geoid issue-19

README.md

@@ -20,14 +20,14 @@ $ conda activate iceroad
 ```
 
 
-#### Running the code
+#### Running whole pipeline
 from the ice-road-copters directory for example one can run:
 ```
 $ python scripts/ice-road-pipeline.py <path-to-directory-of-laz-files> -e <path-to-user-supplied-reference-dem> -a <path-to-ASP-directory> -s <path-to-road-shapefile-to-clip-to> -b 3
 ```
-NOTE: this code assumes you are using a reference DEM (and other airborne lidar data) referenced to the WGS84 ellipsoid (EPSG:4326). If your reference DEM Coordinate Reference System (CRS) is orthometric (Geoid) you must set the `-g  True` flag. This prompts extraction of the CRS metadata and a subsequent coordinate transform. Users must supply a shape file clipped to the roadway through the domain. During co-registration, this code assumes a buffered road area of 3 meters (1.5m on either side of the centerline of the road). This buffered road area can be modifed with `-b` flag.
+Users must supply a shape file clipped to the roadway through the domain. During co-registration, this code assumes a buffered road area of 3 meters (1.5m on either side of the centerline of the road). This buffered road area can be modifed with `-b` flag.
 
-#### Flags
+##### Flags
 
 ```
 -e user_dem      Path to user specifed DEM [one will be downloaded from py3dep if you don't supply one]
@@ -40,28 +40,29 @@ NOTE: this code assumes you are using a reference DEM (and other airborne lidar
 
 -b buffer_meters Total width for the transform area [Default: 3]
 
--g geoid         Is the reference DEM CRS orthometric (geoid height)? Will be auto set to True if you don't supply a DEM [Default: False]
-
 ```
 
+NOTE: this code assumes you are using a reference DEM (and other airborne lidar data) referenced to the same vertical datum (ellipsoid vs. geoid). If your vertical datum differs between the two you can use the `geoid_tool.py` to correct this.
 
-#### Optional: If estimating grain size, install R and the following packages 
+#### Transforming vertical datum
 
-See `ice-road-pipeline.py` for the additional optional flags. This currently requires a target with a known reflectance in order to calibrate lidar reflectance. It assumes wavelength = 1064 nm.
+As stated above, if your vertical datum differs between your point cloud and reference DEM use the following to correct your reference,
 
 ```
-library(raster)
-library(lidR)
-library(rlas)
-library(dplyr)
-library(readr)
-library(terra)
-library(sf)
-library(data.table)
+$ python scripts/geoid_tool.py <path-to-reference-data> -l <path-to-lidar.laz> -t transform_command -a <dir-to-ASP-bin> -d debug
+```
+##### Flags
+
+```
+-l lidar_path            Path to a lidar file you wish to match (it can be small file, just needed for CRS)
+-t transform_command     which type of transformation to do [Options: to_ellipsoid or to_geoid]
+-a asp_dir               Directory with ASP binary files [Can be either ASP or ASP/bin directory]
+-d debug                 turns on debugging logging [Options: True or False]
 
 ```
 
 
+
 ###  Additional information :books:
 The goal of this program is to utilize existing USGS 3DEP 1m topography data (via [py3dep](https://github.com/hyriver/py3dep)) and Ames Stereo Pipeline ([ASP](https://github.com/NeoGeographyToolkit/StereoPipeline)) software to accurately align snow-on airborne lidar point clouds to real world coordinates without the use of ground control points. We also provide an option for a user specified DEM (snow-off).
 
@@ -74,3 +75,21 @@ For example, we used prior knowledge that HWY-21 running through our study site
 After running pdal processing and ASP post-processing, we are able to generate accurate snow depth maps with road differences on the cm scale.
 
 ![snow](./docs/snow.jpeg)
+
+
+
+#### Optional: If estimating grain size, install R and the following packages 
+
+See `ice-road-pipeline.py` for the additional optional flags. This currently requires a target with a known reflectance in order to calibrate lidar reflectance. It assumes wavelength = 1064 nm.
+
+```
+library(raster)
+library(lidR)
+library(rlas)
+library(dplyr)
+library(readr)
+library(terra)
+library(sf)
+library(data.table)
+
+```