nf-core/rangeland

Edit

Input

As most remote sensing workflows, this pipeline relies on numerous sources of data. In the following we will describe the required data and corresponding formats. Mandatory input data consists of satellite data, a digital elevation model, a water vapor database, a data_cube, an area-of-interest specification and an endmember definition.

Satellite data

This pipeline operates on Landsat data. Landsat is a joint NASA/U.S. Geolical Survey satellite mission that provides continuous Earth obersvation data since 1984 at 30m spatial resolution with a temporal revisit frequency of 8-16 days. Landsast carries multispectral optical instruments that observe the land surface in the visible to shortwave infrared spectrum. For infos on Landsat, see here.

Satellite data should be given as a path to a common root of all imagery. This is a common format used in geographic information systems, including FORCE, which is applied in this pipeline. The expected structure underneath the root directory should follow this example:

root
├── 181035
│   └── LE07_L1TP_181035_20061217_20170106_01_T1
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_ANG.txt
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_B1.TIF
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_B2.TIF
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_B3.TIF
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_B4.TIF
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_B5.TIF
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_B6_VCID_1.TIF
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_B6_VCID_2.TIF
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_B7.TIF
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_B8.TIF
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_BQA.TIF
│   |   ├── LE07_L1TP_181035_20061217_20170106_01_T1_GCP.txt
│   |   └── LE07_L1TP_181035_20061217_20170106_01_T1_MTL.txt
|   └── ...
├── 181036
│   └── LE07_L1TP_181036_20061217_20170105_01_T1
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_ANG.txt
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_B1.TIF
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_B2.TIF
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_B3.TIF
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_B4.TIF
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_B5.TIF
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_B6_VCID_1.TIF
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_B6_VCID_2.TIF
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_B7.TIF
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_B8.TIF
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_BQA.TIF
│   |   ├── LE07_L1TP_181036_20061217_20170105_01_T1_GCP.txt
│   |   └── LE07_L1TP_181036_20061217_20170105_01_T1_MTL.txt
|   └── ...
└── ...

Subdirectories of root contain path and row information as commonly used for Landsat imagery. As an example, the sub directory 181036/ contains imagery for path 18 and row 1036.

The next level of subdirectories contains the data for a specific day and from a specific source. Lets look at the example LE07_L1TP_181036_20061217_20170105_01_T1:

• “LE07” corresponds to Landsat 7 Enhanced
• “L1TP” corresponds to Level-1 Terrain Corrected imagery
• “181036” corresponds to the path and row of the imagery, this should match the subdirectory
• “20061217” identifies the 17th December 2006 as the date of acquisition
• “20170105” identifies the 5th January 2017 as the date of (re)processing
• “01” corresponds to version number of the remote sensing product
• “T1” corresponds to the Tier of the data collection, which indicates the Tier 1 landsat collection in this case

On the lowest level of the structure, the actual data is stored. Looking at the contents of LE07_L1TP_181036_20061217_20170105_01_T1, we see that all files share the same prefix, followed by a specification of the specific files contents. These suffixes include:

• “B” followed by a number i identifying the band of the satellite (band 6 has two files as Landsat 7 has two thermal bands)
• “BQA” identifying the quality information band
• “GCP” identifies ground control point information
• “ANG” identifies angle of observation and other geometric information information
• “MTL” identifies meta data

All files within the lowest level of structure belong to a single observation. Files containing imagery (prefix starts with “B”) should be .tif files. Files containing auxiliary data are text files.

This structure is automatically generated when using force to download the data. We strongly suggest users to download data using FORCE (e.g.). For example, executing the following code (e.g. with FORCE in docker) will download data for Landsat 4,5 and 7, in the time range from 1st January 1984 until 31st December 2006, including pictures with up to 70 percent of cloud coverage:

mkdir -p meta
force-level1-csd -u -s "LT04,LT05,LE07" meta
mkdir -p data
force-level1-csd -s "LT04,LT05,LE07" -d "19840101,20061231" -c 0,70 meta/ data/ queue.txt vector/aoi.gpkg

Note that you need to pass an area-of-interest file, see the area of interest section Area of interest for details.

The satellite imagery can be given to the pipeline using:

--input '[path to imagery root]'

The satellite imagery can also be provide as a tar archive. In this case it is mandatory to set --input_tar to true. Moreover, within the tar archive, the structure explained above has to be in place. In the example above 181036/ and 181035/ would need to be in the top level of the archive.

Digital Elevation Model (DEM)

A DEM is necessary for topographic correction of Landsat data, and helps to distinguish between cloud, shadows and water surfaces. Common sources for digital elevation models are Copernicus,Shuttle Radar Topography Mission (SRTM), or Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).

The pipeline expects a path to the Digital Elevation Model root directory as a parameter. Concretely, the expected structure would look like this:

dem
├── <dem_file>.vrt
└── <dem_tifs>/
    └── ...

Here, <dem_file>.vrt orchestrates the single digital elevation files in the <dem_tifs> directory.

The DEM can be given to the pipeline using:

--dem '[path to dem root]'

The digital elevation model can also be provide as a tar archive. In this case it is mandatory to set --dem_tar to true. Moreover, within the tar archive, the structure explained above has to be in place. In the example above <dem_file>.vrt and <dem_tifs>/ would need to be in the top level of the archive.

Water Vapor Database (WVDB)

For atmospheric correction of Landsat data, information on the atmospheric water vapor content is necessary.

The expected format for the wvdb is a directory containing daily water vapor measurements for the area of interest.

We recommend using a precompiled water vapor database, like this one. This global water vapor database can be downloaded by executing this code:

wget -O wvp-global.tar.gz https://zenodo.org/record/4468701/files/wvp-global.tar.gz?download=1
tar -xzf wvp-global.tar.gz --directory wvdb/
rm wvp-global.tar.gz

The WVDB can be given to the pipeline using:

--wvdb '[path to wvdb dir]'

The water vapor database can also be provide as a tar archive. In this case it is mandatory to set --wvdb_tar to true. All files of the wvdb would need to be in the top level of the archive.

Datacube

The datacube definition stores information about the projection and reference grid of the generated datacube. For details see the FORCE main paper.

The datacube definition is passed as a single file using:

--data_cube '[path to datacube definition file]'

Area of interest (AOI)

The area of interest is a geospatial vector dataset that holds the boundary of the targeted area.

AOI is passed as a single using:

--aoi '[path to area of interest file]'

Endmember

For unmixing satellite-observed reflectance into sub-pixel fractions of land surface components (e.g. photosynthetic active vegetation), endmember spectra are necessary.

An example endmember definition (developed in Hostert et al. 2003) looks like this:

320  730  2620 0
560  1450 3100 0
450  2240 3340 0
3670 2750 4700 0
1700 4020 7240 0
710  3220 5490 0

Each colum represents a different endmember. Columns represent Landsat bands (R,G,B, NIR, SWIR1, SWIR2).

The endmembers can be passed in a single text-file using:

--endmember '[path to endmember]'

Pipeline configuration

Users can specify additional parameters to configure how the underlying workflow tools handle the provided data.

Sensor Levels

Data from different satellites can be processed within this workflow. Users may wish to include different satellites in preprocessing and in higher level processing. To control this behavior, two parameters can be set when the pipeline is launched. The first parameter - sensors_level1 - controls the selection of satellites for preprocessing. This parameter should follow the FORCE notation for level 1 processing of satellites. Concretely, a string containing comma-separated satellite identifiers has to be supplied (e.g. "LT04,LT05" to include Landsat 4 and 5). Available options for satellite identifiers are:

• "LT04": Landsat 4 TM
• "LT05": Landsat 5 TM
• "LE07": Landsat 7 ETM+
• "LC08": Landsat 8 OLI
• "S2A": Sentinel-2A MSI
• "S2B": Sentinel-2B MSI

The second parameter - sensors_level2 - controls the selection of satellites for the higher level processing steps. The parameter has to follow the FORCE notation for level 2 processing. In particular, a string containing space-separated satellite identifiers has to be supplied (e.g. "LND04 LND05" to include Landsat 4 and 5). Note that these identifiers differ from those used for the sensors_level1 parameter. More details on available satellite identifiers can be found here, some common options include:

• "LND04": 6-band Landsat 4 TM
• "LND05": 6-band Landsat 5 TM
• "LND07": 6-band Landsat 7 ETM+
• "LND08/09": 6-band Landsat 8-9 OLI
• "SEN2A": 10-band Sentinel-2A
• "SEN2B": 10-band Sentinel-2B

Note that the identifiers specified for both processing levels have to match the data made available to the workflow. In other words, satellite data for e.g. Landsat 5 can’t be processed if it was not supplied using the input parameter.

Both parameters can be passed as using:

--sensors_level1 = '[preprocessing satellite identifier string]'
--sensors_level2 = '[higher level processing satellite identifier string]'

Note that both parameters are optional and are by default set to: "LT04,LT05,LE07,S2A" and "LND04 LND05 LND07". Therefore, by default, the pipeline will use Landsat 4,5,7, and Sentinel 2 for preprocessing, while using Landsat 4,5 and 7 for higher level processing.

Resolution

Resolution of satellite imagery defines the real size of a single pixel. As an example, a resolution of 30 meters indicates that a single pixel in the data covers a 30x30 meters square of the earths surface. Users can customize the resolution that FORCE should assume. This does not necessarily have to match the resolution of the supplied data. FORCE will treat imagery as having the specified resolution. However, passing a resolution not matching the satellite data might lead to unexpected results. Resolution is specified in meters.

A custom resolution can be passed using:

--resolution '[integer]'

The default value is 30, as most Landsat satellite natively provide this resolution.

Temporal extent

In some scenarios, user may be interested to limit the temporal extent of analysis. To enables this, users can specify both start and end date in a string with this syntax: 'YYYY-MM-DD'.

Start and end date can be passed using:

--start_date '[YYYY-MM-DD]'
--end_date   '[YYYY-MM-DD]'

Default values are '1984-01-01' for the start date and '2006-12-31' for the end date.

Group size

The group_size parameters can be ignored in most cases. It defines how many satellite scenes are processed together. The parameters is used to balance the tradeoff between I/O and computational capacities on individual compute nodes. By default, group_size is set to 100.

The group size can be passed using:

--group_size '[integer]'

Higher level processing configuration

During the higher level processing stage, time series analyses of different satellite bands and indexes is performed. The concrete bands and indexes can be defined using the indexes parameter. Spectral unmixing is performed in any case. Thus, passing an empty indexes parameter will restrict time series analyses to the results of spectral unmixing. All available indexes can be found here above the INDEX parameter. The band/index codes need to be passed in a space-separated string. The default value, indexes = "NDVI BLUE GREEN RED NIR SWIR1 SWIR2", enables time series analyses for the NDVI index and the blue, green, red, near-infrared and both shortwave infrared bands. Note that indexes are usually computed based on certain bands. If these bands are not present in the preprocessed data, these indexes can not be computed.

The bands and indexes can be passed using:

--indexes '[index-string]'

In so cases, it may be desirable to analyze the the individual images in a time series. To enable such analysis, the parameter return_tss can be used. If set to true, the pipeline will return time series stacks for each tile and band combination. The option is disabled by default to reduce the output size.

The time series stack output can be enabled using:

--return_tss '[boolean]'

Visualization

The workflow provides two types of results visualization and aggregation. The fine grained mosaic visualization contains all time series analyses results for all tiles in the original resolution. Pyramid visualizations present a broad overview of the same data but at a lower resolution. Both visualizations can be enabled or disabled using the parameters mosaic_visualization and pyramid_visualization. By default, both visualization methods are enabled. Note that the mosaic visualization is required to be enabled when using the test and test_full profiles to allow the pipeline to check the correctness of its results (this is the default behavior, make sure to not disable mosaic when using test profiles) .

The visualizations can be enabled using:

mosaic_visualization  = '[boolean]'
pyramid_visualization = '[boolean]'

FORCE configuration

FORCE supports parallel computations. Users can specify the number of threads FORCE can spawn for a single preprocessing, or higher level processing process. This is archived through the force_threads parameter.

The number of threads can be passed using:

--force_threads '[integer]'

The default value is 2.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/rangeland --input <SATELLITE_DATA_DIR> --dem <DIGITAL_ELEVATION_DIR> --wvdb <WATOR_VAPOR_DIR> --data_cube <DATACUBE_FILE> --aoi <AREA_OF_INTEREST_FILE> --endmember <ENDMEMBER_FILE> --outdir <OUTDIR>  -profile docker

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/rangeland -profile docker -params-file params.yaml

with params.yaml containing:

input: '<PATH_TO_SATELLITE_IMAGERY>'
dem: '<PATH_TO_DEM>'
wvdb: '<PATH_TO_WVDB>'
data_cube: '<PATH_TO_DATACUBE_DEFINITION>'
aoi: '<PATH_TO_AOI_FILE>'
endmember: '<PATH_TO_ENDMEMBER_FILE>'
outdir: './results/'
<...>

You can also generate such YAML/JSON files via nf-core/launch.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you’re running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull nf-core/rangeland

Reproducibility

It is a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you’ll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the nf-core/rangeland releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you’ll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in reproducbility, you can use share and re-use parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Core Nextflow arguments

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended, since it can lead to different results on different machines dependent on the computer enviroment.

• test
  • A profile with a complete configuration for automated testing
  • Includes links to test data so needs no other parameters
• docker
  • A generic configuration profile to be used with Docker
• singularity
  • A generic configuration profile to be used with Singularity
• podman
  • A generic configuration profile to be used with Podman
• shifter
  • A generic configuration profile to be used with Shifter
• charliecloud
  • A generic configuration profile to be used with Charliecloud
• apptainer
  • A generic configuration profile to be used with Apptainer
• wave
  • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
• conda
  • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

-resume

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases you may wish to change which container or conda environment a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the biocontainers or bioconda projects. However in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Azure Resource Requests

To be used with the azurebatch profile by specifying the -profile azurebatch. We recommend providing a compute params.vm_type of Standard_D16_v3 VMs by default but these options can be changed if required.

Note that the choice of VM size depends on your quota and the overall workload during the analysis. For a thorough list, please refer the Azure Sizes for virtual machines in Azure.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'