WetChannelExtraction

This is a Python code to extract wet segments using high resolution Digital Elevation Models (DEMs) and Intensity raster.

A. Installation

The required python library are:

1. arcpy library: this library is included with ArcGIS package. If you install ArcGIS on your computer, the arcpy library will be installed automatically. It is recommended to use 64-bit python which is available on new versions of ArcGIS. 64-bit background geoprocessing is also available for older versions at http://resources.arcgis.com/en/help/main/10.1/index.html#/Background_Geoprocessing_64_bit/002100000040000000/.

2. numpy, scipy, Scikit-learn, and matplotlib libraries.

B. Code structure

1. main.py is the file for setting the parameters and running the code.

2. Wet_Channel_Extraction.py is the main code which calls the functions from Wet_Extraction_Fun.py to delineate wet segments.

3. Pre_Processing.py contains the functions to fix the extent of the input rasters.

C. Inputs

Before running the code, there are some parameters which should be set in Run.py including.

1. The path to the bare-earth DEM (ground_DEM).

2. The path to the vegetation DEM which is created from all LiDAR points (veg_DEM)

3. The path to the intensity raster (int_raster)

4. The path to the flow direction grid (flowdir_raster) and valley network (valley_raster). These two rasters can be extracted using the the code available at https://github.com/MiladHooshyar/DrainageNetworkExtraction.git and are saved as Modified_Fdir_8.tif and valley_network.tif, respectively.

5. The unit of the DEM (unit). It is m for meter and ft for feet.

6. The size of the DEM in meters (pix_per_m).

7. The elevation per meter (ele_per_meter). It is 1 if the unit is meter, 0.3045 if the unit is feet.

8. The minimum area of wet segments (area_thresh).

9. The elevation different between the top of dense vegetation and land surface (diff_thresh).

10. Slope thresholds for differentiation erroneous wet segments (min_slope, max_slope , and min_pos_slope).

11. The maximum distance between two erroneously disconnected segment (max_gap).

After setting the parameters, one can execute Run.py to extract the wet segments. The output files will be saved in a folder called wet_output in the specified Output folder path. comb_wet.tif and wet_connected.tif are original and connected wet segments, respectively.

D. Publications

1. Hooshyar, M., S. Kim, D. Wang, and S. C. Medeiros (2015), Wet channel network extraction by integrating LiDAR intensity and elevation data, Water Resour. Res., 51, 1002910046, doi:10.1002/2015WR018021.