https://data.mundialis.de:/geonetwork/srv/api/records
type: PagedCollection
type: PartialCollectionView
last page: https://data.mundialis.de:/geonetwork/srv/api/records
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mundialis GeoNetwork catalogue
type: Catalog
label: mundialis GeoNetwork catalogue (mundialis)
open search description: eng/portal.opensearch
uri lookup endpoint: eng/rdf.search?any=
publisher: mundialis
themes: GEMET - INSPIRE themes, version 1.0
themes: Continents, countries, sea regions of the world.
themes: INSPIRE priority data set
themes: Classification of spatial data services
themes: Service Type
language: eng
dataset: ERA5-Land monthly: Total precipitation, monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
dataset: MODIS LST Day and Night, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
dataset: Copernicus Digital Elevation Model (DEM) for Europe at 1000 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter DEM dataset
dataset: MODIS NDVI and EVI, 16-day time series for Europe at 1 km resolution
dataset: MODIS NDWI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
dataset: Landcover classification map of Germany 2019 based on Sentinel-2 data
dataset: mundialis
dataset: GEMET - INSPIRE themes, version 1.0
dataset: Continents, countries, sea regions of the world.
dataset: INSPIRE priority data set
dataset: Classification of spatial data services
dataset: Service Type
dataset: Human health and safety
dataset: Meteorological geographical features
dataset: Elevation
dataset: Land cover
dataset: Land use
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2019.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2020.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2021.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2022.zip
dataset: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2023.zip
dataset: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2019.zip
dataset: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2020.zip
dataset: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2021.zip
dataset: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2022.zip
dataset: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2023.zip
dataset: zenodo.6211883
dataset: https://zenodo.org/record/6573852/files/MODIS_NDVI_v01.zip
dataset: https://zenodo.org/record/6573852/files/MODIS_NDVI_EVI.sld
dataset: https://zenodo.org/record/6573852/files/MODIS_NDVI_EVI.qml
dataset: https://zenodo.org/record/6573852/files/MODIS_EVI_v01.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2019.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2020.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2021.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2022.zip
dataset: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2023.zip
dataset: LICENSE.html
dataset: germany 2016 2019 MAJA cd total v02.sld
dataset: germany 2016 2019 MAJA cd total v02.qml
dataset: mundialis GmbH & Co. KG

mundialis
type: Organization

GEMET - INSPIRE themes, version 1.0
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

Continents, countries, sea regions of the world.
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

INSPIRE priority data set
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

Classification of spatial data services
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

Service Type
type: ConceptScheme
uri: https://data.mundialis.de:/geonetwork/srv/eng/thesaurus.download

https://data.mundialis.de/geonetwork/srv/resources/records/6e9f4ea1-7111-4505-8165-a5857e486584
type: CatalogRecord
primary topic: zenodo.12189668
issued: 2024-07-02
modified: 2024-07-02
references: https://data.mundialis.de/geonetwork/srv/resources/records/6e9f4ea1-7111-4505-8165-a5857e486584/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/6e9f4ea1-7111-4505-8165-a5857e486584/formatters/xml
format: application/xml
  type: IMT
  label: XML

ERA5-Land monthly: Total precipitation, monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
type: Dataset
abstract: ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023) Source data: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Total precipitation: Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Processing steps: The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of…
keyword: MOOD-H2020
keyword: Precipitation
keyword: Rift Valley fever
keyword: RVF
keyword: Mauritania
theme: Human health and safety
theme: geoscientificInformation
theme: health
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-18.00 14.00, -18.00 28.00, -4.00 28.00, -4.00 14.00, -18.00 14.00))
temporal: 2019-01-01 / 2023-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: intellectualPropertyRights
license: Licence to Use Copernicus Products (Version 1.2, Nov. 2019): "All users of Copernicus Products must provide clear and visible attribution to the Copernicus programme. The Licensee will communicate to the public the source of the Copernicus Products by crediting the Copernicus Climate Change and Atmosphere Monitoring Services" (full licence available here: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf
license: { "id": "other-open", "name": "Licence to Use Copernicus Products", "url": "https://ads.atmosphere.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf", "quelle": "Source: Copernicus Programme" }
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2019.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2020.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2021.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2022.zip
distribution: https://zenodo.org/records/12189669/files/ERA5_land_prectot_4326_monthly_2023.zip
data quality: Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

https://data.mundialis.de/geonetwork/srv/resources/records/4ccb09d0-99c1-4509-9cda-9195b5755ceb
type: CatalogRecord
primary topic: zenodo.14762335
issued: 2025-01-31
modified: 2025-01-31
references: https://data.mundialis.de/geonetwork/srv/resources/records/4ccb09d0-99c1-4509-9cda-9195b5755ceb/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/4ccb09d0-99c1-4509-9cda-9195b5755ceb/formatters/xml
format: application/xml
  type: IMT
  label: XML

MODIS LST Day and Night, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
type: Dataset
abstract: Land Surface Temperature (LST) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023). Source data: - MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid (MOD11A2 v061): https://lpdaac.usgs.gov/products/mod11a2v061/ The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MOD11A2) Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MOD11A2 is a simple average of all the corresponding MOD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. For the time period January 2019 - December 2023, the LST layer of the original data has been processed. Both day (10:30 am) and night (10:30 pm) overpasses have been processed separately. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers. The time series has been gapfilled with a temporal and a spatial approach. Gaps in the time series were filled with a harmonic analysis of time series using six frequencies to also model relatively short-term changes in LST. Only missing values were replaced by modelled values. The 8-day data are then aggregated to monthly temporal resolution using the average and reprojected to Latitude-Longitude/WGS84. File naming: lst_day/night_monthly_YYYY_MM_30arcsec.tif e.g.: lst_day_monthly_2023_12_30arcsec.tif The date within the filename are year and month of aggregated timestamp. Pixel values: LST * 10 Scaled to Integer, example: value 327 = 32.7 Units are degree Celsius. Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 28N south: 14N west: 18W east: 4W Temporal extent: January 2019 - December 2023 Sp…
keyword: MOOD-H2020
keyword: Land Surface Temperature
keyword: LST
keyword: Rift Valley Fever
keyword: RVF
keyword: Mauritania
theme: Human health and safety
theme: Meteorological geographical features
theme: geoscientificInformation
theme: health
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-18.00 14.00, -18.00 28.00, -4.00 28.00, -4.00 14.00, -18.00 14.00))
temporal: 2019-01-01 / 2023-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge.
license: All LP DAAC current data and products acquired through the LP DAAC have no restrictions on reuse, sale, or redistribution. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For full information on LP DAAC Data Citation and Policies, see: https://lpdaac.usgs.gov/data/data-citation-and-policies/
distribution: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2019.zip
distribution: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2020.zip
distribution: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2021.zip
distribution: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2022.zip
distribution: https://zenodo.org/records/14762335/files/MODIS_LST_4326_monthly_2023.zip
data quality: Derived from: MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid (MOD11A2 v061): https://lpdaac.usgs.gov/products/mod11a2v061/ Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers. The time series has been gapfilled with a temporal and a spatial approach.

https://data.mundialis.de/geonetwork/srv/resources/records/e4c14079-1dd3-44e7-9b9b-a5f8593abfee
type: CatalogRecord
primary topic: zenodo.6211883
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/e4c14079-1dd3-44e7-9b9b-a5f8593abfee/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/e4c14079-1dd3-44e7-9b9b-a5f8593abfee/formatters/xml
format: application/xml
  type: IMT
  label: XML

Copernicus Digital Elevation Model (DEM) for Europe at 1000 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter DEM dataset
type: Dataset
abstract: The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters. The Copernicus DEM for Europe at 1000 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt In order to reproject the data to EU-LAEA projection while reducing the spatial resolution to 1000 m, bilinear resampling was performed in GRASS GIS (using r.proj) and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.
keyword: digital terrain model
keyword: elevation
keyword: Copernicus
keyword: DEM
keyword: DSM
keyword: geomorphometry
keyword: environment
keyword: Europe
keyword: remote sensing
keyword: radar
keyword: space
keyword: MOOD-H2020
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
theme: Elevation
theme: elevation
theme: geoscientificInformation
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-32.00 18.00, -32.00 82.00, 70.00 82.00, 70.00 18.00, -32.00 18.00))
temporal: 2010-01-01 / 2015-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
license: Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International (CC-BY-SA 4.0)
license: { "id": "cc-by-sa/4.0", "name": "Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International", "url": "http://creativecommons.org/licenses/by-sa/4.0/", "quelle": "Source: mundialis" }
license: EO data provided under COPERNICUS by the European Union and ESA. For more information see: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/55df6882-8593-4123-b738-3558c20ad98c
distribution: zenodo.6211883
data quality: derived from Copernicus DEM GLO-30

https://data.mundialis.de/geonetwork/srv/resources/records/4ea3b1e9-d3d0-4c3f-afb1-6445cec3a89d
type: CatalogRecord
primary topic: zenodo.6573852
issued: 2024-07-02
modified: 2024-07-02
references: https://data.mundialis.de/geonetwork/srv/resources/records/4ea3b1e9-d3d0-4c3f-afb1-6445cec3a89d/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/4ea3b1e9-d3d0-4c3f-afb1-6445cec3a89d/formatters/xml
format: application/xml
  type: IMT
  label: XML

MODIS NDVI and EVI, 16-day time series for Europe at 1 km resolution
type: Dataset
abstract: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from MODIS data for Europe at 1 km resolution. Source data: - MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MOD13A1 v006): https://lpdaac.usgs.gov/products/mod13a1v006/ - MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid (MYD13A1 v006): https://lpdaac.usgs.gov/products/myd13a1v006/ The MOD/MYD13A1 Version 6 product provide Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. For the time periods October 2016 - March 2017 and August 2020 - April 2021, the original data has been reprojected to ETRS89-extended / LAEA Europe and aggregated to a 1 km grid. The temporal resolution is 16 days. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". File naming: productCode.acquisitionDate[A (YYYYDDD)]_mosaic_spatialResolution_frequency_VI.tif example: MOD13A1.A2020305_mosaic_1000m_16_days_NDVI.tif The date is Year and Day of Year. Values are NDVI/EVI * 10000. Example: Value 6473 = 0.6473
keyword: NDVI
keyword: EVI
keyword: Vegetation Index
keyword: Europe
keyword: MOOD-H2020
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
theme: Land cover
theme: geoscientificInformation
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-52.00 30.00, -52.00 72.00, 49.00 72.00, 49.00 30.00, -52.00 30.00))
temporal: 2016-10-01 / 2017-03-31
temporal: 2020-08-01 / 2021-04-30
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge.
license: All LP DAAC current data and products acquired through the LP DAAC have no restrictions on reuse, sale, or redistribution. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For full information on LP DAAC Data Citation and Policies, see: https://lpdaac.usgs.gov/data/data-citation-and-policies/
distribution: https://zenodo.org/record/6573852/files/MODIS_NDVI_v01.zip
distribution: https://zenodo.org/record/6573852/files/MODIS_NDVI_EVI.sld
distribution: https://zenodo.org/record/6573852/files/MODIS_NDVI_EVI.qml
distribution: https://zenodo.org/record/6573852/files/MODIS_EVI_v01.zip
data quality: Derived from MODIS Terra/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers.

https://data.mundialis.de/geonetwork/srv/resources/records/e4941c0b-27c0-4522-857f-5e02d719721e
type: CatalogRecord
primary topic: zenodo.14764686
issued: 2025-01-31
modified: 2025-01-31
references: https://data.mundialis.de/geonetwork/srv/resources/records/e4941c0b-27c0-4522-857f-5e02d719721e/formatters/xml
format: text/html
  type: IMT
  label: HTML

https://data.mundialis.de/geonetwork/srv/resources/records/e4941c0b-27c0-4522-857f-5e02d719721e/formatters/xml
format: application/xml
  type: IMT
  label: XML

MODIS NDWI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
type: Dataset
abstract: Normalized Difference Water Index (NDWI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023). Source data: - MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid (MOD09A1 v061): https://lpdaac.usgs.gov/products/mod09a1v061/ The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. For the time period January 2019 - December 2023, the NDWI has been calculated from the Terra MOD09A1 Version 6.1 product. The layers B02 (near infrared) and B06 (shortwave infrared) of the original data have been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layer. The time series has been gapfilled with a temporal and a spatial approach. Gaps in the time series were filled with a harmonic analysis of time series using six frequencies to also model relatively short-term changes in NDWI. Only missing values were replaced by modelled values. NDWI was calculated as the normalized difference of the bands B02 (near infrared) and B06 (shortwave infrared) with: NDWI = (B02 – B06) / (B02 + B06). This NDWI represents vegetation water content. The 8-day data are then aggregated to monthly temporal resolution using the average and reprojected to Latitude-Longitude/WGS84. File naming: ndwi_monthly_YYYY_MM_30arcsec.tif e.g.: ndwi_monthly_2023_12_30arcsec.tif The date within the filename are year an…
keyword: MOOD-H2020
keyword: Normalized Difference Water Index
keyword: NDWI
keyword: Rift Valley Fever
keyword: RVF
keyword: Mauritania
theme: Human health and safety
theme: geoscientificInformation
theme: health
spatial: Polygon
  as WKT: <http://www.opengis.net/def/crs/OGC/1.3/CRS84> Polygon((-18.00 14.00, -18.00 28.00, -4.00 28.00, -4.00 14.00, -18.00 14.00))
temporal: 2019-01-01 / 2023-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: asNeeded
language: eng
license: otherRestrictions
license: license
license: All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge.
license: All LP DAAC current data and products acquired through the LP DAAC have no restrictions on reuse, sale, or redistribution. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For full information on LP DAAC Data Citation and Policies, see: https://lpdaac.usgs.gov/data/data-citation-and-policies/
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2019.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2020.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2021.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2022.zip
distribution: https://zenodo.org/records/14764686/files/MODIS_NDWI_4326_monthly_2023.zip
data quality: Derived from: MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid (MOD09A1 v061): https://lpdaac.usgs.gov/products/mod09a1v061/ NDWI has been calculated from the Terra MOD09A1 Version 6.1 product. The layers B02 (near infrared) and B06 (shortwave infrared) of the original data have been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layer. The time series has been gapfilled with a temporal and a spatial approach.

https://data.mundialis.de/geonetwork/srv/resources/records/36512b46-f3aa-4aa4-8281-7584ec46c813
type: CatalogRecord
primary topic: dataset
issued: 2023-02-28
modified: 2023-02-28
references: https://data.mundialis.de/geonetwork/srv/resources/records/36512b46-f3aa-4aa4-8281-7584ec46c813/formatters/xml
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https://data.mundialis.de/geonetwork/srv/resources/records/36512b46-f3aa-4aa4-8281-7584ec46c813/formatters/xml
format: application/xml
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Landcover classification map of Germany 2019 based on Sentinel-2 data
type: Dataset
abstract: This landcover map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes: 10: forest 20: low vegetation 30: water 40: built-up 50: bare soil 60: agriculture Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets: - OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org) - Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)) - S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523) - Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html) - Contains modified Copernicus Sentinel data (2019), processed by mundialis Processing was performed for blocks of federal states and individual maps were mosaicked afterwards. For each class 100,000 pixels from the potential training a…
keyword: Sentinel-2
keyword: Classification
keyword: Land Cover
keyword: mFUND
keyword: MAJA
keyword: Infrastuktur
keyword: Umwelt
keyword: Regionen und Städte
keyword: mfund-projekt:incora
keyword: mfund-fkz:19F2079C
keyword: incora
keyword: Germany
theme: Land use
theme: Land cover
theme: geoscientificInformation
spatial: Polygon
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temporal: 2019-01-01 / 2019-12-31
publisher: mundialis GmbH & Co. KG
accrual periodicity: irregular
language: eng
license: otherRestrictions
license: license
license: Data licence Germany - attribution - version 2.0 or later (DL-DE->BY-2.0) | Datenlizenz Deutschland - Namensnennung - Version 2.0 oder neuer
license: { "id": "dl-by-de/2.0", "name": "Datenlizenz Deutschland Namensnennung 2.0", "url": "https://www.govdata.de/dl-de/by-2-0", "quelle": "Source: mundialis GmbH & Co. KG" }
distribution: LICENSE.html
data quality: derived from Sentinel-2 MSI - Level 3A-WASP
title: Landcover classification map of Germany 2020 based on Sentinel-2 data
abstract: This landcover map was produced with a classification method developed in the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes: 10: forest 20: low vegetation 30: water 40: built-up 50: bare soil 60: agriculture Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets: - OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org) - Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA)) - S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523) - Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html) - Contains modified Copernicus Sentinel data (2020), processed by mundialis Processing was performed for blocks of federal states and individual maps were mosaicked afterwards. For each class 100,000 pixels from the potential training ar…
temporal: 2020-01-01 / 2020-12-31
title: Change Detection map of Germany 2016-2019 based on Sentinel-2 data
abstract: This change map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data. The map indicates land cover changes between the years 2016 and 2019. It is a difference map from two classifications based on Sentinel-2 MAJA data (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). More information on the two basis classifications can be found here: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/db130a09-fc2e-421d-95e2-1575e7c4b45c https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/36512b46-f3aa-4aa4-8281-7584ec46c813 To keep only significant changes in the change detection map, the following postprocessing steps are applied to the initial difference raster: - Modefilter (3x3) to eliminate isolated pixels and edge effects - Information gain in a 4x4 window compares class distribution within the window from the two timesteps. High values indicate that the class distribution in the window has changed, and thus a change is likely. Gain ranges from 0 to 1, all changes < 0.5 are omitted. - Change areas < 1ha are removed The resulting map has the following nomenclature: 0: No Change 1: Change from low vegetation to forest 2: Change from water to forest 3: Change from built-up to forest 4: Change from bare soil to forest 5: Change from agriculture to forest 6: Change from forest to low vegetation 7: Change from water to low vegetation 8: Change from built-up to low vegetation 9: Change from bare soil to low vegetat…
keyword: Change Detection
keyword: Settlement Dynamics
temporal: 2016-01-01 / 2019-12-31
distribution: germany 2016 2019 MAJA cd total v02.sld
distribution: germany 2016 2019 MAJA cd total v02.qml
title: Copernicus Digital Elevation Model (DEM) for Europe at 30 meter resolution derived from Copernicus Global 30 meter dataset
abstract: Here we provide a mosaic of the Copernicus DEM 30m for Europe and the corresponding hillshade derived from the GLO-30 public instance of the Copernicus DEM. The CRS is the same as the original Copernicus DEM CRS: EPSG:4326. Note that GLO-30 Public provides limited coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters. The Copernicus DEM for Europe at 30 m in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt The pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade …
keyword: digital terrain model
keyword: elevation
keyword: Copernicus
keyword: DEM
keyword: DSM
keyword: geomorphometry
keyword: environment
keyword: Europe
keyword: remote sensing
keyword: radar
keyword: space
keyword: MOOD-H2020
keyword: Tularaemia
keyword: Lyme
keyword: TBE
keyword: WNV
keyword: USUTU
keyword: Mosquito borne Flaviviruses
theme: Elevation
theme: elevation
temporal: 2010-01-01 / 2015-12-31
accrual periodicity: asNeeded
license: Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International (CC-BY-SA 4.0)
license: { "id": "cc-by-sa/4.0", "name": "Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International", "url": "http://creativecommons.org/licenses/by-sa/4.0/", "quelle": "Source: mundialis" }
license: EO data provided under COPERNICUS by the European Union and ESA. For more information see: https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/55df6882-8593-4123-b738-3558c20ad98c
data quality: derived from Copernicus DEM GLO-30 Public
title: Copernicus Digital Elevation Model (DEM) for Europe at 30 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter dataset

https://data.mundialis.de/geonetwork/srv/resources/records/9246503f-6adf-460b-a31e-73a649182d07
type: CatalogRecord
primary topic: dataset
issued: 2023-02-28
modified: 2023-02-28
references: https://data.mundialis.de/geonetwork/srv/resources/records/9246503f-6adf-460b-a31e-73a649182d07/formatters/xml
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https://data.mundialis.de/geonetwork/srv/resources/records/5ef8565b-ea72-4f34-985b-71ab41959230
type: CatalogRecord
primary topic: dataset
issued: 2021-10-25
modified: 2021-10-25
references: https://data.mundialis.de/geonetwork/srv/resources/records/5ef8565b-ea72-4f34-985b-71ab41959230/formatters/xml
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https://data.mundialis.de/geonetwork/srv/resources/records/5ef8565b-ea72-4f34-985b-71ab41959230/formatters/xml
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https://data.mundialis.de/geonetwork/srv/resources/records/f576cda8-d598-478c-b8fe-ad2634c927e8
type: CatalogRecord
primary topic: dataset
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/f576cda8-d598-478c-b8fe-ad2634c927e8/formatters/xml
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https://data.mundialis.de/geonetwork/srv/resources/records/9a382836-47a9-4dac-ad62-f0021e455ab8
type: CatalogRecord
primary topic: dataset
issued: 2023-12-15
modified: 2023-12-15
references: https://data.mundialis.de/geonetwork/srv/resources/records/9a382836-47a9-4dac-ad62-f0021e455ab8/formatters/xml
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https://data.mundialis.de/geonetwork/srv/resources/records/9a382836-47a9-4dac-ad62-f0021e455ab8/formatters/xml
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Human health and safety
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

Meteorological geographical features
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

Elevation
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

Land cover
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

Land use
type: Concept
in scheme: GEMET - INSPIRE themes, version 1.0

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classification map germany 2019 v02.tif
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Copernicus DEM 30m for Europe
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mundialis GmbH & Co. KG
type: Organization

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