{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Water Observations from Space (WOFS)\n",
    "\n",
    "### Background\n",
    "\n",
    "Understanding Australia's flood history is an important part of making better predictions about how we will be affected by flooding in the future.\n",
    "\n",
    "To this end, Geoscience Australia developved the Australian Water Observations from Space (WOFS) algorithm. WOFS provides an estimate of how often water was seen at a particular location. This water detection algorithm is significantly better than the Landsat QA water flag or the NDWI index for water identification. \n",
    "\n",
    "For more information, visit this website: http://www.ga.gov.au/scientific-topics/hazards/flood/wofs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminary steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Supress Warning \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Data Cube Configuration\n",
    "import datacube\n",
    "dc = datacube.Datacube(app = 'my_app')\n",
    "\n",
    "# Import Data Cube API\n",
    "import utils.data_cube_utilities.data_access_api as dc_api  \n",
    "api = dc_api.DataAccessApi()\n",
    "\n",
    "# Import other required packages\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np  \n",
    "import xarray as xr  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define product and extent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Available extents\n",
    "\n",
    "We've listed the available ingested data that you can explore in the ODC Sandbox. The latitude, longitude and time ranges correspond to the boundaries of the ingested data cubes. You'll be able to explore sub-samples of these cubes. You'll also need to provide the platform, product and resolution information for the cube you're subsampling.\n",
    "\n",
    "#### LS8 Caqueta\n",
    "Platform: `'LANDSAT_8'` <br>\n",
    "Product: `'ls8_collection1_AMA_ingest'` <br>\n",
    "Latitude:   `(0.000134747292617865, 1.077843593651382)` <br>\n",
    "Longitude:  `(-74.91935994831539, -73.30266193148462)` <br>\n",
    "Time:       `('2013-04-13', '2018-03-26')` <br>\n",
    "Resolution: `(-0.000269494585236, 0.000269494585236)`\n",
    "\n",
    "#### LS8 Vietnam\n",
    "Platform: `'LANDSAT_8'` <br>\n",
    "Product: `'ls8_collection1_AMA_ingest'` <br>\n",
    "Latitude:  `(10.513927001104687, 12.611133863411238)` <br>\n",
    "Longitude: `(106.79005909290998, 108.91906631627438)` <br>\n",
    "Time: `('2014-01-14', '2016-12-21')` <br>\n",
    "Resolution: `(-0.000269494585236, 0.000269494585236)` <br>\n",
    "\n",
    "#### LS7 Caqueta\n",
    "Platform: `'LANDSAT_7'` <br>\n",
    "Product: `'ls7_collection1_AMA_ingest'` <br>\n",
    "Latitude:  `(0.000134747292617865, 1.077843593651382)` <br>\n",
    "Longitude: `(-74.91935994831539, -73.30266193148462)` <br>\n",
    "Time: `('1999-08-21', '2018-03-25')` <br>\n",
    "Resolution: `(-0.000269494585236, 0.000269494585236)`\n",
    "\n",
    "#### LS7 Lake Baringo\n",
    "Platform: `'LANDSAT_7'` <br>\n",
    "Product: `'ls7_collection1_AMA_ingest'` <br>\n",
    "Latitude:  `(0.4997747685, 0.7495947795)` <br>\n",
    "Longitude: `(35.9742163305, 36.473586859499996)` <br>\n",
    "Time: `('2005-01-08', '2016-12-24')` <br>\n",
    "Resolution: `(-0.000269493, 0.000269493)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CHANGE HERE >>>>>>>>>>>>>>>>>\n",
    "\n",
    "# Select a product and platform\n",
    "platform = \"LANDSAT_8\"\n",
    "product = 'ls8_collection1_AMA_ingest'\n",
    "resolution = (-0.000269494585236, 0.000269494585236)\n",
    "output_crs = 'EPSG:4326'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set extent information\n",
    "\n",
    "You can change the values in this cell to specify the extent of the data cube you wish to analyse.\n",
    "\n",
    "You should select a sub-sample from one of the four data cubes listed above. When subsampling, keep in mind that:\n",
    "* Your latitude and longitude bounds should be within the extents given.\n",
    "* Your area should be small to keep load times reasonable (less than 0.5 square degrees).\n",
    "* Your time period should be within the extents given.\n",
    "\n",
    "You should format the variables as:\n",
    "* `latitude = (min_latitude, max_latitude)`\n",
    "* `longitude = (min_longitude, max_longitude)`\n",
    "* `time_extents = (min_time, max_time)`, where each time has the format: `'YYYY-MM-DD'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CHANGE HERE >>>>>>>>>>>>>>>>>>\n",
    "\n",
    "# Select a sub-region to analyse\n",
    "latitude = (1.0684, 0.8684)\n",
    "longitude  = (-74.8409, -74.6409)\n",
    "time_extents = ('2000-01-01', '2018-01-01')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View the region before loading\n",
    "\n",
    "The next cell will allow you to view the area you'll be analysing by displaying a red bounding box on an interactive map. You can change the extents in the previous cell and rerun the `display_map()` command to see the resulting bounding box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f48f8fe5b38>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The code below renders a map that can be used to view the analysis region.\n",
    "from utils.data_cube_utilities.dc_display_map import display_map\n",
    "\n",
    "display_map(latitude, longitude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataset and the required spectral bands or other parameters\n",
    "The data is loaded by passing the product and area information to the `dc.load()` function. As a part of this load, we also specify the measurements we want in the form of the Landsat bands.\n",
    "\n",
    "The load can take up to a few minutes, so please be patient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "landsat_dataset = dc.load(\n",
    "    latitude=latitude,\n",
    "    longitude=longitude,\n",
    "    platform=platform,\n",
    "    time=time_extents,\n",
    "    product=product,\n",
    "    output_crs=output_crs,\n",
    "    resolution=resolution,\n",
    "    measurements=(\n",
    "        'red',\n",
    "        'green',\n",
    "        'blue',\n",
    "        'nir',\n",
    "        'swir1',\n",
    "        'swir2',\n",
    "        'pixel_qa'\n",
    "    )\n",
    ") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is often useful to print the loaded data to check the dimensions and data variables\n",
    "\n",
    "When looking at the dimensions, the numbers for latitude and longitude correspond to the number of pixels in each dimension and the number for time corresponds to the number of time steps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:    (latitude: 743, longitude: 743, time: 48)\n",
      "Coordinates:\n",
      "  * time       (time) datetime64[ns] 2013-05-06T15:15:22 2013-06-07T15:15:34 ...\n",
      "  * latitude   (latitude) float64 1.068 1.068 1.068 1.068 1.067 1.067 1.067 ...\n",
      "  * longitude  (longitude) float64 -74.84 -74.84 -74.84 -74.84 -74.84 -74.84 ...\n",
      "Data variables:\n",
      "    red        (time, latitude, longitude) int16 2457 2277 2212 2320 2412 ...\n",
      "    green      (time, latitude, longitude) int16 2549 2388 2337 2432 2535 ...\n",
      "    blue       (time, latitude, longitude) int16 2484 2298 2211 2320 2379 ...\n",
      "    nir        (time, latitude, longitude) int16 4441 4291 4221 4304 4371 ...\n",
      "    swir1      (time, latitude, longitude) int16 3193 3052 3053 3080 3173 ...\n",
      "    swir2      (time, latitude, longitude) int16 2335 2211 2199 2230 2315 ...\n",
      "    pixel_qa   (time, latitude, longitude) int32 480 480 480 480 480 480 480 ...\n",
      "Attributes:\n",
      "    crs:      EPSG:4326\n"
     ]
    }
   ],
   "source": [
    "# Displays an overview of the loaded data\n",
    "print(landsat_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Masking out clouds\n",
    "\n",
    "As part of the utilities for the Open Data Cube, we have defined a function to mask clouds based on the quality assurance information for Landsat. The function returns an `xarray.DataArray` object containing the mask. This can then be passed to the `where()` function, which masks the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.data_cube_utilities.clean_mask import landsat_qa_clean_mask\n",
    "\n",
    "cloud_mask = landsat_qa_clean_mask(landsat_dataset, platform=platform)\n",
    "\n",
    "cleaned_dataset = landsat_dataset.where(cloud_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Water Detection Analysis\n",
    "Time series output of the Australian Water Detection from Space (WOFS) results. The results show the percent of time that a pixel is classified as water over the entire time series. BLUE = frequent water, RED = infrequent water.\n",
    "\n",
    "The first step is to classify the dataset, which can be done with the `wofs_classify()` utility function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.data_cube_utilities.dc_water_classifier import wofs_classify\n",
    "\n",
    "ts_water_classification = wofs_classify(landsat_dataset, clean_mask=cloud_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to convert \"no data\" pixels to `nan`. A \"no data\" pixel has a value of `-9999` in Landsat data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_water_classification = ts_water_classification.where(ts_water_classification != -9999).astype(np.float16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the percentage of time that a pixel is classified as water is calculated by taking the average classification value over time and multiplying it by 100. The mean calculation ignores `nan` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "water_classification_percentages = (ts_water_classification.mean(dim=['time']) * 100).wofs.rename('water_classification_percentages')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the results\n",
    "\n",
    "After calculating the water classification percentage, we can plot it both as a 2-dimensional image and 1-dimensional summary.\n",
    "\n",
    "The first step is to choose a colour map and change the colour of `nan` pixels to black. We choose to use the `RdBu` colour map to highlight water in blue and land in red."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import color-scheme and set nans to black\n",
    "from matplotlib.cm import RdBu\n",
    "RdBu.set_bad('black', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following figure, dark blue indicates pixels that experienced significant or constant water over the time series, where dark red indicates pixels that have experienced little or no water over the time series.\n",
    "\n",
    "You can adjust the figure size to avoid distortion. Use the `latitude` and `longitude` dimensions from the `xarray` description to get an idea for the desired aspect ratio. You'll need to add some space in the x-dimension to account for the presence of the colour bar. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CHANGE HERE >>>>>>>>>>>>>>>>>\n",
    "\n",
    "water_classification_percentages.plot(cmap=RdBu, figsize=(14, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By taking the average classification value over the latitude and longitude, we can assess whether the fraction of water pixels has changed significantly over time. It should be noted that clouds can impact the statistical results. The water classification percentage can be displayed on either a linear scale or a logarithmic scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "water_classification_mean_percentages = (ts_water_classification.mean(dim=['latitude', 'longitude']) * 100).wofs.rename('water_classification_percentages')\n",
    "\n",
    "#Linear-scale plot\n",
    "water_classification_mean_percentages.plot(figsize=(15,3), marker='o', linestyle='None')\n",
    "plt.title(\"Percentage of water pixels over time (linear)\")\n",
    "plt.show()\n",
    "\n",
    "#Logarithmic-scale plot\n",
    "water_classification_mean_percentages.plot(figsize=(15,3), marker='o', linestyle='None')\n",
    "plt.title(\"Percentage of water pixels over time (logarithmic)\")\n",
    "plt.gca().set_yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export to GeoTIFF\n",
    "To perform further analysis, use the following cells to download the data in GeoTIFF format. This makes use of the data cube utility function `export_slice_to_geotiff()`. \n",
    "\n",
    "Before exporting, we'll construct an `xarray` dataset to store the water classification percentage data we created earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the water percentage data to a GeoTIFF\n",
    "from utils.data_cube_utilities.import_export import export_slice_to_geotiff\n",
    "\n",
    "# construct the xarray Dataset\n",
    "dataset_to_export = xr.Dataset(coords=water_classification_percentages.coords, attrs=ts_water_classification.attrs)\n",
    "\n",
    "# add the water classification percentages to the new xarray Dataset\n",
    "dataset_to_export['wofs_pct'] = (water_classification_percentages/100).astype(np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The export command on the following line is commented out to avoid overwriting files. If you would like to export data, please change the filename before uncommenting the next line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CHANGE HERE >>>>>>>>>>>>>>>>>>>>>>>>>\n",
    "\n",
    "# export_slice_to_geotiff(dataset_to_export, 'geotiffs/WOFS_Percentage_demo.tif')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the files have been saved in the `geotiffs` folder, which sits inside the `dcal` folder that this notebook is stored in. Use the following cell to list the contents of the `geotiffs` folder.\n",
    "\n",
    "*NOTE:* Starting a command with `!` allows you to run that command in the Jupyter environment's command line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 12K\r\n",
      "drwxrwsr-x 2 jovyan users 4.0K Apr  9 01:57 .\r\n",
      "drwxrwsr-x 5 jovyan users 4.0K May 14 02:28 ..\r\n",
      "-rw-rw-r-- 1 jovyan users   38 Mar 31 23:41 README.md\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lah geotiffs/"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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