nf-core/rangeland

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Introduction

nf-core/rangeland is a geographical best-practice analysis pipeline for remotely sensed imagery. The pipeline processes satellite imagery alongside auxiliary data in multiple steps to arrive at a set of trend files related to land-cover changes. The main pipeline steps are:

1. Read satellite imagery, digital elevation model, endmember definition, water vapor database and area of interest definition

2. Generate allow list and analysis mask to determine which pixels from the satellite data can be used

3. Preprocess data to obtain atmospherically corrected images alongside quality assurance information

4. Classify pixels by applying linear spectral unmixing

5. Time series analyses to obtain trends in vegetation dynamics

6. Create mosaic and pyramid visualizations of the results

7. Read QC (FastQC)

8. Present QC for raw reads (MultiQC)

Usage

To run the pipeline on real data, input data needs to be acquired. Concretely, satellite imagery, water vapor data, a digital elevation model, endmember definitions, a datacube specification, and a area-of-interest specification are required. Please refer to the usage documentation for details on the input structure.

Now, you can run the pipeline using:

nextflow run nf-core/rangeland/main.nf \
   -profile <docker/singularity/.../institute> \
   --input <SATELLITE IMAGES> \
   --dem <DIGITAL ELEVATION MODEL> \
   --wvdb <WATER VAPOR DATA> \
   --data_cube <DATA CUBE> \
   --aoi <AREA OF INTEREST> \
   --endmember <ENDMEMBER SPECIFICATION> \
   --outdir <OUTDIR>

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

The rangeland workflow was originally written by:

• Fabian Lehmann
• David Frantz

The original workflow can be found on github.

Transformation to nf-core/rangeland was conducted by Felix Kummer. nf-core alignment started on the nf-core branch of the original repository.

We thank the following people for their extensive assistance in the development of this pipeline:

• Fabian Lehmann
• Katarzyna Ewa Lewinska.

Acknowledgements

This pipeline was developed and aligned with nf-core as part of the Foundations of Workflows for Large-Scale Scientific Data Analysis (FONDA) initiative.

FONDA can be cited as follows:

The Collaborative Research Center FONDA.

Ulf Leser, Marcus Hilbrich, Claudia Draxl, Peter Eisert, Lars Grunske, Patrick Hostert, Dagmar Kainmüller, Odej Kao, Birte Kehr, Timo Kehrer, Christoph Koch, Volker Markl, Henning Meyerhenke, Tilmann Rabl, Alexander Reinefeld, Knut Reinert, Kerstin Ritter, Björn Scheuermann, Florian Schintke, Nicole Schweikardt, Matthias Weidlich.

Datenbank Spektrum 2021 doi: 10.1007/s13222-021-00397-5

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don’t hesitate to get in touch on the Slack #rangeland channel (you can join with this invite).

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

This pipeline is based one the publication listed below. The publication can be cited as follows:

FORCE on Nextflow: Scalable Analysis of Earth Observation Data on Commodity Clusters

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.