Sliderule data I/O

This notebook will highlight analyzing various coincident elevation measurements. We will find regions with and use slideruleearth.io service to retrieve ICESat-2 and GEDI point elevation measurements.

Note

Keep in mind, these measurements are from different sensor types, close in time, but not at exactly the same time. All measurements also have uncertainties, so we do not expect perfect agreement.

import coincident
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import matplotlib.pyplot as plt
import numpy as np

# For testing OR if you have access to a private cluster set that here
# https://slideruleearth.io/web/rtd/developer_guide/articles/private_clusters.html#private-clusters
# sliderule.init(verbose=True, organization="uw", desired_nodes=5, bypass_dns=True, time_to_live=60)

/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/stable/src/coincident/io/download.py:25: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
  from tqdm.autonotebook import tqdm

%matplotlib inline
# %config InlineBackend.figure_format = 'retina'

Identify a primary dataset

Start by loading a full resolution polygon of a 3DEP LiDAR workunit which has a known start_datetime and end_datatime:

workunit = "CO_WestCentral_2019"
df_wesm = coincident.search.wesm.read_wesm_csv()
gf_lidar = coincident.search.wesm.load_by_fid(
    df_wesm[df_wesm.workunit == workunit].index
)
gf_lidar

	workunit	workunit_id	project	project_id	start_datetime	end_datetime	ql	spec	p_method	dem_gsd_meters	...	seamless_category	seamless_reason	lpc_link	sourcedem_link	metadata_link	geometry	collection	datetime	dayofyear	duration
0	CO_WestCentral_2019	175984	CO_WestCentral_2019_A19	175987	2019-08-21	2019-09-19	QL 2	USGS Lidar Base Specification 1.3	linear-mode lidar	1.0	...	Meets	Meets 3DEP seamless DEM requirements	https://rockyweb.usgs.gov/vdelivery/Datasets/S...	https://prd-tnm.s3.amazonaws.com/index.html?pr...	https://prd-tnm.s3.amazonaws.com/index.html?pr...	MULTIPOLYGON (((-106.13571 38.4146, -106.1702 ...	3DEP	2019-09-04 12:00:00	247	29

1 rows × 33 columns

Search secondary datasets

Provide a list that will be searched in order. The list contains tuples of dataset aliases and the temporal pad in days to search before the primary dataset start and end dates

secondary_datasets = [
    ("gedi", 40),  # +/- 40 days from lidar
    ("icesat-2", 60),  # +/- 60 days from lidar
]

gf_gedi, gf_is2 = coincident.search.cascading_search(
    gf_lidar,
    secondary_datasets,
    min_overlap_area=30,  # km^2
)

Get ICESat-2 ATL06 Data

We’ve identified 7 granules of icesat-2 data to examine, but there is no need to work with the entire granule, which spans a huge geographic extent. Instead we’ll retrieve a subset of elevation values in the area of interest for each granule.

# Hone in on single granule as a pandas series
i = 0
granule_gdf = gf_is2.iloc[[i]]
granule = gf_is2.iloc[i]
granule_gdf.plot()  # needs to be a geodataframe
plt.title(granule.id);
# The icesat-2 track trends N-S, and is crossed by multiple GEDI tracks, resulting in the crosshatched appearance

data_is2 = coincident.io.sliderule.subset_atl06(
    granule_gdf,
    include_worldcover=True,
)

# Plot this data and overlay footprint
fig, ax = plt.subplots(figsize=(8, 10))
plt.scatter(
    x=data_is2.geometry.x,
    y=data_is2.geometry.y,
    c=data_is2.h_li,
    s=10,
)
granule_gdf.dissolve().boundary.plot(ax=ax, color="magenta")
cb = plt.colorbar()
cb.set_label("elevation_hr (m)")

Note

sliderule gets data from the envelope of multipolygon, not only data within intersecting GEDI tracks. Along-track gaps are where there is missing data.

# Here, we specify aoi=granule_gdf to only return GEDI data
# in our icesat-2 track of interest
data_gedi = coincident.io.sliderule.subset_gedi02a(
    gf_gedi,
    aoi=granule_gdf,
    include_worldcover=True,
)

data_gedi

	orbit	track	elevation_lm	solar_elevation	shot_number	flags	worldcover.time_ns	worldcover.fileid	elevation_hr	worldcover.value	beam	sensitivity	srcid	geometry
time_ns
2019-08-13 00:26:33.160042496	3775	3020	1773.083374	18.861452	37750100300143430	130	[2021-06-30T00:00:00.000000000]	[17179869184]	1776.642456	Grassland	1	0.925909	120	POINT (-107.69263 38.75743)
2019-08-13 00:26:33.168306944	3775	3020	1772.384277	18.861008	37750100300143431	130	[2021-06-30T00:00:00.000000000]	[17179869184]	1777.179688	Grassland	1	0.935042	120	POINT (-107.69212 38.7571)
2019-08-13 00:26:33.176571136	3775	3020	1778.180664	18.860561	37750100300143432	130	[2021-06-30T00:00:00.000000000]	[17179869184]	1781.627319	Grassland	1	0.906605	120	POINT (-107.69162 38.75678)
2019-08-13 00:26:33.184835072	3775	3020	1778.053589	18.860117	37750100300143433	130	[2021-06-30T00:00:00.000000000]	[17179869184]	1781.425415	Grassland	1	0.936682	120	POINT (-107.69111 38.75645)
2019-08-13 00:26:33.193099008	3775	3020	1778.005615	18.859673	37750100300143434	130	[2021-06-30T00:00:00.000000000]	[17179869184]	1781.452393	Grassland	1	0.913348	120	POINT (-107.6906 38.75613)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2019-10-15 23:07:56.903731712	4767	21	2445.503906	15.112483	47670800300259542	130	[2021-06-30T00:00:00.000000000]	[68719476736]	2457.810059	Tree cover	8	0.971099	159	POINT (-107.69234 38.51539)
2019-10-15 23:07:56.911997696	4767	21	2439.439453	15.112235	47670800300259543	130	[2021-06-30T00:00:00.000000000]	[68719476736]	2447.930420	Tree cover	8	0.967036	159	POINT (-107.69184 38.51506)
2019-10-15 23:07:56.928529920	4767	21	2432.605957	15.111741	47670800300259545	130	[2021-06-30T00:00:00.000000000]	[68719476736]	2441.134277	Tree cover	8	0.974161	159	POINT (-107.69083 38.5144)
2019-10-15 23:07:56.936795904	4767	21	2433.445312	15.111494	47670800300259546	130	[2021-06-30T00:00:00.000000000]	[68719476736]	2438.831543	Tree cover	8	0.974230	159	POINT (-107.69032 38.51408)
2019-10-15 23:07:56.945061888	4767	21	2437.071777	15.111247	47670800300259547	130	[2021-06-30T00:00:00.000000000]	[68719476736]	2441.822021	Grassland	8	0.968579	159	POINT (-107.68982 38.51375)

10225 rows × 14 columns

# Plot this data and overlay footprint
fig, ax = plt.subplots(figsize=(8, 10))
plt.scatter(
    x=data_gedi.geometry.x,
    y=data_gedi.geometry.y,
    c=data_gedi.elevation_hr,
    s=10,
)
granule_gdf.dissolve().boundary.plot(ax=ax, color="magenta")
cb = plt.colorbar()
cb.set_label("elevation_hr (m)")

NOTE Like ICESat-2, it’s possible to have points falling outside the estimated footprints

Spatial join: nearest points

NOTE: we will not worry about the difference in time of acquisition between adjacent points for now

utm_crs = granule_gdf.estimate_utm_crs()
data_is2_utm = data_is2.to_crs(utm_crs)
data_gedi_utm = data_gedi.to_crs(utm_crs)

# find nearest IS2 point to each GEDI
close_gedi = data_gedi_utm.sjoin_nearest(
    data_is2_utm,
    how="left",
    max_distance=100,  # at most 100m apart
    distance_col="distances",
)
# index is GEDI subset (with _left added), with _right appended to is2 columns + Distances in meters
close_gedi = close_gedi[close_gedi["distances"].notna()]

fig, ax = plt.subplots()
plt.scatter(close_gedi.h_li, close_gedi.elevation_lm)
ax.axline((0, 0), slope=1, color="k", transform=ax.transAxes)
plt.xlabel("ATL06 elevation (m)")
plt.ylabel("GEDI L2A elevation_lm (m)")
plt.title(f"{len(close_gedi)} points within 100m")


# Add statistics and inset histogram to plot
inset_ax = inset_axes(ax, width="40%", height="30%", borderpad=2, loc="lower right")


residuals = close_gedi.h_li - close_gedi.elevation_lm
mean_residual = np.mean(residuals)
median_residual = np.median(residuals)
std_residual = np.std(residuals)
textstr = "\n".join(
    (
        f"Mean: {mean_residual:.2f}",
        f"Median: {median_residual:.2f}",
        f"Std: {std_residual:.2f}",
    )
)
props = dict(boxstyle="round", facecolor="wheat", alpha=0.5)
ax.text(
    0.05,
    0.95,
    textstr,
    transform=ax.transAxes,
    fontsize=10,
    verticalalignment="top",
    bbox=props,
)

_ = inset_ax.hist(residuals, bins=50, range=(-20, 20), color="gray")
inset_ax.axvline(0, color="k", linestyle="-", linewidth=1);

Sample 3DEP

Sample 3DEP 1m DEM at the subset of GEDI Points

# Warning: this relies on TNM API, which is often down :(
close_gedi_3dep = coincident.io.sliderule.sample_raster(
    close_gedi,
    asset_key="usgs3dep-1meter-dem",
    # Restrict to only DEMs derived from specific WESM LiDAR project
    project_name=gf_lidar["project"].iloc[0],
)

# Add 3DEP elevation values to existing dataframe
close_gedi["elevation_3dep"] = close_gedi_3dep.value.values

# Geometries of nearest IS2 points
close_is2 = data_is2_utm.loc[close_gedi.time_ns_right]

# Sample 3DEP at the subset of GEDI Points
close_is2_3dep = coincident.io.sliderule.sample_raster(
    close_is2,
    asset_key="usgs3dep-1meter-dem",
    project_name=gf_lidar["project"].iloc[0],
)

close_is2["elevation_3dep"] = close_is2_3dep.value.values

plt.axvline(0, color="k", linestyle="dashed", linewidth=1)

diff_3dep = close_is2.h_li.values - close_is2.elevation_3dep
label = f"ICESat-2 (median={np.nanmedian(diff_3dep):.2f}, mean={np.nanmean(diff_3dep):.2f}, std={np.nanstd(diff_3dep):.2f}, n={len(diff_3dep)})"
plt.hist(diff_3dep, range=[-5, 5], bins=100, color="m", alpha=0.5, label=label)

diff_3dep = close_gedi.elevation_lm.values - close_gedi.elevation_3dep
label = f"GEDI2A (median={np.nanmedian(diff_3dep):.2f}, mean={np.nanmean(diff_3dep):.2f}, std={np.nanstd(diff_3dep):.2f}, n={len(diff_3dep)})"
plt.hist(diff_3dep, range=[-5, 5], bins=100, color="c", alpha=0.5, label=label)

plt.xlabel("elevation difference (m)")
plt.title("Altimeter Elevation - 3DEP 1m DEM")
plt.xlim(-5, 5)
plt.legend(loc="upper left");