This contains the code notebooks and data (via git lfs) for the ARSET training "Visualizing Land Cover and Land Use Change with NASA Satellite Imagery" as presented in February of 2026.
You can find the unrendered (.Rmd) and rendered (.html) versions of the code under the mkdn/edit directory.
The combined HLS data for 2017 and 2024 is located in the data/rast directory.
Note that you will need to change the data path references in the code to match the location to which you downloaded the files. Your path will not match the ones that I have used.
By the end of this training, attendees will be able to:
- Access NASA Earth observation data (e.g., Harmonized Landsat and Sentinel-2 (HLS)) relevant to LCLUC mapping.
- Convert NASA Earth observation data into distinct land cover and land use (LCLU) classes using supervised and unsupervised machine learning classification methods in the R programming language.
- Recognize the role of classification methods as one part of a change monitoring strategy.
- Compute a change matrix representing the change in LCLU between two dates.
- Create a map in RStudio visualizing the differences in LCLU between two dates.
- Professionals interested in coding to quantify land cover change as it relates to phenomena such as forest composition change, deforestation, urban expansion, habitat loss, and hydro/cryosphere changes.
- Other scientists or analysts interested in leveraging automated tools for decision support around land use management, natural resource management, or other issues that have an impact on how our world is structured across space and time.
(2026). ARSET - Visualizing Land Cover and Land Use Change with NASA Satellite Imagery. https://www.earthdata.nasa.gov/learn/trainings/visualizing-land-cover-l…
