datasets/naip/naip-example.ipynb

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  {
   "cell_type": "markdown",
   "id": "d87f2c82",
   "metadata": {},
   "source": [
    "## Accessing NAIP data with the Planetary Computer STAC API\n",
    "\n",
    "The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) provides US-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR).  NAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) within the [US Department of Agriculture](https://www.usda.gov/) (USDA).  Data are captured at least once every three years for each state.  This dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n",
    "\n",
    "This notebook demonstrates the use of the Planetary Computer STAC API to query for NAIP imagery."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e86824ea",
   "metadata": {},
   "source": [
    "### Environment setup\n",
    "\n",
    "This notebook works with or without an API key, but you will be given more permissive access to the data with an API key.\n",
    "The [Planetary Computer Hub](https://planetarycomputer.microsoft.com/compute) is pre-configured to use your API key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0062a353",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pystac_client\n",
    "import planetary_computer\n",
    "\n",
    "# Set the environment variable PC_SDK_SUBSCRIPTION_KEY, or set it here.\n",
    "# The Hub sets PC_SDK_SUBSCRIPTION_KEY automatically.\n",
    "# pc.settings.set_subscription_key(<YOUR API Key>)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97edd22e-7886-4734-9fb5-7ec4038cd164",
   "metadata": {},
   "source": [
    "### Data access\n",
    "\n",
    "The datasets hosted by the Planetary Computer are available from [Azure Blob Storage](https://docs.microsoft.com/en-us/azure/storage/blobs/). We'll use [pystac-client](https://pystac-client.readthedocs.io/) to search the Planetary Computer's [STAC API](https://planetarycomputer.microsoft.com/api/stac/v1/docs) for the subset of the data that we care about, and then we'll load the data directly from Azure Blob Storage. We'll specify a `modifier` so that we can access the data stored in the Planetary Computer's private Blob Storage Containers. See [Reading from the STAC API](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/) and [Using tokens for data access](https://planetarycomputer.microsoft.com/docs/concepts/sas/) for more."
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2a343e0d-9931-4f38-922b-d2d8e8cc8645",
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
    "    modifier=planetary_computer.sign_inplace,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47354901",
   "metadata": {},
   "source": [
    "### Choose our region and times of interest\n",
    "\n",
    "This area is in South Jordan, Utah, which the Internet says is one of the fastest-growing cities in the US.  Let's see whether we can see some development in this area in the time spanned by our NAIP collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6deaf8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "area_of_interest = {\n",
    "    \"type\": \"Polygon\",\n",
    "    \"coordinates\": [\n",
    "        [\n",
    "            [-111.9839859008789, 40.5389819819361],\n",
    "            [-111.90502166748045, 40.5389819819361],\n",
    "            [-111.90502166748045, 40.57015381856105],\n",
    "            [-111.9839859008789, 40.57015381856105],\n",
    "            [-111.9839859008789, 40.5389819819361],\n",
    "        ]\n",
    "    ],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a38a21c",
   "metadata": {},
   "source": [
    "This collection includes data from 2010, so we'll search for one image near the beginning of that range and one from 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "051121fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "range_old = \"2010-01-01/2013-01-01\"\n",
    "range_new = \"2018-01-01/2021-01-01\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "584b2976",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 Items found in the 'old' range\n",
      "4 Items found in the 'new' range\n"
     ]
    }
   ],
   "source": [
    "search_old = catalog.search(\n",
    "    collections=[\"naip\"], intersects=area_of_interest, datetime=range_old\n",
    ")\n",
    "\n",
    "search_new = catalog.search(\n",
    "    collections=[\"naip\"], intersects=area_of_interest, datetime=range_new\n",
    ")\n",
    "\n",
    "items_old = search_old.item_collection()\n",
    "items_new = search_new.item_collection()\n",
    "\n",
    "print(f\"{len(items_old)} Items found in the 'old' range\")\n",
    "print(f\"{len(items_new)} Items found in the 'new' range\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb22cc7",
   "metadata": {},
   "source": [
    "As seen above, there are multiple items that intersect our area of interest for each year. The following code will choose the item that has the most overlap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9bd9f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.geometry import shape\n",
    "\n",
    "area_shape = shape(area_of_interest)\n",
    "target_area = area_shape.area\n",
    "\n",
    "\n",
    "def area_of_overlap(item):\n",
    "    overlap_area = shape(item.geometry).intersection(shape(area_of_interest)).area\n",
    "    return overlap_area / target_area\n",
    "\n",
    "\n",
    "item_old = sorted(items_old, key=area_of_overlap, reverse=True)[0]\n",
    "item_new = sorted(items_new, key=area_of_overlap, reverse=True)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bab7193",
   "metadata": {},
   "source": [
    "### Render images\n",
    "\n",
    "Each Item has a `rendered_preview` which uses the Planetary Computer's Data API to dynamically create a preview image from the raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c46a5a4-bdbf-401d-bb4d-f89146a19fa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=naip&item=ut_m_4011125_sw_12_1_20110720_20111011&assets=image&asset_bidx=image%7C1%2C2%2C3\"/>"
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(url=item_old.assets[\"rendered_preview\"].href)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a8864522-0c93-42f8-bc22-2af72b29555b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=naip&item=ut_m_4011125_sw_12_060_20180828_20181209&assets=image&asset_bidx=image%7C1%2C2%2C3\"/>"
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     "execution_count": 8,
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    "Image(url=item_new.assets[\"rendered_preview\"].href)"
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  {
   "cell_type": "markdown",
   "id": "22ec75f7-ec8c-4660-8421-d88ec14800c6",
   "metadata": {},
   "source": [
    "To read the raw Cloud Optimized GeoTIFF, use a library like [rioxarray](https://corteva.github.io/rioxarray/html/rioxarray.html) or [rasterio](https://rasterio.readthedocs.io/) and the `image` asset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0ab9b2d6",
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 3, y: 7630, x: 6000)&gt;\n",
       "[137340000 values with dtype=uint8]\n",
       "Coordinates:\n",
       "  * band         (band) int64 1 2 3\n",
       "  * x            (x) float64 4.15e+05 4.15e+05 4.15e+05 ... 4.209e+05 4.209e+05\n",
       "  * y            (y) float64 4.491e+06 4.491e+06 ... 4.483e+06 4.483e+06\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area\n",
       "    scale_factor:   1.0\n",
       "    add_offset:     0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 3</li><li><span class='xr-has-index'>y</span>: 7630</li><li><span class='xr-has-index'>x</span>: 6000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-014d4e05-3284-49ea-a1ee-a6dc31ffa99d' class='xr-array-in' type='checkbox' checked><label for='section-014d4e05-3284-49ea-a1ee-a6dc31ffa99d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[137340000 values with dtype=uint8]</pre></div></div></li><li class='xr-section-item'><input id='section-b95668c4-2625-4650-8806-3d50173111ba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b95668c4-2625-4650-8806-3d50173111ba' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-26fc05c3-ed11-440b-9b83-f0c5693e89b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26fc05c3-ed11-440b-9b83-f0c5693e89b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0ff7f53-5c5a-48ad-9b9f-a5dea015073c' class='xr-var-data-in' type='checkbox'><label for='data-f0ff7f53-5c5a-48ad-9b9f-a5dea015073c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.15e+05 4.15e+05 ... 4.209e+05</div><input id='attrs-68409714-32a2-46d0-a44a-824e6a5cd044' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68409714-32a2-46d0-a44a-824e6a5cd044' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-126212dc-f91e-45c9-8e59-cc6410185014' class='xr-var-data-in' type='checkbox'><label for='data-126212dc-f91e-45c9-8e59-cc6410185014' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([414950.5, 414951.5, 414952.5, ..., 420947.5, 420948.5, 420949.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.491e+06 4.491e+06 ... 4.483e+06</div><input id='attrs-661bf237-fe4e-4ca7-b3b5-c19e7a289487' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-661bf237-fe4e-4ca7-b3b5-c19e7a289487' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d9abd14-edc1-49d6-8093-2f4bea131a76' class='xr-var-data-in' type='checkbox'><label for='data-3d9abd14-edc1-49d6-8093-2f4bea131a76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([4490989.5, 4490988.5, 4490987.5, ..., 4483362.5, 4483361.5, 4483360.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-4bb05185-c835-42a2-a646-6d41586fcf6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bb05185-c835-42a2-a646-6d41586fcf6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7c7c00e-982c-4bcd-8eee-0054613faf6d' class='xr-var-data-in' type='checkbox'><label for='data-a7c7c00e-982c-4bcd-8eee-0054613faf6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;NAD83 / UTM zone 12N&quot;,GEOGCS[&quot;NAD83&quot;,DATUM[&quot;North_American_Datum_1983&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6269&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4269&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-111],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;26912&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314140356</dd><dt><span>inverse_flattening :</span></dt><dd>298.257222101</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>GRS 1980</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>NAD83</dd><dt><span>horizontal_datum_name :</span></dt><dd>North American Datum 1983</dd><dt><span>projected_crs_name :</span></dt><dd>NAD83 / UTM zone 12N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-111.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;NAD83 / UTM zone 12N&quot;,GEOGCS[&quot;NAD83&quot;,DATUM[&quot;North_American_Datum_1983&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6269&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4269&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-111],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;26912&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>414950.0 1.0 0.0 4490990.0 0.0 -1.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-338b2f45-af16-4ca3-818c-58271620ee45' class='xr-section-summary-in' type='checkbox'  checked><label for='section-338b2f45-af16-4ca3-818c-58271620ee45' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (band: 3, y: 7630, x: 6000)>\n",
       "[137340000 values with dtype=uint8]\n",
       "Coordinates:\n",
       "  * band         (band) int64 1 2 3\n",
       "  * x            (x) float64 4.15e+05 4.15e+05 4.15e+05 ... 4.209e+05 4.209e+05\n",
       "  * y            (y) float64 4.491e+06 4.491e+06 ... 4.483e+06 4.483e+06\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area\n",
       "    scale_factor:   1.0\n",
       "    add_offset:     0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import rioxarray\n",
    "\n",
    "ds = rioxarray.open_rasterio(item_old.assets[\"image\"].href).sel(band=[1, 2, 3])\n",
    "ds"
   ]
  }
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