Compare different DEMs for individual glaciers

For most glaciers in the world there are several digital elevation models (DEM) which cover the respective glacier. In OGGM we have currently implemented 10 different open access DEMs to choose from. Some are regional and only available in certain areas (e.g. Greenland or Antarctica) and some cover almost the entire globe. For more information, visit the rgitools documentation about DEMs.

This notebook allows to see which of the DEMs are available for a selected glacier and how they compare to each other. That way it is easy to spot systematic differences and also invalid points in the DEMs.

Input parameters

This notebook can be run as a script with parameters using papermill, but it is not necessary. The following cell contains the parameters you can choose from:

# The RGI Id of the glaciers you want to look for
# Use the original shapefiles or the GLIMS viewer to check for the ID: https://www.glims.org/maps/glims
rgi_id = 'RGI60-11.00897'

# The default is to test for all sources available for this glacier
# Set to a list of source names to override this
sources = None
# Where to write the plots. Default is in the current working directory
plot_dir = ''
# The RGI version to use
# V62 is an unofficial modification of V6 with only minor, backwards compatible modifications
prepro_rgi_version = 62
# Size of the map around the glacier. Currently only 10 and 40 are available
prepro_border = 10
# Degree of processing level.  Currently only 1 is available.
from_prepro_level = 1

Check input and set up

# The sources can be given as parameters
if sources is not None and isinstance(sources, str):
    sources = sources.split(',')
# Plotting directory as well
if not plot_dir:
    plot_dir = './' + rgi_id
import os
plot_dir = os.path.abspath(plot_dir)
import pandas as pd
import numpy as np
from oggm import cfg, utils, workflow, tasks, graphics, GlacierDirectory
import xarray as xr
import geopandas as gpd
import salem
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import itertools

from oggm.utils import DEM_SOURCES
from oggm.workflow import init_glacier_directories
# Make sure the plot directory exists
utils.mkdir(plot_dir);
# Use OGGM to download the data
cfg.initialize()
cfg.PATHS['working_dir'] = utils.gettempdir(dirname='OGGM-DEMS', reset=True)
cfg.PARAMS['use_intersects'] = False
2021-09-15 09:56:29: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2021-09-15 09:56:29: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2021-09-15 09:56:29: oggm.cfg: Multiprocessing: using all available processors (N=2)
2021-09-15 09:56:29: oggm.cfg: PARAMS['use_intersects'] changed from `True` to `False`.

Download the data using OGGM utility functions

Note that you could reach the same goal by downloading the data manually from https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/rgitopo/

# URL of the preprocessed GDirs
gdir_url = 'https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/rgitopo/'
# We use OGGM to download the data
gdir = init_glacier_directories([rgi_id], from_prepro_level=1, prepro_border=10, 
                                 prepro_rgi_version='62', prepro_base_url=gdir_url)[0]
2021-09-15 09:56:30: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2021-09-15 09:56:30: oggm.workflow: Execute entity task gdir_from_prepro on 1 glaciers
2021-09-15 09:56:30: oggm.utils: Downloading https://cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/rgitopo/RGI62/b_010/L1/RGI60-11/RGI60-11.00.tar to /github/home/OGGM/download_cache/cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/rgitopo/RGI62/b_010/L1/RGI60-11/RGI60-11.00.tar...
2021-09-15 09:56:35: oggm.utils: /github/home/OGGM/download_cache/cluster.klima.uni-bremen.de/~oggm/gdirs/oggm_v1.4/rgitopo/RGI62/b_010/L1/RGI60-11/RGI60-11.00.tar verified successfully.

Read the DEMs and store them all in a dataset

if sources is None:
    sources = [src for src in os.listdir(gdir.dir) if src in utils.DEM_SOURCES]
print('RGI ID:', rgi_id)
print('Available DEM sources:', sources)
print('Plotting directory:', plot_dir)
RGI ID: RGI60-11.00897
Available DEM sources: ['NASADEM', 'COPDEM', 'DEM3', 'AW3D30', 'SRTM', 'ASTER', 'MAPZEN', 'TANDEM']
Plotting directory: /__w/tutorials/tutorials/notebooks/RGI60-11.00897
# We use xarray to store the data
ods = xr.Dataset()
for src in sources:
    demfile = os.path.join(gdir.dir, src) + '/dem.tif'
    with xr.open_rasterio(demfile) as ds:
        data = ds.sel(band=1).load() * 1.
        ods[src] = data.where(data > -100, np.NaN)
    
    sy, sx = np.gradient(ods[src], gdir.grid.dx, gdir.grid.dx)
    ods[src + '_slope'] = ('y', 'x'),  np.arctan(np.sqrt(sy**2 + sx**2))

with xr.open_rasterio(gdir.get_filepath('glacier_mask')) as ds:
    ods['mask'] = ds.sel(band=1).load()
# Decide on the number of plots and figure size
ns = len(sources)
x_size = 12
n_cols = 3
n_rows = -(-ns // n_cols)
y_size = x_size / n_cols * n_rows

Raw topography data

smap = salem.graphics.Map(gdir.grid, countries=False)
smap.set_shapefile(gdir.read_shapefile('outlines'))
smap.set_plot_params(cmap='topo')
smap.set_lonlat_contours(add_tick_labels=False)
smap.set_plot_params(vmin=np.nanquantile([ods[s].min() for s in sources], 0.25),
                     vmax=np.nanquantile([ods[s].max() for s in sources], 0.75))

fig = plt.figure(figsize=(x_size, y_size))
grid = AxesGrid(fig, 111,
                nrows_ncols=(n_rows, n_cols),
                axes_pad=0.7,
                cbar_mode='each',
                cbar_location='right',
                cbar_pad=0.1
                )

for i, s in enumerate(sources):
    data = ods[s]
    smap.set_data(data)
    ax = grid[i]
    smap.visualize(ax=ax, addcbar=False, title=s)
    if np.isnan(data).all():
        grid[i].cax.remove()
        continue
    cax = grid.cbar_axes[i]
    smap.colorbarbase(cax)
    
# take care of uneven grids
if ax != grid[-1]:
    grid[-1].remove()
    grid[-1].cax.remove()

plt.savefig(os.path.join(plot_dir, 'dem_topo_color.png'), dpi=150, bbox_inches='tight')
../_images/dem_comparison_19_0.png

Shaded relief

fig = plt.figure(figsize=(x_size, y_size))
grid = AxesGrid(fig, 111,
                nrows_ncols=(n_rows, n_cols),
                axes_pad=0.7,
                cbar_mode='none',
                cbar_location='right',
                cbar_pad=0.1
                )
smap.set_plot_params(cmap='Blues')
smap.set_shapefile()
for i, s in enumerate(sources):
    data = ods[s].copy().where(np.isfinite(ods[s]), 0)
    smap.set_data(data * 0)
    ax = grid[i]
    smap.set_topography(data)
    smap.visualize(ax=ax, addcbar=False, title=s)
    
# take care of uneven grids
if ax != grid[-1]:
    grid[-1].remove()
    grid[-1].cax.remove()

plt.savefig(os.path.join(plot_dir, 'dem_topo_shade.png'), dpi=150, bbox_inches='tight')
../_images/dem_comparison_21_0.png

Slope

fig = plt.figure(figsize=(x_size, y_size))
grid = AxesGrid(fig, 111,
                nrows_ncols=(n_rows, n_cols),
                axes_pad=0.7,
                cbar_mode='each',
                cbar_location='right',
                cbar_pad=0.1
                )

smap.set_topography();
smap.set_plot_params(vmin=0, vmax=0.7, cmap='Blues')

for i, s in enumerate(sources):
    data = ods[s + '_slope']
    smap.set_data(data)
    ax = grid[i]
    smap.visualize(ax=ax, addcbar=False, title=s + ' (slope)')
    cax = grid.cbar_axes[i]
    smap.colorbarbase(cax)
    
# take care of uneven grids
if ax != grid[-1]:
    grid[-1].remove()
    grid[-1].cax.remove()

plt.savefig(os.path.join(plot_dir, 'dem_slope.png'), dpi=150, bbox_inches='tight')
../_images/dem_comparison_23_0.png

Some simple statistics about the DEMs

df = pd.DataFrame()
for s in sources:
    df[s] = ods[s].data.flatten()[ods.mask.data.flatten() == 1]

dfs = pd.DataFrame()
for s in sources:
    dfs[s] = ods[s + '_slope'].data.flatten()[ods.mask.data.flatten() == 1]
df.describe()
NASADEM COPDEM DEM3 AW3D30 SRTM ASTER MAPZEN TANDEM
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean 3028.332505 3014.512451 3061.494096 3023.871970 3032.272219 3031.316346 3023.202610 3066.819092
std 247.615467 259.186768 241.860064 253.597569 246.447678 250.883864 254.144292 258.874115
min 2430.000000 2416.773438 2501.000000 2417.000000 2450.000000 2428.000000 2413.000000 2469.287598
25% 2855.000000 2836.975037 2886.000000 2851.000000 2857.000000 2865.000000 2850.000000 2889.410828
50% 3054.000000 3044.824097 3076.000000 3052.500000 3060.000000 3058.000000 3051.000000 3097.242310
75% 3202.000000 3196.834839 3233.750000 3201.750000 3203.000000 3203.000000 3201.750000 3249.972717
max 3691.000000 3688.330078 3706.000000 3720.000000 3684.000000 3693.000000 3723.000000 3738.977051

Comparison matrix plot

# Table of differences between DEMS
df_diff = pd.DataFrame()
done = []
for s1, s2 in itertools.product(sources, sources):
    if s1 == s2:
        continue
    if (s2, s1) in done:
        continue
    df_diff[s1 + '-' + s2] = df[s1] - df[s2]
    done.append((s1, s2))
# Decide on plot levels
max_diff = df_diff.quantile(0.99).max()
base_levels = np.array([-8, -5, -3, -1.5, -1, -0.5, -0.2, -0.1, 0, 0.1, 0.2, 0.5, 1, 1.5, 3, 5, 8])
if max_diff < 10:
    levels = base_levels
elif max_diff < 100:
    levels = base_levels * 10
elif max_diff < 1000:
    levels = base_levels * 100
else:
    levels = base_levels * 1000
levels = [l for l in levels if abs(l) < max_diff]
if max_diff > 10:
    levels = [int(l) for l in levels]
levels
[-80, -50, -30, -15, -10, -5, -2, -1, 0, 1, 2, 5, 10, 15, 30, 50, 80]
smap.set_plot_params(levels=levels, cmap='PuOr', extend='both')
smap.set_shapefile(gdir.read_shapefile('outlines'))

fig = plt.figure(figsize=(14, 14))
grid = AxesGrid(fig, 111,
                nrows_ncols=(ns - 1, ns - 1),
                axes_pad=0.3,
                cbar_mode='single',
                cbar_location='right',
                cbar_pad=0.1
                )
done = []
for ax in grid:
    ax.set_axis_off()
for s1, s2 in itertools.product(sources, sources):
    if s1 == s2:
        continue
    if (s2, s1) in done:
        continue
    data = ods[s1] - ods[s2]
    ax = grid[sources.index(s1) * (ns - 1) + sources[1:].index(s2)]
    ax.set_axis_on()
    smap.set_data(data)
    smap.visualize(ax=ax, addcbar=False)
    done.append((s1, s2))
    ax.set_title(s1 + '-' + s2, fontsize=8)
    
cax = grid.cbar_axes[0]
smap.colorbarbase(cax);

plt.savefig(os.path.join(plot_dir, 'dem_diffs.png'), dpi=150, bbox_inches='tight')
../_images/dem_comparison_30_0.png

Comparison scatter plot

import seaborn as sns
sns.set(style="ticks")

l1, l2 = (utils.nicenumber(df.min().min(), binsize=50, lower=True), 
          utils.nicenumber(df.max().max(), binsize=50, lower=False))

def plot_unity(xdata, ydata, **kwargs):
    points = np.linspace(l1, l2, 100)
    plt.gca().plot(points, points, color='k', marker=None,
                   linestyle=':', linewidth=3.0)

g = sns.pairplot(df.dropna(how='all', axis=1).dropna(), plot_kws=dict(s=50, edgecolor="C0", linewidth=1));
g.map_offdiag(plot_unity)
for asx in g.axes:
    for ax in asx:
        ax.set_xlim((l1, l2))
        ax.set_ylim((l1, l2))

plt.savefig(os.path.join(plot_dir, 'dem_scatter.png'), dpi=150, bbox_inches='tight')
../_images/dem_comparison_32_0.png

Table statistics

df.describe()
NASADEM COPDEM DEM3 AW3D30 SRTM ASTER MAPZEN TANDEM
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean 3028.332505 3014.512451 3061.494096 3023.871970 3032.272219 3031.316346 3023.202610 3066.819092
std 247.615467 259.186768 241.860064 253.597569 246.447678 250.883864 254.144292 258.874115
min 2430.000000 2416.773438 2501.000000 2417.000000 2450.000000 2428.000000 2413.000000 2469.287598
25% 2855.000000 2836.975037 2886.000000 2851.000000 2857.000000 2865.000000 2850.000000 2889.410828
50% 3054.000000 3044.824097 3076.000000 3052.500000 3060.000000 3058.000000 3051.000000 3097.242310
75% 3202.000000 3196.834839 3233.750000 3201.750000 3203.000000 3203.000000 3201.750000 3249.972717
max 3691.000000 3688.330078 3706.000000 3720.000000 3684.000000 3693.000000 3723.000000 3738.977051
df.corr()
NASADEM COPDEM DEM3 AW3D30 SRTM ASTER MAPZEN TANDEM
NASADEM 1.000000 0.999379 0.997409 0.999553 0.998544 0.999131 0.999643 0.999342
COPDEM 0.999379 1.000000 0.996189 0.999660 0.998394 0.999365 0.999700 0.999974
DEM3 0.997409 0.996189 1.000000 0.996423 0.997038 0.995739 0.996358 0.996175
AW3D30 0.999553 0.999660 0.996423 1.000000 0.998216 0.999315 0.999819 0.999633
SRTM 0.998544 0.998394 0.997038 0.998216 1.000000 0.998518 0.998217 0.998315
ASTER 0.999131 0.999365 0.995739 0.999315 0.998518 1.000000 0.999455 0.999344
MAPZEN 0.999643 0.999700 0.996358 0.999819 0.998217 0.999455 1.000000 0.999687
TANDEM 0.999342 0.999974 0.996175 0.999633 0.998315 0.999344 0.999687 1.000000
df_diff.describe()
NASADEM-COPDEM NASADEM-DEM3 NASADEM-AW3D30 NASADEM-SRTM NASADEM-ASTER NASADEM-MAPZEN NASADEM-TANDEM COPDEM-DEM3 COPDEM-AW3D30 COPDEM-SRTM ... AW3D30-SRTM AW3D30-ASTER AW3D30-MAPZEN AW3D30-TANDEM SRTM-ASTER SRTM-MAPZEN SRTM-TANDEM ASTER-MAPZEN ASTER-TANDEM MAPZEN-TANDEM
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 ... 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean 13.819699 -33.161591 4.460534 -3.939714 -2.983841 5.129894 -38.487375 -46.981290 -9.359164 -17.759413 ... -8.400249 -7.444375 0.669360 -42.947910 0.955873 9.069608 -34.547661 8.113735 -35.503534 -43.617270
std 14.616837 18.533220 9.588316 13.380009 10.892745 9.360127 14.527865 27.893263 8.710851 19.169901 ... 16.555147 9.720664 4.859039 8.722370 14.247553 16.809936 19.220421 8.953622 12.211383 7.972539
min -27.849121 -92.000000 -48.000000 -69.000000 -55.000000 -51.000000 -80.386475 -131.903320 -41.972168 -73.730957 ... -79.000000 -44.000000 -28.000000 -82.822998 -59.000000 -69.000000 -106.757324 -30.000000 -79.663330 -85.858154
25% 4.234375 -43.000000 -2.000000 -10.000000 -9.000000 -1.000000 -48.055481 -59.555908 -14.219543 -29.375549 ... -20.000000 -13.000000 -1.000000 -47.949890 -7.000000 1.000000 -45.801941 2.000000 -43.959106 -48.300476
50% 9.529785 -34.000000 3.000000 -4.000000 -4.000000 3.000000 -42.692627 -45.352905 -9.151611 -17.123169 ... -9.000000 -7.000000 1.000000 -43.253540 0.000000 10.000000 -35.271484 8.000000 -37.429810 -45.182861
75% 18.632935 -25.000000 8.000000 1.000000 3.000000 10.000000 -33.745605 -30.414978 -4.334778 -6.643860 ... 1.000000 -1.000000 3.000000 -38.227905 10.000000 20.750000 -22.963257 14.000000 -28.404724 -40.532715
max 69.138428 91.000000 38.000000 59.000000 40.000000 40.000000 16.636475 86.843262 29.402100 53.235596 ... 75.000000 33.000000 35.000000 -2.486084 69.000000 84.000000 21.226807 46.000000 0.389160 -1.023926

8 rows × 28 columns

df_diff.abs().describe()
NASADEM-COPDEM NASADEM-DEM3 NASADEM-AW3D30 NASADEM-SRTM NASADEM-ASTER NASADEM-MAPZEN NASADEM-TANDEM COPDEM-DEM3 COPDEM-AW3D30 COPDEM-SRTM ... AW3D30-SRTM AW3D30-ASTER AW3D30-MAPZEN AW3D30-TANDEM SRTM-ASTER SRTM-MAPZEN SRTM-TANDEM ASTER-MAPZEN ASTER-TANDEM MAPZEN-TANDEM
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 ... 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean 14.487043 34.688626 7.379117 10.136109 8.758856 7.451212 38.762453 48.232542 10.511262 21.517830 ... 14.691734 9.678682 3.354257 42.947910 10.924798 15.292107 35.206708 9.781231 35.503776 43.617270
std 13.955490 15.486716 7.574302 9.580093 7.128573 7.641868 13.776885 25.668278 7.278676 14.826293 ... 11.346579 7.498083 3.578254 8.722370 9.193391 11.442731 17.984406 7.093540 12.210679 7.972539
min 0.001953 0.000000 0.000000 0.000000 0.000000 0.000000 0.005859 0.087646 0.000977 0.012451 ... 0.000000 0.000000 0.000000 2.486084 0.000000 0.000000 0.166016 0.000000 0.389160 1.023926
25% 4.906738 25.000000 2.000000 3.000000 3.000000 2.000000 33.745605 30.705994 5.184937 10.496765 ... 5.000000 4.000000 1.000000 38.227905 4.000000 6.000000 22.963257 4.000000 28.404724 40.532715
50% 9.772339 34.000000 5.000000 7.000000 7.000000 4.000000 42.692627 45.544189 9.402832 18.754150 ... 13.000000 8.000000 2.000000 43.253540 9.000000 13.000000 35.271484 9.000000 37.429810 45.182861
75% 18.664856 43.000000 10.000000 15.000000 12.000000 10.000000 48.055481 59.782166 14.364990 29.730896 ... 22.000000 14.000000 4.000000 47.949890 16.000000 23.000000 45.801941 14.000000 43.959106 48.300476
max 69.138428 92.000000 48.000000 69.000000 55.000000 51.000000 80.386475 131.903320 41.972168 73.730957 ... 79.000000 44.000000 35.000000 82.822998 69.000000 84.000000 106.757324 46.000000 79.663330 85.858154

8 rows × 28 columns

What’s next?