Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

115

datasets available to search

ShareScore release 0.7.1

Dataset results

115 results for “land use and land cover”

Learn how ShareScore rates datasets ↗

zenodo44/100

Mapping ecosystem types and land cover types in the Seychelles granitic islands, using Earth Engine and Sentinel-2

We share here maps produced using Earth Engine: https://code.earthengine.google.com/?accept_repo=users/bsenterre/gis The maps include a land cover classification based on Sentinel-2, at 10m resolution, using an Object-Based Image Analysis approach, for the Seychelles granitic islands. Based on the land cover, landform (modeled using TauDEM), altitude and expert knowledge, we then derived a model of ecosystem types, with 3 maps: current distribution, potential distribution and prehuman distribution. A report exists (18th May 2022) that describes in detail the methodology, and it is being used for the preparation of a publication. The maps uploaded here are in raster format (geotif), crs=4326, and are accompanied by QGIS legend files (.qml), so they should load in QGIS with their legend automatically.

opencc-by-4.0Jul 2021View details →

zenodo44/100

High resolution and high cadence time series of land surface categories, land use land cover, and land use land cover changes

A prototype of monthly, 10 m resolution land surface categories, land use land cover (LULC) cover, and LULC change maps derived from Sentinel-2 data over three areas within Belgium, Portugal, and Sicily for the period 2018-2020. The LULC and LULC change maps were independently validated by IIASA. All products were generated within the framework of the RapidAI4EO project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356. The data description can be found below. The validation report of the LULC and LULC change maps can be found in validation_LULC.pdf and validation_change.pdf, respectively, and the validation dataset can be found in Lesiv et al. (2023). Data description Increasing the cadence of the land cover updates from the typical (multi-)annual to monthly cadence poses several challenges. First, several land cover types are difficult to discriminate without any knowledge of temporal dynamics. For instance, croplands are characterized by a dynamic of vegetation growth and a harvest period (i.e. cycles of bare soil, sparsely vegetated and vegetated periods). This contrasts with grasslands that often lack the harvest period resulting in a bare soil cover. Without this temporal information, it is difficult to distinguish a vegetated cropland field from grassland. Second, phenological changes may introduce a large intra-class variability and thus also confusion between classes. For example, the shedding of leaves during autumn or wilting of herbaceous vegetation in dry summer periods introduces spectral variability within land cover classes. To overcome these challenges, we developed a workflow with two main phases. The first phase aims to map land surface categories (LSC) at a monthly resolution. The next phase uses the resulting monthly LSC probability time series to classify land cover. Land surface category (LSC) These LSC represent basic, observable bio-geophysical properties (categories) of the Earth surface that can be predicted directly from individual monthly composites. LSC classes contain a set of vegetated and non-vegetated surface categories. Discrete LSC classification legend: <table> <tbody> <tr> <td> Map code </td> <td> Land cover class </td> </tr> <tr> <td> 11 </td> <td> Tree (leaf-on) </td> </tr> <tr> <td> 12 </td> <td> Shrubland (leaf-on) </td> </tr> <tr> <td> 13 </td> <td> Grassland </td> </tr> <tr> <td> 14 </td> <td> Woody vegetation (leaf-off) </td> </tr> <tr> <td> 15 </td> <td> Wilted herbaceous vegetation </td> </tr> <tr> <td> 21 </td> <td> Bare/sparse vegetation </td> </tr> <tr> <td> 22 </td> <td> Water </td> </tr> <tr> <td> 24 </td> <td> Built-up </td> </tr> </tbody> </table> In order to predict the LSC, we trained a CatBoost model (Dorogush et al., 2018) using a DEM, spectral bands and vegetation indices, country, the timing (month) of the spectral data, and the pseudo-probability of a U-Net model trained to segment built-up surfaces as input. Labels were derived by post-processing the land cover labels of the ESA WorldCover product (Zanaga et al., 2021). Please note that the collection of these labels was suboptimal, likely having an impact on the LULC and change maps generated in the prototype. Land use land cover After predicting LSC over the three AOI’s, we trained a CatBoost model using the LSC probabilities over a window of one year, country, and the timing (month) as independent variable. The use of LSC probabilities over multiple months allows to incorporate information about dynamics, which is necessary to discriminate some classes (e.g. cropland and grassland or cropland and bare). Similar to the LSC labels, the LULC labels were derived from the ESA WorldCover product v100 (year 2020), resulting in a similar legend system. The use of a moving window approach to predict LULC allows to (i) incorporate temporal information that is necessary to discriminate land cover classes and (ii) is expected to lead to more consistent land cover maps. It however has the disadvantage that (i) no land cover predictions are available at the beginning and the end of the time series and (ii) the timing of the predicted land cover change is not always accurate. To resolve these issues, we applied a post-processing step that compares and integrates the LULC predictions and cleaned LSC predictions. Discrete LC classification legend: <table> <tbody> <tr> <td> Map code </td> <td> Land cover class </td> </tr> <tr> <td> 10 </td> <td> Tree cover </td> </tr> <tr> <td> 20 </td> <td> Shrubland </td> </tr> <tr> <td> 30 </td> <td> Grassland </td> </tr> <tr> <td> 40 </td> <td> Cropland </td> </tr> <tr> <td> 50 </td> <td> Built-up </td> </tr> <tr> <td> 60 </td> <td> Bare/sparse vegetation </td> </tr> <tr> <td> 80 </td> <td> Permanent water bodies </td> </tr> <tr> <td> 90 </td> <td> Herbaceous wetland </td> </tr> </tbody> </table> Land use land cover change Monthly change maps were finally derived from the land cover maps. The pixel values within the change maps represent the percentage of pixels that changed with respect to the previous month over an area of 90x90m. The maps contain values between 0-100, with larger values assigned to larger change patches. A value of 100 indicates that all pixels within an area of 90x90m around the pixel were flagged as change. Files The zip files contain the following data: <ul> <li>lsc.zip: land surface category maps over the three AOI’s</li> <li>lc.zip: LULC maps over the three AOI’s</li> <li>change.zip: change maps over the three AOI’s</li> </ul> These maps are generated for each month over the period 2018-2020 for each of the tiles (see tiles.gpkg for an overview of all tiles). The files names use the following naming convention: “tile-year-month.tif”. References Myroslava Lesiv, Halyna Bun, & Martina Duerauer. (2023). Validation data set on land cover changes for RapidAI4EO project [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7825963                                                                   Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363. Zanaga, D., et al.., 2021. ESA WorldCover 10 m 2020 v100. <a href="https://doi.org/10.5281/zenodo.5571936 ">https://doi.org/10.5281/zenodo.5571936 </a>

opencc-by-4.0May 2023View details →

zenodo44/100

Zambia land use and land cover field samples

General Description This data set consists of 697 land use and land cover (LULC) sample locations and reference labelling collected over Muchinga and Copperbelt provinces, Zambia. The data available in ESRI shapefile format (spatial reference system: WGS 84, EPSG: 4326) was collected in a field campaign realized between May and June 2023. The dataset can be used for training and to evaluate the performance of the classification models. Land Use and Land Cover Classes The five LULC classes collected are: <table> <tbody> <tr> <td>Label</td> <td>Code</td> </tr> <tr> <td>Forest land</td> <td>1</td> </tr> <tr> <td>Cropland</td> <td>2</td> </tr> <tr> <td>Grassland</td> <td>3</td> </tr> <tr> <td>Wetland</td> <td>4</td> </tr> <tr> <td>Other land</td> <td>5</td> </tr> </tbody> </table> Data set Details <ul> <li>Time period: May / June 2023</li> <li>Type of data: land use and land cover samples</li> <li>How the data was collected: KoboCollect app</li> <li>Coordinate reference system: EPSG:4326</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (27.5472562965098682,-13.8722138995139996 : 32.0291093994995890,-11.2161515458659427)</li> <li>File format: shapefile</li> </ul>

opencc-by-4.0Sep 2023View details →

edi44/100

A Land-use/Land Cover Classification of Baltimore City in 1927

Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 93 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 585 km2 (226 mi2) including all of Baltimore City, and an area immediately adjacent to the city known at the time as the Metropolitan District of Baltimore County. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures a moment of dynamic expansion in the city, just prior to the Great Depression and can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region.

openCC (other)Jul 2021View details →

zenodo40/100

Data from : Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning

This data repository contains the processed lidar metrics for characterizing the habitat structure for classifying main land cover and habitat types in the Lauwersmeer area in the northern part of the Netherlands in the province of Groningen (5754 ha). The lidar metrics were derived from Airborne Laser Scanning (ALS) data using the Actueel Hoogtebestand Nederland 2 (AHN2) openly available dataset from https://www.pdok.nl/.  The derived lidar metrics saved in *.grd file format and contain 32 bands. Each band represents a lidar metric and the water surface was masked out in the dataset. The *l1* in the file name indicates that the file was used for level 1 (wetland) classification and *l23* used for level 2 (land cover types within wetland) and level 3 (reedbed habitats) classification. The lidar metrics were calculated using lidR (<a href="https://github.com/Jean-Romain/lidR">https://github.com/Jean-Romain/lidR</a>) software package. Further details related to the lidar metrics extraction can be found at <a href="https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats">https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats</a> Github repository.

opencc-by-4.0May 2020View details →

zenodo40/100

Kaduna state Land Use Land Cover 2022

The map shows the land use land Cover of Kaduna state as at 2022 showing Build up area, water body,green areas, and open space which was achieved by the use of ESRI LULC data 2019-2022 open Atlas, the data was process using ArcGIS 10.8 using  arctoolbox techniques which includes Extract by mask, Conversion from raster to polygon, working with add field from the attribute table and geoprocessing tool i.e Dissolve where used to analyze the data, and finally the data was switched to layout view to add the map element and the work was exported to a Soft copy for usages in form of an A4 ISO standard respectively

opencc-by-4.0Sep 2023View details →

dryad40/100

Land use and land cover scenarios for the Maurienne valley (French Alps) at 2085 horizon produced using CLUMPY model

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).

opencc-zeroMar 2024View details →

zenodo40/100

LAND USE AND LAND COVER OF THE UPPER AND MIDDLE BASIN OF THE LUJÁN RIVER

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.

opencc-by-4.0Aug 2023View details →

zenodo40/100

Data set: Land use and land cover change in a tropical mountain landscape of northern Ecuador: altitudinal patterns and driving forces

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.

opencc-by-4.0Jan 2022View details →

zenodo40/100

Land use and land cover changes in the contiguous United States from 1630 to 2020

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.

opencc-by-4.0Mar 2022View details →

zenodo40/100

Land Use and Land Cover Mapping of Katanino Forest Reserve, Zambia (2019–2023)

<h1>Overview</h1> 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.  <h1>Maps Accuracy</h1> 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. Table 1:  Land use and land cover maps validation, including overall, producer (PA) and user (UA) accuracies values for each class. <table> <tbody> <tr> <td>   </td> <td> 2019 </td> <td>   </td> <td> 2020 </td> <td>   </td> <td> 2021 </td> <td>   </td> <td> 2022 </td> <td>   </td> <td> 2023 </td> <td>   </td> </tr> <tr> <td>   </td> <td> PA </td> <td> UA </td> <td> PA </td> <td> UA </td> <td> PA </td> <td> UA </td> <td> PA </td> <td> UA </td> <td> PA </td> <td> UA </td> </tr> <tr> <td> Forest </td> <td> 0.87 </td> <td> 0.99 </td> <td> 0.83 </td> <td> 0.99 </td> <td> 0.88 </td> <td> 0.99 </td> <td> 0.91 </td> <td> 0.99 </td> <td> 0.88 </td> <td> 1 </td> </tr> <tr> <td> Non Forest </td> <td> 0.99 </td> <td> 0.86 </td> <td> 0.99 </td> <td> 0.82 </td> <td> 0.99 </td> <td> 0.99 </td> <td> 0.99 </td> <td> 0.89 </td> <td> 1 </td> <td> 0.87 </td> </tr> <tr> <td> Overall Accuracy </td> <td> 0.92 </td> <td>   </td> <td> 0.90 </td> <td>   </td> <td> 0.93 </td> <td>   </td> <td> 0.94 </td> <td>   </td> <td> 0.93 </td> <td>   </td> </tr> </tbody> </table> <h1>Files descripion</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>

opencc-by-4.0May 2024View details →

zenodo40/100

Land use and land cover data for Northern Coast of São Paulo State (Brazil) from 1985 to 2015

Authors: Ana Beatriz Pierri Daunt and Thiago Sanna Freire Silva Product: Land use and land cover maps for 1985, 1990, 1995, 2000, 2005, 2010, 2015 in raster format. Study area: Northern Coast of São Paulo State (Brazil) 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, 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.

opencc-by-4.0Apr 2019View details →

zenodo40/100

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.

opencc-by-4.0Apr 2022View details →

zenodo40/100

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.

opencc-by-4.0Jun 2021View details →

zenodo40/100

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

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 “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  ”.

opencc-by-4.0Aug 2022View details →

dryad40/100

Land use and land cover scenarios for the Maurienne valley (French Alps) at 2085 horizon produced using CLUMPY model

Open the record for dataset details and reuse information.

publicMar 2024View details →

zenodo36/100

Land use and cover (LUC) rasters of the São Lourenço River Basin (2002 - 2014)

The LUC dataset of São Lourenço river basin, a major Pantanal wetland contribution area as provided by the 4th edition of the <a href="https://www.embrapa.br/pantanal/bacia-do-alto-paraguai">Monitoring of Changes in Land cover and Land Use in the Upper Paraguay River Basin - Brazilian portion - Review Period: 2012 to 2014</a> (Embrapa Pantanal, Instituto SOS Pantanal, and WWF-Brasil 2015). For the development of the <a href="https://reginalexavier.github.io/OpenLand/index.html">OpenLand R package</a> (tests and <a href="https://reginalexavier.github.io/OpenLand/articles/openland_vignette.html">vignettes</a>), the original multi-year shape file was clipped to the extent of São Lourenço basin, transformed into a 5-layer <a href="https://rdrr.io/cran/raster/man/stack.html"><code>RasterStack</code></a> and then saved as .RDA file which can be loaded into <a href="https://www.r-project.org/">R</a> (R Core Team, 2019). Five LUC maps (2002, 2008, 2010, 2012 and 2014) compose the time series. The study area of approximately 22,400 km2 is located in the Cerrado Savannah biom in the southeast of the Brazilian state of Mato Grosso. The category names and colors to be associated with the pixel values follow the conventions given by Instituto SOS Pantanal and WWF-Brasil (2015) <a href="https://www.embrapa.br/documents/1354999/1529097/BAP+-+Mapeamento+da+Bacia+do+Alto+Paraguai+-+estudo+completo/e66e3afb-2334-4511-96a0-af5642a56283">(access document here, page 17)</a>. The Portuguese legend acronyms were maintained as defined in the original dataset. The original legend from SOS Pantanal <pre><code class="language-markdown"> _______________________________________________________________________________________________ |Pixel Value |Legend | Class | Use | Category | Colour| |------------|--------|---------------|-------------------|-----------------------------|-------| |2 | Ap | Anthropogenic | Anthropogenic Use | Cattle farming |#FFE4B5| |3 | FF | Natural | NA | Forest formation |#228B22| |4 | SA | Natural | NA | Park savanna |#00FF00| |5 | SG | Natural | NA | Gramineous savanna |#CAFF70| |7 | aa | Anthropogenic | NA | Anthropogenized vegetation |#EE6363| |8 | SF | Natural | NA | Wooded savanna |#00CD00| |9 | Agua | Natural | NA | Water bodies |#436EEE| |10 | Iu | Anthropogenic | Anthropogenic Use | Urban areas |#FFAEB9| |11 | Ac | Anthropogenic | Anthropogenic Use | Crop farming |#FFA54F| |12 | R | Anthropogenic | Anthropogenic Use | Reforestation |#68228B| |13 | Im | Anthropogenic | Anthropogenic Use | Mining areas |#636363| </code></pre>

opencc-by-4.0Feb 2020View details →

zenodo36/100

Land Use and Land Cover Change 2000-2016 in Mozambique

This repository includes land use and land cover maps of Mozambique for 2000, 2005, 2010 and 2016 years. The methodology is based on remote sensing methodology and include satellite image collection and compositing (annual cloud-free and shadow free Landsat images for 2000, 2005, 2010 and 2016),  delineation of a large collection of training plots based on National Land Cover Classification system level 1, supervised classification using a machine learning algorithm (Random Forest) and post-processing steps. The LULCC map for 2016  show area statistics of 45.0% (35.8 Mha) of dry forest, 37.0% (29.3 Mha) of grassland and fallow, 13.7% (10.8 Mha) of cropland 2.0% (1.6 Mha) of wetlands, 1.3% (1 Mha) of other categories (rocks, sands, or bare soils), 0.3% (271,000 ha) of Mangroves, and 0.1% (673.1 ha) of urban areas. The deforestation over the 2000-2016 period is estimated to have been 207,272 ha per year. The methodology and statistics are presented in the report included in this repository. Theses maps are outputs from the study "An Analysis of Land Use Changes and Land Degradation in Mozambique" conducted by Nitidae and CIRAD in the LAUREL project.

opencc-by-4.0Nov 2020View details →

zenodo36/100

Land Use and Land Cover 2019 of Ribaue Mountains (Mount Ribaue and Mount M'paluwe) in Mozambique

This repository includes the land use and land cover map of the Ribaue Mountains and surroundings (Ribaue district, Nampula province, Mozambique), using remote sensing. The Ribaue massif is a series of granite inselbergs in northern Mozambique near the town of Ribaue in Nampula Province. The main area of the massif is made up of the Serra de Ribaue to the west and the Serra de M'paluwe to the east. The inselbergs rise from a relatively flat landscape at ca 500-600 m altitude up to 1675 m on Monte M'paluwe. They form part of a belt of granite rock outcrops, inselbergs and mountains, running NE-SW across Nampula and Zambezia provinces and including Mt Inago (1804 m) and Mt Namuli (2419 m) to the southwest of the Ribaue massif. This belt is considered as a center of endemism. Overall the site supports 15 nationally endemic plant taxa (plants that only occur in Mozambique), 11 near-endemics (plants that are restricted to Mozambique and neighbouring countries) and 10 taxa that are threatened with extinction on the Global IUCN Red List. Steeply sloping granite rock outcrops, mid-altitude moist forest and miombo woodland are the dominant habitat types at the Ribaue massif. The site also includes smaller areas of gallery forest, marsh, seasonal stream gullies, seepage on granite rock, and shaded granite cliffs. The methodology used in this study is based on a classical approach of remote sensing: satellite image collection (cloud-free and shadow free Sentinel 2, 10 m resolution, two season), identification of land use typology (based on field campains), delineation of training plots, supervised classification of land use using a machine learning algorithm (Random Forest) and finally, calculation of land occupation statistics. The methodology and statistics are presented in the report included in the repository.

opencc-by-4.0Oct 2020View details →

zenodo36/100

30 Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel using Landsat Timeseries

30m resolution historically consistent land cover and cover fraction maps over the Sudano-Sahel for the period 1986-2015. These land cover / cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification / regression model, while historical consistency is achieved using the Hidden Markov Model. Validated land cover / cover fraction maps covering the full Sudano-Sahel are provided for 2015 (2015_Sahel.zip), while historical maps are available for four focus areas. The extent of the areas are displayed in 11_study_area.jpeg Each of the zip files contains 14 GeoTIFF files for the respective period and area: <ul> <li>Landsat_LC30_epochYYYY_AREA_bare-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_crops-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_DataDensityIndicator.tif [# overpasses that are used as input for the creation of the maps for this region / epoch]</li> <li>Landsat_LC30_epochYYYY_AREA_discrete-classification-HMM.tif [temporally cleaned discrete classification map using the Hidden Markov Model; legend see below] </li> <li>Landsat_LC30_epochYYYY_AREA_discrete-classification.tif [original discrete classification map; legend see below]</li> <li>Landsat_LC30_epochYYYY_AREA_forest-type-layer.tif [legend see below]</li> <li>Landsat_LC30_epochYYYY_AREA_grass-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_moss-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_shrub-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_snow-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_tree-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_urban-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_water-permanent-coverfraction-layer.tif [0-100%]</li> <li>Landsat_LC30_epochYYYY_AREA_water-seasonal-coverfraction-layer.tif [0-100%]</li> </ul> Discrete classification legend: <ul> <li>0: Unknown. No or not enough satellite data available.</li> <li>20: Shrubs. Woody perennial plants with persistent and woody stems and without any defined main stem being less than 5 m tall. The shrub foliage can be either evergreen or deciduous.</li> <li>30: Herbaceous vegetation. Plants without persistent stem or shoots above ground and lacking definite firm structure. Tree and shrub cover is less than 10 %.</li> <li>40: Cultivated and managed vegetation / agriculture. Lands covered with temporary crops followed by harvest and a bare soil period (e.g., single and multiple cropping systems). Note that perennial woody crops will be classified as the appropriate forest or shrub land cover type.</li> <li>50: Urban / built up. Land covered by buildings and other man-made structures.</li> <li>60: Bare / sparse vegetation. Lands with exposed soil, sand, or rocks and never has more than 10 % vegetated cover during any time of the year.</li> <li>70: Snow and ice. Lands under snow or ice cover throughout the year.</li> <li>80: Permanent water bodies. Lakes, reservoirs, and rivers. Can be either fresh or salt-water bodies.</li> <li>90: Herbaceous wetland. Lands with a permanent mixture of water and herbaceous or woody vegetation. The vegetation can be present in either salt, brackish, or fresh water.</li> <li>100: Moss and lichen.</li> <li>111: Closed forest, evergreen needle leaf. Tree canopy >70 %, almost all needle leaf trees remain green all year. Canopy is never without green foliage.</li> <li>112: Closed forest, evergreen broad leaf. Tree canopy >70 %, almost all broadleaf trees remain green year round. Canopy is never without green foliage.</li> <li>113: Closed forest, deciduous needle leaf. Tree canopy >70 %, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>114: Closed forest, deciduous broad leaf. Tree canopy >70 %, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>115: Closed forest, mixed.</li> <li>116: Closed forest, not matching any of the other definitions.</li> <li>121: Open forest, evergreen needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all needle leaf trees remain green all year. Canopy is never without green foliage.</li> <li>122:Open forest, evergreen broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, almost all broadleaf trees remain green year round. Canopy is never without green foliage.</li> <li>123: Open forest, deciduous needle leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>124: Open forest, deciduous broad leaf. Top layer- trees 15-70 % and second layer- mixed of shrubs and grassland, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.</li> <li>125: Open forest, mixed.</li> <li>126: Open forest, not matching any of the other definitions.</li> <li>200: Oceans, seas. Can be either fresh or salt-water bodies.</li> </ul> Forest type legend: <ul> <li>0: Unknown</li> <li>1: Evergreen needle leaf</li> <li>2: Evergreen broad leaf</li> <li>3: Deciduous needle leaf</li> <li>4: Deciduous broad leaf</li> <li>5: Mix of forest types</li> </ul> More detail on the classification algorithm and the resulting maps can be found in the accompanying paper:  Souverijns, N.; Buchhorn, M.; Horion, S.; Fensholt, R.; Verbeeck, H.; Verbesselt, J.; Herold, M.; Tsendbazar, N.-E.; Bernardino, P.N.; Somers, B.; Van De Kerchove, R. Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series. Remote Sens. 2020, 12, 3817. https://doi.org/10.3390/rs12223817 Please note that a quality layer is available for each of the historical areas / periods (Landsat_LC30_epochYYYY_AREA_DataDensityIndicator.tif). In case a value of 4 or lower is achieved here, the discrete land cover classification / cover fraction for this period / area is highly uncertain. Take this into account when analysing the maps. Furthermore, take note that there is a large difference between the temporally cleaned (Landsat_LC30_epochYYYY_AREA_discrete-classification-HMM.tif) and original discrete land cover classification (Landsat_LC30_epochYYYY_AREA_discrete-classification.tif). We recommend to use the temporally cleaned version in combination with the quality layer.

opencc-by-4.0Dec 2019View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record