burntarea/analyze_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a0de835a",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Burnt Area Analysis\n",
    "## Burnt Area Daily NRT Incremental Product / Monthly Composite / Yearly Composite - Europe, Sentinel-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "66f23ff6-7eef-4fcb-ad74-c0dca7ed56be",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# we will need some imports, let's get them out of the way\n",
    "from affine import Affine\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "import rasterio.windows\n",
    "from rasterio import features\n",
    "from rasterio.enums import MergeAlg, Resampling\n",
    "from rasterio.plot import show\n",
    "from rasterio.vrt import WarpedVRT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8120cfa4",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c257d9dd",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "The easiest way to work with the data is using the [OGC Feature API](https://github.com/opengeospatial/ogcapi-features) to query the [monthly](https://geoservice.dlr.de/eoc/ogc/features/collections/demo:BURNTAREA_S3_EFR_P1D) and [yearly](https://geoservice.dlr.de/eoc/ogc/features/collections/demo:BURNTAREA_S3_EFR_P1Y) products.\n",
    "\n",
    "You can use filtering to query the data serverside. For example we can query all monthly products with a `confidence` of over `95%` and a `burn_severity` of motr than `0.2` (more positive values indicate higher burn severities):\n",
    "https://geoservice.dlr.de/eoc/ogc/features/collections/demo:BURNTAREA_S3_EFR_P1M/items?f=application%2Fgeo%2Bjson&limit=1&filter=confidence_total>95%20AND%20burn_severity>0.2\n",
    "\n",
    "However, let's start with some analysis. First we will need some auxiliary data (the state territory of Greece)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e356f457",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Auxiliary data: Load global country boundaries from Natural Earth (low resolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d493683",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countries = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))  # be aware that this is low-resolution and just used for demo purposes\n",
    "greece = countries[countries[\"iso_a3\"] == \"GRC\"]\n",
    "greece.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "def68865",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# get the bounds, we will use them to query data with later on\n",
    "minx, miny, maxx, maxy = greece.total_bounds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce086ae",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Exploration with Geopandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b9e4aad",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let's get all the burntareas of Greece in the last years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f31ccd8",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_seen</th>\n",
       "      <th>burn_severity</th>\n",
       "      <th>confidence_total</th>\n",
       "      <th>t_stamp_start</th>\n",
       "      <th>area_ha</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09-15 08:57:20+00:00</td>\n",
       "      <td>-0.160346</td>\n",
       "      <td>87.26</td>\n",
       "      <td>2017-01-01 00:00:00+00:00</td>\n",
       "      <td>341.999600</td>\n",
       "      <td>POLYGON ((22.31814 39.41631, 22.31867 39.41901...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-11-02 08:11:13+00:00</td>\n",
       "      <td>-0.084798</td>\n",
       "      <td>88.98</td>\n",
       "      <td>2017-01-01 00:00:00+00:00</td>\n",
       "      <td>8.999999</td>\n",
       "      <td>POLYGON ((22.09033 40.04062, 22.09086 40.04332...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-30 09:09:18+00:00</td>\n",
       "      <td>-0.012951</td>\n",
       "      <td>90.61</td>\n",
       "      <td>2017-01-01 00:00:00+00:00</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>POLYGON ((26.23716 39.86711, 26.23786 39.86977...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-30 09:09:18+00:00</td>\n",
       "      <td>-0.212084</td>\n",
       "      <td>86.09</td>\n",
       "      <td>2017-01-01 00:00:00+00:00</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>POLYGON ((22.32408 39.39330, 22.32461 39.39600...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-12 09:13:02+00:00</td>\n",
       "      <td>-0.007912</td>\n",
       "      <td>90.73</td>\n",
       "      <td>2017-01-01 00:00:00+00:00</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>POLYGON ((22.25734 39.03393, 22.25786 39.03663...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 first_seen  burn_severity  confidence_total  \\\n",
       "0 2017-09-15 08:57:20+00:00      -0.160346             87.26   \n",
       "1 2017-11-02 08:11:13+00:00      -0.084798             88.98   \n",
       "2 2017-08-30 09:09:18+00:00      -0.012951             90.61   \n",
       "3 2017-08-30 09:09:18+00:00      -0.212084             86.09   \n",
       "4 2017-12-12 09:13:02+00:00      -0.007912             90.73   \n",
       "\n",
       "              t_stamp_start     area_ha  \\\n",
       "0 2017-01-01 00:00:00+00:00  341.999600   \n",
       "1 2017-01-01 00:00:00+00:00    8.999999   \n",
       "2 2017-01-01 00:00:00+00:00    9.000000   \n",
       "3 2017-01-01 00:00:00+00:00    9.000000   \n",
       "4 2017-01-01 00:00:00+00:00    9.000000   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((22.31814 39.41631, 22.31867 39.41901...  \n",
       "1  POLYGON ((22.09033 40.04062, 22.09086 40.04332...  \n",
       "2  POLYGON ((26.23716 39.86711, 26.23786 39.86977...  \n",
       "3  POLYGON ((22.32408 39.39330, 22.32461 39.39600...  \n",
       "4  POLYGON ((22.25734 39.03393, 22.25786 39.03663...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter the area of Greece directly\n",
    "\n",
    "yearly_url = f\"https://geoservice.dlr.de/eoc/ogc/features/collections/demo:BURNTAREA_S3_EFR_P1Y/items?f=application%2Fjson&bbox={minx},{miny},{maxx},{maxy}\"\n",
    "\n",
    "yearly_grc = gpd.read_file(yearly_url)  \n",
    "\n",
    "# drop some columns we do not need later on, you can also do this directly\n",
    "yearly_grc.drop([\"id\", \"t_stamp_end\", \"region\", \"name\"], axis=1, inplace=True)  \n",
    "\n",
    "yearly_grc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab8aead",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "- `burn_severity`: This column gives a measure of the loss in vegetation fitness for each burnt area, on a scale between -2 and +2. It is calculated by determining the Normalized Difference Vegetation Index ([NVDI](https://de.wikipedia.org/wiki/Normalized_Difference_Vegetation_Index)) for two points in time, before and after the fire (pre/post). The post fire value is subtracted from the pre fire value (differential NDVI), so that the more positive the result gets, the higher is the loss in vegetation fitness. The column represents the mean value of the totality of differential values within the burnt area perimeter.\n",
    "\n",
    "- `confidence`: The value makes a statement about the estimated accuracy of the burnt area perimeter. It incorporates the severity of the burning together with the number of corresponding active fire measurements, with the result given in percent.\n",
    "\n",
    "- `first seen`: This represents the exact point in time when the first burning inside the final perimeter was detected.\n",
    "\n",
    "- `t_stamp_start` / `t_stamp_stop`: These columns are used to set time range for the query of the whole product. \n",
    "\n",
    "- `geometry`: This is the perimeter of the burnt area in geographic projection (WGS84), provided in the Well-Known Text (WKT) notation.\n",
    "\n",
    "- `area_ha`: This is the exact size of the extent of each burnt area, giben in hectares. While the geometries are provided in WGS84 (latitude/longitude), the determination of this value is based on the Lambert Azimuthal (LAEA) projection. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc1d9b4",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Let's see how much it burnt per year.\n",
    "Note again that we used a low-resolution bounding box of Greece, so don't worry about how correct these numbers are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31ada792",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t_stamp_start</th>\n",
       "      <th>area_ha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>200700.860779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>126189.224045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>147717.111014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>111564.161123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>198513.474203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  t_stamp_start        area_ha\n",
       "0    2017-01-01  200700.860779\n",
       "1    2018-01-01  126189.224045\n",
       "2    2019-01-01  147717.111014\n",
       "3    2020-01-01  111564.161123\n",
       "4    2021-01-01  198513.474203"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_grc_ha = yearly_grc.groupby([\"t_stamp_start\"]).area_ha.sum().reset_index()  # group by year and sum all areas\n",
    "\n",
    "yearly_grc_ha.t_stamp_start = yearly_grc_ha.t_stamp_start.values.astype('datetime64[D]')  # this is just for better plotting\n",
    "yearly_grc_ha.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e0839f83",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x134aba54610>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's have a look\n",
    "years = yearly_grc_ha.t_stamp_start.dt.year\n",
    "plt.xticks(range(math.floor(min(years)), math.ceil(max(years))+1))\n",
    "plt.plot(years, yearly_grc_ha.area_ha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6fcd5ff6",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_seen</th>\n",
       "      <th>burn_severity</th>\n",
       "      <th>confidence_total</th>\n",
       "      <th>t_stamp_start</th>\n",
       "      <th>area_ha</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2596</th>\n",
       "      <td>2021-12-26 07:52:50+00:00</td>\n",
       "      <td>-0.176960</td>\n",
       "      <td>86.89</td>\n",
       "      <td>2021-01-01 00:00:00+00:00</td>\n",
       "      <td>9.000001</td>\n",
       "      <td>POLYGON ((26.42977 41.23592, 26.43052 41.23857...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2600</th>\n",
       "      <td>2021-09-16 08:11:35+00:00</td>\n",
       "      <td>-0.037103</td>\n",
       "      <td>90.07</td>\n",
       "      <td>2021-01-01 00:00:00+00:00</td>\n",
       "      <td>207.000150</td>\n",
       "      <td>POLYGON ((20.33609 40.64541, 20.33563 40.64271...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2601</th>\n",
       "      <td>2021-03-04 09:33:42+00:00</td>\n",
       "      <td>-0.032196</td>\n",
       "      <td>90.18</td>\n",
       "      <td>2021-01-01 00:00:00+00:00</td>\n",
       "      <td>62.999805</td>\n",
       "      <td>POLYGON ((21.10510 40.31302, 21.10608 40.31842...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2602</th>\n",
       "      <td>2021-10-30 08:12:46+00:00</td>\n",
       "      <td>-0.022222</td>\n",
       "      <td>90.40</td>\n",
       "      <td>2021-01-01 00:00:00+00:00</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>POLYGON ((24.13898 41.00044, 24.14245 40.99989...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2606</th>\n",
       "      <td>2021-03-04 08:32:08+00:00</td>\n",
       "      <td>-0.054673</td>\n",
       "      <td>89.67</td>\n",
       "      <td>2021-01-01 00:00:00+00:00</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>POLYGON ((20.31688 40.63660, 20.31734 40.63930...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    first_seen  burn_severity  confidence_total  \\\n",
       "2596 2021-12-26 07:52:50+00:00      -0.176960             86.89   \n",
       "2600 2021-09-16 08:11:35+00:00      -0.037103             90.07   \n",
       "2601 2021-03-04 09:33:42+00:00      -0.032196             90.18   \n",
       "2602 2021-10-30 08:12:46+00:00      -0.022222             90.40   \n",
       "2606 2021-03-04 08:32:08+00:00      -0.054673             89.67   \n",
       "\n",
       "                 t_stamp_start     area_ha  \\\n",
       "2596 2021-01-01 00:00:00+00:00    9.000001   \n",
       "2600 2021-01-01 00:00:00+00:00  207.000150   \n",
       "2601 2021-01-01 00:00:00+00:00   62.999805   \n",
       "2602 2021-01-01 00:00:00+00:00    9.000000   \n",
       "2606 2021-01-01 00:00:00+00:00    9.000000   \n",
       "\n",
       "                                               geometry  \n",
       "2596  POLYGON ((26.42977 41.23592, 26.43052 41.23857...  \n",
       "2600  POLYGON ((20.33609 40.64541, 20.33563 40.64271...  \n",
       "2601  POLYGON ((21.10510 40.31302, 21.10608 40.31842...  \n",
       "2602  POLYGON ((24.13898 41.00044, 24.14245 40.99989...  \n",
       "2606  POLYGON ((20.31688 40.63660, 20.31734 40.63930...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alright, let's take a closer look at 2021. We start by filtering the dataframe again:\n",
    "\n",
    "grc_2021 = yearly_grc[yearly_grc[\"t_stamp_start\"] == \"2021-01-01T00:00:00+00:00\"]\n",
    "grc_2021.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c2b4504",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# that's how it looks like, remember that the border is very low-resolution and we used a bounding box. E.g. Euboea is missing in the background map, but there was a huge fire.\n",
    "\n",
    "base = greece.plot()\n",
    "grc_2021.plot(ax=base, color=\"red\", )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f7a8a6",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "As you can see, a lot has been burnt. Often this is due to e.g. agricultural burning and we can filter them out using landcover data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbf0d90",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Auxiliary data: Download ESA World cover data for Greece\n",
    "\n",
    "We will take the boundary coordinates of Greece to read in the landcover data for Greece in a ~300 meter resolution, like our burnt area data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03ce3955",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Output image transform with the resolution of ~300m.\n",
    "\n",
    "xres = 0.002777777777777777884\n",
    "yres = -0.002777777777777777884\n",
    "\n",
    "dst_width = int((maxx - minx) / xres) \n",
    "dst_height = abs(int((maxy - miny) / yres))\n",
    "dst_transform = Affine(xres, 0.0, minx, \n",
    "                       0.0, yres, maxy)\n",
    "\n",
    "# Read in and warp the LC data - this might take a bit\n",
    "\n",
    "vrt_options = {\n",
    "    \"crs\": yearly_grc.crs,\n",
    "    \"transform\": dst_transform,\n",
    "    \"height\": dst_height, \n",
    "    \"width\": dst_width,\n",
    "    \"resampling\": Resampling.nearest,\n",
    "}\n",
    "\n",
    "with rasterio.Env(AWS_NO_SIGN_REQUEST=\"YES\"):\n",
    "    with rasterio.open(\"/vsis3/esa-worldcover/v100/2020/ESA_WorldCover_10m_2020_v100_Map_AWS.vrt\") as ds:\n",
    "        with WarpedVRT(ds, **vrt_options) as vrt:\n",
    "            window = rasterio.windows.from_bounds(*greece.total_bounds, transform=vrt.transform)\n",
    "            lulc_band = vrt.read(1, window=window)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a5f370",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now that we have the LC as raster we also convert our burnt area product into a raster so we can combine the data more easily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55f4af34",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# get the value we want per pixel in int16\n",
    "\n",
    "burntareas_geom_value = ((geom, int(value)) for geom, value in zip(grc_2021.geometry, round(grc_2021[\"burn_severity\"] * 100,0)))\n",
    "\n",
    "# rasterize it\n",
    "grc_2021_arr = features.rasterize(\n",
    "    burntareas_geom_value,\n",
    "    out_shape=lulc_band.shape,\n",
    "    transform=dst_transform,\n",
    "    all_touched=True,\n",
    "    fill=-5,  # background value\n",
    "    merge_alg=MergeAlg.replace,\n",
    "    dtype=np.int16,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0489dd17",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's have a look, note the difference in burn severity. The raster is zoomed to the burnt area extent.\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize = (10, 10))\n",
    "show(grc_2021_arr, ax = ax)\n",
    "plt.gca()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf7ee01",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We use the LC to filter out any cropland from the burnt area we detected. Cropland has a value of 40 in the LC dataset we are using."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d72af768",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " # create a filter\n",
    "crop = lulc_band == 40\n",
    "\n",
    "# set to background value\n",
    "grc_2021_arr[crop] = -5\n",
    "\n",
    "# let's have a look again, note that the burntarea in the north east was mainly agriculture burning\n",
    "fig, ax = plt.subplots(1, figsize = (10, 10))\n",
    "show(grc_2021_arr, ax = ax)\n",
    "plt.gca()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}