Running the PhoREAL algorithm over Grand Mesa, CO

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

Imports

[1]:

import logging
import sliderule
from sliderule import icesat2

Initialize Client

[2]:

sliderule.init(verbose=True, loglevel=logging.INFO)

[2]:

True

Processing parameters

[3]:

parms = {
    "poly": sliderule.toregion('grandmesa.geojson')['poly'], # subset to Grand Mesa area of interest
    "t0": '2019-11-14T00:00:00Z', # time range is one day - November 14, 2019
    "t1": '2019-11-15T00:00:00Z',
    "srt": icesat2.SRT_LAND, # use the land surface type for ATL03 photon confidence levels
    "len": 100, # generate statistics over a 100m segment
    "res": 100, # generate statistics every 100m
    "pass_invalid": True, # do not perform any segment-level filtering
    "atl08_class": ["atl08_ground", "atl08_canopy", "atl08_top_of_canopy"], # exclude noise and unclassified photons
    "atl08_fields": ["h_dif_ref"], # include these fields as extra columns in the dataframe
    "phoreal": {"binsize": 1.0, "geoloc": "center"} # run the PhoREAL algorithm
}

Make Proessing Request

[4]:

atl08 = sliderule.run("atl03x", parms)

request <AppServer.61671> retrieved 1 resources
Starting proxy for atl03x to process 1 resource(s) with 1 thread(s)
request <AppServer.62515> on ATL03_20191114034331_07370502_006_01.h5 generated dataframe [gt3r] with 343 rows and 21 columns
request <AppServer.62515> on ATL03_20191114034331_07370502_006_01.h5 generated dataframe [gt3l] with 341 rows and 21 columns
request <AppServer.62515> on ATL03_20191114034331_07370502_006_01.h5 generated dataframe [gt2l] with 351 rows and 21 columns
request <AppServer.62515> on ATL03_20191114034331_07370502_006_01.h5 generated dataframe [gt1l] with 355 rows and 21 columns
request <AppServer.62515> on ATL03_20191114034331_07370502_006_01.h5 generated dataframe [gt2r] with 354 rows and 21 columns
request <AppServer.62515> on ATL03_20191114034331_07370502_006_01.h5 generated dataframe [gt1r] with 357 rows and 21 columns
Successfully completed processing resource [1 out of 1]: ATL03_20191114034331_07370502_006_01.h5
Writing arrow file: /tmp/tmpd6vg4yk5
Closing arrow file: /tmp/tmpd6vg4yk5

Print Resulting GeoDataFrame

[5]:

atl08

[5]:

	h_dif_ref	pflags	region	gt	photon_count	solar_elevation	ground_photon_count	canopy_h_metrics	rgt	h_te_median	...	x_atc	y_atc	h_max_canopy	h_min_canopy	srcid	vegetation_photon_count	cycle	canopy_openness	photon_start	geometry
time_ns
2019-11-14 03:46:36.981117952	5.799881	0	2	30	118	-44.032406	20	[1.5048828, 1.5048828, 1.5048828, 1.5048828, 2...	737	1823.442383	...	4.322300e+06	33.324844	7.769043	0.508423	0	98	5	1.399428	1069838	POINT (-108.08651 38.84584)
2019-11-14 03:46:36.995268096	6.815710	0	2	30	112	-44.032173	24	[2.0375977, 2.0375977, 2.0375977, 2.0375977, 2...	737	1861.372314	...	4.322400e+06	33.237431	8.363403	1.092041	0	88	5	1.633684	1069974	POINT (-108.08663 38.84675)
2019-11-14 03:46:37.009418240	5.844150	16	2	30	102	-44.031933	102	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...	737	1893.611084	...	4.322501e+06	33.146454	0.000000	0.000000	0	0	5	NaN	1070092	POINT (-108.08674 38.84765)
2019-11-14 03:46:37.023568128	3.316736	0	2	30	105	-44.031689	94	[1.1791992, 1.1791992, 1.1791992, 1.1791992, 1...	737	1898.847656	...	4.322602e+06	33.228848	1.179199	0.519409	0	11	5	0.206181	1070200	POINT (-108.08685 38.84855)
2019-11-14 03:46:37.037718016	-1.645961	0	2	30	91	-44.031452	27	[1.569458, 1.569458, 1.569458, 1.569458, 2.542...	737	1892.505737	...	4.322702e+06	33.172836	4.684448	0.580322	0	64	5	0.921298	1070306	POINT (-108.08696 38.84945)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2019-11-14 03:46:41.909418240	-3.708359	0	2	50	105	-43.974560	103	[0.6296387, 0.6296387, 0.6296387, 0.6296387, 0...	737	1728.333130	...	4.357472e+06	-3154.093750	0.629639	0.536987	5	2	5	0.046326	1132271	POINT (-108.08859 39.16396)
2019-11-14 03:46:41.923518464	-2.790664	0	2	50	95	-43.974361	87	[0.8251953, 0.8251953, 0.8251953, 0.8251953, 0...	737	1724.484131	...	4.357573e+06	-3153.931152	6.393188	0.567627	5	8	5	2.033772	1132378	POINT (-108.08870 39.16486)
2019-11-14 03:46:41.937768448	-2.073942	0	2	50	97	-43.974068	79	[0.81433105, 0.81433105, 0.81433105, 0.8143310...	737	1718.757690	...	4.357674e+06	-3154.187988	7.938599	0.540161	5	18	5	2.606790	1132482	POINT (-108.08881 39.16577)
2019-11-14 03:46:41.952018432	-1.922682	0	2	50	103	-43.973827	93	[1.4121094, 1.4121094, 1.4121094, 1.4121094, 1...	737	1713.514893	...	4.357775e+06	-3154.338867	3.148071	0.556641	5	10	5	0.854210	1132585	POINT (-108.08892 39.16668)
2019-11-14 03:46:41.959268352	-1.415894	16	2	50	5	-43.973728	5	[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...	737	1712.941406	...	4.357827e+06	-3154.295410	0.000000	0.000000	5	0	5	NaN	1132689	POINT (-108.08898 39.16714)

2101 rows × 25 columns

Plot Canopy Height

[6]:

canopy_gt1l = atl08[atl08['gt'] == icesat2.GT1L]
canopy_gt1l.plot.scatter(x='x_atc', y='h_canopy')

[6]:

<Axes: xlabel='x_atc', ylabel='h_canopy'>

Plot Landcover

[7]:

atl08.plot('landcover', legend=True)

[7]:

<Axes: >

Create and Plot 75th percentile Across All Ground Tracks

[8]:

atl08['75'] = atl08.apply(lambda row : row["canopy_h_metrics"][icesat2.P['75']], axis = 1)
atl08.plot.scatter(x='x_atc', y='75')

[8]:

<Axes: xlabel='x_atc', ylabel='75'>