Update README.md

README.md

@@ -4,31 +4,53 @@
 </p>
 These instructions are designed for the Above Science Cloud (ASC) environment, but you will need to install the required packages and conda env  beforhand, regardless!
 
-[comment]: <> (![ASC](https://above.nasa.gov/images/ASC_logo.jpg 80x)
-
 <p align="center">
     <img src="https://above.nasa.gov/images/ASC_logo.jpg" width="200" alt="ASC" text-align="center"/>  
 </p>
 
-## Workflow for LUT-based classification
-* This method was used for the Daring Lake scenes.
+## Workflow for extracting stats for all lakes observed by UAVSAR:
+1. Run classification in Matlab and analysis/reclass_rasters.py
+2. analysis/upscaleFromLandcover.ipynb
+3. analysis/Raster2PercentageLakePoly.py (Polygonizes landcover map to lake polygons, with attributes for EM%, whether or not it is a border lake, and whether it was observed by AirSWOT CIR camera.)
 
-0. Activate conda environment. On ASC, the command would be:
 
->```
->conda activate base
->```
-1. Move original GRD files to default_grd folder, using the script [polsar_pro/psp_mv_hgt_slope_inc_function.sh](polsar_pro/psp_mv_hgt_slope_inc_function.sh).
+## Workflow for extracting stats for dozen or so visited lakes:
+1. Run classification in Matlab and analysis/reclass_rasters.py
+2. analysis/Raster2PercentagePoly.py (Convert a land cover raster to a shapefile, with attributes for fractional class coverage)
+3. analysis/calcVegAreaFromPts.py (An automatic way of calculating littoral fraction from a shapefile of water bodies and a list of points)
+4. analysis/Average_em.ipynb (Averages em_fraction for each site along all available dates)
+
+Note: If running over maximum ROI, use the ROI file: 'ROI-analysis_albers.shp'. If running me under ROI common to all acquisition dates, use ROI file: 'ROI-analysis_albers-sub.shp'. When toggling between these two ROIs, be sure to to update vars 'file_basenames' and 3 others with tag #sub-roi.
+
+## Workflow for sensitivity figure:
+
+Be sure to update file paths in [python_env.py](analysis/python_env.py) and filepath vars in [Raster2PercentageLakePoly.py](analysis/Raster2PercentageLakePoly.py) before running
 
-2. Run python script [radiocal_example_script_ek.py](https://github.com/ekcomputer/UAVSAR-Radiometric-Calibration/blob/master/python/radiocal_example_script_ek.py) located in an additional forked [repo](https://github.com/ekcomputer/UAVSAR-Radiometric-Calibration).
+## Workflow for further analysis and plots:
+1. lake-histograms-multi-temporal.ipynb
+2. lake-bar-chart-time-series.ipynb
+3. lake-histogram-by-area-etc.ipynb
 
-3. Run polsar pro workflow in parallel using the following command:
+## Workflow for Polarimetric SAR decomposition using PolSAR Pro and Random Forests classification in Matlab
+1. Run polsar pro workflow in parallel using the following command:
 >```
 > bash polsar_pro/parallel_cat_run.sh polsar_pro/psp_workflow_function.sh [path/to/textfile/with/input/IDs] [number of cores]
 >```
 > Number of cores: 2 recommended to conserve memory on ASC, since each function call will need ~40 GB of mem max dand cause un-noticed errors if memory limit is exceeded
 If using slurm to run a batch job, substitute `slurm` for `bash` in the above command.
 
-4. In matlab: [trainingImageImport.m](trainingImageImport.m)
+2. In matlab: [trainingImageImport.m](trainingImageImport.m)
 
-5. If reclassifying raster, use python: [analysis/reclass_rasters.py](analysis/reclass_rasters.py)
+3. If reclassifying raster, use python: [analysis/reclass_rasters.py](analysis/reclass_rasters.py)
+4. 
+## Workflow for Look-up Table (LUT)-based classification
+* This method was used for the Daring Lake scenes (CSD) only.
+
+1. Activate conda environment. On ASC, the command would be:
+
+>```
+>conda activate base
+>```
+2. Move original GRD files to default_grd folder, using the script [polsar_pro/psp_mv_hgt_slope_inc_function.sh](polsar_pro/psp_mv_hgt_slope_inc_function.sh).
+3. Run python script [radiocal_example_script_ek.py](https://github.com/ekcomputer/UAVSAR-Radiometric-Calibration/blob/master/python/radiocal_example_script_ek.py) located in an additional forked [repo](https://github.com/ekcomputer/UAVSAR-Radiometric-Calibration).
+4. Continue with step 1. of regular workflow, [above](#workflow-for-look-up-table-(lut)-based-classification).