Raster data consist of a matrix of cells (or pixels) arranged as rows and columns (or grids), with each cell containing a value representing information, such as temperature. Raster are derived from UAV or airborne sensors, as well as space-borne sensors on satellites:
- EarthExplorer – various remote sensing data sets can be downloaded such as Landsat 8 using a simple interface.
- NASA Sci Earth Observation – satellite images as well as spatial data sets of global products such as water vapor, temperatue, net radiation etc.
- ESA SciHub – Free access to Sentinel data isavailable through the ESA portal
- Sentinel Hub – Get satellite imagery on your table without worrying about synchronization issues, storage, processing, de-compression algorithms, meta-data or sensor bands.
- SEPAL – Allows users to query and process satellite data quickly and efficiently, tailor their products for local needs, and produce sophisticated and relevant geospatial analyses quickly.
- NOAA Coastal – Coastal community leaders use the content found in this NOAA-sponsored website to address issues commonly associated with a changing climate and a growing population.
- getSpatialData –
getSpatialDatais an R package in an early development stage that ultimately aims to enable homogeneous and reproducible workflows to query, preview, analyze, select, order and download various kinds of spatial datasets from open sources.
- Alsaka Satelite Facility – Get SAR and InSAR data from around the globe.
- GlobCover – GlobeCover is a global 300m landcover classifcation approach based on MERIS onboard ENVISAT for 2004-2006 and 2009.
- The GLC2000 – global landcover data set is available on a 1 km spatial resolution and for the year 2000 based on VEGETATION onboard SPOT 4.
- MODIS Landcover products MCD12 are available on the NASA webpage. A good overview of documentation and download location can be found here.
- Global Forest Change – Very important information about forest cover change of the last years can be checked online.
- EARTHENV – this page by the Map Of Life project is providing 1km consensus land-cover product for biodiversity and ecosystem modelling.
- Worldclim.org – Is not a remote sensing product but an interpolation of climate variables. However it is listed here due to its usefulness and lack for alternative data sets.
- ERA5 provides hourly estimates of a large number of atmospheric, land and oceanic climate variables.
- ESA Climate Office – The CCI aims to realise the full potential of the long-term global Earth Observation archives that ESA has established over the past 30 years, as a significant and timely contribution to the ECV databases required by UNFCCC.
Vector data are commonly lines (e.g. roads), polygons (e.g. national borders) or points (e.g. in-situ data measurements). In the following a range of vector resources are listed:
- Naturalearthdata – cultural and physical vector GIS datasets are available on a global scale. Additionally some raster datasets are provided that provide hillshade relief for your map
- Open Street Map – crowdsourced data of various types. The accuracy and quantity differes between regions but are most often highly accurate. Also see OSM or Geofabrik
- Open Topography – provides a portal to high spatial resolution topographic data and tools. In particular, it houses LiDAR data, which is a rare, precious resource nowadays.
- United Nations Environment Programme – (UNEP) is the authoritative source for UN data. For example, it holds more than 500 variables such as freshwater, climate and health.
- Earth Observations (NEO) – Imagine seeing daily snapshots of climate and environmental conditions of Earth. NASA’s Earth Observations (NEO) is like a real-time climate snapshot of the world.
- South East Asia Data – Raster and vector data for large parts of South East Asia to outline fundamental vector and raster capabilities.
- Indonesia Time-Series Data Remote Sensing Data (Landsat and Sentinel-2) time-series for 2 decades covering a small area in Indonesia.various data sets for an exercise on the African continent:
- Kongo Sentinel-2 Szene I
- Kongo Sentinel-2 Szene II
- Kongo Nationalpark Shapefile
- Small Kongo dataset
- Google Earth Engine classification Script
- Classification comparison between Sentinel 1 and Sentinel 2
- Resultdata from our textbooks:
- “Remote Sensing and GIS for Ecologists” – Deforestation pattern in Brazil.
- “Getting Started with Spatial Data” – Landcover information from Bavaria.
some recommended plugins to look at are listed below. Many more are available and listed here that might suit your specific needs:
- OSM downloader
- SRTM downloader
- Sentinel Hub
- Copernicus Global Land tools
- OSM place search
- Google Maps Geocoder
- Profile tool
- Data Plotly
- Resource Sharing
- Processing R
- EnMap toolbox
- Time series viewer plugin
- Gee timeseries explorer
- Digitizing tool
- Point sampling tool
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