Topography of the Trail Islands in British Columbia, Canada

This is a lidar point cloud (ground reflections only) sliced to the small Trail Islands to the North of Vancouver. The islands have some nice looking topography and their isolated nature creates problems for some interpolation methods.

Original source: LidarBC

Pre-processing: Source code for preparation of the original dataset for redistribution in Ensaio

import pandas as pd
import pygmt

import ensaio

Download and cache the data and return the path to it on disk

fname = ensaio.fetch_british_columbia_lidar(version=1)
print(fname)

/home/runner/work/_temp/cache/ensaio/v1/british-columbia-lidar.csv.xz

Load the CSV formatted data with pandas

data = pd.read_csv(fname)
data

	longitude	latitude	elevation_m
0	-123.813753	49.460263	-0.67
1	-123.813725	49.460274	-0.96
2	-123.813764	49.460254	-0.78
3	-123.813744	49.460262	-0.61
4	-123.813737	49.460265	-0.62
...	...	...	...
829728	-123.807407	49.455007	-1.24
829729	-123.807410	49.454995	-1.25
829730	-123.807416	49.454980	-1.19
829731	-123.807428	49.454966	-1.21
829732	-123.807432	49.454963	-1.20

829733 rows × 3 columns

Make a PyGMT map with the data points colored by the elevation.

fig = pygmt.Figure()
fig.basemap(
    region=[
        data.longitude.min(),
        data.longitude.max(),
        data.latitude.min(),
        data.latitude.max(),
    ],
    projection="M15c",
    frame=True,
)
pygmt.makecpt(cmap="viridis", series=[data.elevation_m.min(), data.elevation_m.max()])
fig.plot(
    x=data.longitude, y=data.latitude, fill=data.elevation_m, cmap=True, style="c0.05c"
)
fig.colorbar(frame='af+l"elevation [m]"')
fig.show()