notebooks/DEACoastlines_generation_raster.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEA Coastlines raster generation <img align=\"right\" src=\"https://github.com/GeoscienceAustralia/dea-notebooks/raw/develop/Supplementary_data/dea_logo.jpg\">\n",
    "\n",
    "This code conducts raster generation for DEA Coastlines:\n",
    "\n",
    "* Load stack of all available Landsat 5, 7, 8 and 9 satellite imagery for a location using [ODC Virtual Products](https://docs.dea.ga.gov.au/notebooks/Frequently_used_code/Virtual_products.html)\n",
    "* Convert each satellite image into a remote sensing water index (MNDWI)\n",
    "* For each satellite image, model ocean tides into a grid based on exact time of image acquisition\n",
    "* Interpolate tide heights into spatial extent of image stack using the [FES2014 global tide model](https://github.com/GeoscienceAustralia/dea-coastlines/wiki/Setting-up-tidal-models-for-DEA-Coastlines)\n",
    "* Mask out high and low tide pixels by removing all observations acquired outside of 50 percent of the observed tidal range centered over mean sea level\n",
    "* Combine tidally-masked data into annual median composites representing the most representative position of the coastline at approximately mean sea level each year\n",
    "\n",
    "This is an interactive version of the code intended for prototyping; to run this analysis at scale, use the [command line tools](DEACoastlines_generation_CLI.ipynb).\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started\n",
    "Set working directory to top level of repository to ensure links work correctly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/jovyan/Robbi/dea-coastlines\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load packages\n",
    "\n",
    "First we import the required Python packages, then we connect to the database, and load the catalog of virtual products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 22.0.2; however, version 22.3.1 is available.\n",
      "You should consider upgrading via the '/env/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install -r requirements.in --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
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       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
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       "                    <strong>Dashboard: </strong> <a href=\"/user/robbi.bishoptaylor@ga.gov.au/proxy/8787/status\" target=\"_blank\">/user/robbi.bishoptaylor@ga.gov.au/proxy/8787/status</a>\n",
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       "                    <strong>Total memory:</strong> 59.21 GiB\n",
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       "                        <strong>Workers:</strong> 1\n",
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       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"/user/robbi.bishoptaylor@ga.gov.au/proxy/8787/status\" target=\"_blank\">/user/robbi.bishoptaylor@ga.gov.au/proxy/8787/status</a>\n",
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       "                        <strong>Total threads:</strong> 7\n",
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       "                        <strong>Started:</strong> Just now\n",
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       "                            <strong>Local directory: </strong> /home/jovyan/Robbi/dea-coastlines/dask-worker-space/worker-u8bhfcwu\n",
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       "\n",
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   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext line_profiler\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import dask\n",
    "import xarray as xr\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "from functools import partial\n",
    "from datacube.utils.geometry import Geometry\n",
    "\n",
    "# Load DEA Coastlines and DEA tools code\n",
    "import coastlines.raster\n",
    "import coastlines.utils\n",
    "from dea_tools.dask import create_local_dask_cluster\n",
    "from dea_tools.coastal import model_tides, pixel_tides\n",
    "from dea_tools.datahandling import parallel_apply\n",
    "\n",
    "# Connect to datacube\n",
    "import datacube\n",
    "import odc.geo.xr\n",
    "\n",
    "dc = datacube.Datacube(app=\"DEACoastlines\")\n",
    "\n",
    "# Create local dask client for parallelisation\n",
    "client = create_local_dask_cluster(return_client=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setup\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set analysis parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "study_area = 8\n",
    "raster_version = 'development'\n",
    "start_year = 1988\n",
    "end_year = 2021\n",
    "config_path = 'configs/dea_coastlines_config_development.yaml'\n",
    "\n",
    "# study_area = 8\n",
    "# raster_version = 'testing'\n",
    "# start_year = 1988\n",
    "# end_year = 2021\n",
    "# config_path = 'configs/dea_coastlines_config.yaml'\n",
    "\n",
    "# Load analysis params from config file\n",
    "config = coastlines.utils.load_config(\n",
    "    config_path=config_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load supplementary data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Albers grid cells used to process the analysis\n",
    "gridcell_gdf = (\n",
    "    gpd.read_file(config['Input files']['grid_path']).to_crs(\n",
    "        epsg=4326).set_index('id'))\n",
    "gridcell_gdf.index = gridcell_gdf.index.astype(int).astype(str)\n",
    "gridcell_gdf = gridcell_gdf.loc[[str(study_area)]]\n",
    "gridcell_gdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data\n",
    "### Create spatiotemporal query\n",
    "This establishes the spatial and temporal extent used to search for Landsat satellite data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create query based on analysis gridcell extent\n",
    "geopoly = Geometry(gridcell_gdf.iloc[0].geometry, crs=gridcell_gdf.crs)\n",
    "query = {\n",
    "    'geopolygon': geopoly.buffer(0.05),\n",
    "    'time': (str(start_year - 1), str(end_year + 1)),\n",
    "    'dask_chunks': {\n",
    "        'time': 1,\n",
    "        'x': 2048,\n",
    "        'y': 2048\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load satellite data as MNDWI\n",
    "This step loads satellite data from Landsat 5, 7, 8 and 9, and returns the data as a cloud-masked array converted to the Modified Normalised Difference Water Index (MNDWI).\n",
    "For Digital Earth Australia Coastlines, this is achieved using [ODC Virtual Products](https://docs.dea.ga.gov.au/notebooks/Frequently_used_code/Virtual_products.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\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.Dataset&gt;\n",
       "Dimensions:      (time: 1764, y: 461, x: 392)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1987-05-27T23:27:59.980430 ... 2022-12...\n",
       "  * y            (y) float64 -4.242e+06 -4.242e+06 ... -4.256e+06 -4.256e+06\n",
       "  * x            (x) float64 3.371e+05 3.371e+05 ... 3.488e+05 3.488e+05\n",
       "    spatial_ref  int32 32655\n",
       "Data variables:\n",
       "    mndwi        (time, y, x) float32 dask.array&lt;chunksize=(1, 461, 392), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    crs:           epsg:32655\n",
       "    grid_mapping:  spatial_ref</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-2837e57e-2792-4581-9eb1-a347a757b9c5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2837e57e-2792-4581-9eb1-a347a757b9c5' 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'>time</span>: 1764</li><li><span class='xr-has-index'>y</span>: 461</li><li><span class='xr-has-index'>x</span>: 392</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-59878604-b210-44ee-a9a1-92acad16cbf2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-59878604-b210-44ee-a9a1-92acad16cbf2' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1987-05-27T23:27:59.980430 ... 2...</div><input id='attrs-ef83aeca-70bf-4bc7-9df7-965b9f92f6b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef83aeca-70bf-4bc7-9df7-965b9f92f6b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dfba993-6f36-491f-aac0-0aedc343b275' class='xr-var-data-in' type='checkbox'><label for='data-4dfba993-6f36-491f-aac0-0aedc343b275' 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>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1987-05-27T23:27:59.980430000&#x27;, &#x27;1987-07-30T23:29:25.571665000&#x27;,\n",
       "       &#x27;1987-08-22T23:35:53.388409000&#x27;, ..., &#x27;2022-12-21T00:10:03.092193000&#x27;,\n",
       "       &#x27;2022-12-29T00:10:02.868450000&#x27;, &#x27;2022-12-30T00:04:17.864346000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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'>-4.242e+06 ... -4.256e+06</div><input id='attrs-b8078e70-55d5-4fe1-87e2-cc554837e555' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8078e70-55d5-4fe1-87e2-cc554837e555' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9a7d86b-a71e-4835-8750-e0c849167c5a' class='xr-var-data-in' type='checkbox'><label for='data-e9a7d86b-a71e-4835-8750-e0c849167c5a' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-30.0</dd><dt><span>crs :</span></dt><dd>epsg:32655</dd></dl></div><div class='xr-var-data'><pre>array([-4242150., -4242180., -4242210., ..., -4255890., -4255920., -4255950.])</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'>3.371e+05 3.371e+05 ... 3.488e+05</div><input id='attrs-e9f979dd-1dc2-454f-81b3-d9cd71683044' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9f979dd-1dc2-454f-81b3-d9cd71683044' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3ce24ee-226a-4211-a09b-f942061e9564' class='xr-var-data-in' type='checkbox'><label for='data-b3ce24ee-226a-4211-a09b-f942061e9564' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>30.0</dd><dt><span>crs :</span></dt><dd>epsg:32655</dd></dl></div><div class='xr-var-data'><pre>array([337110., 337140., 337170., ..., 348780., 348810., 348840.])</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'>int32</div><div class='xr-var-preview xr-preview'>32655</div><input id='attrs-a6759cd5-507c-41f9-8194-1014c4b74fa6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6759cd5-507c-41f9-8194-1014c4b74fa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6bf6154-37e3-4c56-b48e-791073831fab' class='xr-var-data-in' type='checkbox'><label for='data-e6bf6154-37e3-4c56-b48e-791073831fab' 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>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 55N&quot;,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;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,147],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;32655&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr-var-data'><pre>array(32655, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e645db72-d037-4ab8-bb6d-9371341c074c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e645db72-d037-4ab8-bb6d-9371341c074c' 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>mndwi</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 461, 392), meta=np.ndarray&gt;</div><input id='attrs-0528930f-a936-4a4e-b02c-da6480e811f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0528930f-a936-4a4e-b02c-da6480e811f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf79f5cb-9482-452e-b920-46670a8bc546' class='xr-var-data-in' type='checkbox'><label for='data-bf79f5cb-9482-452e-b920-46670a8bc546' 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 :</span></dt><dd>epsg:32655</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>nodata :</span></dt><dd>nan</dd></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> 1.19 GiB </td>\n",
       "                        <td> 705.91 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1764, 461, 392) </td>\n",
       "                        <td> (1, 461, 392) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 115025 Tasks </td>\n",
       "                        <td> 1764 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e399ec3b-5900-481a-b773-d55231cfc912' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e399ec3b-5900-481a-b773-d55231cfc912' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>epsg:32655</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (time: 1764, y: 461, x: 392)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1987-05-27T23:27:59.980430 ... 2022-12...\n",
       "  * y            (y) float64 -4.242e+06 -4.242e+06 ... -4.256e+06 -4.256e+06\n",
       "  * x            (x) float64 3.371e+05 3.371e+05 ... 3.488e+05 3.488e+05\n",
       "    spatial_ref  int32 32655\n",
       "Data variables:\n",
       "    mndwi        (time, y, x) float32 dask.array<chunksize=(1, 461, 392), meta=np.ndarray>\n",
       "Attributes:\n",
       "    crs:           epsg:32655\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load MNDWI virtual product\n",
    "ds = coastlines.raster.load_water_index(\n",
    "    dc,\n",
    "    query,\n",
    "    yaml_path=config[\"Virtual product\"][\"virtual_product_path\"],\n",
    "    product_name=config[\"Virtual product\"][\"virtual_product_name\"],\n",
    "    mask_terrain_shadow=False,\n",
    ")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tidal modelling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Interpolate tides into each satellite timestep\n",
    "For each satellite timestep, model tide heights into a low-resolution 5 x 5 km grid (matching resolution of the FES2014 tidal model), then reproject modelled tides into the spatial extent of our satellite image. Add  this new data as a new variable in our satellite dataset to allow each satellite pixel to be analysed and filtered/masked based on the tide height at the exact moment of satellite image acquisition. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating reduced resolution tide modelling array\n",
      "Modelling tides using FES2014 tide model\n",
      "Reprojecting tides into original array\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1764/1764 [00:06<00:00, 257.56it/s]\n"
     ]
    }
   ],
   "source": [
    "ds[\"tide_m\"], tides_lowres = pixel_tides(ds, resample=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot example interpolated tide surface for a single timestep:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot\n",
    "timestep = 15\n",
    "ds_i = ds[\"tide_m\"].isel(time=timestep)\n",
    "ds_lowres_i = tides_lowres.isel(time=timestep)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 8))\n",
    "ds_lowres_i.plot.imshow(\n",
    "    ax=axes[0],\n",
    "    robust=True,\n",
    "    cmap=\"viridis\",\n",
    "    vmin=ds_i.min().item(),\n",
    "    vmax=ds_i.max().item(),\n",
    ")\n",
    "ds_i.plot.imshow(\n",
    "    ax=axes[1],\n",
    "    robust=True,\n",
    "    cmap=\"viridis\",\n",
    "    vmin=ds_i.min().item(),\n",
    "    vmax=ds_i.max().item(),\n",
    ")\n",
    "for ax in axes:\n",
    "    gridcell_gdf.to_crs(ds.odc.geobox.crs).plot(\n",
    "        ax=ax, facecolor=\"none\", edgecolor=\"black\"\n",
    "    )\n",
    "axes[0].set_title(\"Low resolution (5 x 5 km) modelled tides\")\n",
    "axes[1].set_title(\"Modelled tides reprojected into input satellite grid\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate per-pixel tide cutoffs\n",
    "Based on the entire time-series of tide heights, compute the max and min satellite-observed tide height for each pixel, then calculate tide cutoffs used to restrict our data to satellite observations centred over mid-tide (0 m Above Mean Sea Level)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine tide cutoff\n",
    "tide_cutoff_min, tide_cutoff_max = coastlines.raster.tide_cutoffs(ds, tides_lowres, tide_centre=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate yearly composites\n",
    "Export tidally-masked MNDWI median composites for each year, and three-yearly composites used to gapfill poor data coverage areas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If output folder doesn't exist, create it\n",
    "output_dir = f\"data/interim/raster/{raster_version}/{study_area}_{raster_version}\"\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# Iterate through each year and export annual and 3-year gapfill composites\n",
    "coastlines.raster.export_annual_gapfill(\n",
    "    ds, output_dir, tide_cutoff_min, tide_cutoff_max, start_year, end_year\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Close Dask client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.nanny - WARNING - Worker process still alive after 3.9999988555908206 seconds, killing\n"
     ]
    }
   ],
   "source": [
    "client.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "***\n",
    "\n",
    "## Additional information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**License:** The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). \n",
    "Digital Earth Australia data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n",
    "\n",
    "**Contact:** For assistance with any of the Python code or Jupyter Notebooks in this repository, please post a [Github issue](https://github.com/GeoscienceAustralia/dea-coastlines/issues/new).\n",
    "\n",
    "**Last modified:** November 2022\n",
    "\n",
    "**To cite:**\n",
    "\n",
    "> Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. (2021). Mapping Australia's dynamic coastline at mean sea level using three decades of Landsat imagery. Remote Sensing of Environment, 267, 112734. Available: https://doi.org/10.1016/j.rse.2021.112734\n",
    ">\n",
    "> Nanson, R., Bishop-Taylor, R., Sagar, S., Lymburner, L., (2022). Geomorphic insights into Australia's coastal change using a national dataset derived from the multi-decadal Landsat archive. Estuarine, Coastal and Shelf Science, 265, p.107712. Available: https://doi.org/10.1016/j.ecss.2021.107712\n",
    ">\n",
    "> Bishop-Taylor, R., Sagar, S., Lymburner, L., Alam, I., Sixsmith, J. (2019). Sub-pixel waterline extraction: characterising accuracy and sensitivity to indices and spectra. Remote Sensing, 11 (24):2984. Available: https://doi.org/10.3390/rs11242984"
   ]
  }
 ],
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