Boulder Watershed Demo

Process ATL03 data from the Boulder Watershed region and produce a customized ATL06 elevation dataset.

What is demonstrated

• The icesat2.atl06p API is used to perform a SlideRule parallel processing request of the Boulder Watershed region
• The matplotlib and geopandas packages are used to plot the data returned by SlideRule

Points of interest

This is a simple notebook showing how a region of interest can be processed by SlideRule and the results analyzed using pandas DataFrames and Matplotlib.

import logging
import geopandas as gpd
import matplotlib.pyplot as plt
from sliderule import icesat2

SlideRule Configuration

# Configure ICESat-2 API
icesat2.init("slideruleearth.io")
# Configure Region of Interest
region = [ {"lon":-105.82971551223244, "lat": 39.81983728534918},
           {"lon":-105.30742121965137, "lat": 39.81983728534918},
           {"lon":-105.30742121965137, "lat": 40.164048017973755},
           {"lon":-105.82971551223244, "lat": 40.164048017973755},
           {"lon":-105.82971551223244, "lat": 39.81983728534918} ]

Execute ATL06 Algorithm using SlideRule

%%time

# Build ATL06 Request
parms = {
    "poly": region,
    "srt": icesat2.SRT_LAND,
    "cnf": icesat2.CNF_SURFACE_HIGH,
    "ats": 10.0,
    "cnt": 10,
    "len": 40.0,
    "res": 20.0
}

# Request ATL06 Data
gdf = icesat2.atl06p(parms)

# Display Statistics
print("Reference Ground Tracks: {}".format(gdf["rgt"].unique()))
print("Cycles: {}".format(gdf["cycle"].unique()))
print("Received {} elevations".format(len(gdf)))

Reference Ground Tracks: [554 973 996  28 531  51]
Cycles: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21]
Received 308983 elevations
CPU times: user 12 s, sys: 283 ms, total: 12.3 s
Wall time: 36.2 s

Plot Region

# Calculate Extent
lons = [p["lon"] for p in region]
lats = [p["lat"] for p in region]
lon_margin = (max(lons) - min(lons)) * 0.1
lat_margin = (max(lats) - min(lats)) * 0.1

# Create Plot
fig,(ax1,ax2) = plt.subplots(num=None, ncols=2, figsize=(12, 6))
box_lon = [e["lon"] for e in region]
box_lat = [e["lat"] for e in region]

# Plot SlideRule Ground Tracks
ax1.set_title("SlideRule Zoomed Ground Tracks")
gdf.plot(ax=ax1, column=gdf["h_mean"], cmap='winter_r', s=1.0, zorder=3)
ax1.plot(box_lon, box_lat, linewidth=1.5, color='r', zorder=2)
ax1.set_xlim(min(lons) - lon_margin, max(lons) + lon_margin)
ax1.set_ylim(min(lats) - lat_margin, max(lats) + lat_margin)
ax1.set_aspect('equal', adjustable='box')

# Plot SlideRule Global View
ax2.set_title("SlideRule Global Reference")
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world.plot(ax=ax2, color='0.8', edgecolor='black')
gdf.plot(ax=ax2, marker='o', color='red', markersize=2.5, zorder=3)
ax2.set_xlim(-180,180)
ax2.set_ylim(-90,90)
ax2.set_aspect('equal', adjustable='box')

# Show Plot
plt.tight_layout()

/tmp/ipykernel_2187566/3994963524.py:22: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.
  world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))