Running list of TODOs

[orianac]

For MVP for Washington:

• basic form of training dataset:
  • All glas shots translated to biomass using one allometric equation (Cindy) [done]
  • Look up sampling strategy of GLAS and allometric equation assumptions wrt leaf conditions (Cindy/Ori) [done]
  • Calculate seasonal average for each year from Landsat with spatially continuous map for WA (Ori + Joe) (relatedly, decide on Landsat data structure) (snap to a uniform Hansen 30m grid x annually)
  • Extract Landsat variables to use into a tabular format (all raw bands)
• set up ML model for training (Cindy)
  • random forest + XGBoost!
  • set up inference function
• Set up inference inputs
  • extract the same landsat variables into tabular format for all of washington
• Plotting function from ML model output (altair)(Ori) (lat/lon/time)
  • spatial maps
  • time series
• Set up validation dataset
  • Find 4 well-respected datasets

To expand to global:

• Transforming Harris et al spreadsheet into python
  • Mask of column 2 (ecoregion + NLCD) -> allometric equation
  • allometric equation = dictionary of functions
  • height metrics = another dictionary of functions [done]
  • parameter to indicate whether to preprocess (whether input is smooth or raw)

Improvements by April:
GLAS/biomass:

• apply glas filtering based on Harris et al (Cindy) [done]
• double check how GLAS elevation should be calculated from GLAH14 data
• decide whether we should use smoothed or raw wf to make height metric calculations
• Double check terrain calculations by reading Duncanson et al more closely
• potentially change the raw extracted glas data into the original variable name
• interpolate between bins (currently at 15cm intervals)
• double check that compression ratio does not change during the valid signal part (between sig beg and sig end)
• Figure out which allometric equations can be used for leaf off conditionsAllometric equations are trained predominantly upon leaf-on conditions, so we should determine whether estimates for leaf-off conditions are valid. This is relevant for our reporting/updating interval- proposal: update bi-annually after the end of the growing season in each hemisphere (September and March(?)).

Landsat

• Masking clouds (potentially via https://github.com/ubarsc/python-fmask or potentially using *_BQA.TIF files in LANDSAT archive
• Smoothing LANDSAT images using CCDC
• Grabbing multiple LANDSAT pixels for each GLAS record? GLAS has 70 m diameter and LANDSAT is 30m so could use 4 LANDSAT? Bounding box of all LANDSAT pixels?

ML model

• Training different model for each ecoregion
• Incorporating a climate dataset into the training of the model (Others have used Worldclim, though we could use Terraclim)
• out of sample validation