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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "import pystac_client\n",
+ "import planetary_computer\n",
+ "import odc.stac\n",
+ "import geopandas as gpd\n",
+ "import dask.distributed\n",
+ "import matplotlib.pyplot as plt\n",
+ "import rioxarray\n",
+ "from datetime import datetime, timedelta\n",
+ "\n",
+ "\n",
+ "\n",
+ "nps = gpd.read_file(\"/vsicurl/https://huggingface.co/datasets/cboettig/biodiversity/resolve/main/data/NPS.gdb\")\n",
+ "calfire = gpd.read_file(\"/vsicurl/https://huggingface.co/datasets/cboettig/biodiversity/resolve/main/data/fire22_1.gdb\", layer = \"firep22_1\")\n",
+ "# fire = gpd.read_file(\"/vsizip/vsicurl/https://edcintl.cr.usgs.gov/downloads/sciweb1/shared/MTBS_Fire/data/composite_data/burned_area_extent_shapefile/mtbs_perimeter_data.zip\"\n",
+ "\n",
+ "# extract and reproject the Joshua Tree NP Polygon\n",
+ "jtree = nps[nps.PARKNAME == \"Joshua Tree\"].to_crs(calfire.crs)\n",
+ "\n",
+ "# All Fires in the DB that intersect the Park\n",
+ "jtree_fires = jtree.overlay(calfire, how=\"intersection\")\n",
+ "\n",
+ "# Extract a polygon if interest. > 2015 for Sentinel, otherwise we can use LandSat\n",
+ "recent = jtree_fires[jtree_fires.YEAR_ > \"2015\"]\n",
+ "big = recent[recent.Shape_Area == recent.Shape_Area.max()].to_crs(\"EPSG:4326\")\n",
+ "\n",
+ "# Get bounding box + dates before & after fire for STAC search\n",
+ "box = big.buffer(0.01).bounds.to_numpy()[0] # Fire bbox + buffer\n",
+ "alarm_date = datetime.strptime(big.ALARM_DATE.item(), \"%Y-%m-%dT%H:%M:%S+00:00\") \n",
+ "before_date = alarm_date - timedelta(days=14)\n",
+ "after_date = alarm_date + timedelta(days=14)\n",
+ "search_dates = before_date.strftime(\"%Y-%m-%d\") + \"/\" + after_date.strftime(\"%Y-%m-%d\")\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "def stac_search(box, datetime): \n",
+ " # STAC Search for this imagery in space/time window\n",
+ " items = (\n",
+ " pystac_client.Client.\n",
+ " open(\"https://cmr.earthdata.nasa.gov/stac/LPCLOUD\").\n",
+ " search(collections=[\"HLSL30.v2.0\"],\n",
+ " bbox=box,\n",
+ " datetime=datetime).\n",
+ " item_collection())\n",
+ " return items\n",
+ "\n",
+ "def compute_ndvi(items, box):\n",
+ " # Time to compute:\n",
+ " client = dask.distributed.Client()\n",
+ " # landsat_bands = [\"nir08\", \"swir16\"]\n",
+ " sentinel_bands = [\"B05\", \"B04\", \"Fmask\"] # NIR, SWIR, and Cloud Mask\n",
+ "\n",
+ " # The magic of gdalwarper. Can also resample, reproject, and aggregate on the fly\n",
+ " data = odc.stac.load(items,\n",
+ " bands=sentinel_bands,\n",
+ " bbox=box,\n",
+ " crs=\"EPSG:32611\"\n",
+ " )\n",
+ "\n",
+ " red = data[\"B04\"].astype(\"float\")\n",
+ " nir = data[\"B05\"].astype(\"float\")\n",
+ "\n",
+ " # can resample and aggregate in xarray. compute with dask\n",
+ " ndvi = ((nir - red) / (nir + red)).compute()\n",
+ " \n",
+ " return ndvi\n"
+ ]
+ },
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+ " \n",
+ " B04 \n",
+ " \n",
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+ " \n",
+ " \n",
+ " \n",
+ " href \n",
+ " \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T11SNT.2022160T181605.v2.0/HLS.L30.T11SNT.2022160T181605.v2.0.B04.tif\" \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " title \n",
+ " \"Download HLS.L30.T11SNT.2022160T181605.v2.0.B04.tif\" \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
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+ " \n",
+ " \n",
+ " VAA \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " href \n",
+ " \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T11SNT.2022160T181605.v2.0/HLS.L30.T11SNT.2022160T181605.v2.0.VAA.tif\" \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " title \n",
+ " \"Download HLS.L30.T11SNT.2022160T181605.v2.0.VAA.tif\" \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " SZA \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " href \n",
+ " \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T11SNT.2022160T181605.v2.0/HLS.L30.T11SNT.2022160T181605.v2.0.SZA.tif\" \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " title \n",
+ " \"Download HLS.L30.T11SNT.2022160T181605.v2.0.SZA.tif\" \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " \n",
+ " B10 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " href \n",
+ " \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T11SNT.2022160T181605.v2.0/HLS.L30.T11SNT.2022160T181605.v2.0.B10.tif\" \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " title \n",
+ " \"Download HLS.L30.T11SNT.2022160T181605.v2.0.B10.tif\" \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
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+ " \n",
+ " SAA \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " href \n",
+ " \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T11SNT.2022160T181605.v2.0/HLS.L30.T11SNT.2022160T181605.v2.0.SAA.tif\" \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " title \n",
+ " \"Download HLS.L30.T11SNT.2022160T181605.v2.0.SAA.tif\" \n",
+ " \n",
+ " \n",
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+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " B03 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " href \n",
+ " \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T11SNT.2022160T181605.v2.0/HLS.L30.T11SNT.2022160T181605.v2.0.B03.tif\" \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " title \n",
+ " \"Download HLS.L30.T11SNT.2022160T181605.v2.0.B03.tif\" \n",
+ " \n",
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+ " \n",
+ " B07 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " href \n",
+ " \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T11SNT.2022160T181605.v2.0/HLS.L30.T11SNT.2022160T181605.v2.0.B07.tif\" \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " title \n",
+ " \"Download HLS.L30.T11SNT.2022160T181605.v2.0.B07.tif\" \n",
+ " \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " \n",
+ " B01 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " href \n",
+ " \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T11SNT.2022160T181605.v2.0/HLS.L30.T11SNT.2022160T181605.v2.0.B01.tif\" \n",
+ " \n",
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+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " title \n",
+ " \"Download HLS.L30.T11SNT.2022160T181605.v2.0.B01.tif\" \n",
+ " \n",
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+ " \n",
+ " \n",
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+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " B05 \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " href \n",
+ " \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T11SNT.2022160T181605.v2.0/HLS.L30.T11SNT.2022160T181605.v2.0.B05.tif\" \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " title \n",
+ " \"Download HLS.L30.T11SNT.2022160T181605.v2.0.B05.tif\" \n",
+ " \n",
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+ " \n",
+ " \n",
+ " B09 \n",
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+ " \n",
+ " \n",
+ " href \n",
+ " \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T11SNT.2022160T181605.v2.0/HLS.L30.T11SNT.2022160T181605.v2.0.B09.tif\" \n",
+ " \n",
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+ " \n",
+ " \n",
+ " title \n",
+ " \"Download HLS.L30.T11SNT.2022160T181605.v2.0.B09.tif\" \n",
+ " \n",
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+ " \n",
+ " browse \n",
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+ " \n",
+ " \n",
+ " href \n",
+ " \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/HLSL30.020/HLS.L30.T11SNT.2022160T181605.v2.0/HLS.L30.T11SNT.2022160T181605.v2.0.jpg\" \n",
+ " \n",
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+ " \n",
+ " \n",
+ " type \n",
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+ " \n",
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+ " \n",
+ " \n",
+ " title \n",
+ " \"Download HLS.L30.T11SNT.2022160T181605.v2.0.jpg\" \n",
+ " \n",
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+ " \n",
+ " metadata \n",
+ " \n",
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+ " \n",
+ " \n",
+ " href \n",
+ " \"https://cmr.earthdata.nasa.gov/search/concepts/G2289704610-LPCLOUD.xml\" \n",
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+ " \n",
+ " bbox \n",
+ " [] 4 items \n",
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+ " \n",
+ " 0 \n",
+ " -116.612659 \n",
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+ " \n",
+ " \n",
+ " stac_extensions \n",
+ " [] 1 items \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " \"https://stac-extensions.github.io/eo/v1.0.0/schema.json\" \n",
+ " \n",
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+ " \n",
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+ " \"HLSL30.v2.0\" \n",
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+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "items = stac_search(box, search_dates)\n",
+ "items"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "href = \"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T11SNT.2022135T182205.v2.0/HLS.L30.T11SNT.2022135T182205.v2.0.B04.tif\"\n",
+ "import rioxarray\n",
+ "import xarray\n",
+ "import rasterio\n",
+ "import os\n",
+ "from pathlib import Path\n",
+ "cookies = os.path.expanduser(\"~/.urs_cookies\")\n",
+ "Path(cookies).touch()\n",
+ "\n",
+ "with rasterio.Env(GDAL_HTTP_COOKIEFILE=cookies, \n",
+ " GDAL_HTTP_COOKIEJAR=cookies, \n",
+ " GDAL_HTTP_NETRC=True):\n",
+ " x = xarray.open_dataset(href)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\n",
+ "Name: WGS 84\n",
+ "Axis Info [ellipsoidal]:\n",
+ "- Lat[north]: Geodetic latitude (degree)\n",
+ "- Lon[east]: Geodetic longitude (degree)\n",
+ "Area of Use:\n",
+ "- name: World.\n",
+ "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
+ "Datum: World Geodetic System 1984 ensemble\n",
+ "- Ellipsoid: WGS 84\n",
+ "- Prime Meridian: Greenwich"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "big.crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.11/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n",
+ "Perhaps you already have a cluster running?\n",
+ "Hosting the HTTP server on port 42151 instead\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "client = dask.distributed.Client()\n",
+ "# landsat_bands = [\"nir08\", \"swir16\"]\n",
+ "sentinel_bands = [\"B05\", \"B04\"] # NIR, SWIR, and Cloud Mask\n",
+ "\n",
+ "\n",
+ "with rasterio.Env(GDAL_HTTP_COOKIEFILE=cookies, \n",
+ " GDAL_HTTP_COOKIEJAR=cookies, \n",
+ " GDAL_HTTP_NETRC=True):\n",
+ " # The magic of gdalwarper. Can also resample, reproject, and aggregate on the fly\n",
+ " data = odc.stac.load(items,\n",
+ " bands=sentinel_bands,\n",
+ " geopolygon=big,\n",
+ " resolution = 30\n",
+ " )\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
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+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "
<xarray.DataArray 'B04' (time: 7, latitude: 1, longitude: 1)>\n",
+ "array([[[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]]], dtype=float32)\n",
+ "Coordinates:\n",
+ " * latitude (latitude) float64 45.0\n",
+ " * longitude (longitude) float64 -105.0\n",
+ " spatial_ref int32 4326\n",
+ " * time (time) datetime64[ns] 2022-05-15T18:22:05.159000 ... 2022-06... nan nan nan nan nan nan nan
array([[[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]]], dtype=float32) Coordinates: (4)
Indexes: (3)
PandasIndex
PandasIndex(Index([45.0], dtype='float64', name='latitude')) PandasIndex
PandasIndex(Index([-105.0], dtype='float64', name='longitude')) PandasIndex
PandasIndex(DatetimeIndex(['2022-05-15 18:22:05.159000', '2022-05-16 18:15:32.443000',\n",
+ " '2022-05-23 18:21:39.848000', '2022-05-31 18:22:08.503000',\n",
+ " '2022-06-01 18:15:30.038000', '2022-06-08 18:21:41.323000',\n",
+ " '2022-06-09 18:16:05.238000'],\n",
+ " dtype='datetime64[ns]', name='time', freq=None)) Attributes: (0)
"
+ ],
+ "text/plain": [
+ "\n",
+ "array([[[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]],\n",
+ "\n",
+ " [[nan]]], dtype=float32)\n",
+ "Coordinates:\n",
+ " * latitude (latitude) float64 45.0\n",
+ " * longitude (longitude) float64 -105.0\n",
+ " spatial_ref int32 4326\n",
+ " * time (time) datetime64[ns] 2022-05-15T18:22:05.159000 ... 2022-06..."
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.B04"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "red = data[\"B04\"].astype(\"float\")\n",
+ "nir = data[\"B05\"].astype(\"float\")\n",
+ "\n",
+ "# can resample and aggregate in xarray. compute with dask\n",
+ "ndvi = ((nir - red) / (nir + red)).compute()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ndvi.isel(time=0).plot.imshow(cmap=\"viridis\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# write first and last date to tif\n",
+ "ndvi.isel(time=0).rio.to_raster(raster_path=\"before.tif\", driver=\"COG\")\n",
+ "ndvi.isel(time=(ndvi.time.size-1)).rio.to_raster(raster_path=\"after.tif\", driver=\"COG\")\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "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": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}