Topographic Hillshading with Matplotlib

Introduction

This lab demonstrates how to use Matplotlib to create topographic hillshading plots using varying blend modes and vertical exaggeration. The purpose of hillshading is for visual purposes to create a 3D-like effect on 2D maps. In this lab, we will learn how to change the blend modes and vertical exaggeration to achieve different visual effects.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Import Required Libraries

We begin by importing the required libraries, including Matplotlib, NumPy, and LightSource.

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.cbook import get_sample_data
from matplotlib.colors import LightSource

Load the Data

Next, we load the sample elevation data using the get_sample_data function from Matplotlib. We then extract the elevation data and the cell size of the grid.

dem = get_sample_data('jacksboro_fault_dem.npz')
z = dem['elevation']
dx, dy = dem['dx'], dem['dy']

Specify the Cell Size

If you need topographically accurate vertical exaggeration, or you don't want to guess at what vert_exag should be, you'll need to specify the cell size of the grid (i.e. the dx and dy parameters). Otherwise, any vert_exag value you specify will be relative to the grid spacing of your input data. In this step, we calculate the dx and dy values in meters.

dy = 111200 * dy
dx = 111200 * dx * np.cos(np.radians(dem['ymin']))

Specify Light Source and Colormap

We specify the LightSource object by setting the azimuth and altitude of the light source. We also set the colormap to be used in the plot.

ls = LightSource(azdeg=315, altdeg=45)
cmap = plt.cm.gist_earth

Create the Plot

We create a 4x3 plot grid to show the hillshaded plots with different blend modes and vertical exaggeration. We first show the hillshade intensity image in the first row, and then place hillshaded plots with different blend modes in the rest of the rows. We use a for loop to iterate through the different vertical exaggeration values and blend modes.

fig, axs = plt.subplots(nrows=4, ncols=3, figsize=(8, 9))
plt.setp(axs.flat, xticks=[], yticks=[])

for col, ve in zip(axs.T, [0.1, 1, 10]):
    col[0].imshow(ls.hillshade(z, vert_exag=ve, dx=dx, dy=dy), cmap='gray')
    for ax, mode in zip(col[1:], ['hsv', 'overlay', 'soft']):
        rgb = ls.shade(z, cmap=cmap, blend_mode=mode,
                       vert_exag=ve, dx=dx, dy=dy)
        ax.imshow(rgb)

Label the Plot

We label the rows and columns of the plot grid using the set_title and set_ylabel functions. We also add a title for the vertical exaggeration and blend mode groups.

for ax, ve in zip(axs[0], [0.1, 1, 10]):
    ax.set_title(f'{ve}', size=18)
for ax, mode in zip(axs[:, 0], ['Hillshade', 'hsv', 'overlay', 'soft']):
    ax.set_ylabel(mode, size=18)

axs[0, 1].annotate('Vertical Exaggeration', (0.5, 1), xytext=(0, 30),
                   textcoords='offset points', xycoords='axes fraction',
                   ha='center', va='bottom', size=20)
axs[2, 0].annotate('Blend Mode', (0, 0.5), xytext=(-30, 0),
                   textcoords='offset points', xycoords='axes fraction',
                   ha='right', va='center', size=20, rotation=90)
fig.subplots_adjust(bottom=0.05, right=0.95)

Display the Plot

Finally, we display the plot using the show function.

plt.show()

Summary

In this lab, we learned how to create topographic hillshading plots using Matplotlib. We used varying blend modes and vertical exaggeration to achieve different visual effects. We also learned how to specify the cell size of the grid for topographically accurate vertical exaggeration.