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
2021-05-18 08:09:45: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2021-05-18 08:09:45: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2021-05-18 08:09:45: oggm.cfg: Multiprocessing: using all available processors (N=2)
2021-05-18 08:09:45: 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]
2021-05-18 08:09:45: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2021-05-18 08:09:45: oggm.workflow: Execute entity task gdir_from_prepro on 1 glaciers
2021-05-18 08:09:45: 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...
2021-05-18 08:09:48: 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: ['ASTER', 'SRTM', 'AW3D30', 'NASADEM', 'COPDEM', 'MAPZEN', 'DEM3', '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()
ASTER SRTM AW3D30 NASADEM COPDEM MAPZEN DEM3 TANDEM
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean 3031.316346 3032.272219 3023.871970 3028.332505 3014.512695 3023.202610 3061.494096 3066.820068
std 250.883864 246.447678 253.597569 247.615467 259.186615 254.144292 241.860064 258.874268
min 2428.000000 2450.000000 2417.000000 2430.000000 2416.773438 2413.000000 2501.000000 2469.287598
25% 2865.000000 2857.000000 2851.000000 2855.000000 2836.975037 2850.000000 2886.000000 2889.410828
50% 3058.000000 3060.000000 3052.500000 3054.000000 3044.824097 3051.000000 3076.000000 3097.242310
75% 3203.000000 3203.000000 3201.750000 3202.000000 3196.834839 3201.750000 3233.750000 3249.972717
max 3693.000000 3684.000000 3720.000000 3691.000000 3688.330078 3723.000000 3706.000000 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
[-50, -30, -15, -10, -5, -2, -1, 0, 1, 2, 5, 10, 15, 30, 50]
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()
ASTER SRTM AW3D30 NASADEM COPDEM MAPZEN DEM3 TANDEM
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean 3031.316346 3032.272219 3023.871970 3028.332505 3014.512695 3023.202610 3061.494096 3066.820068
std 250.883864 246.447678 253.597569 247.615467 259.186615 254.144292 241.860064 258.874268
min 2428.000000 2450.000000 2417.000000 2430.000000 2416.773438 2413.000000 2501.000000 2469.287598
25% 2865.000000 2857.000000 2851.000000 2855.000000 2836.975037 2850.000000 2886.000000 2889.410828
50% 3058.000000 3060.000000 3052.500000 3054.000000 3044.824097 3051.000000 3076.000000 3097.242310
75% 3203.000000 3203.000000 3201.750000 3202.000000 3196.834839 3201.750000 3233.750000 3249.972717
max 3693.000000 3684.000000 3720.000000 3691.000000 3688.330078 3723.000000 3706.000000 3738.977051
df.corr()
ASTER SRTM AW3D30 NASADEM COPDEM MAPZEN DEM3 TANDEM
ASTER 1.000000 0.998518 0.999315 0.999131 0.999365 0.999455 0.995739 0.999344
SRTM 0.998518 1.000000 0.998216 0.998544 0.998394 0.998217 0.997038 0.998315
AW3D30 0.999315 0.998216 1.000000 0.999553 0.999660 0.999819 0.996423 0.999633
NASADEM 0.999131 0.998544 0.999553 1.000000 0.999379 0.999643 0.997409 0.999342
COPDEM 0.999365 0.998394 0.999660 0.999379 1.000000 0.999700 0.996189 0.999974
MAPZEN 0.999455 0.998217 0.999819 0.999643 0.999700 1.000000 0.996358 0.999687
DEM3 0.995739 0.997038 0.996423 0.997409 0.996189 0.996358 1.000000 0.996175
TANDEM 0.999344 0.998315 0.999633 0.999342 0.999974 0.999687 0.996175 1.000000
df_diff.describe()
ASTER-SRTM ASTER-AW3D30 ASTER-NASADEM ASTER-COPDEM ASTER-MAPZEN ASTER-DEM3 ASTER-TANDEM SRTM-AW3D30 SRTM-NASADEM SRTM-COPDEM ... NASADEM-COPDEM NASADEM-MAPZEN NASADEM-DEM3 NASADEM-TANDEM COPDEM-MAPZEN COPDEM-DEM3 COPDEM-TANDEM MAPZEN-DEM3 MAPZEN-TANDEM DEM3-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 -0.955873 7.444375 2.983841 16.803539 8.113735 -30.177750 -35.503534 8.400249 3.939714 17.759413 ... 13.819699 5.129894 -33.161591 -38.487375 -8.689804 -46.981290 -52.307072 -38.291485 -43.617270 -5.325784
std 14.247553 9.720664 10.892745 12.309234 8.953622 24.464741 12.211383 16.555147 13.380009 19.169901 ... 14.616837 9.360127 18.533220 14.527865 8.057372 27.893263 1.883556 24.467068 7.972539 27.721822
min -69.000000 -33.000000 -40.000000 -21.495605 -30.000000 -112.000000 -79.663330 -75.000000 -59.000000 -53.235596 ... -27.849121 -51.000000 -92.000000 -80.386475 -44.972168 -131.903320 -79.299805 -123.000000 -85.858154 -131.650635
25% -10.000000 1.000000 -3.000000 8.315002 2.000000 -42.000000 -43.959106 -1.000000 -1.000000 6.643860 ... 4.234375 -1.000000 -43.000000 -48.055481 -11.912292 -59.555908 -52.462891 -50.000000 -48.300476 -21.998291
50% 0.000000 7.000000 4.000000 14.929077 8.000000 -29.000000 -37.429810 9.000000 4.000000 17.123169 ... 9.529785 3.000000 -34.000000 -42.692627 -7.221680 -45.352905 -52.428711 -38.000000 -45.182861 -6.978149
75% 7.000000 13.000000 9.000000 24.004944 14.000000 -20.000000 -28.404724 20.000000 10.000000 29.375549 ... 18.632935 10.000000 -25.000000 -33.745605 -4.038086 -30.414978 -52.394043 -25.000000 -40.532715 7.438965
max 59.000000 44.000000 55.000000 52.883789 46.000000 94.000000 0.389160 79.000000 69.000000 73.730957 ... 69.138428 40.000000 91.000000 16.636475 32.682129 86.843262 -23.296631 100.000000 -1.023926 79.397461

8 rows × 28 columns

df_diff.abs().describe()
ASTER-SRTM ASTER-AW3D30 ASTER-NASADEM ASTER-COPDEM ASTER-MAPZEN ASTER-DEM3 ASTER-TANDEM SRTM-AW3D30 SRTM-NASADEM SRTM-COPDEM ... NASADEM-COPDEM NASADEM-MAPZEN NASADEM-DEM3 NASADEM-TANDEM COPDEM-MAPZEN COPDEM-DEM3 COPDEM-TANDEM MAPZEN-DEM3 MAPZEN-TANDEM DEM3-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 10.924798 9.678682 8.758856 17.410436 9.781231 33.658173 35.503776 14.691734 10.136109 21.517830 ... 14.487043 7.451212 34.688626 38.762453 9.364936 48.232542 52.307072 40.133623 43.617270 21.395372
std 9.193391 7.498083 7.128573 11.434442 7.093540 19.397900 12.210679 11.346579 9.580093 14.826293 ... 13.955490 7.641868 15.486716 13.776885 7.261413 25.668278 1.883556 21.310154 7.972539 18.411083
min 0.000000 0.000000 0.000000 0.000977 0.000000 0.000000 0.389160 0.000000 0.000000 0.012451 ... 0.001953 0.000000 0.000000 0.005859 0.000488 0.087646 23.296631 0.000000 1.023926 0.001709
25% 4.000000 4.000000 3.000000 8.656433 4.000000 21.000000 28.404724 5.000000 3.000000 10.496765 ... 4.906738 2.000000 25.000000 33.745605 4.394409 30.705994 52.394043 26.000000 40.532715 7.188965
50% 9.000000 8.000000 7.000000 15.060913 9.000000 30.000000 37.429810 13.000000 7.000000 18.754150 ... 9.772339 4.000000 34.000000 42.692627 7.369751 45.544189 52.428711 38.000000 45.182861 16.839111
75% 16.000000 14.000000 12.000000 24.004944 14.000000 43.000000 43.959106 22.000000 15.000000 29.730896 ... 18.664856 10.000000 43.000000 48.055481 12.140320 59.782166 52.462891 50.000000 48.300476 30.157959
max 69.000000 44.000000 55.000000 52.883789 46.000000 112.000000 79.663330 79.000000 69.000000 73.730957 ... 69.138428 51.000000 92.000000 80.386475 44.972168 131.903320 79.299805 123.000000 85.858154 131.650635

8 rows × 28 columns

What’s next?