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
2021-06-07 14:33:32: oggm.cfg: Reading default parameters from the OGGM `params.cfg` configuration file.
2021-06-07 14:33:32: oggm.cfg: Multiprocessing switched OFF according to the parameter file.
2021-06-07 14:33:32: oggm.cfg: Multiprocessing: using all available processors (N=2)
2021-06-07 14:33:32: 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]
2021-06-07 14:33:33: oggm.workflow: init_glacier_directories from prepro level 1 on 1 glaciers.
2021-06-07 14:33:33: oggm.workflow: Execute entity task gdir_from_prepro on 1 glaciers
2021-06-07 14:33:33: 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...
2021-06-07 14:33:36: 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: ['AW3D30', 'COPDEM', 'DEM3', 'NASADEM', 'SRTM', 'TANDEM', 'MAPZEN', 'ASTER']
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()
AW3D30 COPDEM DEM3 NASADEM SRTM TANDEM MAPZEN ASTER
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean 3023.871970 3014.512695 3061.494096 3028.332505 3032.272219 3066.820068 3023.202610 3031.316346
std 253.597569 259.186615 241.860064 247.615467 246.447678 258.874268 254.144292 250.883864
min 2417.000000 2416.773438 2501.000000 2430.000000 2450.000000 2469.287598 2413.000000 2428.000000
25% 2851.000000 2836.975037 2886.000000 2855.000000 2857.000000 2889.410828 2850.000000 2865.000000
50% 3052.500000 3044.824097 3076.000000 3054.000000 3060.000000 3097.242310 3051.000000 3058.000000
75% 3201.750000 3196.834839 3233.750000 3202.000000 3203.000000 3249.972717 3201.750000 3203.000000
max 3720.000000 3688.330078 3706.000000 3691.000000 3684.000000 3738.977051 3723.000000 3693.000000

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()
AW3D30 COPDEM DEM3 NASADEM SRTM TANDEM MAPZEN ASTER
count 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000 3218.000000
mean 3023.871970 3014.512695 3061.494096 3028.332505 3032.272219 3066.820068 3023.202610 3031.316346
std 253.597569 259.186615 241.860064 247.615467 246.447678 258.874268 254.144292 250.883864
min 2417.000000 2416.773438 2501.000000 2430.000000 2450.000000 2469.287598 2413.000000 2428.000000
25% 2851.000000 2836.975037 2886.000000 2855.000000 2857.000000 2889.410828 2850.000000 2865.000000
50% 3052.500000 3044.824097 3076.000000 3054.000000 3060.000000 3097.242310 3051.000000 3058.000000
75% 3201.750000 3196.834839 3233.750000 3202.000000 3203.000000 3249.972717 3201.750000 3203.000000
max 3720.000000 3688.330078 3706.000000 3691.000000 3684.000000 3738.977051 3723.000000 3693.000000
df.corr()
AW3D30 COPDEM DEM3 NASADEM SRTM TANDEM MAPZEN ASTER
AW3D30 1.000000 0.999660 0.996423 0.999553 0.998216 0.999633 0.999819 0.999315
COPDEM 0.999660 1.000000 0.996189 0.999379 0.998394 0.999974 0.999700 0.999365
DEM3 0.996423 0.996189 1.000000 0.997409 0.997038 0.996175 0.996358 0.995739
NASADEM 0.999553 0.999379 0.997409 1.000000 0.998544 0.999342 0.999643 0.999131
SRTM 0.998216 0.998394 0.997038 0.998544 1.000000 0.998315 0.998217 0.998518
TANDEM 0.999633 0.999974 0.996175 0.999342 0.998315 1.000000 0.999687 0.999344
MAPZEN 0.999819 0.999700 0.996358 0.999643 0.998217 0.999687 1.000000 0.999455
ASTER 0.999315 0.999365 0.995739 0.999131 0.998518 0.999344 0.999455 1.000000
df_diff.describe()
AW3D30-COPDEM AW3D30-DEM3 AW3D30-NASADEM AW3D30-SRTM AW3D30-TANDEM AW3D30-MAPZEN AW3D30-ASTER COPDEM-DEM3 COPDEM-NASADEM COPDEM-SRTM ... NASADEM-SRTM NASADEM-TANDEM NASADEM-MAPZEN NASADEM-ASTER SRTM-TANDEM SRTM-MAPZEN SRTM-ASTER TANDEM-MAPZEN TANDEM-ASTER MAPZEN-ASTER
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 9.359164 -37.622126 -4.460534 -8.400249 -42.947910 0.669360 -7.444375 -46.981290 -13.819699 -17.759413 ... -3.939714 -38.487375 5.129894 -2.983841 -34.547661 9.069608 0.955873 43.617270 35.503534 -8.113735
std 8.710851 24.010492 9.588316 16.555147 8.722370 4.859039 9.720664 27.893263 14.616837 19.169901 ... 13.380009 14.527865 9.360127 10.892745 19.220421 16.809936 14.247553 7.972539 12.211383 8.953622
min -29.402100 -121.000000 -38.000000 -79.000000 -82.822998 -28.000000 -44.000000 -131.903320 -69.138428 -73.730957 ... -69.000000 -80.386475 -51.000000 -55.000000 -106.757324 -69.000000 -59.000000 1.023926 -0.389160 -46.000000
25% 4.334778 -49.000000 -8.000000 -20.000000 -47.949890 -1.000000 -13.000000 -59.555908 -18.632935 -29.375549 ... -10.000000 -48.055481 -1.000000 -9.000000 -45.801941 1.000000 -7.000000 40.532715 28.404724 -14.000000
50% 9.151611 -36.000000 -3.000000 -9.000000 -43.253540 1.000000 -7.000000 -45.352905 -9.529785 -17.123169 ... -4.000000 -42.692627 3.000000 -4.000000 -35.271484 10.000000 0.000000 45.182861 37.429810 -8.000000
75% 14.219543 -25.000000 2.000000 1.000000 -38.227905 3.000000 -1.000000 -30.414978 -4.234375 -6.643860 ... 1.000000 -33.745605 10.000000 3.000000 -22.963257 20.750000 10.000000 48.300476 43.959106 -2.000000
max 41.972168 109.000000 48.000000 75.000000 -2.486084 35.000000 33.000000 86.843262 27.849121 53.235596 ... 59.000000 16.636475 40.000000 40.000000 21.226807 84.000000 69.000000 85.858154 79.663330 30.000000

8 rows × 28 columns

df_diff.abs().describe()
AW3D30-COPDEM AW3D30-DEM3 AW3D30-NASADEM AW3D30-SRTM AW3D30-TANDEM AW3D30-MAPZEN AW3D30-ASTER COPDEM-DEM3 COPDEM-NASADEM COPDEM-SRTM ... NASADEM-SRTM NASADEM-TANDEM NASADEM-MAPZEN NASADEM-ASTER SRTM-TANDEM SRTM-MAPZEN SRTM-ASTER TANDEM-MAPZEN TANDEM-ASTER MAPZEN-ASTER
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 10.511262 39.443754 7.379117 14.691734 42.947910 3.354257 9.678682 48.232542 14.487043 21.517830 ... 10.136109 38.762453 7.451212 8.758856 35.206708 15.292107 10.924798 43.617270 35.503776 9.781231
std 7.278676 20.882402 7.574302 11.346579 8.722370 3.578254 7.498083 25.668278 13.955490 14.826293 ... 9.580093 13.776885 7.641868 7.128573 17.984406 11.442731 9.193391 7.972539 12.210679 7.093540
min 0.000977 0.000000 0.000000 0.000000 2.486084 0.000000 0.000000 0.087646 0.001953 0.012451 ... 0.000000 0.005859 0.000000 0.000000 0.166016 0.000000 0.000000 1.023926 0.389160 0.000000
25% 5.184937 26.000000 2.000000 5.000000 38.227905 1.000000 4.000000 30.705994 4.906738 10.496765 ... 3.000000 33.745605 2.000000 3.000000 22.963257 6.000000 4.000000 40.532715 28.404724 4.000000
50% 9.402832 36.000000 5.000000 13.000000 43.253540 2.000000 8.000000 45.544189 9.772339 18.754150 ... 7.000000 42.692627 4.000000 7.000000 35.271484 13.000000 9.000000 45.182861 37.429810 9.000000
75% 14.364990 49.000000 10.000000 22.000000 47.949890 4.000000 14.000000 59.782166 18.664856 29.730896 ... 15.000000 48.055481 10.000000 12.000000 45.801941 23.000000 16.000000 48.300476 43.959106 14.000000
max 41.972168 121.000000 48.000000 79.000000 82.822998 35.000000 44.000000 131.903320 69.138428 73.730957 ... 69.000000 80.386475 51.000000 55.000000 106.757324 84.000000 69.000000 85.858154 79.663330 46.000000

8 rows × 28 columns

What’s next?