{ "cells": [ { "cell_type": "markdown", "id": "a1433e0d", "metadata": {}, "source": [ "# Query and Stack Imagery into a GIF Using stackstac and Dask\n", "\n", "**Attribution**: This tutorial is developed based on the [example](https://stackstac.readthedocs.io/en/latest/examples/gif.html) provided in the stackstac documentation. \n", "\n", "We'll load all the [Landsat-8 (Collection 2, Level 2)](https://planetarycomputer.microsoft.com/dataset/landsat-8-c2-l2) data that's available from [Microsoft's Planetary Computer](https://planetarycomputer.microsoft.com/) over a small region on the coast of [Cape Cod](https://www.google.com/maps/place/Chatham,+MA/@41.7498076,-70.2026227,10.73z/data=!4m13!1m7!3m6!1s0x89fb15440149e94d:0x1f9c0efa001cb20b!2sCape+Cod!3b1!8m2!3d41.6687897!4d-70.2962408!3m4!1s0x89fb142168afbe53:0x714436ec7d485a53!8m2!3d41.6821432!4d-69.9597359), Massachusetts, USA.\n", "\n", "Using nothing but standard xarray syntax, we'll mask cloudy pixels with the Landsat QA band and reduce the data down to biannual median composites. We will use Dask to make this much faster. \n", "\n", "[Animated](https://geogif.readthedocs.io/en/latest/) as a GIF, we can watch the coastline move over the years due to [longshore drift](https://en.wikipedia.org/wiki/Longshore_drift)." ] }, { "cell_type": "markdown", "id": "12713e89", "metadata": {}, "source": [ "You can access this notebook (in a Docker image) on this [GitHub repo](https://github.com/HamedAlemo/dask-tutorial)." ] }, { "cell_type": "code", "execution_count": 1, "id": "1f282b25-b790-4c33-bab1-6f479e51d305", "metadata": { "tags": [] }, "outputs": [], "source": [ "import dask\n", "import pystac_client\n", "import planetary_computer\n", "import leafmap\n", "import geogif\n", "import stackstac" ] }, { "cell_type": "markdown", "id": "56105e54-1f0d-4ba2-97a5-b8de15d778eb", "metadata": {}, "source": [ "Start your Dask cluster:" ] }, { "cell_type": "code", "execution_count": 2, "id": "779d87e1-e10b-4b05-a3ec-04882781a0db", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask.distributed import Client, LocalCluster\n", "cluster = LocalCluster()\n", "client = Client(cluster)\n", "client" ] }, { "cell_type": "markdown", "id": "5c493e73-52eb-4ba5-b1df-ad057fdf42c5", "metadata": {}, "source": [ "Interactively pick the area of interest from a map. Just move the map around and re-run all cells to generate the timeseries somewhere else!" ] }, { "cell_type": "code", "execution_count": 3, "id": "2b9cbced-693f-4c14-a1e9-5d00509f9e4f", "metadata": { "tags": [] }, "outputs": [], "source": [ "import leafmap" ] }, { "cell_type": "code", "execution_count": 4, "id": "e8489395-a3da-4ea8-adb3-01462d5b5feb", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6c29ee8be5e64de390f0e4bf26c267f8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map(center=[41.64933994767867, -69.94438630063088], controls=(ZoomControl(options=['position', 'zoom_in_text',…" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = leafmap.Map(center=[41.64933994767867, -69.94438630063088], zoom=12, height=\"800px\")\n", "m" ] }, { "cell_type": "code", "execution_count": 5, "id": "64a8d51d-3eae-4839-a34f-0d91b43cd4ef", "metadata": { "tags": [] }, "outputs": [], "source": [ "bbox = (m.west, m.south, m.east, m.north)" ] }, { "cell_type": "markdown", "id": "3e7c17dc-7a70-4fdd-bbe9-149fac625559", "metadata": {}, "source": [ "## Search for STAC items\n", "\n", "Use [pystac-client](https://github.com/stac-utils/pystac-client) to connect to Microsoft's STAC API endpoint and search for Landsat-8 scenes." ] }, { "cell_type": "code", "execution_count": 6, "id": "c0f5cbed-74fb-4110-abf0-311edf6432a8", "metadata": { "tags": [] }, "outputs": [], "source": [ "catalog = pystac_client.Client.open(\n", " \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n", " modifier=planetary_computer.sign_inplace,\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "id": "81cd0d2e-58d5-4734-bd32-71d818233a51", "metadata": { "tags": [] }, "outputs": [], "source": [ "search = catalog.search(\n", " collections=['landsat-8-c2-l2'],\n", " bbox=bbox,\n", ")" ] }, { "cell_type": "markdown", "id": "6650815b-9745-40ba-9775-e1d33cf5ee94", "metadata": {}, "source": [ "Load all the STAC items:" ] }, { "cell_type": "code", "execution_count": 8, "id": "c96a7a55-b962-4460-97fd-84c2ddb458e2", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "201" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "items = search.get_all_items()\n", "len(items)" ] }, { "cell_type": "markdown", "id": "046d1b98-f5eb-481d-957b-32a7e1ee7484", "metadata": {}, "source": [ "These are the footprints of all the items we'll use:" ] }, { "cell_type": "code", "execution_count": 9, "id": "b27d491c-3391-4eaf-ac94-3955f97b8ed6", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6c29ee8be5e64de390f0e4bf26c267f8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map(bottom=391020.0, center=[41.64933994767867, -69.94438630063088], controls=(ZoomControl(options=['position'…" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.add_geojson(items.to_dict(), zoom=12)\n", "m" ] }, { "cell_type": "markdown", "id": "c0cdf4fa-88d8-40ec-83b5-ddeaba95b6e1", "metadata": {}, "source": [ "## Create an xarray with stacksatc\n", "\n", "Set `bounds_latlon=bbox` to automatically clip to our area of interest (instead of using the full footprints of the scenes)." ] }, { "cell_type": "code", "execution_count": 10, "id": "62e0380d-364c-4012-9cba-d43d7ab73519", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 104 ms, sys: 4.8 ms, total: 109 ms\n", "Wall time: 106 ms\n" ] }, { "data": { "text/html": [ "
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       "  * time                         (time) datetime64[ns] 2013-03-22T15:19:00.54...\n",
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<xarray.DataArray 'stackstac-0bffcf6778474275db65519a313c614c' (time: 201,\n",
       "                                                                band: 3,\n",
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       "  * time                         (time) datetime64[ns] 2013-03-22T15:19:00.54...\n",
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       "  * band                         (band) object 'red' 'green' 'blue'\n",
       "Attributes:\n",
       "    spec:        RasterSpec(epsg=32619, bounds=(410130.0, 4599750.0, 432540.0...\n",
       "    crs:         epsg:32619\n",
       "    transform:   | 30.00, 0.00, 410130.00|\\n| 0.00,-30.00, 4622790.00|\\n| 0.0...\n",
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" ], "text/plain": [ "\n", "dask.array\n", "Coordinates: (12/27)\n", " * time (time) datetime64[ns] 2013-03-22T15:19:00.54...\n", " id (time) " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# What's the typical interval between scenes?\n", "cloud_free_stack.time.diff(\"time\").dt.days.plot.hist();" ] }, { "cell_type": "markdown", "id": "7edb78de-adf3-41a5-9fb7-e2d90065cea7", "metadata": {}, "source": [ "## Make biannual median composites\n", "\n", "The Landsat-8 scenes appear to typically be 5-15 days apart. Let's composite that down to a 6-month interval.\n", "\n", "Since the cloudy pixels we masked with NaNs will be ignored in the `median`, this should give us a decent cloud-free-ish image for each." ] }, { "cell_type": "code", "execution_count": 17, "id": "850986d1-d704-43d4-a910-6ea4fe7c9dad", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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       "    instruments                object {'oli', 'tirs'}\n",
       "    landsat:collection_number  <U2 '02'\n",
       "    platform                   <U9 'landsat-8'\n",
       "    ...                         ...\n",
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