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Incorporate NOAA and NCALM to search (#37)
* removed (commented-out) CONUS/AK search constraint * removed CONUS/AK constraint (cryocloud dev instead of local) * updated CONUS/AK (not sure why my branch didn't have this already) and win-64 compat * WSL build complete * Deleted CONUS/AK test and box import * added shapely make_valid to cascading_search * Return rasters for cop30 and ESA search * Added ODC dependency and fixed formatting * Added test for .io.xarray and synced search main.py * Cleaned test_xarray and added aoi, large_aoi to init * test_xarray matplotlib import inside test func * removed depends_on_optional from xarray test * Implemented a messy OpenTopo search and test for NOAA and NCALM * Updated opentopo search to skip metadata with missing temporal info and accept arguments from _validate_temporal_bounds * Added opentopo example notebook for NOAA/NCALM search * Added messy NEON search * Added NEON to opentopo example nb and renamed * Addressed comments in PR #37 (75c19843f61d524d9257b8cad60a33ae5baff7eee6a9fa7b89a0ab5971db9357) * Added LVIS Available on STAC * ignore requests type, address pylint --------- Co-authored-by: Scott Henderson <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,282 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Additional Aerial LiDAR Datasets\n", | ||
| "\n", | ||
| "This notebook will highlight the additional aerial LiDAR datasets besides USGS 3dep that are available in the `coincident` package and how to grab those datasets' footprints using spatial and temporal search parameters.\n", | ||
| "\n", | ||
| "Other Supported Catalogs:\n", | ||
| "- NOAA Coastal LiDAR\n", | ||
| "- NCALM LiDAR\n", | ||
| "- NEON LiDAR" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import coincident\n", | ||
| "import geopandas as gpd" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## OpenTopography API\n", | ||
| "\n", | ||
| "`coincident` supports the use of the [OpenTopo /otCatalog API](https://portal.opentopography.org/apidocs/) to access additional aerial LiDAR data.\n", | ||
| "\n", | ||
| "\n", | ||
| "opentopo datasets currently supported includes the [NOAA Coastal LiDAR Catalog](https://coast.noaa.gov/htdata/lidar1_z/) and [NCALM Aerial LiDAR Catalog](https://calm.geo.berkeley.edu/ncalm/dtc.html). \n", | ||
| "\n", | ||
| "```{note}\n", | ||
| "The NCALM Aerial LiDAR Catalog also includes user-submitted flights\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# first, we'll search for NOAA Coastal LiDAR missions in Washington\n", | ||
| "# we'll inspect 2018 arbitrarily\n", | ||
| "aoi = gpd.read_file(\n", | ||
| " \"https://raw.githubusercontent.com/unitedstates/districts/refs/heads/gh-pages/states/WA/shape.geojson\"\n", | ||
| ")\n", | ||
| "date = \"2018\"\n", | ||
| "aoi.plot();" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "gf_noaa = coincident.search.search(dataset=\"noaa\", intersects=aoi, datetime=[date])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "gf_noaa" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "gf_noaa.explore(column=\"title\", cmap=\"Set1\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# now, let's see if there were any NCALM missions from the same year\n", | ||
| "gf_ncalm = coincident.search.search(dataset=\"ncalm\", intersects=aoi, datetime=[date])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "gf_ncalm" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "gf_ncalm.explore(column=\"id\", cmap=\"Set3\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## NEON API\n", | ||
| "\n", | ||
| "We also support the use of the [NEON Python API](https://www.neonscience.org/resources/learning-hub/tutorials/neon-api-intro-requests-py).\n", | ||
| "\n", | ||
| "```{warning}\n", | ||
| "Searching large areas and/or large time periods in the NEON catalog will take a long time due to the limited spatiotemporal search supported by the API.\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%%time\n", | ||
| "gf_neon = coincident.search.search(dataset=\"neon\", intersects=aoi, datetime=[date])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "gf_neon" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "m = gf_noaa.explore(color=\"blue\")\n", | ||
| "gf_ncalm.explore(m=m, color=\"black\")\n", | ||
| "gf_neon.explore(m=m, color=\"deeppink\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## NASA LVIS\n", | ||
| "\n", | ||
| "Currently, only a select number of LVIS flghts are supported due to the limited amount of LVIS products hosted on STAC catalogs:\n", | ||
| "- [ABoVE LVIS L2 Geolocated Surface Elevation Product, Version 1](https://nsidc.org/data/ablvis2/versions/1)\n", | ||
| " - Search extent limited to bbox [-158, 48, -104, 72] and 2017-06-29 to 2017-07-17\n", | ||
| "- [AfriSAR LVIS L2 Geolocated Surface Elevation Product, Version 1](https://nsidc.org/data/aflvis2/versions/1)\n", | ||
| " - Search extent limited to bbox [8, -2, 12, 1] and 2016-02-20 to 2016-03-08\n", | ||
| "\n", | ||
| "```{warning}\n", | ||
| "Searching highly complex multi-polygons might break the STAC search, so simplifying your polygon before searching is recommended.\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 12, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# first, let's look at the ABoVE product in Canada\n", | ||
| "aoi = gpd.read_file(\n", | ||
| " \"https://gist.githubusercontent.com/M1r1k/d5731bf39e1dfda5b53b4e4c560d968d/raw/c774258085ddc11776591ce95f2240d0fd0657a2/canada_provinces.geo.json\"\n", | ||
| ")\n", | ||
| "aoi = aoi[aoi[\"name\"] == \"Yukon\"].reset_index(drop=True)\n", | ||
| "# reduce complexity of multipolygon input\n", | ||
| "aoi.geometry = aoi.geometry.convex_hull" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 13, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "gf_lvis_ab = coincident.search.search(dataset=\"ablvis2_1\", intersects=aoi)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "print(gf_lvis_ab.shape)\n", | ||
| "gf_lvis_ab.head(2)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "m = aoi.explore(color=\"gray\")\n", | ||
| "gf_lvis_ab.clip(aoi).explore(m=m, column=\"datetime\", cmap=\"inferno\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 16, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# now, let's look at AfriSAR LiDAR\n", | ||
| "# this polygon is already simplified\n", | ||
| "aoi = gpd.read_file(\n", | ||
| " \"https://raw.githubusercontent.com/glynnbird/countriesgeojson/refs/heads/master/gabon.geojson\"\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 17, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "gf_lvis_af = coincident.search.search(\n", | ||
| " dataset=\"aflvis2_1\", intersects=aoi, datetime=[\"2016-02-20\"]\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "print(gf_lvis_af.shape)\n", | ||
| "gf_lvis_af.head(2)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "gf_lvis_af.explore(column=\"datetime\", cmap=\"inferno\")" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "dev", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.12.8" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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|
@@ -10,4 +10,5 @@ quickstart | |
| cascading_search | ||
| sliderule | ||
| contextual_data | ||
| additional_lidar | ||
| ``` | ||
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