PhoREAL / SlideRule Example¶

Demonstrate running the PhoREAL algorithm in SlideRule to produce canopy metrics over the Grand Mesa, Colorado region.

Imports¶

In [1]:
import warnings
warnings.filterwarnings("ignore") # suppress warnings
In [2]:
import matplotlib.pyplot as plt
import matplotlib
import geopandas
import logging
import sliderule
from sliderule import icesat2

Initialize Client¶

  • Organization currently set to "utexas"; if you want to be a member of the utexas SlideRule organization, make a request through the SlideRule provisioning system (https://ps.slideruleearth.io); otherwise, remove the organization parameter to default to the public SlideRule cluster.
  • Notebook only processes one granule, so one desired_node is sufficient
In [3]:
icesat2.init("slideruleearth.io", verbose=True, loglevel=logging.INFO)

Processing parameters¶

  • 100m segments stepped every 100m
  • Subsetted to the Grand Mesa region
  • Time range is one day, Nov 14, 2019
  • Only processing ground, canopy, and top of canopy photons
  • Request the "h_dif_ref" variable as an ancillary field to be included in the results
  • Running PhoREAL algorithm using a binsize of 1m, and geolocating each segment at the center of the segment
  • Sending reconstructed waveforms along with metrics (for diagnostics and demonstration purposes only)
In [4]:
parms = {
    "poly": sliderule.toregion('grandmesa.geojson')['poly'],
    "t0": '2019-11-14T00:00:00Z',
    "t1": '2019-11-15T00:00:00Z',
    "srt": icesat2.SRT_LAND,
    "len": 100,
    "res": 100,
    "pass_invalid": True, 
    "atl08_class": ["atl08_ground", "atl08_canopy", "atl08_top_of_canopy"],
    "atl08_fields": ["h_dif_ref"],
    "phoreal": {"binsize": 1.0, "geoloc": "center", "use_abs_h": False, "send_waveform": True}
}

Make Atl08 Request¶

In [5]:
atl08 = icesat2.atl08p(parms, keep_id=True)

Identified 1 resources to process
Starting proxy for atl08 to process 1 resource(s) with 1 thread(s)
request <AppServer.129> processing initialized on ATL03_20191114034331_07370502_006_01.h5 ...
Successfully completed processing resource [1 out of 1]: ATL03_20191114034331_07370502_006_01.h5

Print Resulting GeoDataFrame¶

In [6]:
atl08
Out[6]:
h_canopy h_mean_canopy ph_count landcover gnd_ph_count x_atc extent_id rgt snowcover canopy_h_metrics ... cycle spot segment_id h_te_median gt geometry waveform num_bins binsize h_dif_ref
time
2019-11-14 03:46:36.935118336 2.261719 1.156670 102 30 75 -6.882999e+10 3319153278633115650 737 1 (1.479736328125, 1.479736328125, 1.47973632812... ... 5 6 215507 1958.305542 10 POINT (-108.12262 38.83912) [0.20588235557079315, 0.05882352963089943] 2 1.0 0.963623
2019-11-14 03:46:36.949218304 3.137817 1.304013 113 30 54 -6.882593e+10 3319153278633115654 737 1 (1.4913330078125, 1.4913330078125, 1.491333007... ... 5 6 215512 1964.416748 10 POINT (-108.12272 38.84002) [0.3362831771373749, 0.1592920422554016, 0.026... 3 1.0 -3.216064
2019-11-14 03:46:36.963318272 4.442627 1.834195 103 30 49 -6.882186e+10 3319153278633115658 737 1 (1.5015869140625, 1.5015869140625, 1.501586914... ... 5 6 215517 1976.178833 10 POINT (-108.12283 38.84092) [0.2330097109079361, 0.1650485396385193, 0.097... 4 1.0 -5.043213
2019-11-14 03:46:36.977417984 5.067871 2.465483 103 30 35 -6.881773e+10 3319153278633115662 737 1 (1.7940673828125, 1.7940673828125, 1.794067382... ... 5 6 215522 1991.423218 10 POINT (-108.12295 38.84182) [0.20388349890708923, 0.27184465527534485, 0.1... 6 1.0 2.426270
2019-11-14 03:46:36.980918016 5.811401 2.816030 114 20 19 -6.927786e+10 3319153279706857474 737 1 (1.5048828125, 1.5048828125, 1.5048828125, 1.5... ... 5 4 215529 1822.414673 30 POINT (-108.08651 38.84583) [0.1666666716337204, 0.21052631735801697, 0.21... 8 1.0 5.912720
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2019-11-14 03:46:42.293068288 8.225342 3.273977 322 30 192 -6.805046e+10 3319153278633117075 737 1 (1.5029296875, 1.5029296875, 1.5029296875, 1.5... ... 5 5 217288 1786.887939 20 POINT (-108.16125 39.15930) [0.14596273005008698, 0.043478261679410934, 0.... 8 1.0 -3.731567
2019-11-14 03:46:42.293818368 0.000000 NaN 272 40 272 -6.828026e+10 3319153279706858887 737 1 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ... 5 3 217294 1709.912354 40 POINT (-108.12461 39.16313) [0.0] 1 1.0 -0.086914
2019-11-14 03:46:42.295218176 10.701416 4.089300 202 40 191 -6.851079e+10 3319153280780600667 737 1 (0.8890380859375, 0.8890380859375, 0.889038085... ... 5 1 217301 1716.896362 60 POINT (-108.08792 39.16696) [0.024752475321292877, 0.0, 0.0049504949711263... 11 1.0 -2.204712
2019-11-14 03:46:42.302418176 0.000000 NaN 51 40 51 -6.827817e+10 3319153279706858891 737 1 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ... 5 3 217299 1707.600586 40 POINT (-108.12468 39.16368) [0.0] 1 1.0 -0.086914
2019-11-14 03:46:42.304318208 1.410645 0.724375 174 20 161 -6.804797e+10 3319153278633117079 737 1 (1.41064453125, 1.41064453125, 1.41064453125, ... ... 5 5 217293 1784.938721 20 POINT (-108.16134 39.16002) [0.07471264153718948] 1 1.0 -1.957886

2112 rows × 25 columns

Plot Canopy Height¶

In [7]:
canopy_gt1l = atl08[atl08['gt'] == icesat2.GT1L]
canopy_gt1l.plot.scatter(x='x_atc', y='h_canopy')
Out[7]:
<Axes: xlabel='x_atc', ylabel='h_canopy'>
No description has been provided for this image

Plot Landcover¶

In [8]:
atl08.plot('landcover')
Out[8]:
<Axes: >
No description has been provided for this image

Create and Plot 75th percentile Across All Ground Tracks¶

In [9]:
atl08['75'] = atl08.apply(lambda row : row["canopy_h_metrics"][icesat2.P['75']], axis = 1)
atl08.plot.scatter(x='x_atc', y='75')
Out[9]:
<Axes: xlabel='x_atc', ylabel='75'>
No description has been provided for this image

Create Sample Waveform Plots¶

In [10]:
num_plots = 5
waveform_index = [96, 97, 98, 100, 101]
fig,ax = plt.subplots(num=1, ncols=num_plots, sharey=True, figsize=(12, 6))
for x in range(num_plots):
    ax[x].plot([x for x in range(len(canopy_gt1l['waveform'][waveform_index[x]]))], canopy_gt1l['waveform'][waveform_index[x]], zorder=1, linewidth=1.0, color='mediumseagreen')
plt.show()
No description has been provided for this image

Make Atl06 Request¶

  • Below we run an ATL06-SR processing request on the same source data using the same parameters. Because the keep_id argument is set to true here and above when we made the ATL08 request, we can merge the resulting dataframes and have a single table of both elevation data using the customized ATL06-SR algorithm, and vegatation data using the PhoREAL algorithm.
In [11]:
atl06 = icesat2.atl06p(parms, keep_id=True)

Identified 1 resources to process
Starting proxy for atl06 to process 1 resource(s) with 1 thread(s)
request <AppServer.130> processing initialized on ATL03_20191114034331_07370502_006_01.h5 ...
request <AppServer.130> processing of ATL03_20191114034331_07370502_006_01.h5 complete (691067/0/0)
request <AppServer.130> processing complete (2112/1/2112/0)
Successfully completed processing resource [1 out of 1]: ATL03_20191114034331_07370502_006_01.h5

Merge Atl06 and Atl08 GeoDataFrames¶

In [12]:
gdf = geopandas.pd.merge(atl08, atl06, on='extent_id', how='left', suffixes=('.atl08','.atl06')).set_axis(atl08.index)
gdf
Out[12]:
h_canopy h_mean_canopy ph_count landcover gnd_ph_count x_atc.atl08 extent_id rgt.atl08 snowcover canopy_h_metrics ... rms_misfit x_atc.atl06 spot.atl06 cycle.atl06 rgt.atl06 dh_fit_dx h_mean y_atc geometry.atl06 h_dif_ref.atl06
time
2019-11-14 03:46:36.935118336 2.261719 1.156670 102 30 75 -6.882999e+10 3319153278633115650 737 1 (1.479736328125, 1.479736328125, 1.47973632812... ... 0.699817 4321857.0 6 5 737 0.164871 1958.905980 3224.564941 POINT (-108.12262 38.83912) 0.062566
2019-11-14 03:46:36.949218304 3.137817 1.304013 113 30 54 -6.882593e+10 3319153278633115654 737 1 (1.4913330078125, 1.4913330078125, 1.491333007... ... 1.607597 4321957.0 6 5 737 0.043268 1965.811905 3224.207520 POINT (-108.12272 38.84002) -1.459010
2019-11-14 03:46:36.963318272 4.442627 1.834195 103 30 49 -6.882186e+10 3319153278633115658 737 1 (1.5015869140625, 1.5015869140625, 1.501586914... ... 2.103739 4322057.0 6 5 737 0.123389 1976.876691 3224.200439 POINT (-108.12283 38.84092) -4.185234
2019-11-14 03:46:36.977417984 5.067871 2.465483 103 30 35 -6.881773e+10 3319153278633115662 737 1 (1.7940673828125, 1.7940673828125, 1.794067382... ... 2.184336 4322157.5 6 5 737 0.266099 1991.758298 3224.361816 POINT (-108.12295 38.84182) -0.171245
2019-11-14 03:46:36.980918016 5.811401 2.816030 114 20 19 -6.927786e+10 3319153279706857474 737 1 (1.5048828125, 1.5048828125, 1.5048828125, 1.5... ... 3.317562 4322298.0 4 5 737 0.288814 1829.816771 33.340397 POINT (-108.08651 38.84583) 5.809160
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2019-11-14 03:46:42.293068288 8.225342 3.273977 322 30 192 -6.805046e+10 3319153278633117075 737 1 (1.5029296875, 1.5029296875, 1.5029296875, 1.5... ... 2.215102 4357558.0 5 5 737 -0.059700 1788.645095 3146.364502 POINT (-108.16125 39.15931) -3.117488
2019-11-14 03:46:42.293818368 0.000000 NaN 272 40 272 -6.828026e+10 3319153279706858887 737 1 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ... 0.159687 4357678.5 3 5 737 -0.040557 1709.930448 -46.399494 POINT (-108.12461 39.16314) -0.178279
2019-11-14 03:46:42.295218176 10.701416 4.089300 202 40 191 -6.851079e+10 3319153280780600667 737 1 (0.8890380859375, 0.8890380859375, 0.889038085... ... 0.388871 4357818.5 1 5 737 -0.052055 1715.391574 -3243.738037 POINT (-108.08794 39.16714) -2.204712
2019-11-14 03:46:42.302418176 0.000000 NaN 51 40 51 -6.827817e+10 3319153279706858891 737 1 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... ... 0.000000 4357778.5 3 5 737 -0.034187 1706.306431 -46.261078 POINT (-108.12472 39.16404) -0.086914
2019-11-14 03:46:42.304318208 1.410645 0.724375 174 20 161 -6.804797e+10 3319153278633117079 737 1 (1.41064453125, 1.41064453125, 1.41064453125, ... ... 0.302310 4357658.0 5 5 737 -0.082557 1783.404110 3146.425537 POINT (-108.16136 39.16020) -2.475322

2112 rows × 43 columns

In [ ]: