datasets/hgb/hgb-example.ipynb

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    "## Accessing Harmonized Global Biomass data with the Planetary Computer STAC API\n",
    "\n",
    "The Harmonized Global Biomass (HGB) dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution.\n",
    "\n",
    "This notebook provides an example of accessing HGB data using the Planetary Computer STAC API, inspecting the data assets in the catalog, and doing some simple processing and plotting of the data from the Cloud Optimized GeoTIFF source."
   ]
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   "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. The Planetary Computer Hub is pre-configured to use your API key."
   ]
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pystac_client\n",
    "import planetary_computer\n",
    "import rioxarray\n",
    "import dask.distributed"
   ]
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   "cell_type": "markdown",
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   "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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   "execution_count": 2,
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     "text": [
      "Returned 1 Items\n"
     ]
    }
   ],
   "source": [
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
    "    modifier=planetary_computer.sign_inplace,\n",
    ")\n",
    "\n",
    "items = list(catalog.get_collection(\"hgb\").get_all_items())\n",
    "print(f\"Returned {len(items)} Items\")"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll see we only returned a single item for the entire collection. This is a bit different from other types of datasets on the Planetary Computer. Each of the assets are represented by a single global raster at a single temporal resolution.\n",
    "\n",
    "Let's see what assets are associated with this item:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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     "text": [
      "aboveground: Global above-ground biomass\n",
      "belowground: Global below-ground biomass\n",
      "aboveground_uncertainty: Uncertainty associated with global above-ground biomass\n",
      "belowground_uncertainty: Uncertainty associated with global below-ground biomass\n",
      "tilejson: TileJSON with default rendering\n",
      "rendered_preview: Rendered preview\n"
     ]
    }
   ],
   "source": [
    "item = items[0]\n",
    "print(*[f\"{key}: {asset.title}\" for key, asset in item.assets.items()], sep=\"\\n\")"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the variable of interest"
   ]
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   "cell_type": "code",
   "execution_count": 4,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (y: 52201, x: 129600)\n",
       "Coordinates:\n",
       "  * x            (x) float64 -180.0 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
       "  * y            (y) float64 84.0 84.0 83.99 83.99 ... -60.99 -61.0 -61.0 -61.0\n",
       "    spatial_ref  int64 0\n",
       "Data variables:\n",
       "    biomass      (y, x) uint16 dask.array&lt;chunksize=(2560, 2560), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    AREA_OR_POINT:       Area\n",
       "    DataType:            Generic\n",
       "    OVR_RESAMPLING_ALG:  NEAREST\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.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b16876f1-ea26-4183-9eaf-266f14cf0f8e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b16876f1-ea26-4183-9eaf-266f14cf0f8e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 52201</li><li><span class='xr-has-index'>x</span>: 129600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a90f5b24-3a80-4350-83d3-ea34e3921d53' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a90f5b24-3a80-4350-83d3-ea34e3921d53' class='xr-section-summary' >Coordinates: <span>(3)</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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -180.0 ... 180.0 180.0</div><input id='attrs-81418aa2-2336-44ef-a043-1991d9c952ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81418aa2-2336-44ef-a043-1991d9c952ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a03bbe58-ac27-40a1-b4ac-b5f5fe88875b' class='xr-var-data-in' type='checkbox'><label for='data-a03bbe58-ac27-40a1-b4ac-b5f5fe88875b' 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([-179.998611, -179.995833, -179.993056, ...,  179.993056,  179.995833,\n",
       "        179.998611])</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'>84.0 84.0 83.99 ... -61.0 -61.0</div><input id='attrs-f9107ed2-2055-4344-a9a5-88240e73ca2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9107ed2-2055-4344-a9a5-88240e73ca2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf32a8e2-54e3-4a04-859f-d293a4fd3ef5' class='xr-var-data-in' type='checkbox'><label for='data-cf32a8e2-54e3-4a04-859f-d293a4fd3ef5' 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([ 83.998611,  83.995833,  83.993056, ..., -60.995833, -60.998611,\n",
       "       -61.001389])</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-420743f3-404d-4222-9e20-06bd20f4d135' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-420743f3-404d-4222-9e20-06bd20f4d135' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-579248aa-ce83-4360-8313-daf67dfd5e48' class='xr-var-data-in' type='checkbox'><label for='data-579248aa-ce83-4360-8313-daf67dfd5e48' 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>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&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;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</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>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&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;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>-180.00000000000006 0.00277777777777778 0.0 84.00000000000001 0.0 -0.00277777777777778</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-8078d00a-b066-473b-8d8a-660edb7ad193' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8078d00a-b066-473b-8d8a-660edb7ad193' class='xr-section-summary' >Data variables: <span>(1)</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>biomass</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2560, 2560), meta=np.ndarray&gt;</div><input id='attrs-b505cd37-b1ff-4e38-9b96-2c5f32e55838' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b505cd37-b1ff-4e38-9b96-2c5f32e55838' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee9feb1a-8aa2-4634-a20a-b4442f04b638' class='xr-var-data-in' type='checkbox'><label for='data-ee9feb1a-8aa2-4634-a20a-b4442f04b638' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 12.60 GiB </td>\n",
       "                        <td> 12.50 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (52201, 129600) </td>\n",
       "                        <td> (2560, 2560) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 3 Graph Layers </td>\n",
       "                        <td> 1071 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> uint16 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fec1210f-3254-415b-bd47-e082712ac082' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fec1210f-3254-415b-bd47-e082712ac082' class='xr-section-summary' >Attributes: <span>(5)</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>DataType :</span></dt><dd>Generic</dd><dt><span>OVR_RESAMPLING_ALG :</span></dt><dd>NEAREST</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.Dataset>\n",
       "Dimensions:      (y: 52201, x: 129600)\n",
       "Coordinates:\n",
       "  * x            (x) float64 -180.0 -180.0 -180.0 -180.0 ... 180.0 180.0 180.0\n",
       "  * y            (y) float64 84.0 84.0 83.99 83.99 ... -60.99 -61.0 -61.0 -61.0\n",
       "    spatial_ref  int64 0\n",
       "Data variables:\n",
       "    biomass      (y, x) uint16 dask.array<chunksize=(2560, 2560), meta=np.ndarray>\n",
       "Attributes:\n",
       "    AREA_OR_POINT:       Area\n",
       "    DataType:            Generic\n",
       "    OVR_RESAMPLING_ALG:  NEAREST\n",
       "    scale_factor:        1.0\n",
       "    add_offset:          0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da = rioxarray.open_rasterio(\n",
    "    item.assets[\"aboveground\"].href, chunks=dict(x=2560, y=2560)\n",
    ")\n",
    "\n",
    "# Transform our data array to a dataset by selecting the only data variable ('band')\n",
    "# renaming it to something useful ('biomass')\n",
    "ds = da.to_dataset(dim=\"band\").rename({1: \"biomass\"})\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot global aboveground biomass\n",
    "\n",
    "We'll be loading in a global 300m raster, so let's spin up a distributed Dask cluster to parallelize our reads and computation. You can use the dashboard link to monitor resources and progress on the cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/user/taugspurger@microsoft.com/proxy/8787/status\n"
     ]
    }
   ],
   "source": [
    "dask_client = dask.distributed.Client()\n",
    "print(dask_client.dashboard_link)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this global plot, it's ok to lose some detail in our rendering. First we'll downsample the entire dataset by a factor of 100 on each spatial dimension and drop drop any values at zero or below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.7 s, sys: 1.49 s, total: 18.2 s\n",
      "Wall time: 42.6 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(522, 1296)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "factor = 100\n",
    "coarse_biomass = (\n",
    "    ds.biomass.coarsen(dim={\"x\": factor, \"y\": factor}, boundary=\"trim\").mean().compute()\n",
    ")\n",
    "\n",
    "# Filter out nodata\n",
    "coarse_biomass = coarse_biomass.where(coarse_biomass > 0)\n",
    "coarse_biomass.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our dataset nicely reduced, we can plot the above ground biomass for the planet. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-hgb-hgb-example.ipynb/7.png\"/>"
      ],
      "text/plain": [
       "<Figure size 1296x522 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h, w = coarse_biomass.shape\n",
    "dpi = 100\n",
    "fig = plt.figure(frameon=False, figsize=(w / dpi, h / dpi), dpi=dpi)\n",
    "ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0])\n",
    "ax.set_axis_off()\n",
    "fig.add_axes(ax)\n",
    "coarse_biomass.plot(cmap=\"Greens\", add_colorbar=False)\n",
    "plt.show();"
   ]
  }
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