Terrestrial Ecosystem Science & Services

Data Sources for geospatial and remote sensing analysis

This page signposts useful data sources for geospatial ecology in R, including best options and supporting evidence.

  • rstac rapid access, search and download spacetime earth observation data via SpatioTemporal Asset Catalog (STAC) linked with Microsoft Planetary Computer (see MPC data catalogue). This is usually the most efficient way to access most satellite-derived datasets for TESS Lab research. Data sources and products include Landsat, ESA WorldCover, Fire (e.g., burned area), Weather and climate (e.g., ECMWF, inc. ERA5); Infrastructure such as building footprints, MODIS, and ESA Sentinel data.
  • rsi simplified access to geospatial data from STAC (SpatioTemporal Asset Catalog) and calculating spectral indices, also includes a global Digital Terrain Model (DTM) (by default the Copernicus 30 m DEM which is generally considered to be the best non-commercial product ([1], [2], [3], see also [4], [5]), although the NASADEM is also very good).
  • Weather and climate
    • chirps API Client for the Climate Hazards Center ‘CHIRPS’ and ‘CHIRTS’. The ‘CHIRPS’ data is a quasi-global (50°S – 50°N) high-resolution (0.05 arc-degrees) rainfall data set, which incorporates satellite imagery and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. Many independent studies have highlighted that CHIRPS outperforms nearly all other precipitation products in Africa across most metrics ([1], [2], [3], [4], [5], [6], [7], [8], [9]), although TAMSAT has similar performance with slightly better correspondence with daily totals but slightly worse correspondence for multi-day totals and slightly finer spatial resolution.
    • ‘CHIRTS’ is a quasi-global (60°S – 70°N), a high-resolution data set of daily maximum and minimum temperatures.
  • This Geospatial Data Catalog signposts over 18,000 (free) geospatial datasets, providing information: Dataset description including key variables, Time-series, Spatial resolution. It was built by Rob Johnsen, who builds geospatial platforms for the World Bank and you can add new datasets.
  • Chewie for efficient download, management and manipulation of the GEDI 2A, 2B, 1B, 4A spaceborne LiDAR products.
  • rayvista a small plugin for the rayshader package, to create 3D visualisations of any location on Earth.
  • chmloader global Canopy Height Model (CHM) at 1 m spatial resolution (after Tolan et al., 2024).
  • rnaturalearth facilitates access to Natural Earth vector and raster data including physical (e.g., coastline, lakes, glaciated areas) datasets and cultural (e.g., country boundaries, airports, roads, railroads) features.
  • OpenStreetMap gives access to open street map raster images.
  • mapme.biodiversity allows to download and process a number open datasets related to biodiversity conservation providing efficient routines and parallelization options. Datasets include among others the Global Forest Watch, ESA/Copernicus Landcover, WorldClim and NASA FIRMS.
  • marmap for downloading, manipulating, and plotting bathymetric and topographic data in R (querying NOAA’s ETOPO1 database).
  • osfr facilitates access to open research materials and data in the Open Science Framework (OSF).
  • cshapes has historical country boundaries (1886-today).
  • gbifdb a high-performance interface to the Global Biodiversity Information Facility (GBIF). ‘gbifdb’ provides enhanced performance for R users performing large-scale analyses on servers and cloud computing providers, providing full support for arbitrary ‘SQL’ or ‘dplyr’ operations on the complete ‘GBIF’ data tables.
  • 𝗿𝗴𝗲𝗲𝗱𝗶𝗺 to search, composite, and download ‘Google Earth Engine’ imagery with ‘reticulate’ bindings for the ‘Python’ module ‘geedim’ with documentation here. (NB. there is an alternative way to use GDAL to extract assets but the process is quite involved (creating a Google Cloud bucket, and getting a key into the system variables… etc.)).
  • If using tmap, this web explorer shows the different basemaps available (“Esri.WorldImagery” is one of the true colour satellite imagery options).
  • Other geospatial data sets (for importing to R)