Cascading search

In the Quickstart notebook we covered searching datasets one-by-one and gradually reducing the spatial domain of our search based on overlapping footprints.

coincident also provides a cascading_search()[#coincident.search.cascading_search] method as a convenience to perform this same type of search in one go.

import coincident
import geopandas as gpd
import xyzservices.providers as xyz
import matplotlib.pyplot as plt

/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/src/coincident/io/download.py:27: 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...	http://prd-tnm.s3.amazonaws.com/index.html?pre...	http://prd-tnm.s3.amazonaws.com/index.html?pre...	MULTIPOLYGON (((-106.17143 38.42061, -106.3208...	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 = [
    ("maxar", 14),  # +/- 14 days from lidar
    ("gedi", 40),  # +/- 40 days from lidar
    ("icesat-2", 60),
]

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

Visualize results

Below we visualize cropped footprints from each secondary dataset.

gf_maxar = dataframes[0]
print(len(gf_maxar))
m = gf_lidar.explore(
    style_kwds=dict(fill=None, color="black"), tiles=xyz.CartoDB.Positron
)  # basemap
gf_maxar.explore(m=m, column="datetime")

7

Make this Notebook Trusted to load map: File -> Trust Notebook

gf_gedi = dataframes[1]
print(len(gf_gedi))
m = gf_lidar.explore(
    style_kwds=dict(fill=None, color="black"), tiles=xyz.CartoDB.Positron
)  # basemap
gf_gedi.explore(m=m, column="datetime")

14

Make this Notebook Trusted to load map: File -> Trust Notebook

gf_is2 = dataframes[2]
print(len(gf_maxar))
m = gf_lidar.explore(
    style_kwds=dict(fill=None, color="black"), tiles=xyz.CartoDB.Positron
)  # basemap
gf_is2.explore(m=m, column="datetime")

7

Make this Notebook Trusted to load map: File -> Trust Notebook

fig, ax = plt.subplots(figsize=(10, 5))
for df in dataframes:
    if "constellation" in df.columns:
        label = df.constellation.iloc[0]
    else:
        label = df.collection.iloc[0]
    plt.scatter(x=df["datetime"], y=df["collection"], s=50, marker="d", label=label)

# NOTE: probably a more robust way to set aspect depending on date range
ax.set_aspect(6)
plt.axvline(
    gf_lidar.start_datetime.iloc[0],
    color="black",
    linestyle="--",
    linewidth=0.5,
    label="LiDAR",
)
plt.axvline(gf_lidar.end_datetime.iloc[0], color="black", linestyle="--", linewidth=0.5)
plt.title("CO_WestCentral_2019 Overlaps")
plt.legend(loc="lower right")
fig.autofmt_xdate()

Save results

The footprints of the last secondary dataset show where we have spatial intersections across all datasets. We save this a single MultiPolygon to use in QGIS or geojson.io

gf_is2.dissolve()[["geometry"]].to_file("/tmp/CO_WestCentral_2019_overlaps.geojson")

Summary

• The CO_WestCentral_2019 USGS 3DEP LiDAR was acquired between 2019-08-21 and 2019-09-19

• We found 7 Maxar Stereo acquisitions with 14 days of the LiDAR

• We found 14 GEDI acquisitions that overlap the lidar+stereo footprints within 40 days of LiDAR

• We found 7 ICESat-2 acquisitions that overlap combined lidar+stereo+GEDI footprints within 60 days of LiDAR

The final ‘overlap’ polygons have at least a 30k^2 area in which at least two of the datasets intersect. Acquisition dates for any given footprint vary from 60 days before the LiDAR was acquired through 60 days afterwards.