Reading and Downloading DEM and LPC Products

The coincident.io.xarray and coincident.io.download modules support the option to load DEMs into memory via odc-stac or download DEMs into local directories.

There is also support for Lidar Point Cloud (LPC) spatial filtering for aerial lidar catalogs, where the user can return a GeoDataFrame with the respective .laz tile filename, download url, and geometry (epsg 4326) for each tile intersecting an input aoi. These laz files can then be downloaded locally with coincident.io.download.download_files()

There is specific support for USGS 3DEP EPT readers where the user can return a PDAL pipeline configured with the EPT URL, the AOI’s bounds, and polygon WKT, all in the EPT’s spatial reference system.

Note

Coincident does not support the processing of lidar point cloud products. Please see the lidar_tools repository for information on processing the returned GeoDataFrame with lidar point cloud products.

import coincident
import geopandas as gpd
from shapely.geometry import box

/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

3DEP and NEON overlapping Flights

Note

For all of these functions, you will need identification metadata from the coincident.search.search functions for each respective catalog

Search

workunit = "CO_CentralEasternPlains_1_2020"
df_wesm = coincident.search.wesm.read_wesm_csv()
gf_usgs = coincident.search.wesm.load_by_fid(
    df_wesm[df_wesm.workunit == workunit].index
)

gf_usgs

	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_CentralEasternPlains_1_2020	192973	CO_CentralEasternPlains_2020_D20	192976	2020-05-09	2020-06-10	QL 2	USGS Lidar Base Specification 2.1	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 (((-103.7138 39.8837, -103.7138 3...	3DEP	2020-05-25	146	32

1 rows × 33 columns

# We will examine the 'sourcedem' 1m
# NOTE: it's important to note the *vertical* CRS of the data
gf_usgs.iloc[0]

workunit                                 CO_CentralEasternPlains_1_2020
workunit_id                                                      192973
project                                CO_CentralEasternPlains_2020_D20
project_id                                                       192976
start_datetime                                      2020-05-09 00:00:00
end_datetime                                        2020-06-10 00:00:00
ql                                                                 QL 2
spec                                  USGS Lidar Base Specification 2.1
p_method                                              linear-mode lidar
dem_gsd_meters                                                      1.0
horiz_crs                                                          6342
vert_crs                                                           5703
geoid                                                           GEOID18
lpc_pub_date                                        2020-12-11 00:00:00
lpc_update                                                          NaT
lpc_category                                                      Meets
lpc_reason                                  Meets 3DEP LPC requirements
sourcedem_pub_date                                  2020-12-10 00:00:00
sourcedem_update                                                    NaT
sourcedem_category                                                Meets
sourcedem_reason                     Meets 3DEP source DEM requirements
onemeter_category                                                 Meets
onemeter_reason                         Meets 3DEP 1-m DEM requirements
seamless_category                                                 Meets
seamless_reason                    Meets 3DEP seamless DEM requirements
lpc_link              https://rockyweb.usgs.gov/vdelivery/Datasets/S...
sourcedem_link        http://prd-tnm.s3.amazonaws.com/index.html?pre...
metadata_link         http://prd-tnm.s3.amazonaws.com/index.html?pre...
geometry              MULTIPOLYGON (((-103.7138 39.8837, -103.7138 3...
collection                                                         3DEP
datetime                                            2020-05-25 00:00:00
dayofyear                                                           146
duration                                                             32
Name: 0, dtype: object

Now, we’ll explore an overlapping NEON flight

gf_neon = coincident.search.search(
    dataset="neon", intersects=gf_usgs, datetime=["2020"]
)

/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/.pixi/envs/dev/lib/python3.12/site-packages/geopandas/array.py:403: UserWarning: Geometry is in a geographic CRS. Results from 'sjoin_nearest' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  warnings.warn(

gf_neon.head()

	id	title	start_datetime	end_datetime	product_url	geometry
0	ARIK	Arikaree River NEON	2020-06-01	2020-06-30	https://data.neonscience.org/api/v0/data/DP3.3...	POLYGON ((-102.60902 39.69825, -102.60871 39.7...

m = gf_usgs.explore(color="black")
gf_neon.explore(m=m, column="id", popups=True)

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

Subset data

We will evaluate small subset of this overlap for deomstrative purposes.

Let’s subset based on some contextual LULC data

gf_wc = coincident.search.search(
    dataset="worldcover",
    intersects=gf_neon,
    datetime=["2020"],
)

dswc = coincident.io.xarray.to_dataset(
    gf_wc,
    bands=["map"],
    aoi=gf_neon,
    mask=True,
    resolution=0.00027,  # ~30m
)
dswc = dswc.rename(map="landcover")
dswc = dswc.compute()

# arbitrary bbox that will be our subset area (all cropland)
bbox_geometry = box(-102.505, 39.675, -102.49, 39.685)
aoi = gpd.GeoDataFrame(geometry=[bbox_geometry], crs="EPSG:4326")

ax = coincident.plot.plot_esa_worldcover(dswc)
aoi.plot(ax=ax, facecolor="none", edgecolor="black", linestyle="--", linewidth=2)
from matplotlib.lines import Line2D

custom_line = Line2D([0], [0], color="black", linestyle="--", lw=2)
ax.legend([custom_line], ["Area of Interest"], loc="upper right", fontsize=10)
ax.set_title("ESA WorldCover");

Actually read in the DEMs

datetime_str = gf_neon.end_datetime.item()
site_id = gf_neon.id.item()
datetime_str, site_id

('2020-06-30', 'ARIK')

%%time
da_neon_dem = coincident.io.xarray.load_neon_dem(
    aoi, datetime_str=datetime_str, site_id=site_id, product="dsm"
)

/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/.pixi/envs/dev/lib/python3.12/site-packages/rioxarray/_io.py:1146: RuntimeWarning: TIFFReadDirectoryCheckOrder:Invalid TIFF directory; tags are not sorted in ascending order
  if riods.subdatasets:
/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/.pixi/envs/dev/lib/python3.12/site-packages/rioxarray/_io.py:1146: RuntimeWarning: TIFFReadDirectoryCheckOrder:Invalid TIFF directory; tags are not sorted in ascending order
  if riods.subdatasets:
/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/.pixi/envs/dev/lib/python3.12/site-packages/rioxarray/_io.py:1146: RuntimeWarning: TIFFReadDirectoryCheckOrder:Invalid TIFF directory; tags are not sorted in ascending order
  if riods.subdatasets:

/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/.pixi/envs/dev/lib/python3.12/site-packages/rioxarray/_io.py:1146: RuntimeWarning: TIFFReadDirectoryCheckOrder:Invalid TIFF directory; tags are not sorted in ascending order
  if riods.subdatasets:
/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/.pixi/envs/dev/lib/python3.12/site-packages/rioxarray/_io.py:1146: RuntimeWarning: TIFFReadDirectoryCheckOrder:Invalid TIFF directory; tags are not sorted in ascending order
  if riods.subdatasets:

CPU times: user 181 ms, sys: 33.2 ms, total: 214 ms
Wall time: 1.35 s

/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/.pixi/envs/dev/lib/python3.12/site-packages/rioxarray/_io.py:1146: RuntimeWarning: TIFFReadDirectoryCheckOrder:Invalid TIFF directory; tags are not sorted in ascending order
  if riods.subdatasets:

da_neon_dem

<xarray.DataArray 'elevation' (y: 1146, x: 1318)> Size: 6MB
dask.array<getitem, shape=(1146, 1318), dtype=float32, chunksize=(808, 1000), chunktype=numpy.ndarray>
Coordinates:
  * x            (x) float64 11kB 7.14e+05 7.14e+05 ... 7.153e+05 7.153e+05
  * y            (y) float64 9kB 4.395e+06 4.395e+06 ... 4.396e+06 4.396e+06
    spatial_ref  int64 8B 0

xarray.DataArray

'elevation'

• y: 1146
• x: 1318

• dask.array<chunksize=(338, 40), meta=np.ndarray>

  Array Chunk Bytes 5.76 MiB 3.08 MiB Shape (1146, 1318) (808, 1000) Dask graph 6 chunks in 64 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • x
    (x)
    float64
    7.14e+05 7.14e+05 ... 7.153e+05

    axis :

    X

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    units :

    metre

    array([713960.5, 713961.5, 713962.5, ..., 715275.5, 715276.5, 715277.5],
          shape=(1318,))

  • y
    (y)
    float64
    4.395e+06 4.395e+06 ... 4.396e+06

    axis :

    Y

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    units :

    metre

    array([4394662.5, 4394663.5, 4394664.5, ..., 4395805.5, 4395806.5, 4395807.5],
          shape=(1146,))

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    PROJCS["WGS 84 / UTM zone 13N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32613"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    projected_crs_name :

    WGS 84 / UTM zone 13N

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    -105.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    spatial_ref :

    PROJCS["WGS 84 / UTM zone 13N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32613"]]

    GeoTransform :

    713960.0 1.0 0.0 4394662.0 0.0 1.0

    array(0)

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Index([713960.5, 713961.5, 713962.5, 713963.5, 713964.5, 713965.5, 713966.5,
           713967.5, 713968.5, 713969.5,
           ...
           715268.5, 715269.5, 715270.5, 715271.5, 715272.5, 715273.5, 715274.5,
           715275.5, 715276.5, 715277.5],
          dtype='float64', name='x', length=1318))

  • y
    PandasIndex

    PandasIndex(Index([4394662.5, 4394663.5, 4394664.5, 4394665.5, 4394666.5, 4394667.5,
           4394668.5, 4394669.5, 4394670.5, 4394671.5,
           ...
           4395798.5, 4395799.5, 4395800.5, 4395801.5, 4395802.5, 4395803.5,
           4395804.5, 4395805.5, 4395806.5, 4395807.5],
          dtype='float64', name='y', length=1146))

• Attributes: (0)

usgs_project = gf_usgs["project"].item()
usgs_project

'CO_CentralEasternPlains_2020_D20'

%%time
da_usgs_dem = coincident.io.xarray.load_usgs_dem(aoi, usgs_project)

CPU times: user 576 ms, sys: 618 ms, total: 1.19 s
Wall time: 2.81 s

da_usgs_dem

<xarray.DataArray 'elevation' (y: 1146, x: 1317)> Size: 6MB
dask.array<getitem, shape=(1146, 1317), dtype=float32, chunksize=(1146, 1317), chunktype=numpy.ndarray>
Coordinates:
  * x            (x) float64 11kB 7.14e+05 7.14e+05 ... 7.153e+05 7.153e+05
  * y            (y) float64 9kB 4.396e+06 4.396e+06 ... 4.395e+06 4.395e+06
    spatial_ref  int64 8B 0

xarray.DataArray

'elevation'

• y: 1146
• x: 1317

• dask.array<chunksize=(1146, 1317), meta=np.ndarray>

  Array Chunk Bytes 5.76 MiB 5.76 MiB Shape (1146, 1317) (1146, 1317) Dask graph 1 chunks in 8 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • x
    (x)
    float64
    7.14e+05 7.14e+05 ... 7.153e+05

    axis :

    X

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    units :

    metre

    array([713961.499973, 713962.499973, 713963.499973, ..., 715275.499973,
           715276.499973, 715277.499973], shape=(1317,))

  • y
    (y)
    float64
    4.396e+06 4.396e+06 ... 4.395e+06

    axis :

    Y

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    units :

    metre

    array([4395807.500046, 4395806.500046, 4395805.500046, ..., 4394664.500046,
           4394663.500046, 4394662.500046], shape=(1146,))

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    PROJCS["NAD83 / UTM zone 13N",GEOGCS["NAD83",DATUM["North_American_Datum_1983",SPHEROID["GRS 1980",6378137,298.257222101,AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","6269"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4269"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","26913"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    inverse_flattening :

    298.257222101

    reference_ellipsoid_name :

    GRS 1980

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    NAD83

    horizontal_datum_name :

    North American Datum 1983

    projected_crs_name :

    NAD83 / UTM zone 13N

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    -105.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    spatial_ref :

    PROJCS["NAD83 / UTM zone 13N",GEOGCS["NAD83",DATUM["North_American_Datum_1983",SPHEROID["GRS 1980",6378137,298.257222101,AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","6269"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4269"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","26913"]]

    GeoTransform :

    713960.999973323 1.0 0.0 4395808.000045877 0.0 -1.0

    array(0)

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Index([713961.499973323, 713962.499973323, 713963.499973323, 713964.499973323,
           713965.499973323, 713966.499973323, 713967.499973323, 713968.499973323,
           713969.499973323, 713970.499973323,
           ...
           715268.499973323, 715269.499973323, 715270.499973323, 715271.499973323,
           715272.499973323, 715273.499973323, 715274.499973323, 715275.499973323,
           715276.499973323, 715277.499973323],
          dtype='float64', name='x', length=1317))

  • y
    PandasIndex

    PandasIndex(Index([4395807.500045877, 4395806.500045877, 4395805.500045877,
           4395804.500045877, 4395803.500045877, 4395802.500045877,
           4395801.500045877, 4395800.500045877, 4395799.500045877,
           4395798.500045877,
           ...
           4394671.500045877, 4394670.500045877, 4394669.500045877,
           4394668.500045877, 4394667.500045877, 4394666.500045877,
           4394665.500045877, 4394664.500045877, 4394663.500045877,
           4394662.500045877],
          dtype='float64', name='y', length=1146))

• Attributes: (0)

da_usgs_dem.coarsen(x=5, y=5, boundary="trim").mean().plot.imshow();

Download

Note

coincident.io.download.download_neon_dem needs the NEON site’s start_datetime OR end_datetime to work

gf_neon

	id	title	start_datetime	end_datetime	product_url	geometry
0	ARIK	Arikaree River NEON	2020-06-01	2020-06-30	https://data.neonscience.org/api/v0/data/DP3.3...	POLYGON ((-102.60902 39.69825, -102.60871 39.7...

local_output_dir = "/tmp"

coincident.io.download.download_neon_dem(
    aoi=aoi,
    datetime_str=gf_neon.start_datetime.item(),
    site_id=gf_neon.id.item(),
    product="dsm",
    output_dir=local_output_dir,
)

# USGS_1M_13_x71y440_CO_CentralEasternPlains_2020_D20.tif:  236MB
coincident.io.download.download_usgs_dem(
    aoi=aoi,
    project=usgs_project,
    output_dir=local_output_dir,
    save_parquet=True,  # save a STAC-like geoparquet of the tiles you download
)

Finally, you can grab the LPC tile metadata. For USGS 3DEP data, you can also return a PDAL pipeline based on the available EPT data. This PDAL pipeline will be returned as a JSON file where the user can add their own custom parameters (additional filters, writers, etc.) to this pipeline dictionary before executing it with PDAL.

Note

coincident.io.download.fetch_lpc_tiles needs the NEON site’s end_datetime OR start_datetime to work if you are in fact accessing NEON data

%%time
gf_neon_lpc_tiles = coincident.io.download.fetch_lpc_tiles(
    aoi=aoi,
    dataset_id=gf_neon.id.item(),
    provider="NEON",
    datetime_str=gf_neon.end_datetime.item(),
)

CPU times: user 53.5 ms, sys: 5.14 ms, total: 58.7 ms
Wall time: 329 ms

gf_neon_lpc_tiles.head()

	name	url	geometry
0	NEON_D10_ARIK_DP1_715000_4395000_classified_po...	https://storage.googleapis.com/neon-aop-produc...	POLYGON ((-102.48149 39.67754, -102.48116 39.6...
1	NEON_D10_ARIK_DP1_714000_4394000_classified_po...	https://storage.googleapis.com/neon-aop-produc...	POLYGON ((-102.49347 39.66879, -102.49314 39.6...
2	NEON_D10_ARIK_DP1_713000_4395000_classified_po...	https://storage.googleapis.com/neon-aop-produc...	POLYGON ((-102.50479 39.67804, -102.50447 39.6...
3	NEON_D10_ARIK_DP1_713000_4394000_classified_po...	https://storage.googleapis.com/neon-aop-produc...	POLYGON ((-102.50511 39.66904, -102.50479 39.6...
4	NEON_D10_ARIK_DP1_714000_4395000_classified_po...	https://storage.googleapis.com/neon-aop-produc...	POLYGON ((-102.49314 39.67779, -102.49281 39.6...

%%time
gf_usgs_lpc_tiles = coincident.io.download.fetch_lpc_tiles(
    aoi=aoi, dataset_id=usgs_project, provider="USGS"
)

CPU times: user 10.2 ms, sys: 0 ns, total: 10.2 ms
Wall time: 1.37 s

gf_usgs_lpc_tiles.head()

	name	url	geometry
0	5fded821d34e30b9123e230c	https://rockyweb.usgs.gov/vdelivery/Datasets/S...	POLYGON ((-102.50479 39.66904, -102.50479 39.6...
1	5fded821d34e30b9123e230e	https://rockyweb.usgs.gov/vdelivery/Datasets/S...	POLYGON ((-102.50447 39.67804, -102.50447 39.6...
2	5fded82fd34e30b9123e235a	https://rockyweb.usgs.gov/vdelivery/Datasets/S...	POLYGON ((-102.49314 39.66879, -102.49314 39.6...
3	5fded830d34e30b9123e235c	https://rockyweb.usgs.gov/vdelivery/Datasets/S...	POLYGON ((-102.49281 39.67779, -102.49281 39.6...
4	5fded837d34e30b9123e23a8	https://rockyweb.usgs.gov/vdelivery/Datasets/S...	POLYGON ((-102.48149 39.66854, -102.48149 39.6...

m = gf_usgs_lpc_tiles.explore(color="black")
gf_neon_lpc_tiles.explore(m=m)

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

Now, we can download the laz files

coincident.io.download.download_files(
    gf_neon_lpc_tiles["url"], output_dir=local_output_dir
)

pdal_pipeline = coincident.io.download.build_usgs_ept_pipeline(
    aoi, workunit=gf_usgs.workunit.item(), output_dir=local_output_dir
)

pdal_pipeline

{'pipeline': [{'type': 'readers.ept',
   'filename': 'https://s3-us-west-2.amazonaws.com/usgs-lidar-public/CO_CentralEasternPlains_1_2020/ept.json',
   'bounds': '(([-11410804.4037645068, -11409134.6114026085], [4818825.9525320893, 4820272.3693662276]))'},
  {'type': 'filters.crop',
   'polygon': 'POLYGON ((-11409134.611402608 4818825.952532089, -11409134.611402608 4820272.369366228, -11410804.403764507 4820272.369366228, -11410804.403764507 4818825.952532089, -11409134.611402608 4818825.952532089))'},
  {'type': 'writers.las',
   'filename': 'CO_CentralEasternPlains_1_2020_EPT_subset_pipeline.laz',
   'compression': 'laszip'}]}

NCALM

Search

aoi = gpd.read_file(
    "https://raw.githubusercontent.com/unitedstates/districts/refs/heads/gh-pages/states/WA/shape.geojson"
)
aoi.plot();

gf_ncalm = coincident.search.search(
    dataset="ncalm", intersects=aoi, datetime=["2018-09-19"]
)

gf_ncalm

	id	name	title	start_datetime	end_datetime	geometry
0	OTLAS.072019.6339.1	WA18_Wall	High-Resolution Mapping of Goat Rock Volcano, WA	2018-09-19	2018-09-20	POLYGON ((-121.46701 46.48376, -121.45914 46.4...

Now, let’s subset to a small AOI for convenience sake

buffer_size = 0.01
centroid = gf_ncalm.centroid
mini_aoi = gpd.GeoDataFrame(
    geometry=[
        box(
            centroid.x - buffer_size,
            centroid.y - buffer_size,
            centroid.x + buffer_size,
            centroid.y + buffer_size,
        )
    ],
    crs="EPSG:4326",
)

/tmp/ipykernel_1139/991173689.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  centroid = gf_ncalm.centroid
/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/.pixi/envs/dev/lib/python3.12/site-packages/shapely/geometry/polygon.py:91: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
  return [float(c) for c in o]

m = gf_ncalm.explore()
mini_aoi.explore(m=m, color="red")

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

Note

The NCALM DEMs are not tiled, so reading in the data and downloading takes a longer time

Subset data

%%time
da_ncalm_dtm = coincident.io.xarray.load_ncalm_dem(
    aoi=mini_aoi, product="dtm", dataset_id=gf_ncalm["name"].item()
)

CPU times: user 6.36 s, sys: 3.58 s, total: 9.94 s
Wall time: 23.5 s

da_ncalm_dtm

<xarray.DataArray 'elevation' (y: 2254, x: 1579)> Size: 14MB
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       ...,
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]],
      shape=(2254, 1579), dtype=float32)
Coordinates:
  * x            (x) float64 13kB 6.226e+05 6.226e+05 ... 6.242e+05 6.242e+05
  * y            (y) float64 18kB 5.152e+06 5.152e+06 ... 5.15e+06 5.15e+06
    spatial_ref  int64 8B 0
Attributes:
    AREA_OR_POINT:  Area
    scale_factor:   1.0
    add_offset:     0.0

xarray.DataArray

'elevation'

• y: 2254
• x: 1579

• nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan

  array([[nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         ...,
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan],
         [nan, nan, nan, ..., nan, nan, nan]],
        shape=(2254, 1579), dtype=float32)

• Coordinates: (3)
  • x
    (x)
    float64
    6.226e+05 6.226e+05 ... 6.242e+05

    axis :

    X

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    units :

    metre

    array([622599.000001, 622600.000001, 622601.000001, ..., 624175.000001,
           624176.000001, 624177.000001], shape=(1579,))

  • y
    (y)
    float64
    5.152e+06 5.152e+06 ... 5.15e+06

    axis :

    Y

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    units :

    metre

    array([5152454.000016, 5152453.000016, 5152452.000016, ..., 5150203.000016,
           5150202.000016, 5150201.000016], shape=(2254,))

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    projected_crs_name :

    WGS 84 / UTM zone 10N

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    -123.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    spatial_ref :

    PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    GeoTransform :

    622598.5000010493 1.0000000000086315 0.0 5152454.500016331 0.0 -1.0000000000086808

    array(0)

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Index([622599.0000010493, 622600.0000010493, 622601.0000010493,
           622602.0000010493, 622603.0000010494, 622604.0000010494,
           622605.0000010494, 622606.0000010494, 622607.0000010494,
           622608.0000010494,
           ...
           624168.0000010629, 624169.0000010629, 624170.0000010629,
           624171.0000010629, 624172.0000010629, 624173.0000010629,
           624174.0000010629, 624175.0000010629, 624176.0000010629,
           624177.0000010629],
          dtype='float64', name='x', length=1579))

  • y
    PandasIndex

    PandasIndex(Index([5152454.000016331, 5152453.000016331, 5152452.000016331,
           5152451.000016331, 5152450.000016331, 5152449.000016331,
           5152448.000016331, 5152447.000016331, 5152446.000016331,
            5152445.00001633,
           ...
           5150210.000016311, 5150209.000016311, 5150208.000016311,
           5150207.000016311, 5150206.000016311, 5150205.000016311,
           5150204.000016311, 5150203.000016311, 5150202.000016311,
           5150201.000016311],
          dtype='float64', name='y', length=2254))

• Attributes: (3)

  AREA_OR_POINT :

  Area

  scale_factor :

  1.0

  add_offset :

  0.0

da_ncalm_dtm.coarsen(x=5, y=5, boundary="trim").mean().plot.imshow();

Download

Note

You will not see a download progress bar for downloading NCALM DEMs as opposed to USGS and NEON provider DEMs

%%time
coincident.io.download.download_ncalm_dem(
    aoi=mini_aoi,
    dataset_id=gf_ncalm["name"].item(),
    product="dtm",
    output_dir=local_output_dir,
)

CPU times: user 6.18 s, sys: 1.64 s, total: 7.81 s
Wall time: 20.6 s

%%time
gf_ncalm_lpc_tiles = coincident.io.download.fetch_lpc_tiles(
    aoi=mini_aoi, dataset_id=gf_ncalm.name.item(), provider="NCALM"
)

CPU times: user 64.3 ms, sys: 5.45 ms, total: 69.7 ms
Wall time: 449 ms

gf_ncalm_lpc_tiles.head()

	name	url	geometry
0	WA18_Wall/622000_5150000.laz	https://opentopography.s3.sdsc.edu/pc-bulk/WA1...	POLYGON ((-121.39723 46.49234, -121.39697 46.5...
1	WA18_Wall/622000_5151000.laz	https://opentopography.s3.sdsc.edu/pc-bulk/WA1...	POLYGON ((-121.39697 46.50133, -121.3967 46.51...
2	WA18_Wall/622000_5152000.laz	https://opentopography.s3.sdsc.edu/pc-bulk/WA1...	POLYGON ((-121.3967 46.51033, -121.39644 46.51...
3	WA18_Wall/623000_5150000.laz	https://opentopography.s3.sdsc.edu/pc-bulk/WA1...	POLYGON ((-121.3842 46.49216, -121.38394 46.50...
4	WA18_Wall/623000_5151000.laz	https://opentopography.s3.sdsc.edu/pc-bulk/WA1...	POLYGON ((-121.38394 46.50115, -121.38367 46.5...

m = gf_ncalm.explore()
gf_ncalm_lpc_tiles.explore(m=m, color="red")
mini_aoi.explore(m=m, color="black")

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

NOAA

Search

aoi = gpd.read_file(
    "https://raw.githubusercontent.com/unitedstates/districts/refs/heads/gh-pages/states/FL/shape.geojson"
)
gf_noaa = coincident.search.search(
    dataset="noaa", intersects=aoi, datetime=["2022-10-27"]
)

buffer_size = 0.02
centroid = gf_noaa.centroid
mini_aoi = gpd.GeoDataFrame(
    geometry=[
        box(
            centroid.x - buffer_size,
            centroid.y - buffer_size,
            centroid.x + buffer_size,
            centroid.y + buffer_size,
        )
    ],
    crs="EPSG:4326",
)

/tmp/ipykernel_1139/2106933600.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  centroid = gf_noaa.centroid
/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/.pixi/envs/dev/lib/python3.12/site-packages/shapely/geometry/polygon.py:91: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
  return [float(c) for c in o]

m = gf_noaa.explore()
mini_aoi.explore(m=m, color="red")

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

the name and id being identical is expected

gf_noaa

	id	name	title	start_datetime	end_datetime	geometry
0	10149	10149	2022 NOAA NGS Topobathy Lidar: Big Bend WMA, FL	2022-10-27	2022-11-29	POLYGON ((-83.4654 29.36713, -83.35291 29.4196...

Note

Our coincident.search.search(dataset=”noaa”) returns the dataset ids “Lidar Datasets at NOAA Digital Coast” whereas coincident.io.xarray.load_noaa_dem() requires the ids from the “Imagery and Elevation Raster Datasets at NOAA Digital Coast” dataset. The corresponding elevation raster dataset id is the same as the lidar dataset id + 1. e.g. “Great Bay NERR UAS Lidar” has id 10175 for lidar data and id 10176 for dem data

noaa_dem_id = int(gf_noaa.id.item()) + 1
print(f"NOAA LiDAR id: {gf_noaa.id.item()}  NOAA DEM id: {noaa_dem_id}")

NOAA LiDAR id: 10149  NOAA DEM id: 10150

Warning

The larger the NOAA flight, the longer the below function takes regardless of your input AOI

Subset

%%time
da_noaa_dem = coincident.io.xarray.load_noaa_dem(mini_aoi, noaa_dem_id)

CPU times: user 1.67 s, sys: 282 ms, total: 1.96 s
Wall time: 3.69 s

da_noaa_dem

<xarray.DataArray 'elevation' (y: 3909, x: 3964)> Size: 62MB
dask.array<getitem, shape=(3909, 3964), dtype=float32, chunksize=(3909, 2625), chunktype=numpy.ndarray>
Coordinates:
  * x            (x) float64 32kB 2.237e+05 2.237e+05 ... 2.276e+05 2.276e+05
  * y            (y) float64 31kB 3.304e+06 3.304e+06 ... 3.3e+06 3.3e+06
    spatial_ref  int64 8B 0

xarray.DataArray

'elevation'

• y: 3909
• x: 3964

• dask.array<chunksize=(3909, 1339), meta=np.ndarray>

  Array Chunk Bytes 59.11 MiB 39.14 MiB Shape (3909, 3964) (3909, 2625) Dask graph 2 chunks in 28 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • x
    (x)
    float64
    2.237e+05 2.237e+05 ... 2.276e+05

    axis :

    X

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    units :

    metre

    array([223661.5, 223662.5, 223663.5, ..., 227622.5, 227623.5, 227624.5],
          shape=(3964,))

  • y
    (y)
    float64
    3.304e+06 3.304e+06 ... 3.3e+06

    axis :

    Y

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    units :

    metre

    array([3303908.5, 3303907.5, 3303906.5, ..., 3300002.5, 3300001.5, 3300000.5],
          shape=(3909,))

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    COMPD_CS["NAD83(2011) / UTM zone 17N + NAVD88 height - Geoid18 (Meters)",PROJCS["NAD83(2011) / UTM zone 17N",GEOGCS["NAD83(2011)",DATUM["NAD83_National_Spatial_Reference_System_2011",SPHEROID["GRS 1980",6378137,298.257222101,AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","1116"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","6318"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-81],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","6346"]],VERT_CS["NAVD88 height",VERT_DATUM["North American Vertical Datum 1988",2005,AUTHORITY["EPSG","5103"]],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Gravity-related height",UP],AUTHORITY["EPSG","5703"]]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    inverse_flattening :

    298.257222101

    reference_ellipsoid_name :

    GRS 1980

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    NAD83(2011)

    horizontal_datum_name :

    NAD83 (National Spatial Reference System 2011)

    projected_crs_name :

    NAD83(2011) / UTM zone 17N

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    -81.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    geopotential_datum_name :

    North American Vertical Datum 1988

    spatial_ref :

    COMPD_CS["NAD83(2011) / UTM zone 17N + NAVD88 height - Geoid18 (Meters)",PROJCS["NAD83(2011) / UTM zone 17N",GEOGCS["NAD83(2011)",DATUM["NAD83_National_Spatial_Reference_System_2011",SPHEROID["GRS 1980",6378137,298.257222101,AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","1116"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","6318"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-81],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","6346"]],VERT_CS["NAVD88 height",VERT_DATUM["North American Vertical Datum 1988",2005,AUTHORITY["EPSG","5103"]],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Gravity-related height",UP],AUTHORITY["EPSG","5703"]]]

    GeoTransform :

    223660.99999999977 1.0 0.0 3303908.9999999995 0.0 -1.0

    array(0)

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Index([223661.49999999977, 223662.49999999977, 223663.49999999977,
           223664.49999999977, 223665.49999999977, 223666.49999999977,
           223667.49999999977, 223668.49999999977, 223669.49999999977,
           223670.49999999977,
           ...
           227615.49999999977, 227616.49999999977, 227617.49999999977,
           227618.49999999977, 227619.49999999977, 227620.49999999977,
           227621.49999999977, 227622.49999999977, 227623.49999999977,
           227624.49999999977],
          dtype='float64', name='x', length=3964))

  • y
    PandasIndex

    PandasIndex(Index([3303908.4999999995, 3303907.4999999995, 3303906.4999999995,
           3303905.4999999995, 3303904.4999999995, 3303903.4999999995,
           3303902.4999999995, 3303901.4999999995, 3303900.4999999995,
           3303899.4999999995,
           ...
           3300009.4999999995, 3300008.4999999995, 3300007.4999999995,
           3300006.4999999995, 3300005.4999999995, 3300004.4999999995,
           3300003.4999999995, 3300002.4999999995, 3300001.4999999995,
           3300000.4999999995],
          dtype='float64', name='y', length=3909))

• Attributes: (0)

da_noaa_dem.coarsen(x=50, y=50, boundary="trim").mean().plot.imshow();

buffer_size = 0.008
centroid = gf_noaa.centroid
mini_aoi = gpd.GeoDataFrame(
    geometry=[
        box(
            centroid.x - buffer_size,
            centroid.y - buffer_size,
            centroid.x + buffer_size,
            centroid.y + buffer_size,
        )
    ],
    crs="EPSG:4326",
)
m = gf_noaa.explore()
mini_aoi.explore(m=m, color="red")

/tmp/ipykernel_1139/1549685662.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  centroid = gf_noaa.centroid
/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/.pixi/envs/dev/lib/python3.12/site-packages/shapely/geometry/polygon.py:91: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
  return [float(c) for c in o]

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

Download

Note

You will not see a download progress bar for downloading NOAA DEMs as opposed to USGS and NEON provider DEMs

%%time
coincident.io.download.download_noaa_dem(
    aoi=mini_aoi, dataset_id=noaa_dem_id, output_dir=local_output_dir
)

CPU times: user 2.26 s, sys: 491 ms, total: 2.76 s
Wall time: 3.71 s

list(mini_aoi.bounds.values)

[array([-83.84683768,  29.80678818, -83.83083768,  29.82278818])]

gf_noaa.id.item()

'10149'

%%time
gf_noaa_lpc_tiles = coincident.io.download.fetch_lpc_tiles(
    aoi=mini_aoi, dataset_id=gf_noaa.id.item(), provider="NOAA"
)

CPU times: user 1.86 s, sys: 26.8 ms, total: 1.88 s
Wall time: 3.73 s

gf_noaa_lpc_tiles

	name	url	geometry
0	20221114_225500e_3302500n.copc.laz	https://noaa-nos-coastal-lidar-pds.s3.amazonaw...	POLYGON ((-83.8353 29.82259, -83.83543 29.8270...
1	20221114_226000e_3302000n.copc.laz	https://noaa-nos-coastal-lidar-pds.s3.amazonaw...	POLYGON ((-83.83 29.81819, -83.83013 29.8227, ...
2	20221114_226000e_3302500n.copc.laz	https://noaa-nos-coastal-lidar-pds.s3.amazonaw...	POLYGON ((-83.83013 29.8227, -83.83026 29.8272...

m = gf_noaa.explore()
gf_noaa_lpc_tiles.explore(m=m, color="red")

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

G-LiHT

Search

aoi = gpd.read_file(
    "https://raw.githubusercontent.com/unitedstates/districts/refs/heads/gh-pages/states/MD/shape.geojson"
)
aoi = aoi.simplify(0.01)
aoi.explore()

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

gf_gliht = coincident.search.search(
    dataset="gliht",
    intersects=aoi,
    datetime=["2017-07-31"],
)

gf_gliht = gf_gliht.iloc[[0]]

gf_gliht

	assets	bbox	collection	geometry	id	links	stac_extensions	stac_version	type	datetime	end_datetime	start_datetime	storage:schemes	dayofyear
0	{'001/GLDSMT_SERC_CalTarps_31July2017_am_l0s0/...	{'xmin': -76.5520244, 'ymin': 38.8724263, 'xma...	GLDSMT_001	POLYGON ((-76.55112 38.87243, -76.54378 38.872...	GLDSMT_SERC_CalTarps_31July2017_am_l0s0	[{'href': 'https://cmr.earthdata.nasa.gov/stac...	[https://stac-extensions.github.io/storage/v2....	1.1.0	Feature	2017-07-31 04:00:00+00:00	2017-08-01 03:59:59+00:00	2017-07-31 04:00:00+00:00	{'aws': {'bucket': 'lp-prod-protected', 'platf...	212

Now, let’s create a small areal subset of the data as we have for the other examples

c = gf_gliht.geometry.centroid
mini_aoi = gpd.GeoDataFrame(
    geometry=[box(c.x - 0.0045, c.y - 0.0045, c.x + 0.0045, c.y + 0.0045)],
    crs="EPSG:4326",
)

/tmp/ipykernel_1139/1428141286.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  c = gf_gliht.geometry.centroid
/home/docs/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/.pixi/envs/dev/lib/python3.12/site-packages/shapely/geometry/polygon.py:91: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead
  return [float(c) for c in o]

m = gf_gliht.explore()
mini_aoi.explore(color="black", m=m)

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

As seen in the Additional Aerial LiDAR Datasets example, NASA G-LiHT has many different provided gridded datasets. The following collections below are the current datasets supported by coincident.

    - 'ortho': Orthorectified aerial imagery
    - 'chm': Canopy height model
    - 'dsm': Digital surface model
    - 'dtm': Digital terrain model
    - 'hyperspectral_ancillary': Ancillary HSI data
    - 'radiance': Hyperspectral aradiance
    - 'reflectance': Hyperspectral surface reflectance
    - 'hyperspectral_vegetation': HSI-derived veg indices

Note

Not all G-LiHT flights will contain every single product listed. For example, a flight may have ‘dsm’ data but not ‘ortho’ data.

# we'll need the dataset id from our initial search
gf_gliht.id.item()

'GLDSMT_SERC_CalTarps_31July2017_am_l0s0'

Important

Unlike the G-LiHT search, you will need NASA Earthdata credentials to read in and download the gridded datasets from G-LiHT. You can register for a free account here: https://urs.earthdata.nasa.gov/users/new

and set:

%%time
da_chm = coincident.io.xarray.load_gliht_raster(
    aoi=mini_aoi,
    dataset_id=gf_gliht.id.item(),
    product="chm",
)

---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
File <timed exec>:1

File ~/checkouts/readthedocs.org/user_builds/coincident/checkouts/104/src/coincident/io/xarray.py:734, in load_gliht_raster(aoi, dataset_id, product)
    728 if not username or not password:
    729     msg_no_ed_cres = """
    730     EARTHDATA_USER and EARTHDATA_PASS must be set in the environment.
    731     os.environ["EARTHDATA_USER"] = "username"
    732     os.environ["EARTHDATA_PASS"] ="password"
    733     """
--> 734     raise OSError(msg_no_ed_cres)
    736 # Use the href from the first found data asset to create the grid template
    737 first_href = data_assets[asset_keys_to_load[0]]["href"]

OSError: 
        EARTHDATA_USER and EARTHDATA_PASS must be set in the environment.
        os.environ["EARTHDATA_USER"] = "username"
        os.environ["EARTHDATA_PASS"] ="password"
        

da_chm

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[65], line 1
----> 1 da_chm

NameError: name 'da_chm' is not defined

da_chm.plot.imshow(cmap="Greens");

Download

Note

Since there are so many gridded products for G-LiHT, you can specify multiple products to download in one call. This download will automatically skip any products that aren’t available for the specific flight among the current datasets supported by coincident

coincident.io.download.download_gliht_raster(
    aoi=mini_aoi,
    dataset_id=gf_gliht.id.item(),
    products=["chm", "dsm", "dtm"],
    output_dir="/tmp",
)