Skip to content

Fractional cover - MODIS, CSIRO Land and Water algorithm

License

Notifications You must be signed in to change notification settings

juan-guerschman/geoglam

 
 

Repository files navigation

Geoglam Fractional cover - MODIS, CSIRO Land and Water algorithm

Vegetation fractional cover represents the exposed proportion of Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV) and Bare Soil (BS) within each pixel. In forested canopies the photosynthetic or non-photosynthetic portions of trees may obscure those of the grass layer and/or bare soil. The MODIS Fractional Cover product is derived from the MODIS Nadir BRDF-Adjusted Reflectance (NBAR) product (MCD43A4, collection 5). A suite of derivative are also produced, namely total vegetation cover (PV+NPV), monthly fractional cover and total vegetation cover, monthly anomaly of total cover against the time series, and three-monthly total cover difference. MODIS fractional cover has been validated for Australia.

Enivronment setup

  • git clone https://github.com/nci/geoglam.git into your local directory
  • Run setup_env.sh (i.e. bash setup_env.sh) to set up miniconda environment. You only need to run this step once
  • Open submit_pbs_jobs.sh and fill up NCI_PROJECT and BASE_DIR. NCI_PROJECT is your NCI project number, fr1, for example. BASE_DIR is the full path of the to-be-generated geoglam products. For example, /short///geoglam_data

Usage

  • Once the miniconda environment is set up, run submit_pbs_jobs.sh to submit PBS jobs for each tile in the tile csv file. There are a few example tile csv files under the tile_files directory.
  • Once the PBS jobs finished, run utils/parse_logs.py (i.e. python utils/parse_logs.py ) to extract any failed tiles. If there are failed tiles, a csv file that contains the failed tiles will be written under the current working directory.
  • You might want to archive the log files for future references. To do so, run utils/archive_log_files.sh

About

Fractional cover - MODIS, CSIRO Land and Water algorithm

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Perl 81.9%
  • Python 15.6%
  • Shell 2.5%