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https://github.com/cboettig/modis-lai-forecast

https://github.com/cboettig/modis-lai-forecast

Science Score: 23.0%

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Created over 3 years ago · Last pushed about 3 years ago
Metadata Files
Readme License

README.md

modis-lai-forecast

project team: John Smith, David Durden, Emma Mendelsohn, Carl Boettiger

This repo holds code for a spatially explicit forecasting challenge pipeline to benchmark spatial models using MODIS leaf index data. In this example we focus on locations of wildfire burns and recovery.

Workflow overview

efi-spatial (3)

Site selection

Our goal is to select locations across a variety of environments and burn conditions. Currently we have two sites selected from Monitoring Trends in Burn Severity (MTBS). These shapefiles are available in the /shp directory. - California August complex fire - Colorado East Troublesome

Functions

Functions are stored in the R/ directory.

  • fire_bbox() reads in a fire boundary shapefile and determines a bounding box for grabbing MODIS data with a padding option.
  • ingest_planetary_data() downloads data from Microsoft planetary comuputer and returns a gdalcube data cube proxy object.
  • create_target_file() subsets the data cube, pulls data for a given data and serializes target geotiff to disk.
  • spat_climatology() creates climatology predictions and serializes prediction geotiff to disk. Predictions are created using an ensemble of historical data within a given month. If historical data is missing, values are treated as NA and bootstrap re-sampling is performed using previous monthly data.
  • scoring_spat_ensemble() assigns CRPS (Continuous Ranked Probability Scores) and Logarithmic Scores for a given target file and ensemble forecast. Serializes scored geotiff to disk.
  • na_bootstrap_fun() is used internally for re-sampling during creation of climatological forecasts. The function takes a vector x of (possibly missing) data and fills NA values using a bootstrap re-sampling of non-NA values.

Environment

This project uses renv for package management. Use renv::restore() to load project packages.

Next steps

  • Ingest additional fire sites. Potential locations
    • NEON GRSM: https://www.neonscience.org/
    • NEON SOAP: https://www.neonscience.org/field-sites/soap
    • Arizona rapid burn/recovery
    • Eastern canada fires
  • Ingest addition data streams (e.g., burn intensity from MTBS)
  • Deployment for submissions

Owner

  • Name: Carl Boettiger
  • Login: cboettig
  • Kind: user
  • Company: UC Berkeley

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  • emmamendelsohn (2)
  • cboettig (1)
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Dependencies

.github/workflows/spatial-forecast.yaml actions
  • actions/checkout v3 composite
  • r-lib/actions/setup-renv v2 composite
Dockerfile docker
  • eco4cast/rocker-binder latest build