Including ArcticDEM Samples
2022-12-23
Overview
This tutorial walks you through the steps necessary to sample the ArcticDEM at the location of each elevation generated by an atl06p request. The DEM value is returned as an additional column in the GeoDataFrame.
For a full set of code associated with this tutorial, see: https://github.com/ICESat2-SlideRule/sliderule-python/blob/development/examples/arcticdem.ipynb.
Prerequisites: This walk-through assumes you have already installed the SlideRule Python client and familiar with how to use it. See the installation instructions in the reference documentation if you need help installing the SlideRule Python client. See the Making Your First Request tutorial if you’ve never made a SlideRule processing request before.
Background
The ArcticDEM is a high-resolution digital surface model of the Arctic, produced by the Polar Geospatial Center at the University of Minnesota, and hosted in a public S3 bucket in the AWS us-west-2 data center. SlideRule is able to associate a elevations sampled from the ArcticDEM with each elevation generated from ICESat-2’s photon cloud data. The combined dataset provides a side-by-side comparison of ATL06-SR elevations with ArcticDEM elevations.
Sampling the ArcticDEM mosaic in an atl06p request
The “samples” parameter is used to request ArcticDEM samples be included in atl06p responses. For the ArcticDEM, there are two possible values that can be provided: “arcticdem-mosaic” and “arcticdem-strips”.
Step 1: Import and initialize the SlideRule Python package for ICESat-2.
>>> from sliderule import sliderule, icesat2
>>> icesat2.init("slideruleearth.io")
Step 2: Create parameters for a typical atl06p processing request.
>>> grand_mesa = sliderule.toregion('dicksonfjord.geojson')
>>> parms = {
"poly": grand_mesa["poly"],
"srt": icesat2.SRT_LAND,
"cnf": icesat2.CNF_SURFACE_HIGH,
"len": 40.0,
"res": 20.0
}
The dicksonfjord.geojson file used in this example can be downloaded by navigating to our downloads page; alternatively, you can create your own GeoJSON file at geojson.io. Be sure that your region of interest is in the arctic, otherwise there will be no data in the ArcticDEM for it.
Step 3: Specify sampling the ArcticDEM mosaic.
>>> parms["samples"] = {"mosaic": {"asset": "arcticdem-mosaic", "radius": 10.0, "zonal_stats": True}}
Step 4: Issue the processing request to SlideRule, for only a single granule (to keep the data volume down).
>>> resource = "ATL03_20190314093716_11600203_005_01.h5"
>>> gdf = icesat2.atl06p(parms, resources=[resource])
When this completes (~10 seconds), the gdf variable should now contain all of the results of the elevations calculated by SlideRule with a corresponding column for the “arcticdem-mosaic”. Note that the granule provided must be in the region of interest.
Step 5: Display a summary of the results.
>>> print(gdf)
segment_id rgt n_fit_photons spot h_sigma gt delta_time cycle rms_misfit dh_fit_dy ... geometry mosaic.time mosaic.max mosaic.mad mosaic.mean mosaic.median mosaic.value mosaic.count mosaic.stdev mosaic.min
time
2019-03-14 09:40:46.282934432 405226 1160 12 1 0.551360 10 3.779165e+07 2 0.874532 0.0 ... POINT (-26.27920 72.77984) 1.176077e+09 558.497131 2.760759 550.909552 550.701904 550.909729 81 3.328835 544.854675
2019-03-14 09:40:46.284351344 405227 1160 32 1 0.246645 10 3.779165e+07 2 1.308783 0.0 ... POINT (-26.27924 72.77993) 1.176077e+09 568.031189 4.222864 559.327384 559.479126 559.673889 81 4.824281 551.360779
2019-03-14 09:40:46.285767320 405227 1160 20 1 1.144088 10 3.779165e+07 2 1.318949 0.0 ... POINT (-26.27928 72.78002) 1.176077e+09 573.887756 3.402086 566.768132 567.251282 567.251282 81 4.031628 558.077698
2019-03-14 09:40:46.291444880 405229 1160 15 1 0.187902 10 3.779165e+07 2 0.263953 0.0 ... POINT (-26.27943 72.78037) 1.176077e+09 599.084351 1.593114 594.913225 594.901855 594.901855 81 1.911160 591.075134
2019-03-14 09:40:46.292858704 405230 1160 27 1 0.059746 10 3.779165e+07 2 0.269889 0.0 ... POINT (-26.27947 72.78046) 1.176077e+09 602.492004 1.780210 598.034786 597.679504 597.673462 81 2.093415 594.528076
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2019-03-14 09:40:48.353273584 405838 1160 27 2 0.053913 20 3.779165e+07 2 0.280013 0.0 ... POINT (-26.32802 72.88865) 1.183334e+09 1495.614258 0.709607 1494.015764 1494.007690 1494.036499 81 0.834192 1492.483643
2019-03-14 09:40:48.354683848 405839 1160 15 2 0.114113 20 3.779165e+07 2 0.330651 0.0 ... POINT (-26.32806 72.88874) 1.183334e+09 1494.552612 0.687070 1492.995177 1492.975098 1492.975098 81 0.806113 1491.506592
2019-03-14 09:40:48.356088496 405839 1160 8 2 0.046961 20 3.779165e+07 2 0.132604 0.0 ... POINT (-26.32810 72.88882) 1.183334e+09 1493.258545 0.591286 1491.916643 1491.894531 1491.894531 81 0.698308 1490.576050
2019-03-14 09:40:48.357494344 405840 1160 16 2 0.032820 20 3.779165e+07 2 0.124317 0.0 ... POINT (-26.32814 72.88891) 1.183334e+09 1492.435181 0.542995 1491.242427 1491.285156 1491.201050 81 0.642991 1489.934570
2019-03-14 09:40:48.358898136 405840 1160 19 2 0.028111 20 3.779165e+07 2 0.117033 0.0 ... POINT (-26.32818 72.88900) 1.183334e+09 1492.308105 0.614543 1490.753858 1490.765381 1490.621094 81 0.731349 1489.323242
1650 rows × 25 columns
For a full description of all of the fields returned from the atl06p function, see the elevations documentation.