Link to code
Prepared by: Alex Mikulas, PhD, CACHE postdoctoral associate
Date: March 16, 2026
Original Authors:
Finn Roberts, IPUMS Senior Data Analyst
Rebecca Luttinen, IPUMS Global Health Data Analyst
Devon Kristiansen, IPUMS Global Health Research Manager
Jude Mikal, Senior Research Fellow, University of Minnesota College of Pharmacy
Specific purpose of code: The below code resources offer a comprehensive outline for downloading, processing, aggregating, and integrating global vegetation coverage data for use in demographic and health research. Vegetation data come from the Normalized Difference Vegetation Index (NVDI), the Visible Infrared Imaging Radiometer Suite (VIIRS), and Moderate Resolution Imaging Spectroradiometer (MODUS).
Ultimately, these resources allow users to aggregate environmental data into spatially relevant scales and integrate it into a variety of social and health data sources to better measure environmental context or exposure.
The IPUMS DHS Spatial Analysis and Health Research Hub has numerous resources on using environmental data in health research. While many resources in the hub are used with DHS data integration, the data, code, and analysis resources can be altered for data integration into any spatially identified aging and health dataset.
Link to code:
- [Start here!] From MODIS to VIIRS: The Latest Source for NDVI Data
General Application: This code and associated resources allow researchers to build a vegetation coverage dataset that can be integrated into any individual or aggregate dataset that has temporal and spatial specificity. The data extend from 1981 to current, with 10 to 20-day increments and up to 20-meter raster resolution.
How does or could this code allow researchers to assess research questions related to aging or life course?: This code can be used to create environmental context and exposure to greenspace and vegetation variables that can be used cross-sectionally or longitudinally, and at spatially detailed scales. It can be integrated into health surveys to provide environmental context, aggregated data to identify locations with high concentrations of aging adults and changing vegetation or greenspace, etc. In longitudinal datasets, researchers could chart an individual’s longitudinal exposure to vegetation and other relevant environmental features over the life course.
Data sets used:
Publicly available climate and weather data.
Links to data (also repeated in code examples):
Coding Language: R
Tools and Packages used: terra, sf, dplyr, ggplot2, ggspatial, patchwork, lubridate (likely others)
Output(s): datasets, maps
Spatial extent: Global dataset, raster data at 20 – 250-meter resolution
Temporal extent: 1981 to current; 10-20 day increments
Published papers that use this code:
Moisa, M., Roba, Z., Purohit, S., Deribew, K., & Gemeda, D. (2025). Evaluating the impact of land use and land cover change on soil moisture variability using GIS and remote sensing technology in southwestern Ethiopia. Environmental Monitoring and Assessment, 197. https://doi.org/10.1007/s10661-025-14301-1
Grace, K., Kristiansen, D., Boyle, E. H., & Luetke, M. (2023). Investigating Seasonal Agriculture, Contraceptive Use, and Pregnancy in Burkina Faso. The Professional Geographer. https://www.tandfonline.com/doi/full/10.1080/00330124.2023.2199316