nf-core pipeline · earth-observation, landsat, satellite-imagery
nf-core/rangeland
Processes satellite imagery and auxiliary data to analyse land-cover change and vegetation trends.
What nf-core/rangeland is
nf-core/rangeland is an earth observation analysis pipeline designed for processing remotely sensed satellite imagery alongside auxiliary geospatial datasets. Developed as part of the Foundations of Workflows for Large-Scale Scientific Data Analysis (FONDA) initiative, it translates the FORCE on Nextflow workflow into an nf-core pipeline.
The workflow addresses the computational requirements of monitoring vegetation dynamics and environmental change over time. By transforming raw satellite scenes into harmonised, analysis-ready datasets and applying classification techniques, it produces time series trend data for specified areas of interest.
What the pipeline does
The pipeline ingests raw satellite imagery alongside auxiliary data: digital elevation models, water vapour databases, endmember definitions, datacube specifications, and area of interest boundaries. It evaluates pixel suitability by generating an allow list and analysis mask, followed by atmospheric correction to produce level 2 analysis-ready data alongside quality assurance information.
Temporally and spatially overlapping preprocessed observations are merged before pixels are classified using linear spectral unmixing. The pipeline then carries out time series analyses to quantify vegetation dynamic trends into level 3 data, generates mosaic and pyramid visualisations, and collates software version details with MultiQC.
Key facts
- Pipeline type
- Earth observation satellite image processing
- First release
- 1.0.0 (2025-01-17)
- Required inputs
- Satellite imagery, digital elevation model, water vapour database, datacube definition, area of interest, endmember definition
- Primary outputs
- Level 2 analysis-ready data, Level 3 vegetation dynamic trend files, visualisations (mosaics and pyramids), MultiQC report
- Classification method
- Linear spectral unmixing
What you can use it for
- Vegetation trend analysis
- Conducting time series analyses across satellite data to derive long-term trends in vegetation dynamics over an area of interest.
- Land-cover classification
- Applying linear spectral unmixing using reference endmembers to quantify land-cover composition across satellite scenes.
- Analysis-ready data generation
- Preprocessing satellite scenes with digital elevation models and water vapour data to produce atmospherically corrected level 2 data.
Run it here
The research agent runs nf-core/rangeland with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Automates the full workflow from raw satellite data to level 3 vegetation dynamic trend outputs.
- Applies atmospheric correction coupled with digital elevation and water vapour modelling to yield level 2 analysis-ready data.
- Uses linear spectral unmixing to resolve mixed pixel compositions.
- Builds mosaic and pyramid visualisations for downstream viewing of processed results.
Watch out for
- Requires specific auxiliary inputs, including an area of interest, datacube definition, endmember definitions, water vapour data, and a digital elevation model.
- Pipeline execution parameters must be provided via the command line or parameter files, as custom Nextflow configuration files cannot be used to set parameters.
Questions
Can I run nf-core/rangeland without my own cluster?
The research agent runs nf-core/rangeland with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/rangeland under?
MIT, like every nf-core pipeline.
What auxiliary files are required to run the pipeline?
In addition to satellite imagery, runs require a digital elevation model, a water vapour database, endmember definitions, a datacube definition, and an area of interest definition.
What technique does the pipeline use to classify imagery?
The pipeline classifies image pixels by applying linear spectral unmixing against specified endmember definitions.
What data levels does the pipeline generate?
It generates level 2 analysis-ready data with atmospheric corrections and quality assurance information, as well as level 3 trend files detailing vegetation dynamics.
How to cite it
Lehmann, F., Frantz, D., Becker, S., Leser, U., Hostert, P. (2021). FORCE on Nextflow: Scalable Analysis of Earth Observation Data on Commodity Clusters. In CIKM Workshops.
Written from nf-core/rangeland's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.