NOAA Coastal LiDAR

This notebook illustrates search, directly loading DEMs, and downloading DEMs and LPCs for local processing

Dataset	Alias	Type	Start	End	Extent	Source
NOAA Coastal LiDAR	noaa	LiDAR	1996-10-09		US Territories	NOAA

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

Search

We’ll search for data in Florida

aoi = gpd.read_file(
    "https://raw.githubusercontent.com/unitedstates/districts/refs/heads/gh-pages/states/FL/shape.geojson"
)
m = aoi.explore(color="black")

gf_noaa = coincident.search.search(
    dataset="noaa", intersects=aoi, datetime=["2022-10-27"]
)
print(f"Found {len(gf_noaa)} NOAA scenes")
gf_noaa

# Plot the entire footprint for this scene and a subset region
buffer_size = 0.02
centroid = gf_noaa.geometry.centroid[0]
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(color="black")
mini_aoi.explore(m=m, color="magenta")

Get Data

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}")

Stream

First we’ll show how to stream gridded DEMs directly into Xarray

Warning

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

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

da_noaa_dem

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

Download

Note

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

DEM

%%time
local_output_dir = "/tmp"

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

!ls -tlrh /tmp/clipped*tif

LPC Tiles

%%time

# Even smaller AOI
buffer_size = 0.008
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",
)

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

gf_noaa_lpc_tiles

m = mini_aoi.explore(color="black")
gf_noaa_lpc_tiles.explore(m=m, color="magenta")