lidar 0.1.6

Features

• Smoothing DEMs using mean, median, and Gaussian filters (see filtering.py)

• Extracting depressions from DEMs (see filling.py).

• Filtering out small artifact depressions based on user-specified minimum depression size (see filling.py).

• Generating refined DEMs with small depressions filled but larger depressions kept intact (see filling.py).

• Delineating depression nested hierarchy using the level-set method (see slicing.py).

• Delineating mount nested hierarchy using the level-set method (see mounts.py).

• Computing topological and geometric properties of depressions, including size, volume, mean depth, maximum depth, lowest elevation, spill elevation, perimeter, major axis length, minor axis length, elongatedness, eccentricity, orientation, and area-bbox-ratio (see slicing.py).

• Exporting depression properties as a csv file (see slicing.py).

Using It

Install the Python package using the following command:

pip install lidar

And use:

import os
import pkg_resources
import lidar

# identify the sample data directory of the package
package_name = 'lidar'
data_dir = pkg_resources.resource_filename(package_name, 'data/')

# use the sample dem. Change it to your own dem if needed
in_dem = os.path.join(data_dir, 'dem.tif')
# set output directory. By default, use the temp directory under user's home directory
out_dir = os.path.join(os.path.expanduser("~"), "temp")

# parameters for identifying sinks and delineating nested depressions
min_size = 1000      # minimum number of pixels as a depression
min_depth = 0.5      # minimum depth as a depression
interval = 0.3       # slicing interval for the level-set method
bool_shp = False     # output shapefiles for each individual level

# extracting sinks based on user-defined minimum depression size
out_dem = os.path.join(out_dir, "median.tif")
in_dem = MedianFilter(in_dem, kernel_size=3, out_file=out_dem)
sink_path = lidar.ExtractSinks(in_dem, min_size, out_dir)
dep_id_path, dep_level_path = lidar.DelineateDepressions(sink_path, min_size, min_depth, interval, out_dir, bool_shp)

Check the example.py for more details.

Examples

The images below show working examples of the level set method for delineating nested depressions in the Cottonwood Lake Study Area (CLSA), North Dakota. More test datasets (e.g., the Pipestem watershed in the Prairie Pothole Region of North Dakota) can be downloaded from http://gishub.org/2018-JAWRA-Data

The following example was conducted on a 64-bit Linux machine with a quad-core Intel i7-7700 CPU and 16 GB RAM. The average running time of the algorithm for this DEM was 0.75 seconds.

Publications

The level-set algorithm used in the lidar package has been published in the following articles.

• Wu, Q., Lane, C.R., Wang, L., Vanderhoof, M.K., Christensen, J.R., & Liu, H. (2018). Efficient Delineation of Nested Depression Hierarchy in Digital Elevation Models for Hydrological Analysis Using Level-Set Methods. Journal of the American Water Resources Association. (forthcoming)

Credits

• This algorithms are built on richdem, numpy, scipy, scikit-image, and pygdal.

• This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

0.1.5 (2018-05-16)

0.1.3 (2018-05-15)

0.1.0 (2018-05-14)

• First release on PyPI.