVER OF THE UPPER AND MIDDLE BASIN OF THE LUJÁN RIVER
<p>The general objective of this work was to update the zoning and characterization of the land use and land cover of the upper and middle basin of the Luján River from high-resolution satellite images. Eight categories of land cover and land use were determined: Agricultural (A), Agricultural-livestock (AGa), Livestock (G), Horticultural (H), Forest (M), Water bodies (Ag), Low density city (Cb) and High density city (Ca). For the classification, high-resolution satellite images provided by Google Earth by the constellation of satellites of the company Maxar Technologies in the years 2021 and 2022. he scale used for the digitalization of the land use and land cover on the screen with digital GIS tools was 1:2,000. For the visual interpretation, different advanced vector digitization tools of the QGIS software were used on the aforementioned satellite images. All generated layers were georeferenced according to the WGS84 system with POSGAR 2007 - FAJA 5 projection.</p>
GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with fine classification system from 1985 to 2022
<div> <p>GLC_FCS30D is the first global fine land cover dynamic product at a 30-meter resolution that adopts continuous change detection. It utilizes a refined classification system containing 35 land-cover categories and covers the time span from 1985 to 2022. Before the year 2000, the update cycle was every 5 years, while after 2000, it is updated annually. In specific, it developed by combining the continuous change detection method, local adaptive updating models and the spatiotemporal optimization algorithm from dense time-series Landsat imagery, and was validated to achieve an overall accuracy of 80.88% (±0.27%) for the basic classification system 10 major land-cover types) and 73.24% (±0.30%) for the LCCS level-1 validation system (17 LCCS land-cover types).</p> <p>The GLC_FCS30D has been compressed into 36 zip files, and<strong> <em>the details about the GLC_FCS30D can be found in the User's guides.</em></strong></p> </div>
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
<p>The benchmark code is available at: <a href="https://github.com/Junjue-Wang/LoveDA">https://github.com/Junjue-Wang/LoveDA</a></p> <p><strong>Highlights: </strong></p> <ol> <li>5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan</li> <li>Focus on different geographical environments between Urban and Rural</li> <li>Advance both semantic segmentation and domain adaptation tasks</li> <li>Three considerable challenges: multi-scale objects, complex background samples, and inconsistent class distributions</li> </ol> <p><strong>Reference:</strong></p> <pre><code>@inproceedings{wang2021loveda, title={Love{DA}: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation}, author={Junjue Wang and Zhuo Zheng and Ailong Ma and Xiaoyan Lu and Yanfei Zhong}, booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks}, editor = {J. Vanschoren and S. Yeung}, year={2021}, volume = {1}, pages = {}, url={https://datasets-benchmarks proceedings.neurips.cc/paper/2021/file/4e732ced3463d06de0ca9a15b6153677-Paper-round2.pdf} }</code></pre> <p><strong>License:</strong></p> <p>The owners of the data and of the copyright on the data are RSIDEA, Wuhan University. Use of the Google Earth images must respect the "Google Earth" terms of use. All images and their associated annotations in LoveDA can be used for academic purposes only, <strong>but any commercial use is prohibited. (CC BY-NC-SA 4.0)</strong></p>
A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types
<p>Dataset accompanying manuscript <em>"A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types". </em>Datasets contain a wall-to-wall map of vegetation types covering the study area of terrestrial Norway, produced using three methods for assembling individual predictions from Distribution models (<em>probability-based method</em>, <em>performance-based method</em> and <em>prevalence-based method</em>). </p>
Copernicus Global Land Service: Global biome cluster layer for the 100m global land cover processing line
<p><strong>A map of 73 global biome clusters, geographic areas that were grouped to optimize the global 100m land cover processing.</strong></p> <p>In order to group Earth Observation data for faster processing or adaptation of algorithms to specific regions, the 100m global land cover (CGLS-LC100) algorithm uses a Global Biome Cluster layer. The term <em>biome cluster</em> hereby refers to a geographic area which has similar bio-geophysical parameters and, therefore, can be grouped for processing. In other words, the biome cluster layer can be seen as an ecological regionalisation which outlines areas of similar environmental conditions, ecological processes, and biotic communities (Coops et al., 2018). There are already several global regionalisation layers existing, e.g. Ecoregions 2017 global dataset (Dinerstein et al., 2017), Geiger-Koeppen global ecozones after Olofsson update (Olofsson et al., 2012), Global ecological zones for FAO forest reporting with update 2010 (FAO, 2012). But several tests in the CGLS-LC100 workflow have shown that the existing layers did not provide the required global and continental classification accuracy. These findings go along with Coops et al. (2018) who stated that "<em>Most regionalisations are made based on subjective criteria, and cannot be readily revised, leading to outstanding questions with respect to how to optimally develop and define them."</em></p> <p>Therefore, we decided to develop a customized ecological regionalisation layer which performs best with the given PROBA-V remote sensing data and the specifications of the CGLS-LC100 product. It groups spectral similar areas and helps to optimize the later classification/regression to regional patterns. Input into the layer creation were well-known existing datasets which were combined, re-grouped and advanced based on prior CGLS-LC100 classification results and local mapping knowledge of the workflow developer. To ensure that this layer is clearly separable from other existing regionalisations and not mistakenly interpreted as an eco-region layer, we decide to call it <em>biome clusters</em> <em>layer</em>.</p> <p>The following steps outline the global biome clusters layer generation:</p> <ul> <li>Spatial union of Ecoregions 2017 dataset (Dinerstein et al., 2017), Geiger-Koeppen dataset (Olofsson et al., 2012) and Global FAO eco-regions datasets (FAO, 2012);</li> <li>Regrouping and dissolving by using experience from first global CGLS-LC100 mapping results and subjective mapping experience of the developer;</li> <li>Refinement of the biome clusters in the High North latitudes via incorporation of a Global tree-line layer (Alaska Geobotany Center, 2003);</li> <li>Manual improvement of borders between biome clusters to reduce classification artefacts by using a DEM and mapping experience from previous projects and continental test runs;</li> <li>Usage of a global land/sea mask, the Sentinel-2 tiling grid and PROBA-V imaging extent to extend the borders of the biome clusters into the sea to make sure that also small islands on the coastline are correctly processed.</li> </ul> <p>When developing a regionalisation, the definition of the clusters and the boundaries that delineate them in time and space is the key challenge. Overall, the map distinguishes <strong>73 global biome clusters</strong>.</p>
2017–2018 Land Cover Map of Pyrénées-Atlantiques
<p>This archive contains:</p> <p><br> ├── classification_dpt64_16_classes.tif : the 16 classes land cover map</p> <p>├── classification_dpt64_16_classes_confusion_matrix.png : the confusion matrix. Have a look at it, it is performed on a different dataset than the one used for training the classifier.</p> <p>├── classification_dpt64_21_classes.tif : the 21 classes land cover map including post-treatments (<a href="https://outlook.office.com/owa/redir.aspx?REF=IplRG6_B9OG8Rjvmh5Epv5dpru9Rpr5pwX-IxbR_kY-VTC7cUdfZCAFodHRwczovL2ZyYW1hZ2l0Lm9yZy9TY2h3YWFiL3Byb2pldF9wcmVkYXRldXJzNjQvLS9ibG9iL21haW4vc2NyaXB0cy9DbGFzc2lmaWNhdGlvblBvc3RQcm9jZXNzLnB5">https://framagit.org/Schwaab/projet_predateurs64/-/blob/main/scripts/ClassificationPostProcess.py</a>)</p> <p>├── colorFile.txt : color file for symbology</p> <p>├── configfile_iota2.cfg : iota2 configuration file (in case you are already using iota2. If not, what are you waiting for ?)</p> <p>├── document_methodologique.pdf : technical report (french) for the classification </p> <p>├── nomenclature.txt : nomenclature file</p> <p>├── reference_data_2018.shp : the training and validation data set in its 2018 version (for crops)</p> <p>├── reference_data_2019.shp : the training and validation data set in its 2019 version (for crops)</p> <p>├── reference_photo_interpretation.shp : the part of the training and validation data set that has been photo interpreted with a field giving the potential species or combinations of associated vegetation</p> <p>├── reference_tree_nomenclature.png : a visual about the reference data</p> <p>├── stratification_3_zones.shp : the stratification layer that has helped improve classification results. It is based on landscape entities (<a href="https://outlook.office.com/owa/redir.aspx?REF=-inpMPbdxkotoNFtrpo0ptiZmF3YJRSM91bnORcpCHKVTC7cUdfZCAFodHRwczovL2RhdGEubGU2NC5mci9leHBsb3JlL2RhdGFzZXQvZW50aXRlLXBheXNhZ2VyZS8.">https://data.le64.fr/explore/dataset/entite-paysagere/</a>) </p> <p>├── style_16_classes.qml : the Qgis style layer 16 classes</p> <p>└── style_21_classes.qml : the Qgis style layer 21 classes<br> </p> <p>Description:</p> <p><br> The land cover map of the French department Pyrénées-Atlantiques (64) is based on Sentinel-2 (L2A level) satellite images performed with Iota² chain (<a href="https://outlook.office.com/owa/redir.aspx?REF=xF93blZK9Vxs-N7pnBh9q489HLjMtSEZRU6VkhywO-qVTC7cUdfZCAFodHRwczovL2ZyYW1hZ2l0Lm9yZy9pb3RhMi1wcm9qZWN0L2lvdGEyLw..">https://framagit.org/iota2-project/iota2/</a>). The algorithm used is Random Forest. The time series used ranges from 2017 to 2018.</p> <p>During the development phase of this classification, the collection of additional training data on the photo-interpreted classes 'landes basses' (low heath shrublands), 'landes hautes' (high heath shrublands) and 'landes hautes avec arbres' (high heath shrublands with young-growth forest) has led to a remarkable increase of the number of pixels of these classes and with it the visual quality of the map. However, this increase has been linked with only minor to almost no significant improvement of the F-scores on these classes. Some are still massively confused with other land covers like grasslands and broadleaf mature forests. Especially the mixed class 'landes hautes avec arbres' (high heath shrublands with young-growth forest).<br> <br> We take it as a limit of the reference data that is built from divers data sources and would always beneficiate from more training samples of shrubby classes and a better precision of the class 'forêt de feuillus' (broadleaf mature forests). But this could also show the limit of pixel-oriented classifications for mixed/textured classes (classes with high intra-class heterogeneity). Experimentations using a contextual method – the Auto-context method now being included in Iota2 thanks to Dawa Derksen and Iota2 developers (<a href="https://outlook.office.com/owa/redir.aspx?REF=-QXKxpHR73RRUGsNGVs-FtQHPORNqP2y2wLSVVK_L-uVTC7cUdfZCAFodHRwOi8vbGFubmlzdGVyLnVwcy10bHNlLmZyL29zby9kb25uZWVzd3d3X1RoZWlhT1NPL2lvdGEyX2RvY3VtZW50YXRpb24vZGV2ZWxvcC9hdXRvQ29udGV4dC5odG1s">http://lannister.ups-tlse.fr/oso/donneeswww_TheiaOSO/iota2_documentation/develop/autoContext.html</a>) – has unfortunately not been conclusive on that matter yet.</p>
Chesapeake Land Cover dataset - Learning on the prior extension
<p>This dataset extends the "Chesapeake Land Cover" dataset at <a href="https://lila.science/datasets/chesapeakelandcover">https://lila.science/datasets/chesapeakelandcover</a> with an additional <em>layer </em>of data containing a prior of observing a 1m Chesapeake Conservancy land cover class label given an NLCD label. Specifically, for each <em>tile </em>in the original "Chesapeake Land Cover" dataset, this dataset contains another tile (named with a "_prior_from_cooccurrences_101_31_no_osm_no_buildings.tif" suffix) containing the prior probabilities of observing a four class version of the land cover classes at a 1m resolution. The prior probability is given by the normalized co-occurrence matrix between NLCD classes and the Chesapeake 1m land cover labels in each state with additional spatial smoothing (using a gaussian filter with a standard deviation of 31 pixels and a cutoff of 101 pixels) to reduce the block artifacts caused by the relatively low-resolution of the NLCD labels (30m) compared to the LC labels (1m). <strong>Note:</strong> the prior is a 4-class mapping of the 6-class labels. In the 4-class version of the dataset the "barren land", "impervious (other)", and "impervious (road)" classes are combined.</p> <p>This dataset also includes the per-state co-occurrence matrices. Each of these is a matrix, <span class="math-tex">\(C\)</span>, with size 7 x 17, where an entry <span class="math-tex">\(C_{ij}\)</span> gives the normalized count of land cover class <span class="math-tex">\(i\)</span> for given NLCD label <span class="math-tex">\(j\)</span>. The class indices <span class="math-tex">\(i\)</span> and <span class="math-tex">\(j\)</span> correspond to the indices used in the Chesapeake Conservancy land cover label and NLCD layers.</p> <p>The dataset is packaged in a way such that it can simply be unzipped over an existing copy of the "Chesapeake Land Cover" (i.e. follows the same directory structure). E.g., given a directory that contains the original dataset "cvpr_chesapeake_landcover/", and the file "cvpr_chesapeake_landcover_prior_extension.zip", run `unzip cvpr_chesapeake_landcover_prior_extension.zip` to create the merged dataset.</p> <p> </p> <p>Version 1.1 fixes an issue with the geotiff metadata that caused GDAL to interpret the 4th band as a nodata mask in some settings.</p>
Labeled points for four land cover classes referring to the year 2019
<p>This dataset provides hand-labeled points for four different land cover classes in coastal areas (sand, seawater, grass, trees).</p> <p>It was created based on photointerpretation of high-resolution imagery in Google Earth Pro and QGIS, referring to the year 2019.</p> <p>This dataset was used for the random forest classification of satellite imagery in the following manuscript: </p> <p>"<em>Satellite image processing for the coarse-scale investigation of sandy coastal areas</em>".</p> <p>If you use any part of this dataset, please cite as follows:</p>
Data set: Land use and land cover change in a tropical mountain landscape of northern Ecuador: altitudinal patterns and driving forces
<p>Tropical mountain ecosystems are threatened by land use pressures, compromising their capacity to provide multiple ecosystem services. The analysis of landscape changes and their proximate driving forces is often qualitative and sectorial oriented, although local patterns and numerous interactions among socio-economic, demographic, and biophysical factors shape these socio-ecological systems. We characterized land use land cover (LULC) dynamics using Markov-chain probabilities by elevation and geographic settings and then, implementing the DPSIR holistic approach, we integrated them with a variety of freely available geospatial and temporal data into a Generalized Additive Model (GAM) to uncover the factors driving such landscape dynamics in a sensitive region of the northern Ecuadorian Andes. Our results demonstrated a dynamic and clear geographical pattern of distinct LULC transitions through time, explained by different combination of socio-economic factors, demographic and infrastructure variables and environmental parameters, from which topographic variables were the main drivers of change in this landscape. We found that deforestation of remnant native forest and agricultural expansion still occur in higher elevations, while land conversion toward anthropic environments, particularly significant expansion of floriculture and urban areas were observed in lower elevations to the east of the studied territory. Our findings also revealed an unexpected stability trend of paramo and a successional recovery of previous agricultural land to the west and center of the territory, which could be explained by agricultural land abandonment. However, the very low probability of persistence of montane forests found overall, highlights the greater threat to permanently lose the already vulnerable mountain native biodiversity. The methodological approach and our findings, demonstrating dynamic patterns through space and time and their explanatory drivers, could help local authorities and stakeholder to improve sustainably resource land management in vulnerable landscapes such as the tropical Andes in northern Ecuador.</p>
Saudi Arabian Capitals Urban Land Cover Maps: 1985-2019
<p>A CCDC algorithm was used to produce 13 sets of geographical maps. Each set represents one capital city of Saudi Arabia: Riyadh (which also serves as the country’s capital), Buridah, Ha’il, Dammam, Makkah, Madinah, Arar, Skakah Tabuk, Albaha, Abha, Jazan, and Najran. Each of the 13 datasets contains 35 annual maps from 1985 to 2019. The file format of these datasets is the .hdr file. The dataset is free to download. </p> <p>In the case of using the urban land cover maps, please cites the website and the paper:</p> <ol> <li>Aljaddani AH, Song X-P, Zhu Z. Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a Landsat Time Series. <em>Remote Sensing</em>. 2022; 14(10):2382. https://doi.org/10.3390/rs14102382</li> <li>Aljaddani, Amal H, Song, Xiao-Peng, & Zhu, Zhe. (2022). Saudi Arabian Capitals Urban Land Cover Maps: 1985-2019 (Version: 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6210073</li> </ol> <p> </p> <p> </p>
EnviroAtlas land cover dataset
<p>This dataset contains imagery, land cover, and object layers for four of the cities covered by the EPA’s EnviroAtlas dataset: Pittsburgh, PA, Phoenix, AZ, Durham, NC, and Austin, TX. The dataset consists of 10 test tiles and 5 validation tiles in each city, with an additional 10 training tiles and 8 (total) validation tiles in Pittsburgh, subsetting the total area that the EnviroAtlas dataset covers in each city. The EnviroAtlas label definitions are explained in <a href="https://pubmed.ncbi.nlm.nih.gov/32844040/">Pilant et al. 2020</a>. In the processed prior files included in this dataset, channels correspond to:</p> <ul> <li>0: water</li> <li>1: impervious surface</li> <li>2: soil and barren</li> <li>3: trees and forest (and shrub in AZ)</li> <li>4: grass and herbaceous</li> </ul> <p>Specifically, this dataset is in the same format as the "<a href="https://lila.science/datasets/chesapeakelandcover">Chesapeake Land Cover</a>" dataset and contains the following layers:</p> <ul> <li>NAIP 1m aerial imagery (year = 2010 for Pittsburgh, P and Phoenix, AZ, and year = 2012 for Durham NC, and Austin TX <ul> <li><a href="https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/index">https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/index</a></li> <li><a href="https://planetarycomputer.microsoft.com/dataset/naip">https://planetarycomputer.microsoft.com/dataset/naip</a></li> </ul> </li> <li>NLCD 2016 <ul> <li><a href="https://www.mrlc.gov/data">https://www.mrlc.gov/data</a></li> </ul> </li> <li>OpenStreetMap roads <ul> <li><a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></li> </ul> </li> <li>OpenStreetMap waterways, waterbodies, water= max(waterways, waterbodies) <ul> <li><a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></li> </ul> </li> <li>Microsoft building footprints <ul> <li><a href="https://github.com/Microsoft/USBuildingFootprints">https://github.com/Microsoft/USBuildingFootprints</a></li> </ul> </li> <li>EnviroAtlas 1m land cover labels: <ul> <li><a href="https://www.epa.gov/enviroatlas/about-data">https://www.epa.gov/enviroatlas/about-data </a></li> </ul> </li> <li>Prior probabilities of the 1m land cover labels (with OSM and building data fused into prior)</li> <li>Prior probabilities of the 1m land cover labels (without OSM or building data)</li> </ul> <p>For more information on how the OpenStreetMap layers are downloaded and processed, as well as how the prior probabilities are calculated, see the accompanying GitHub repository: </p> <p> </p> <p>Version 1.1 fixes an issue with the spatial index file.</p>
Land cover for MEGAN v3.2
<p>2020, 2017 and 2013 land cover for MEGAN v3.2. Citation: 高超, 张学磊, 修艾军, 等. 中国生物源挥发性有机物 (BVOCs) 时空排放特征研究[J]. 环境科学学报, 2019, 39(12): 4140-4151.</p>
Generating Annual 4-m Land Cover Products over 28 Metropolises in China during 2000-2020
<p>A 4-m fine-grained land cover products over the 28 provincial capital cities in China (mainly in temperate monsoon climate and subtropical monsoon climate zone, and categorized into 7 classes, i.e., cropland, forest, grass, shrub, water, impervious surface, and bare land) is generated through the proposed model; A 4-m annual land cover products during 2000-2020 in Wuhan, China is generated through the proposed model.</p> <p>To validate the effectiveness of the derived products, three verification approaches are employed. 1) evaluation by the consistency with the contemporary products including 10-m Esri Land Cover, 10-m FROMGLC10, and 30-m GlobeLand30 ; 2) evaluation by the publicly certified 4-m annotation dataset GID; 3) evaluation by the third-party-created annotation samples. The validation results can be viewed in the manuscript, and the reproduction executation details can be viewed at Readme.txt.</p> <p>The products is in .tif format, and validaiton region is in ENVI/hdr format. Users need to install ENVI software or Arcgis to read the product.</p> <p>"open_source_DETNet_code_and_simple_demo.rar" is open source code and the corresponding intruction.</p>
Land use and land cover changes in the contiguous United States from 1630 to 2020
<p>Through integrating multi-source data including high-resolution remote sensing image-based land use and land cover (LULC) data, model-based land use products, and historical land archives, we reconstructed historical LULC at an annual time scale and 1 km x 1 km resolution in the contiguous United States (CONUS) from 1630 to 2020. Compared to other historical LULC datasets, our data can capture the major characters of LULC as well as provide more accurate information with higher spatial and temporal resolution. The LULC data can be used for regional studies in a wide range of topics including LULC impacts on the ecosystem, biodiversity, water resource, carbon and nitrogen cycles, and greenhouse gas emissions.</p>
Theia OSO Land Cover Map 2106
<p>http://osr-cesbio.ups-tlse.fr/~oso/posts/2017-03-30-carte-s2-2016/</p>
Derivation of plant functional type (PFT) maps from the ESA CCI Land Cover product
<p><em>This package supplements the following paper submitted to ESSD: <strong>Gross and net land cover changes of the main plant functional types derived from the annual ESA CCI land cover maps (1992-2015).</strong></em></p> <p><em>Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A. and Peng, S.: Gross and net land cover changes based on plant functional types derived from the annual ESA CCI land cover maps, Earth Syst. Sci. Data Discuss., 1–23, doi:10.5194/essd-2017-74, 2017.</em></p> <p><em>This package contains the protocol of converting the original annual ESA CCI Land Cover product into plant functional types (PFTs) that can be used by land surface models and the corresponding cross-walking table.</em></p> <p><em>The original ESA LC class data and translated PFTs in 2000 as an example are attached in the .zip file. The annual ESA CCI PFT maps from 1992 to 2015 at half degree resolution are also added in a .zip file.</em></p>
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 7. 3D–диаграммы пространственного распределениЯ обилиЯ моллюска B. cylindrica (А), фитомассы (В), проективного покрытиЯ (С), твердости грунта на глубине 5–10 см (D) на участке № 1 в 2010 г. (единицы иЗмерениЯ осей Х и Y даны в метрах). Fig. 7. 3D–diagrams of the abundance spatial distribution of the snail B. cylindrica (A), phytomass (B), plants projective cover (C), 0–10 cm layer soil penetration resistance (D) at the site 1 in 2010. (axes X and Y presented in meters).
5m land cover for baseline and 3-30-300 scenarios in Paris, Aarhus, and Velika Gorica
<p>This dataset supports scenario analysis using a high-resolution (5m) land cover classification of three European cities: Paris Region (France), Aarhus Municipality (Denmark), and Grad Velika Gorica (Croatia). The scenarios are: current (baseline) land cover, and a created new land cover that meets the 3-30-300 rule for urban greening (Konijnendijk 2023). In the 3-30-300 scenario, every building has two or more tree raster cells within a 30 m buffer, every neighbourhood has 30% or more green and blue space cover within a 300 m buffer, and each building has an accessible green space of at least 1 ha within 300 m. This rule was applied to the urban footprint of each city. In Paris, this applied only to the four central départements and not the entire Paris Region, Île-de-France. </p> <p> </p> <p><strong>Associated Paper</strong></p> <p>The full methodology behind the datasets is described in the following paper. This paper analyses the extent to which each city currently meets, and measures the land cover change required to meet the 3-30-300 rule. Please also cite this paper when using the dataset.</p> <p>Owen, D., Fitch, A., Fletcher, D., Knopp, J., Levin, G., Farley, K., Banzhaf, E., Zandersen, M., Grandin, G., Jones, L. 2024. Opportunities and constraints of implementing the 3-30-300 rule for urban greening. <em>Urban Forestry & Urban Greening,</em> <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ufug.2024.128393" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.ufug.2024.128393</span></a></p> <p> </p> <p><strong>Original Data Sources</strong></p> <p>These layers are based on the high resolution land cover layers produced by Knopp (2021, 2022a, 2022b). For Paris, the baseline land cover was modified, using the 10 m land cover by Wu (2022), to reclassify trees to either coniferous or deciduous. </p> <p> </p> <p><strong>Data</strong></p> <p><strong><em>LC_Classification_Lookup_Table.docx</em></strong></p> <p>This word document is a lookup table for the baseline and 3-30-300 scenario land cover classification.</p> <p><strong><em>Baseline_and_3_30_300_HRLC_all_cities.zip </em></strong></p> <p>This file contains the baseline and 3-30-300 scenarios for Velika Gorica, Aarhus, and the four central départements of Paris Region (clipped to a 1km buffer). These files include all interventions from the 3-30-300 rule.</p> <p><strong><em>Original_and_Final_HRLC_3_30_300_Paris_Region.zip</em></strong></p> <p>This file contains the baseline and 3-30-300 scenario for the entire Paris Region only. Whilst there is land cover data for the entire Paris Region, the interventions from the 3-30-300 rule were only applied to the four central départements. This file has been uploaded separately because the file size is greater.</p> <p> </p> <p><strong>References</strong></p> <p>Knopp, J. M. (2021). High resolution land cover 2015 Aarhus, Denmark [Data set]. In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Version v1, Vol. 16, pp. 6545–6555). Zenodo. <a href="https://doi.org/10.5281/zenodo.5215792" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5215792</a></p> <p>Knopp, J. (2022a). High resolution land cover 2016 Velika Gorica (Version v1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7107514" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7107514</a></p> <p>Knopp, J. (2022b). High resolution land cover 2017 Ile-de-France [Data set]. REGREEN - Fostering nature‐based solutions for smart, green and healthy urban transitions in Europe and China. Horizon2020 Grant No. 821016. <a href="https://doi.org/10.5281/zenodo.7110027" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7110027</a></p> <p>Konijnendijk, C.C., 2023. Evidence-based guidelines for greener, healthier, more resilient neighbourhoods: Introducing the 3–30–300 rule. <em>Journal of forestry research</em>, <em>34</em>(3), pp.821-830.</p> <p>Owen, D., Fitch, A., Fletcher, D., Knopp, J., Levin, G., Farley, K., Banzhaf, E., Zandersen, M., Grandin, G., & Jones, L. (2024). Opportunities and constraints of implementing the 3–30–300 rule for urban greening. Urban Forestry & Urban Greening, 98, 128393. <a href="https://doi.org/10.1016/j.ufug.2024.128393">https://doi.org/10.1016/j.ufug.2024.128393 </a> </p> <p>Wanben Wu. (2022). Europe and China Refined Land cover (ECRLC) (10m) (Version V2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5846090" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5846090</a></p> <p> </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)
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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.