{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cda76d61",
   "metadata": {},
   "source": [
    "# Fire Progressions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54666b51",
   "metadata": {},
   "source": [
    "`wxee` is designed for processing weather data, but it can also be useful for remote sensing data. In this example, we'll look at how `wxee` can work with data from the GOES-16 and MODIS to visualize fire progressions over time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fa491a3",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56349485",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install wxee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6bc5c66a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:07:04.390889Z",
     "start_time": "2021-09-12T04:07:02.299951Z"
    }
   },
   "outputs": [],
   "source": [
    "import ee\n",
    "import wxee\n",
    "\n",
    "ee.Authenticate()\n",
    "wxee.Initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299b65e2",
   "metadata": {},
   "source": [
    "## Daily Progressions from MODIS\n",
    "One of the products captured by the MODIS sensor is fire hotspot detections. We'll use `wxee` to load daily hotspots for the 2021 Caldor fire in California and visualize them by the time of burning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4482e615",
   "metadata": {},
   "source": [
    "### Downloading MODIS Data to xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc9a357",
   "metadata": {},
   "source": [
    "To start with, we'll load 14 days of MODIS data as a `TimeSeries`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1eb24867",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:07:23.368631Z",
     "start_time": "2021-09-12T04:07:21.139222Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mMODIS/006/MOD14A1\u001b[0m\n",
      "\tImages: 21\n",
      "\tStart date: 2021-08-15 00:00:00 UTC\n",
      "\tEnd date: 2021-09-04 00:00:00 UTC\n",
      "\tMean interval: 1.00 days\n"
     ]
    }
   ],
   "source": [
    "modis = wxee.TimeSeries(\"MODIS/006/MOD14A1\").filterDate(\"2021-08-15\", \"2021-09-05\").select(\"FireMask\")\n",
    "\n",
    "modis.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fa23a8e",
   "metadata": {},
   "source": [
    "The `FireMask` band contains codes indicating the confidence of fire detections. First, we'll use `map` to turn those codes into binary images of fire presence, copying properties over so we don't lose any time information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "681ebbad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:07:25.494470Z",
     "start_time": "2021-09-12T04:07:25.483415Z"
    }
   },
   "outputs": [],
   "source": [
    "fire_masks = modis.map(lambda img: img.eq(9).copyProperties(img, img.propertyNames()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3090624d",
   "metadata": {},
   "source": [
    "Now we'll specify a bounding box around the Caldor fire and download the `TimeSeries` to an `xarray.Dataset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7cdd11f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:07:31.739366Z",
     "start_time": "2021-09-12T04:07:27.255165Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b4ad73c2b5f4777a2634273b2d60837",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Requesting:   0%|          | 0/21 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc06f53b8c7042ff85f21a1c4d03b197",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0/21 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "region = ee.Geometry.Polygon(\n",
    "        [[[-120.70580745719522, 38.90798203224902],\n",
    "          [-120.70580745719522, 38.51793451346024],\n",
    "          [-119.90014827750772, 38.51793451346024],\n",
    "          [-119.90014827750772, 38.90798203224902]]]\n",
    ")\n",
    "\n",
    "ds = fire_masks.wx.to_xarray(scale=1000, region=region, crs=\"EPSG:26910\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e61039b",
   "metadata": {},
   "source": [
    "### Visualizing the Progression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cf55ac2",
   "metadata": {},
   "source": [
    "The result will be 14 binary fire masks. Let's take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad721c8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:07:35.337767Z",
     "start_time": "2021-09-12T04:07:32.722696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7f85bec59130>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x648 with 22 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.FireMask.plot(col=\"time\", col_wrap=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcb937ad",
   "metadata": {},
   "source": [
    "That shows us how the fire grew over time, but maybe we want a single summary image describing that progression. Let's calculate the first day that a given pixel burned. Rather than storing specific times, we'll represent them as time elapsed since the beginning of the time series."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "107cd931",
   "metadata": {},
   "source": [
    "First, get the start time of the time series and calculate the number of hours elapsed between each time step and the start time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ef848e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:07:37.975577Z",
     "start_time": "2021-09-12T04:07:37.960248Z"
    }
   },
   "outputs": [],
   "source": [
    "start = ds.time.min()\n",
    "delta_days = (ds.time - start).dt.days"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc29040",
   "metadata": {},
   "source": [
    "Now, we'll multiply the time delta by the fire mask. Because non-fire pixels have a value of 0, this will give us a 3D array with elapsed days since start for each hotspot pixel and values of 0 for all other pixels. We cast from a `timedelta` to an `int` to allow plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "06009d24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:07:39.483393Z",
     "start_time": "2021-09-12T04:07:39.467099Z"
    }
   },
   "outputs": [],
   "source": [
    "delta_days_fire = (ds.FireMask * delta_days).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1206abca",
   "metadata": {},
   "source": [
    "Finally, we can mask non-hotspot pixels (values of 0) and take the minimum over time to get the first time, in days elapsed since start, that each pixel burned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "023c2199",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:07:40.804848Z",
     "start_time": "2021-09-12T04:07:40.785846Z"
    }
   },
   "outputs": [],
   "source": [
    "first_burned = delta_days_fire.where(delta_days_fire != 0).min(\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672f339e",
   "metadata": {},
   "source": [
    "And of course, plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca029779",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:07:41.818595Z",
     "start_time": "2021-09-12T04:07:41.483874Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f85ada61af0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_burned.plot(size=8, cmap=\"inferno_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74fb9f1c",
   "metadata": {},
   "source": [
    "## Hourly Progressions from GOES-16"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72d89999",
   "metadata": {},
   "source": [
    "Unlike MODIS which captures daily hotspots, GOES-16 captures hotspots every 5 minutes! This is great for tracking fast-moving fires, but it also means we have to deal with a lot more data. We can use `wxee` and the `aggregate_time` method to aggregate those 5-minute hotspots to hourly hotspots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c14d7c6",
   "metadata": {},
   "source": [
    "### Downloading GOES-16 data to xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a6bfc8",
   "metadata": {},
   "source": [
    "First, we'll load GOES-16 data over a time window that experienced explosive fire growth in the Western Cascades of Oregon in 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40f505a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:07:52.603734Z",
     "start_time": "2021-09-12T04:07:45.027527Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mNOAA/GOES/16/FDCC\u001b[0m\n",
      "\tImages: 288\n",
      "\tStart date: 2020-09-08 00:01:16 UTC\n",
      "\tEnd date: 2020-09-08 23:56:16 UTC\n",
      "\tMean interval: 5.00 minutes\n"
     ]
    }
   ],
   "source": [
    "ts = wxee.TimeSeries(\"NOAA/GOES/16/FDCC\").select(\"Mask\").filterDate(\"2020-09-08\", \"2020-09-09\")\n",
    "ts.describe(\"minute\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e786bec",
   "metadata": {},
   "source": [
    "Like with MODIS, we'll have to convert mask codes to a binary fire mask. For GOES, we'll use the codes 10, 11, 30, and 31."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f576d1f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:07:52.617388Z",
     "start_time": "2021-09-12T04:07:52.610487Z"
    }
   },
   "outputs": [],
   "source": [
    "def fire_mask(img):\n",
    "    mask = ee.Image(0).rename(\"fire\")\n",
    "    mask = (mask\n",
    "        .where(img.eq(10), 1)\n",
    "        .where(img.eq(11), 1)\n",
    "        .where(img.eq(30), 1)\n",
    "        .where(img.eq(31), 1)\n",
    "    )\n",
    "    # Copy the properties from the original images to avoid losing time data.\n",
    "    return mask.copyProperties(img, img.propertyNames())\n",
    "\n",
    "# Convert each mask class image into a binary fire presence mask\n",
    "fire_masks = ts.map(lambda img: fire_mask(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5231808e",
   "metadata": {},
   "source": [
    "Now we can aggregate the 288 5-minute masks to 24 hourly masks. We'll use a max reducer, meaning that an hourly hotspot was detected at least once within the hour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d78b650",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:08:57.985957Z",
     "start_time": "2021-09-12T04:07:52.618797Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mNOAA/GOES/16/FDCC\u001b[0m\n",
      "\tImages: 24\n",
      "\tStart date: 2020-09-08 00:01:16 UTC\n",
      "\tEnd date: 2020-09-08 23:01:16 UTC\n",
      "\tMean interval: 60.00 minutes\n"
     ]
    }
   ],
   "source": [
    "hourly_fire = fire_masks.aggregate_time(\"hour\", reducer=ee.Reducer.max())\n",
    "hourly_fire.describe(\"minute\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ac0ee9c",
   "metadata": {},
   "source": [
    "Select a region around the largest 2020 fire, Beachie Creek."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cfa366ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:08:57.990827Z",
     "start_time": "2021-09-12T04:08:57.987781Z"
    }
   },
   "outputs": [],
   "source": [
    "geom = ee.Geometry.Polygon(\n",
    "    [[[-122.77807114256022, 45.370803623985665],\n",
    "      [-122.77807114256022, 44.519360582318896],\n",
    "      [-121.46520493162272, 44.519360582318896],\n",
    "      [-121.46520493162272, 45.370803623985665]]]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf2ac4af",
   "metadata": {},
   "source": [
    "And download the data to an `xarray.Dataset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7b87dc8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:10:00.799796Z",
     "start_time": "2021-09-12T04:08:57.992221Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "720876dc5e784c33ac7d1b26c439ce18",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Requesting:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8956afc1b0d846a19b3ab9ccab16d3ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = hourly_fire.wx.to_xarray(region=geom, scale=2_000, crs=\"EPSG:26910\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d30702d",
   "metadata": {},
   "source": [
    "### Visualizing the Progression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a62da28",
   "metadata": {},
   "source": [
    "Like before, we'll convert our time series of fire masks into a single image showing when each area burned in time elapsed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ac48efe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:10:00.811035Z",
     "start_time": "2021-09-12T04:10:00.801256Z"
    }
   },
   "outputs": [],
   "source": [
    "start = ds.time.min()\n",
    "# Convert milliseconds to hours\n",
    "delta_hours = (ds.time - start) / 3_600_000_000_000\n",
    "\n",
    "delta_hours_fire = (ds.fire * delta_hours).astype(int)\n",
    "\n",
    "first_burned = delta_hours_fire.where(delta_hours_fire != 0).min(\"time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c09d75",
   "metadata": {},
   "source": [
    "And plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b31a7bb7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-12T04:10:01.044421Z",
     "start_time": "2021-09-12T04:10:00.812254Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f85bc3f40a0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_burned.plot(size=8, cmap=\"inferno_r\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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