The Quadtree reduces the amount of displacement data by subsampling the InSAR displacement map. For efficient forward modelling it is important to have reasonably sized dataset.

The quadtree is made from hierarchically organized QuadNode, a slice through of the tree’s nodes is then called leaves.

Kite realises the quadtree concept from Jónsson et al. (2002) [1].

Note

All nodes of the Quadtree are built upon initialisation an instance.

The graphical user interface (GUI) spool offers an interactive parametrisation of the quadtree. Start the program, click on tab Quadtree. Detailed instruction can be found in spool’s tutorial.

Start spool and open a QuadTree container.
spool insar_displacement_scene.npz


The quadtree can also be parametrised by a python script. This example modifies the

from kite import Scene
sc = Scene.import_data('test/data/20110214_20110401_ml4_sm.unw.geo_ig_dsc_ionnocorr.mat')

# For convenience we set an abbreviation to the quadtree

qt.epsilon = 0.024        # Variance threshold
qt.nan_allowed = 0.9      # Percentage of NaN values allowed per tile/leaf
qt.tile_size_max = 12000  # Maximum leaf size in [m] or [deg]
qt.tile_size_min = 250    # Minimum leaf size in [m] or [deg]

print(qt.reduction_rms)   # In units of [m] or [deg]
# >>> 0.234123152

for l in qt.leafs:
print l

# We save the scene in kite's format
sc.save('kite_scene')

# Or export the quadtree to CSV file
qt.export('/tmp/tree.csv')


Footnotes

 [1] Jónsson, Sigurjón, Howard Zebker, Paul Segall, and Falk Amelung. 2002. “Fault Slip Distribution of the 1999 Mw 7.1 Hector Mine, California, Earthquake, Estimated from Satellite Radar and GPS Measurements.” Bulletin of the Seismological Society of America 92 (4): 1377–89. doi:10.1785/0120000922.