Lab7 - LiDAR data analysis and visualization with whitebox and leafmap**

Lab7 - LiDAR data analysis and visualization with whitebox and leafmap**#

LIDAR .LAS or .LAZ file can be downloaded from: https://apps.nationalmap.gov/lidar-explorer/#/

Create a new conda env to install required packages:

conda create -n geo python
conda activate geo
conda install -c conda-forge mamba
mamba install -c conda-forge pygis
pip install laspy[lazrs]

Uncomment the following line to install packages in Google Colab.

# !pip install leafmap

# !pip install laspy[lazrs]

Import libraries#

import os
import leafmap
import whitebox

c:\Users\C00553090\AppData\Local\miniconda3\envs\hypercoast\lib\site-packages\pandas\core\computation\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.7.3' currently installed).
  from pandas.core.computation.check import NUMEXPR_INSTALLED

Set up whitebox#

wbt = whitebox.WhiteboxTools()
wbt.set_working_dir(os.getcwd())
wbt.set_verbose_mode(False)

Download sample data#

filename = "USGS_LPC_Barataria_and_Jean_Lafitte_LiDAR_15RYN5357.laz"

Read LAS/LAZ data#

# laz = leafmap.read_lidar(filename) is part of the leafmap library, 
# and it is used to read LiDAR data (LAS or LAZ format) into a Python object for further processing and visualization.
laz = leafmap.read_lidar(filename)

laz

<LasData(1.2, point fmt: <PointFormat(1, 0 bytes of extra dims)>, 37891 points, 3 vlrs)>

# LAS 1.0 is basic and lacks many modern features.
# LAS 1.4 supports additional attributes like full waveform and higher precision.
# LAS 1.2 is a versatile format and widely supported by LiDAR processing tools.
str(laz.header.version)

'1.2'

Upgrade file version#

#Some modern tools (like GIS or analysis software) may require LAS 1.4 to work with advanced datasets. 

las = leafmap.convert_lidar(laz, file_version="1.4")

str(las.header.version)

'1.4'

Write LAS data#

# The command leafmap.write_lidar(las, "LA.las") is used to save LiDAR data to a file in the specified LAS or LAZ format. Here's a detailed breakdown:
leafmap.write_lidar(las, "LA.las")

Histogram analysis#

# The command wbt.lidar_histogram("LA.las", "histogram.html") is part of the WhiteboxTools (WBT) library and is used to generate a histogram of the LiDAR data stored in the LAS file (LA.las). 
# it generates an interactive histogram of the attribute distributions in the LiDAR file LA.las and saves it as an HTML file (histogram.html). 
# This tool helps analyze and visualize the data's attributes, such as elevation or intensity, in a user-friendly format.

wbt.lidar_histogram("LA.las", "histogram.html")

Visualize LiDAR data#

leafmap.view_lidar("LA.las")

Remove outliers#

# The command wbt.lidar_elevation_slice("LA.las", "LA_rm.las", minz=0, maxz=450) is used to filter LiDAR points based on their elevation (Z values). It keeps only the points within a specified elevation range and removes all others.
wbt.lidar_elevation_slice("LA.las", "LA_rm.las", minz=0, maxz=450)

Visualize LiDAR data after removing outliers#

leafmap.view_lidar("LA_rm.las", cmap="terrain")

Create DSM#

# The command is used to create a Digital Surface Model (DSM) from a LiDAR point cloud file.
wbt.lidar_digital_surface_model(
    "LA_rm.las", "dsm.tif", resolution=1.0, minz=0, maxz=450
)

# The command is used to assign a coordinate system (CRS) to the file dsm.tif.
leafmap.add_crs("dsm.tif", epsg=26915)

Visualize DSM#

m = leafmap.Map()
m.add_raster("dsm.tif", palette="terrain", layer_name="DSM")
m

Create DEM#

# Create a Digital Terrain Model (DTM) by removing objects like trees, buildings, or other above-ground features from a Digital Surface Model (DSM).
wbt.remove_off_terrain_objects("dsm.tif", "dem.tif", filter=25, slope=15.0)

Visualize DEM#

m.add_raster("dem.tif", palette="terrain", layer_name="DEM")
m

Create CHM#

chm = wbt.subtract("dsm.tif", "dem.tif", "chm.tif")

m.add_raster("chm.tif", palette="gist_earth", layer_name="CHM")
m