esbae_script_06a_merge_sbae_ceo (2).ipynb

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   "cell_type": "markdown",
   "id": "d0b45b24-6cc1-44b4-ba38-dd9b053e49dc",
   "metadata": {
    "tags": []
   },
   "source": [
    "# V - Merging esbae with CEO data \n",
    "### This script serves to merge validated data from CEO with eSBAE points for quick estimation of areas\n",
    "#### This script requires minimum an m16 instance and select the python 3 kernel\n",
    "###### For more information contact aurelie.shapiro@fao.org or remi.dannunzio@fao.org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7899a542-decb-4052-8f0d-44d83d23ba6d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "# Set the display option to a large value to prevent text wrapping\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b5e710-f28e-4862-ac7f-f072e4672886",
   "metadata": {},
   "source": [
    "#### combine all CEO sample files\n",
    "##### download your CEO sample files from collect.earth and upload to SEPAL using FileZilla.\n",
    "##### add as many files as you have projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dbf603c5-ed47-4e36-b271-26e6200a3737",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#DRC\n",
    "ceo1 = pd.read_csv('/home/sepal-user/module_results/esbae/DRC/ceo-CAFI-DDD-RDC-eSBAE-validation-1-sample-data_prov.csv')\n",
    "ceo2 = pd.read_csv('/home/sepal-user/module_results/esbae/DRC/ceo-CAFI-DDD-RDC-eSBAE-validation-2-sample-data.csv')\n",
    "ceo3 = pd.read_csv('/home/sepal-user/module_results/esbae/DRC/ceo-CAFI-DDD-RDC-eSBAE-validation-3-sample-data.csv')\n",
    "#GAB\n",
    "#ceo1 = pd.read_csv('/home/sepal-user/module_results/esbae/GAB/ceo-CAFI-DDD-Gabon-eSBAE-validation-1-sample-data.csv')\n",
    "#ceo2 = pd.read_csv('/home/sepal-user/module_results/esbae/GAB/ceo-CAFI-DDD-Gabon-eSBAE-validation-2-sample-data.csv')\n",
    "#ceo3 = pd.read_csv('/home/sepal-user/module_results/esbae/GAB/ceo-CAFI-DDD-Gabon-eSBAE-validation-3-sample-data.csv')\n",
    "#ceo4 = pd.read_csv('/home/sepal-user/module_results/esbae/GAB/ceo-CAFI-DDD-Gabon-eSBAE-validation-4-sample-data.csv')\n",
    "#ceo5 = pd.read_csv('/home/sepal-user/module_results/esbae/GAB/ceo-CAFI-DDD-Gabon-eSBAE-validation-5-sample-data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f1635a7-2e97-4128-be10-901c426e72e8",
   "metadata": {},
   "source": [
    "##### drop duplicates from ceo collections\n",
    "###### keep first duplicate by date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa3ab8ff-9649-44f5-99c4-90089d9a6f51",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['OBJECTID', 'Join_Count', 'TARGET_FID', 'plotid', 'sampleid', 'lon', 'lat', 'email', 'flagged', 'collection_time', 'analysis_duration', 'imagery_title', 'imagery_attributions', 'sample_geom', 'pl_aspect', 'pl_brightness_max', 'pl_red_mean', 'pl_greenness_max', 'pl_cusum_confidence', 'pl_cusum_change_date', 'pl_nir_max', 'pl_red_min', 'pl_bfast_means', 'pl_elevation', 'pl_red_sd', 'pl_cnc_1520', 'pl_swir1_sd', 'pl_index', 'pl_tmf_sub', 'pl_dw_tree_prob__min', 'pl_bs_slope_max', 'pl_red_max', 'pl_images', 'pl_dist', 'pl_bfast_magnitude', 'pl_ccdc_magnitude', 'pl_simple_combined', 'pl_tmf_defyear', 'pl_greenness_mean', 'pl_tmf_2019', 'pl_swir2_sd', 'pl_nir_sd', 'pl_gfc_tc00', 'pl_greenness_min', 'pl_tmf_degyear', 'pl_tmf_2020', 'pl_tmf_2017', 'pl_bs_slope_min', 'pl_lang_tree_height', 'pl_bs_slope_sd', 'pl_dw_class_mode', 'pl_swir1_mean', 'pl_wetness_sd', 'pl_bs_slope_mean', 'pl_ccdc_change_date', 'pl_dw_tree_prob_mean', 'pl_nir_mean', 'pl_dw_tree_prob__stddev', 'pl_tmf_2016', 'pl_nir_min', 'pl_esri_lc17', 'pl_wetness_mean', 'pl_ndfi_min', 'pl_wetness_min', 'pl_point_id', 'pl_kmeans', 'pl_swir2_mean', 'pl_esri_lc19', 'pl_slope', 'pl_cusum_magnitude', 'pl_esri_lc18', 'pl_gfc_loss', 'pl_esri_lc20', 'pl_swir1_max', 'pl_swir1_min', 'pl_mon_images', 'pl_esa_lc20', 'pl_brightness_min', 'pl_brightness_mean', 'pl_potapov_tree_height', 'pl_gfc_lossyear', 'pl_wetness_max', 'pl_swir2_max', 'pl_bfast_change_date', 'pl_gfc_gain', 'pl_brightness_sd', 'pl_tmf_2018', 'pl_dw_tree_prob__max', 'pl_strata', 'pl_ndfi_max', 'pl_esri_lc21', 'pl_swir2_min', 'pl_esa_lc21', 'pl_ndfi_sd', 'pl_tmf_main', 'pl_greenness_sd', 'pl_chg_prob', 'pl_ndfi_mean', 'forêt ou non-forêt en 2015?', \"Y'a t'il de la régéneration\", 'Changement entre 2015-2022', 'Type de Changement (2015-2022)', 'Exploitation forestière artisanale (<5ha)', 'Activité minière artisanale', 'Infrastructure routière', 'Infrastructure d’urbanisation ou habitations', 'Exploitation forestière industrielle (>5ha)', 'Autre moteur', 'Descriptif autre moteur', 'Activité minière industrielle', 'Agriculture industrielle', 'Agriculture paysanne', 'Année de Changement', 'Type de non-forêt en 2015', 'Type de forêt en 2015', 'Commentaires', 'SCE_SEM', 'SCE_GEO', 'MODIF', 'ORIGINE', 'NOM', 'CODE_INS', 'SURFACE', 'CHEF_LIEU']\n"
     ]
    }
   ],
   "source": [
    "column_names = ceo1.columns.tolist()\n",
    "# Print the list of column names\n",
    "print(column_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86bae5e0-8169-40be-98ff-f7cc8fa3b044",
   "metadata": {},
   "source": [
    "##### merge all ceo files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30ee1ce6-6324-44b3-a48e-a1d86ff37808",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#make a list of the ceo files first\n",
    "#ceo_files = [ceo1, ceo2, ceo3,ceo4,ceo5]\n",
    "ceo_files = [ceo1, ceo2, ceo3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e405fab6-a8a0-4461-96e4-091177c712f3",
   "metadata": {},
   "source": [
    "##### add a column named \"interpreted\" which = 1 when the point has been validated in CEO, otherwise 0\n",
    "##### count the number of validated points per ceo file\n",
    "##### replace forest and non-forest types with codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9c12ded-8591-4487-9494-68b2081e3987",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ceo 1 # of validated points: 498, # of not validated points: 0\n",
      "ceo 2 # of validated points: 6590, # of not validated points: 88\n",
      "ceo 3 # of validated points: 3342, # of not validated points: 2629\n"
     ]
    }
   ],
   "source": [
    "# List to store the modified DataFrames\n",
    "modified_ceo_files = []\n",
    "\n",
    "# Initialize collection counter\n",
    "collection_counter = 1\n",
    "\n",
    "for i, ceo in enumerate(ceo_files):\n",
    "       \n",
    "    # Sort the DataFrame by 'date' in descending order\n",
    "    ceo.sort_values(by='collection_time', ascending=True, inplace=True)\n",
    "\n",
    "    # Remove duplicates keeping the last occurrence (the most recent date)\n",
    "    ceo.drop_duplicates(subset='pl_point_id', keep='first', inplace=True)\n",
    "\n",
    "    # Reset the index\n",
    "    ceo.reset_index(drop=True, inplace=True)\n",
    "    \n",
    "    # Add 'collection' column with an increasing number\n",
    "    ceo['collection'] = collection_counter\n",
    "    \n",
    "    # Add 'interpreted' column with 1 when 'email' is not null, otherwise 0\n",
    "    ceo['interpreted'] = np.where(ceo['email'].notna(), 1, 0)\n",
    "    \n",
    "    # Calculate total count for each 'interpreted' value\n",
    "    interpreted_counts = ceo['interpreted'].value_counts().to_dict()\n",
    "    \n",
    "    # Append the modified DataFrame to the list\n",
    "    modified_ceo_files.append(ceo)\n",
    "    \n",
    "    # Print count for the current DataFrame\n",
    "    print(f\"ceo {i + 1} # of validated points: {interpreted_counts.get(1, 0)}, # of not validated points: {interpreted_counts.get(0, 0)}\")\n",
    "    \n",
    "    # Increment the collection counter\n",
    "    collection_counter += 1\n",
    "\n",
    "# Concatenate the modified DataFrames into one merged DataFrame\n",
    "merged_ceo = pd.concat(modified_ceo_files, ignore_index=True)\n",
    "\n",
    "\n",
    "# Specify the columns you want to keep in the merged DataFrame\n",
    "columns_to_keep = ['plotid', 'sampleid', 'lon', 'lat', 'email', 'flagged', 'collection_time', 'analysis_duration', \n",
    "                    'sample_geom', 'pl_point_id', 'pl_chg_prob', 'pl_simple_combined', 'pl_strata', 'forêt ou non-forêt en 2015?', \"Y'a t'il de la régéneration\", \n",
    "                    'Changement entre 2015-2022', 'Type de Changement (2015-2022)', 'Exploitation forestière artisanale (<5ha)', \n",
    "                    'Activité minière artisanale', 'Infrastructure routière', 'Infrastructure d’urbanisation ou habitations', \n",
    "                    'Exploitation forestière industrielle (>5ha)', 'Autre moteur', 'Descriptif autre moteur', 'Activité minière industrielle', \n",
    "                    'Agriculture industrielle', 'Agriculture paysanne', 'Année de Changement', 'Type de non-forêt en 2015', 'Type de forêt en 2015', 'Commentaires', 'collection', 'interpreted']\n",
    "\n",
    "# Select only the specified columns\n",
    "merged_ceo = merged_ceo[columns_to_keep]\n",
    "\n",
    "# Create a dictionary to map old column names to new column names\n",
    "column_mapping = {\n",
    "    'pl_point_id': 'point_id',\n",
    "    'pl_chg_prob': 'chg_prob', \n",
    "    'pl_simple_combined': 'simple_combined', \n",
    "    'pl_strata': 'stratum',\n",
    "    'forêt ou non-forêt en 2015?': 'Ref_FNF_2015', \n",
    "    \"Y'a t'il de la régéneration\": 'Ref_Regeneration',\n",
    "    'Changement entre 2015-2022': 'Ref_Change_1522', \n",
    "    'Type de Changement (2015-2022)': 'Ref_Change_Type_1522', \n",
    "    'Exploitation forestière artisanale (<5ha)': 'ArtFor',\n",
    "    'Activité minière artisanale': 'ArtMine',\n",
    "    'Infrastructure routière': 'InfraR', \n",
    "    'Infrastructure d’urbanisation ou habitations': 'Urb',\n",
    "    'Exploitation forestière industrielle (>5ha)':'IndFor', \n",
    "    'Autre moteur': 'Other', \n",
    "    'Descriptif autre moteur': 'Other_Desc', \n",
    "    'Activité minière industrielle': 'IndMine', \n",
    "    'Agriculture industrielle': 'IndAg', \n",
    "    'Agriculture paysanne': 'ArtAg', \n",
    "    'Année de Changement': 'Ref_Year', \n",
    "    'Type de non-forêt en 2015':'Ref_NFtype', \n",
    "    'Type de forêt en 2015':'Ref_Ftype'\n",
    "}\n",
    "\n",
    "# Use the rename() method to rename the columns\n",
    "merged_ceo = merged_ceo.rename(columns=column_mapping)\n",
    "\n",
    "# Define the replacements as a dictionary\n",
    "replace_dict = {\n",
    "    'Ref_FNF_2015': {'non-forêt': '0', 'forêt': '1'},\n",
    "    'Ref_Ftype': {'1 - forêt dense': '1', '2 - forêt dense sèche': '2', '3 - forêt secondaire': '3','4 - forêt claire sèche': '4','7 - forêt mangrove': '7',\n",
    "                 '8 - forêt marécageuse': '8', '9 - forêt galérie': '9','10 - plantation forestière': '10', '11- savane arborée': '11'},\n",
    "    'Ref_NFtype': {'12 - savane arbustive': '12', '13 - savane herbacée': '13', '14 - prairie aquatique': '14', '15 - sol nu végétation éparse': '15',\n",
    "                  '16 - terres cultivées': '16', '17 - zone baties': '17', '18 - eau': '18'},\n",
    "    'Ref_Change_1522': {'oui':'1','non':'0'},\n",
    "    'ArtFor':{'oui':'1','non':'0'},\n",
    "    'IndFor':{'oui':'1','non':'0'},\n",
    "    'ArtAg':{'oui':'1','non':'0'},\n",
    "    'IndAg':{'oui':'1','non':'0'},\n",
    "    'ArtMine':{'oui':'1','non':'0'},\n",
    "    'IndMine':{'oui':'1','non':'0'},\n",
    "    'Urb':{'oui':'1','non':'0'},\n",
    "    'InfraR':{'oui':'1','non':'0'},\n",
    "    'Other':{'oui':'1','non':'0'}\n",
    "}\n",
    "\n",
    "# Use the replace() method to replace strings in specified columns\n",
    "merged_ceo.replace(replace_dict, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f69be9dc-43c2-4070-b63e-8d05f720209e",
   "metadata": {
    "tags": []
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   "source": [
    "merged_ceo_interpreted = merged_ceo[merged_ceo['interpreted'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e2c49c5-65ce-48fe-8983-6e4163dc038f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['plotid', 'sampleid', 'lon', 'lat', 'email', 'flagged', 'collection_time', 'analysis_duration', 'sample_geom', 'point_id', 'chg_prob', 'simple_combined', 'stratum', 'Ref_FNF_2015', 'Ref_Regeneration', 'Ref_Change_1522', 'Ref_Change_Type_1522', 'ArtFor', 'ArtMine', 'InfraR', 'Urb', 'IndFor', 'Other', 'Other_Desc', 'IndMine', 'IndAg', 'ArtAg', 'Ref_Year', 'Ref_NFtype', 'Ref_Ftype', 'Commentaires', 'collection', 'interpreted']\n"
     ]
    }
   ],
   "source": [
    "ceo_column_names = merged_ceo_interpreted.columns.tolist()\n",
    "# Print the list of column names\n",
    "print(ceo_column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6bdb8226-1032-4724-92f7-f092444b46a4",
   "metadata": {
    "tags": []
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       "      <td>1358102</td>\n",
       "      <td>1358102</td>\n",
       "      <td>17.355111</td>\n",
       "      <td>-2.651903</td>\n",
       "      <td>mbenzadaudet@yahoo.fr</td>\n",
       "      <td>False</td>\n",
       "      <td>01/07/2023 07:38</td>\n",
       "      <td>603.4 secs</td>\n",
       "      <td>POINT(17.355111 -2.651903)</td>\n",
       "      <td>1358102</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>déforestation</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1360479</td>\n",
       "      <td>1360479</td>\n",
       "      <td>17.874516</td>\n",
       "      <td>-0.407201</td>\n",
       "      <td>mbenzadaudet@yahoo.fr</td>\n",
       "      <td>False</td>\n",
       "      <td>01/07/2023 07:39</td>\n",
       "      <td>291.9 secs</td>\n",
       "      <td>POINT(17.874516 -0.407201)</td>\n",
       "      <td>1360479</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>non</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    plotid  sampleid        lon       lat                  email flagged  \\\n",
       "0  1344074   1344074  16.778148 -3.403462  mbenzadaudet@yahoo.fr   False   \n",
       "1  1348314   1348314  17.570499 -1.133539  mbenzadaudet@yahoo.fr   False   \n",
       "2  1346683   1346683  17.261760 -1.433204  mbenzadaudet@yahoo.fr   False   \n",
       "3  1358102   1358102  17.355111 -2.651903  mbenzadaudet@yahoo.fr   False   \n",
       "4  1360479   1360479  17.874516 -0.407201  mbenzadaudet@yahoo.fr   False   \n",
       "\n",
       "    collection_time analysis_duration                 sample_geom  point_id  \\\n",
       "0  01/07/2023 07:29        467.9 secs  POINT(16.778148 -3.403462)   1344074   \n",
       "1  01/07/2023 07:31        250.3 secs  POINT(17.570499 -1.133539)   1348314   \n",
       "2  01/07/2023 07:31        336.6 secs   POINT(17.26176 -1.433204)   1346683   \n",
       "3  01/07/2023 07:38        603.4 secs  POINT(17.355111 -2.651903)   1358102   \n",
       "4  01/07/2023 07:39        291.9 secs  POINT(17.874516 -0.407201)   1360479   \n",
       "\n",
       "   chg_prob  simple_combined  stratum Ref_FNF_2015 Ref_Regeneration  \\\n",
       "0      0.24             0.24        2            0              non   \n",
       "1      0.03             0.03        1            1              NaN   \n",
       "2      0.02             0.02        1            1              NaN   \n",
       "3      0.42             0.42        2            1              NaN   \n",
       "4      0.55             0.55        3            0              non   \n",
       "\n",
       "  Ref_Change_1522 Ref_Change_Type_1522 ArtFor ArtMine InfraR  Urb IndFor  \\\n",
       "0             NaN                  NaN    NaN     NaN    NaN  NaN    NaN   \n",
       "1               0                  NaN    NaN     NaN    NaN  NaN    NaN   \n",
       "2               0                  NaN    NaN     NaN    NaN  NaN    NaN   \n",
       "3               1        déforestation      1       0      0    0      0   \n",
       "4             NaN                  NaN    NaN     NaN    NaN  NaN    NaN   \n",
       "\n",
       "  Other Other_Desc IndMine IndAg ArtAg  Ref_Year Ref_NFtype Ref_Ftype  \\\n",
       "0   NaN        NaN     NaN   NaN   NaN       NaN         12       NaN   \n",
       "1   NaN        NaN     NaN   NaN   NaN       NaN        NaN         8   \n",
       "2   NaN        NaN     NaN   NaN   NaN       NaN        NaN         8   \n",
       "3     0        NaN       0     0     0    2020.0        NaN         1   \n",
       "4   NaN        NaN     NaN   NaN   NaN       NaN         18       NaN   \n",
       "\n",
       "  Commentaires  collection  interpreted  \n",
       "0          NaN           1            1  \n",
       "1          NaN           1            1  \n",
       "2          NaN           1            1  \n",
       "3          NaN           1            1  \n",
       "4          NaN           1            1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_ceo_interpreted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4ff26c6c-3975-49b6-a148-9ec7c4559731",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "merged_ceo_interpreted = merged_ceo_interpreted.sort_values(by='collection', ascending=True)\n",
    "\n",
    "# Remove duplicates keeping the last occurrence (the most recent date)\n",
    "merged_ceo_nodup = merged_ceo_interpreted.drop_duplicates(subset='point_id', keep='first')\n",
    "\n",
    "# Reset the index\n",
    "merged_ceo_nodup = merged_ceo_nodup.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d96fd19-6809-413a-9696-c3991d50efbc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5679\n",
       "1    4751\n",
       "Name: Ref_FNF_2015, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_ceo_nodup['Ref_FNF_2015'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c29d4ef8-380e-40d1-8348-7dba22f200e2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3808\n",
       "1     943\n",
       "Name: Ref_Change_1522, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_ceo_nodup['Ref_Change_1522'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0097c4de-9bca-4a73-bcf5-1c9afc87f88b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "déforestation    743\n",
       "dégradation      200\n",
       "Name: Ref_Change_Type_1522, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_ceo_nodup['Ref_Change_Type_1522'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a37a3028-90b5-4dd1-a25e-efe01d0a5ad8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    10430\n",
       "Name: interpreted, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_ceo_nodup['interpreted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67e302cd-0a1b-4995-96df-0c912cf70bff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    6590\n",
       "3    3342\n",
       "1     498\n",
       "Name: collection, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_ceo_nodup['collection'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "04549d39-bee9-4f57-b112-cad1d8b8d259",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16    2995\n",
       "13    1548\n",
       "1     1408\n",
       "4     1360\n",
       "3     1072\n",
       "12     626\n",
       "2      483\n",
       "9      287\n",
       "15     221\n",
       "18     148\n",
       "14      98\n",
       "8       89\n",
       "17      43\n",
       "11      43\n",
       "7        5\n",
       "10       4\n",
       "Name: Ref_LCtype, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_ceo_nodup['Ref_LCtype'] = merged_ceo_nodup['Ref_Ftype'].fillna(merged_ceo_nodup['Ref_NFtype'])\n",
    "merged_ceo_nodup['Ref_LCtype'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e453b46-551d-4204-be40-4803c4d25e60",
   "metadata": {},
   "source": [
    "##### Check if values in 'pointid' are unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb09c9af-db98-4eee-92e5-d205a1016f3e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# this might take some time\n",
    "#unique_check = merged_ceo_nodup['point_id'].nunique() == len(merged_ceo_nodup['point_id'])\n",
    "\n",
    "# Print the result\n",
    "#if unique_check:\n",
    "#    print(\"All values are unique!\")\n",
    "#else:\n",
    "#    print(\"F$%§!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d21c47-12fb-4d92-b151-5f1d1c140461",
   "metadata": {},
   "source": [
    "##### if there are duplicates determine which points and which project (collection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8d3c9161-5a6b-4e9c-963d-3e4964f0329d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Find duplicate values in the 'ID' column\n",
    "#duplicate_ids = merged_ceo_nodup[merged_ceo_nodup.duplicated(subset='point_id', keep='first')]\n",
    "\n",
    "# If you want to print specific columns along with duplicates, you can do:\n",
    "# For example, print only 'ID' and 'Name' columns\n",
    "#print(\"\\nDuplicate Rows with 'ID' and 'Name':\")\n",
    "#print(duplicate_ids[['point_id', 'collection']])\n",
    "#len(duplicate_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "540971c0-b71a-4555-b2ab-69e75c8c250a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#merged_ceo_nodup.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef10186-78cf-4634-83fd-7073f85cf3b0",
   "metadata": {},
   "source": [
    "## add new columns for change and year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a06d1d2-744d-4e25-a2fc-6a5f64b0467d",
   "metadata": {},
   "source": [
    "#### clean up columns and replace values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "57263f3a-6939-435c-ac66-b9ebe93f5875",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NF        5679\n",
       "Stable    3808\n",
       "Def        743\n",
       "Deg        200\n",
       "Name: Ref_Change_Type_1522, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(x):\n",
    "  if x['Ref_FNF_2015'] == '0' : return 'NF'\n",
    "  elif x['Ref_Change_Type_1522'] == 'déforestation': return 'Def'\n",
    "  elif x['Ref_Change_Type_1522'] == 'dégradation' : return 'Deg'\n",
    "  else: return 'Stable'\n",
    "\n",
    "merged_ceo_nodup['Ref_Change_Type_1522'] = merged_ceo_nodup.apply(f, axis=1)\n",
    "merged_ceo_nodup['Ref_Change_Type_1522'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d31939f7-ce73-4b77-abe5-17b99aa943c4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Def2016      71\n",
      "Def2017     107\n",
      "Def2018     127\n",
      "Def2019     136\n",
      "Def2020     124\n",
      "Def2021     107\n",
      "Def2022      71\n",
      "Deg2016      54\n",
      "Deg2017      18\n",
      "Deg2018      24\n",
      "Deg2019      33\n",
      "Deg2020      33\n",
      "Deg2021      21\n",
      "Deg2022      17\n",
      "NF         5679\n",
      "Stable     3808\n",
      "Name: Ref_Change_Year_1522, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "def y(x):\n",
    "  if x['Ref_Change_Type_1522'] == 'Def' and x['Ref_Year'] == 2016: return 'Def2016'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Def' and x['Ref_Year'] == 2017: return 'Def2017'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Def' and x['Ref_Year'] == 2018: return 'Def2018'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Def' and x['Ref_Year'] == 2019: return 'Def2019'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Def' and x['Ref_Year'] == 2020: return 'Def2020'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Def' and x['Ref_Year'] == 2021: return 'Def2021'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Def' and x['Ref_Year'] == 2022: return 'Def2022'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Deg' and x['Ref_Year'] == 2016: return 'Deg2016'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Deg' and x['Ref_Year'] == 2017: return 'Deg2017'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Deg' and x['Ref_Year'] == 2018: return 'Deg2018'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Deg' and x['Ref_Year'] == 2019: return 'Deg2019'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Deg' and x['Ref_Year'] == 2020: return 'Deg2020'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Deg' and x['Ref_Year'] == 2021: return 'Deg2021'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Deg' and x['Ref_Year'] == 2022: return 'Deg2022'\n",
    "  elif x['Ref_Change_Type_1522'] == 'NF' : return 'NF'\n",
    "  elif x['Ref_Change_Type_1522'] == 'Stable' : return 'Stable'\n",
    "  else: return ''\n",
    "\n",
    "merged_ceo_nodup['Ref_Change_Year_1522'] = merged_ceo_nodup.apply(y, axis=1)\n",
    "\n",
    "annual_counts = merged_ceo_nodup['Ref_Change_Year_1522'].value_counts()\n",
    "annual_counts = annual_counts.sort_index()\n",
    "print(annual_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eec38754-782c-4810-afab-b66aa9697456",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_ceo_nodup['Def2016'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Def2016', 1, 0)\n",
    "merged_ceo_nodup['Def2017'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Def2017', 1, 0)\n",
    "merged_ceo_nodup['Def2018'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Def2018', 1, 0)\n",
    "merged_ceo_nodup['Def2019'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Def2019', 1, 0)\n",
    "merged_ceo_nodup['Def2020'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Def2020', 1, 0)\n",
    "merged_ceo_nodup['Def2021'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Def2021', 1, 0)\n",
    "merged_ceo_nodup['Def2022'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Def2022', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d6b46afa-fa39-4678-bb0a-98f222e9d4dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_ceo_nodup['Deg2016'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Deg2016', 1, 0)\n",
    "merged_ceo_nodup['Deg2017'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Deg2017', 1, 0)\n",
    "merged_ceo_nodup['Deg2018'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Deg2018', 1, 0)\n",
    "merged_ceo_nodup['Deg2019'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Deg2019', 1, 0)\n",
    "merged_ceo_nodup['Deg2020'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Deg2020', 1, 0)\n",
    "merged_ceo_nodup['Deg2021'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Deg2021', 1, 0)\n",
    "merged_ceo_nodup['Deg2022'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Deg2022', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7d71783a-2664-41d0-947c-45572341cc31",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_ceo_nodup['Defall'] = np.where(merged_ceo_nodup['Ref_Change_Type_1522'] == 'Def', 1, 0)\n",
    "merged_ceo_nodup['Degall'] = np.where(merged_ceo_nodup['Ref_Change_Type_1522'] == 'Deg', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0926ff1a-84a1-400b-bc07-7c72cfcbbdae",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_ceo_nodup['Stable'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'Stable', 1, 0)\n",
    "merged_ceo_nodup['NF'] = np.where(merged_ceo_nodup['Ref_Change_Year_1522'] == 'NF', 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a2951865-3ca2-42c9-a68c-9ce45fd46fef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10430"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_counts.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "096ffe12-cde7-4f0d-bd5e-3b2fa00212c1",
   "metadata": {
    "tags": []
   },
   "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>Ref_Change_Year_1522</th>\n",
       "      <th>Def2016</th>\n",
       "      <th>Def2017</th>\n",
       "      <th>Def2018</th>\n",
       "      <th>Def2019</th>\n",
       "      <th>Def2020</th>\n",
       "      <th>Def2021</th>\n",
       "      <th>Def2022</th>\n",
       "      <th>Deg2016</th>\n",
       "      <th>Deg2017</th>\n",
       "      <th>Deg2018</th>\n",
       "      <th>Deg2019</th>\n",
       "      <th>Deg2020</th>\n",
       "      <th>Deg2021</th>\n",
       "      <th>Deg2022</th>\n",
       "      <th>NF</th>\n",
       "      <th>Stable</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stratum</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3134.0</td>\n",
       "      <td>2212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2526.0</td>\n",
       "      <td>1451.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ref_Change_Year_1522  Def2016  Def2017  Def2018  Def2019  Def2020  Def2021  \\\n",
       "stratum                                                                      \n",
       "1                         NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2                        10.0     14.0     12.0     20.0     22.0     31.0   \n",
       "3                        61.0     93.0    115.0    116.0    102.0     76.0   \n",
       "\n",
       "Ref_Change_Year_1522  Def2022  Deg2016  Deg2017  Deg2018  Deg2019  Deg2020  \\\n",
       "stratum                                                                      \n",
       "1                         NaN      NaN      NaN      NaN      NaN      1.0   \n",
       "2                        32.0      6.0      6.0      7.0     13.0     10.0   \n",
       "3                        39.0     48.0     12.0     17.0     20.0     22.0   \n",
       "\n",
       "Ref_Change_Year_1522  Deg2021  Deg2022      NF  Stable  \n",
       "stratum                                                 \n",
       "1                         NaN      NaN    19.0   145.0  \n",
       "2                        10.0      8.0  3134.0  2212.0  \n",
       "3                        11.0      9.0  2526.0  1451.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check points by change and stratum\n",
    "ceo_table = pd.pivot_table(merged_ceo_nodup, values='interpreted', index=['stratum'],\n",
    "                       columns=['Ref_Change_Year_1522'], aggfunc=\"count\")\n",
    "ceo_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3dced9a5-9eb6-4f06-bc1a-19375d9ae2e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#export cleaned CEO data\n",
    "ceo_out = '/home/sepal-user/module_results/esbae/DRC/DRC_esbae_all_ceo_clean.csv'\n",
    "merged_ceo_nodup.to_csv(ceo_out,index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7fa54de4-a842-46fc-bb90-c0c413baf516",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ceo_phaseI = pd.read_csv('/home/sepal-user/CAFI_DDD/all_ceo_validation_TMF_2023_uniqueID.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1f82921-49ef-481e-b69c-7abebc352f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_ceo_nodup = pd.read_csv('/home/sepal-user/module_results/esbae/GAB/GAB_esbae_all_ceo_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e833515d-6228-4f54-a977-1e9db1a1a50f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['OID_', 'PLOTID', 'CEO_PLOTID', 'LON', 'LAT', 'ISO', 'CEO_Ref_Co',\n",
       "       'Ref_FNF', 'Ref_LCover', 'Ref_Change', 'Ref_Chan_1', 'countDrive',\n",
       "       'Ref_InfraR', 'Ref_Urb', 'Ref_ArtAg', 'Ref_IndAg', 'Ref_ArtFor',\n",
       "       'Ref_IndFor', 'Ref_ArtMin', 'Ref_IndMin', 'Ref_Other', 'Source',\n",
       "       'Ref_ChDate', 'Change_Bin', 'UniqueID'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ceo_phaseI.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b5493dd-37d0-486e-8daf-29b413318e0e",
   "metadata": {},
   "source": [
    "#### read all esbae points with augmented data (output from script 5a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "994e19b2-3bd9-400d-9919-b9373beca822",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1548365"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV files\n",
    "#DRC\n",
    "#esbae = pd.read_csv('/home/sepal-user/module_results/esbae/DRC/DRC_esbae_2015_2022_model.csv')\n",
    "esbae = pd.read_csv('/home/sepal-user/module_results/esbae/DRC/DRC_esbae_2015_2022_model_terr.csv')\n",
    "#GAB\n",
    "#esbae = pd.read_csv('/home/sepal-user/module_results/esbae/GAB/GAB_esbae_2015_2022_model.csv')\n",
    "len(esbae)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b817a5b-862a-4db6-95a0-f77aa062d75d",
   "metadata": {},
   "source": [
    "##### Rename columns to be same as ceo file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0f35df58-6413-4d11-b9ff-7c2f652da0a6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "esbae = esbae.rename(columns={'PLOTID': 'plotid'})\n",
    "esbae = esbae.rename(columns={'LAT': 'lat'})\n",
    "esbae = esbae.rename(columns={'LON': 'lon'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f0ad0f19-b552-4019-915d-a02890b6183b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images                    int64\n",
      "mon_images                int64\n",
      "bfast_change_date       float64\n",
      "bfast_magnitude         float64\n",
      "bfast_means             float64\n",
      "cusum_change_date       float64\n",
      "cusum_confidence        float64\n",
      "cusum_magnitude         float64\n",
      "red_mean                float64\n",
      "red_sd                  float64\n",
      "red_min                 float64\n",
      "red_max                 float64\n",
      "nir_mean                float64\n",
      "nir_sd                  float64\n",
      "nir_min                 float64\n",
      "nir_max                 float64\n",
      "swir1_mean              float64\n",
      "swir1_sd                float64\n",
      "swir1_min               float64\n",
      "swir1_max               float64\n",
      "swir2_mean              float64\n",
      "swir2_sd                float64\n",
      "swir2_min               float64\n",
      "swir2_max               float64\n",
      "ndfi_mean               float64\n",
      "ndfi_sd                 float64\n",
      "ndfi_min                float64\n",
      "ndfi_max                float64\n",
      "brightness_mean         float64\n",
      "brightness_sd           float64\n",
      "brightness_min          float64\n",
      "brightness_max          float64\n",
      "greenness_mean          float64\n",
      "greenness_sd            float64\n",
      "greenness_min           float64\n",
      "greenness_max           float64\n",
      "wetness_mean            float64\n",
      "wetness_sd              float64\n",
      "wetness_min             float64\n",
      "wetness_max             float64\n",
      "bs_slope_mean           float64\n",
      "bs_slope_sd             float64\n",
      "bs_slope_max            float64\n",
      "bs_slope_min            float64\n",
      "ccdc_change_date        float64\n",
      "ccdc_magnitude          float64\n",
      "aspect                  float64\n",
      "dw_class_mode           float64\n",
      "dw_tree_prob__max       float64\n",
      "dw_tree_prob__min       float64\n",
      "dw_tree_prob__stdDev    float64\n",
      "dw_tree_prob_mean       float64\n",
      "elevation               float64\n",
      "esa_lc20                float64\n",
      "esa_lc21                float64\n",
      "esri_lc17               float64\n",
      "esri_lc18               float64\n",
      "esri_lc19               float64\n",
      "esri_lc20               float64\n",
      "esri_lc21               float64\n",
      "gfc_gain                float64\n",
      "gfc_loss                float64\n",
      "gfc_lossyear            float64\n",
      "gfc_tc00                float64\n",
      "lang_tree_height        float64\n",
      "potapov_tree_height     float64\n",
      "slope                   float64\n",
      "tmf_2016                float64\n",
      "tmf_2017                float64\n",
      "tmf_2018                float64\n",
      "tmf_2019                float64\n",
      "tmf_2020                float64\n",
      "tmf_defyear             float64\n",
      "tmf_degyear             float64\n",
      "tmf_main                float64\n",
      "tmf_sub                 float64\n",
      "geometry                 object\n",
      "point_id                  int64\n",
      "cnc_1520                float64\n",
      "chg_prob                float64\n",
      "simple_combined         float64\n",
      "stratum                   int64\n",
      "kmeans                    int64\n",
      "plotid                    int64\n",
      "lon                     float64\n",
      "lat                     float64\n",
      "SCE_SEM                  object\n",
      "SCE_GEO                  object\n",
      "MODIF                    object\n",
      "ORIGINE                  object\n",
      "NOM                      object\n",
      "CODE_INS                float64\n",
      "SURFACE                 float64\n",
      "CHEF_LIEU                object\n",
      "ID_SEPAL                float64\n",
      "BLABLA                  float64\n",
      "index_right             float64\n",
      "GID_2                    object\n",
      "GID_0                    object\n",
      "COUNTRY                  object\n",
      "GID_1                    object\n",
      "NAME_1                   object\n",
      "NL_NAME_1               float64\n",
      "NAME_2                   object\n",
      "VARNAME_2               float64\n",
      "NL_NAME_2               float64\n",
      "TYPE_2                   object\n",
      "ENGTYPE_2                object\n",
      "CC_2                    float64\n",
      "HASC_2                  float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(esbae.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "449f951d-7aaf-452f-80cf-ad8305b7daaa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1548365"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(esbae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "80f1e8b3-709f-4a87-8b2d-5445afc35bab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['images', 'mon_images', 'bfast_change_date', 'bfast_magnitude',\n",
       "       'bfast_means', 'cusum_change_date', 'cusum_confidence',\n",
       "       'cusum_magnitude', 'red_mean', 'red_sd',\n",
       "       ...\n",
       "       'GID_1', 'NAME_1', 'NL_NAME_1', 'NAME_2', 'VARNAME_2', 'NL_NAME_2',\n",
       "       'TYPE_2', 'ENGTYPE_2', 'CC_2', 'HASC_2'],\n",
       "      dtype='object', length=110)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eSBAE_cols = esbae.columns\n",
    "eSBAE_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d699564-4605-445b-b157-8100ee053f03",
   "metadata": {},
   "source": [
    "### Merge ceo and sbae tables\n",
    "#### replace validated points in esbae points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc76170a-f34e-4ff7-bbce-aaddc01d8054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10430"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make list of point_ids of validated points\n",
    "validated_id_list = merged_ceo_nodup['point_id'].tolist()\n",
    "len(validated_id_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c2d4c3d0-a285-4d4d-8d9e-b7235e043a14",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Merge ceo and sbae on \"pointid\" with an outer join\n",
    "esbae_ceo = pd.merge(esbae, merged_ceo_nodup, on='point_id', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ac1c6e9f-3b5b-440d-88cd-8fb0f5916580",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Fill NaN values in 'stratum' with values from the esbae DataFrame\n",
    "esbae_ceo['stratum'] = esbae_ceo['stratum_y'].fillna(esbae_ceo['stratum_x'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "186fce90-2a65-4d4b-a8ec-6d64709168b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1550108"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(esbae_ceo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2706fbbf-4744-41c5-bd58-03c4b5a4bff2",
   "metadata": {
    "tags": []
   },
   "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>images</th>\n",
       "      <th>mon_images</th>\n",
       "      <th>bfast_change_date</th>\n",
       "      <th>bfast_magnitude</th>\n",
       "      <th>bfast_means</th>\n",
       "      <th>cusum_change_date</th>\n",
       "      <th>cusum_confidence</th>\n",
       "      <th>cusum_magnitude</th>\n",
       "      <th>red_mean</th>\n",
       "      <th>red_sd</th>\n",
       "      <th>red_min</th>\n",
       "      <th>red_max</th>\n",
       "      <th>nir_mean</th>\n",
       "      <th>nir_sd</th>\n",
       "      <th>nir_min</th>\n",
       "      <th>nir_max</th>\n",
       "      <th>swir1_mean</th>\n",
       "      <th>swir1_sd</th>\n",
       "      <th>swir1_min</th>\n",
       "      <th>swir1_max</th>\n",
       "      <th>swir2_mean</th>\n",
       "      <th>swir2_sd</th>\n",
       "      <th>swir2_min</th>\n",
       "      <th>swir2_max</th>\n",
       "      <th>ndfi_mean</th>\n",
       "      <th>ndfi_sd</th>\n",
       "      <th>ndfi_min</th>\n",
       "      <th>ndfi_max</th>\n",
       "      <th>brightness_mean</th>\n",
       "      <th>brightness_sd</th>\n",
       "      <th>brightness_min</th>\n",
       "      <th>brightness_max</th>\n",
       "      <th>greenness_mean</th>\n",
       "      <th>greenness_sd</th>\n",
       "      <th>greenness_min</th>\n",
       "      <th>greenness_max</th>\n",
       "      <th>wetness_mean</th>\n",
       "      <th>wetness_sd</th>\n",
       "      <th>wetness_min</th>\n",
       "      <th>wetness_max</th>\n",
       "      <th>bs_slope_mean</th>\n",
       "      <th>bs_slope_sd</th>\n",
       "      <th>bs_slope_max</th>\n",
       "      <th>bs_slope_min</th>\n",
       "      <th>ccdc_change_date</th>\n",
       "      <th>ccdc_magnitude</th>\n",
       "      <th>aspect</th>\n",
       "      <th>dw_class_mode</th>\n",
       "      <th>dw_tree_prob__max</th>\n",
       "      <th>dw_tree_prob__min</th>\n",
       "      <th>dw_tree_prob__stdDev</th>\n",
       "      <th>dw_tree_prob_mean</th>\n",
       "      <th>elevation</th>\n",
       "      <th>esa_lc20</th>\n",
       "      <th>esa_lc21</th>\n",
       "      <th>esri_lc17</th>\n",
       "      <th>esri_lc18</th>\n",
       "      <th>esri_lc19</th>\n",
       "      <th>esri_lc20</th>\n",
       "      <th>esri_lc21</th>\n",
       "      <th>gfc_gain</th>\n",
       "      <th>gfc_loss</th>\n",
       "      <th>gfc_lossyear</th>\n",
       "      <th>gfc_tc00</th>\n",
       "      <th>lang_tree_height</th>\n",
       "      <th>potapov_tree_height</th>\n",
       "      <th>slope</th>\n",
       "      <th>tmf_2016</th>\n",
       "      <th>tmf_2017</th>\n",
       "      <th>tmf_2018</th>\n",
       "      <th>tmf_2019</th>\n",
       "      <th>tmf_2020</th>\n",
       "      <th>tmf_defyear</th>\n",
       "      <th>tmf_degyear</th>\n",
       "      <th>tmf_main</th>\n",
       "      <th>tmf_sub</th>\n",
       "      <th>geometry</th>\n",
       "      <th>point_id</th>\n",
       "      <th>cnc_1520</th>\n",
       "      <th>chg_prob_x</th>\n",
       "      <th>simple_combined_x</th>\n",
       "      <th>stratum_x</th>\n",
       "      <th>kmeans</th>\n",
       "      <th>plotid_x</th>\n",
       "      <th>lon_x</th>\n",
       "      <th>lat_x</th>\n",
       "      <th>SCE_SEM</th>\n",
       "      <th>SCE_GEO</th>\n",
       "      <th>MODIF</th>\n",
       "      <th>ORIGINE</th>\n",
       "      <th>NOM</th>\n",
       "      <th>CODE_INS</th>\n",
       "      <th>SURFACE</th>\n",
       "      <th>CHEF_LIEU</th>\n",
       "      <th>ID_SEPAL</th>\n",
       "      <th>BLABLA</th>\n",
       "      <th>index_right</th>\n",
       "      <th>GID_2</th>\n",
       "      <th>GID_0</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>GID_1</th>\n",
       "      <th>NAME_1</th>\n",
       "      <th>NL_NAME_1</th>\n",
       "      <th>NAME_2</th>\n",
       "      <th>VARNAME_2</th>\n",
       "      <th>NL_NAME_2</th>\n",
       "      <th>TYPE_2</th>\n",
       "      <th>ENGTYPE_2</th>\n",
       "      <th>CC_2</th>\n",
       "      <th>HASC_2</th>\n",
       "      <th>plotid_y</th>\n",
       "      <th>sampleid</th>\n",
       "      <th>lon_y</th>\n",
       "      <th>lat_y</th>\n",
       "      <th>email</th>\n",
       "      <th>flagged</th>\n",
       "      <th>collection_time</th>\n",
       "      <th>analysis_duration</th>\n",
       "      <th>sample_geom</th>\n",
       "      <th>chg_prob_y</th>\n",
       "      <th>simple_combined_y</th>\n",
       "      <th>stratum_y</th>\n",
       "      <th>Ref_FNF_2015</th>\n",
       "      <th>Ref_Regeneration</th>\n",
       "      <th>Ref_Change_1522</th>\n",
       "      <th>Ref_Change_Type_1522</th>\n",
       "      <th>ArtFor</th>\n",
       "      <th>ArtMine</th>\n",
       "      <th>InfraR</th>\n",
       "      <th>Urb</th>\n",
       "      <th>IndFor</th>\n",
       "      <th>Other</th>\n",
       "      <th>Other_Desc</th>\n",
       "      <th>IndMine</th>\n",
       "      <th>IndAg</th>\n",
       "      <th>ArtAg</th>\n",
       "      <th>Ref_Year</th>\n",
       "      <th>Ref_NFtype</th>\n",
       "      <th>Ref_Ftype</th>\n",
       "      <th>Commentaires</th>\n",
       "      <th>collection</th>\n",
       "      <th>interpreted</th>\n",
       "      <th>Ref_LCtype</th>\n",
       "      <th>Ref_Change_Year_1522</th>\n",
       "      <th>Def2016</th>\n",
       "      <th>Def2017</th>\n",
       "      <th>Def2018</th>\n",
       "      <th>Def2019</th>\n",
       "      <th>Def2020</th>\n",
       "      <th>Def2021</th>\n",
       "      <th>Def2022</th>\n",
       "      <th>Deg2016</th>\n",
       "      <th>Deg2017</th>\n",
       "      <th>Deg2018</th>\n",
       "      <th>Deg2019</th>\n",
       "      <th>Deg2020</th>\n",
       "      <th>Deg2021</th>\n",
       "      <th>Deg2022</th>\n",
       "      <th>Defall</th>\n",
       "      <th>Degall</th>\n",
       "      <th>Stable</th>\n",
       "      <th>NF</th>\n",
       "      <th>stratum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>127.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2016.082</td>\n",
       "      <td>-663.263855</td>\n",
       "      <td>-2.929723</td>\n",
       "      <td>2015.710</td>\n",
       "      <td>0.516891</td>\n",
       "      <td>5808.547516</td>\n",
       "      <td>9673.072153</td>\n",
       "      <td>264.749422</td>\n",
       "      <td>8049.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9673.072153</td>\n",
       "      <td>264.749422</td>\n",
       "      <td>8049.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9673.072153</td>\n",
       "      <td>264.749422</td>\n",
       "      <td>8049.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9673.072153</td>\n",
       "      <td>264.749422</td>\n",
       "      <td>8049.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9673.072153</td>\n",
       "      <td>264.749422</td>\n",
       "      <td>8049.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9673.072153</td>\n",
       "      <td>264.749422</td>\n",
       "      <td>8049.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9673.072153</td>\n",
       "      <td>264.749422</td>\n",
       "      <td>8049.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9673.072153</td>\n",
       "      <td>264.749422</td>\n",
       "      <td>8049.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>44.330723</td>\n",
       "      <td>9.525018</td>\n",
       "      <td>72.820237</td>\n",
       "      <td>15.758345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>471.539795</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>POINT (21.814596 3.176039)</td>\n",
       "      <td>1786794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.037333</td>\n",
       "      <td>0.037333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1786794.0</td>\n",
       "      <td>21.814596</td>\n",
       "      <td>3.176039</td>\n",
       "      <td>INS</td>\n",
       "      <td>UNDP/OCHA</td>\n",
       "      <td>2015-04-28</td>\n",
       "      <td>Numerisation Saint Moulin</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>405.0</td>\n",
       "      <td>54449.141681</td>\n",
       "      <td>Gbadolite</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>COD.20.2_1</td>\n",
       "      <td>COD</td>\n",
       "      <td>Democratic Republic of the Congo</td>\n",
       "      <td>COD.20_1</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Businga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Territoire</td>\n",
       "      <td>Territory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2017.721</td>\n",
       "      <td>328.460571</td>\n",
       "      <td>1.608799</td>\n",
       "      <td>2016.408</td>\n",
       "      <td>0.064274</td>\n",
       "      <td>1201.461111</td>\n",
       "      <td>9954.327778</td>\n",
       "      <td>104.017425</td>\n",
       "      <td>9213.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9954.327778</td>\n",
       "      <td>104.017425</td>\n",
       "      <td>9213.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9954.327778</td>\n",
       "      <td>104.017425</td>\n",
       "      <td>9213.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9954.327778</td>\n",
       "      <td>104.017425</td>\n",
       "      <td>9213.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9954.327778</td>\n",
       "      <td>104.017425</td>\n",
       "      <td>9213.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9954.327778</td>\n",
       "      <td>104.017425</td>\n",
       "      <td>9213.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9954.327778</td>\n",
       "      <td>104.017425</td>\n",
       "      <td>9213.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9954.327778</td>\n",
       "      <td>104.017425</td>\n",
       "      <td>9213.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>-2.921160</td>\n",
       "      <td>3.150850</td>\n",
       "      <td>5.662446</td>\n",
       "      <td>-11.455737</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>477.131683</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>POINT (21.819697 3.185814)</td>\n",
       "      <td>1786795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061333</td>\n",
       "      <td>0.061333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1786795.0</td>\n",
       "      <td>21.819697</td>\n",
       "      <td>3.185814</td>\n",
       "      <td>INS</td>\n",
       "      <td>UNDP/OCHA</td>\n",
       "      <td>2015-04-28</td>\n",
       "      <td>Numerisation Saint Moulin</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>405.0</td>\n",
       "      <td>54449.141681</td>\n",
       "      <td>Gbadolite</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>COD.20.2_1</td>\n",
       "      <td>COD</td>\n",
       "      <td>Democratic Republic of the Congo</td>\n",
       "      <td>COD.20_1</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Businga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Territoire</td>\n",
       "      <td>Territory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2015.512</td>\n",
       "      <td>-646.093933</td>\n",
       "      <td>-5.213411</td>\n",
       "      <td>2017.636</td>\n",
       "      <td>0.427163</td>\n",
       "      <td>5255.813768</td>\n",
       "      <td>9652.822283</td>\n",
       "      <td>275.581389</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9652.822283</td>\n",
       "      <td>275.581389</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9652.822283</td>\n",
       "      <td>275.581389</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9652.822283</td>\n",
       "      <td>275.581389</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9652.822283</td>\n",
       "      <td>275.581389</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9652.822283</td>\n",
       "      <td>275.581389</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9652.822283</td>\n",
       "      <td>275.581389</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9652.822283</td>\n",
       "      <td>275.581389</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.325556</td>\n",
       "      <td>9.713268</td>\n",
       "      <td>41.748108</td>\n",
       "      <td>-29.690243</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>407.467529</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>POINT (21.814248 3.156549)</td>\n",
       "      <td>1786824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019333</td>\n",
       "      <td>0.019333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1786824.0</td>\n",
       "      <td>21.814248</td>\n",
       "      <td>3.156549</td>\n",
       "      <td>INS</td>\n",
       "      <td>UNDP/OCHA</td>\n",
       "      <td>2015-04-28</td>\n",
       "      <td>Numerisation Saint Moulin</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>405.0</td>\n",
       "      <td>54449.141681</td>\n",
       "      <td>Gbadolite</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>COD.20.2_1</td>\n",
       "      <td>COD</td>\n",
       "      <td>Democratic Republic of the Congo</td>\n",
       "      <td>COD.20_1</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Businga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Territoire</td>\n",
       "      <td>Territory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>127.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2015.512</td>\n",
       "      <td>-720.501404</td>\n",
       "      <td>-8.295720</td>\n",
       "      <td>2016.258</td>\n",
       "      <td>0.105731</td>\n",
       "      <td>2415.789493</td>\n",
       "      <td>9715.193219</td>\n",
       "      <td>191.880403</td>\n",
       "      <td>8834.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9715.193219</td>\n",
       "      <td>191.880403</td>\n",
       "      <td>8834.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9715.193219</td>\n",
       "      <td>191.880403</td>\n",
       "      <td>8834.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9715.193219</td>\n",
       "      <td>191.880403</td>\n",
       "      <td>8834.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9715.193219</td>\n",
       "      <td>191.880403</td>\n",
       "      <td>8834.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9715.193219</td>\n",
       "      <td>191.880403</td>\n",
       "      <td>8834.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9715.193219</td>\n",
       "      <td>191.880403</td>\n",
       "      <td>8834.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9715.193219</td>\n",
       "      <td>191.880403</td>\n",
       "      <td>8834.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>-8.626105</td>\n",
       "      <td>7.777670</td>\n",
       "      <td>22.566297</td>\n",
       "      <td>-29.511456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>405.362030</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>POINT (21.819349 3.166323)</td>\n",
       "      <td>1786825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019333</td>\n",
       "      <td>0.019333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1786825.0</td>\n",
       "      <td>21.819349</td>\n",
       "      <td>3.166323</td>\n",
       "      <td>INS</td>\n",
       "      <td>UNDP/OCHA</td>\n",
       "      <td>2015-04-28</td>\n",
       "      <td>Numerisation Saint Moulin</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>405.0</td>\n",
       "      <td>54449.141681</td>\n",
       "      <td>Gbadolite</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>COD.20.2_1</td>\n",
       "      <td>COD</td>\n",
       "      <td>Democratic Republic of the Congo</td>\n",
       "      <td>COD.20_1</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Businga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Territoire</td>\n",
       "      <td>Territory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>121.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2015.534</td>\n",
       "      <td>-342.082703</td>\n",
       "      <td>-3.743933</td>\n",
       "      <td>2020.244</td>\n",
       "      <td>0.354583</td>\n",
       "      <td>3881.995676</td>\n",
       "      <td>9718.401122</td>\n",
       "      <td>240.410580</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9718.401122</td>\n",
       "      <td>240.410580</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9718.401122</td>\n",
       "      <td>240.410580</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9718.401122</td>\n",
       "      <td>240.410580</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9718.401122</td>\n",
       "      <td>240.410580</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9718.401122</td>\n",
       "      <td>240.410580</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9718.401122</td>\n",
       "      <td>240.410580</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9718.401122</td>\n",
       "      <td>240.410580</td>\n",
       "      <td>8530.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>14.225275</td>\n",
       "      <td>9.960431</td>\n",
       "      <td>45.675925</td>\n",
       "      <td>-15.207377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>471.921875</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>POINT (21.818997 3.146832)</td>\n",
       "      <td>1786858</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1786858.0</td>\n",
       "      <td>21.818997</td>\n",
       "      <td>3.146832</td>\n",
       "      <td>INS</td>\n",
       "      <td>UNDP/OCHA</td>\n",
       "      <td>2015-04-28</td>\n",
       "      <td>Numerisation Saint Moulin</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>405.0</td>\n",
       "      <td>54449.141681</td>\n",
       "      <td>Gbadolite</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>COD.20.2_1</td>\n",
       "      <td>COD</td>\n",
       "      <td>Democratic Republic of the Congo</td>\n",
       "      <td>COD.20_1</td>\n",
       "      <td>Nord-Ubangi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Businga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Territoire</td>\n",
       "      <td>Territory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   images  mon_images  bfast_change_date  bfast_magnitude  bfast_means  \\\n",
       "0   127.0        92.0           2016.082      -663.263855    -2.929723   \n",
       "1   121.0        93.0           2017.721       328.460571     1.608799   \n",
       "2   128.0        92.0           2015.512      -646.093933    -5.213411   \n",
       "3   127.0        92.0           2015.512      -720.501404    -8.295720   \n",
       "4   121.0        87.0           2015.534      -342.082703    -3.743933   \n",
       "\n",
       "   cusum_change_date  cusum_confidence  cusum_magnitude     red_mean  \\\n",
       "0           2015.710          0.516891      5808.547516  9673.072153   \n",
       "1           2016.408          0.064274      1201.461111  9954.327778   \n",
       "2           2017.636          0.427163      5255.813768  9652.822283   \n",
       "3           2016.258          0.105731      2415.789493  9715.193219   \n",
       "4           2020.244          0.354583      3881.995676  9718.401122   \n",
       "\n",
       "       red_sd  red_min  red_max     nir_mean      nir_sd  nir_min  nir_max  \\\n",
       "0  264.749422   8049.0  10000.0  9673.072153  264.749422   8049.0  10000.0   \n",
       "1  104.017425   9213.0  10000.0  9954.327778  104.017425   9213.0  10000.0   \n",
       "2  275.581389   8530.0  10000.0  9652.822283  275.581389   8530.0  10000.0   \n",
       "3  191.880403   8834.0  10000.0  9715.193219  191.880403   8834.0  10000.0   \n",
       "4  240.410580   8530.0  10000.0  9718.401122  240.410580   8530.0  10000.0   \n",
       "\n",
       "    swir1_mean    swir1_sd  swir1_min  swir1_max   swir2_mean    swir2_sd  \\\n",
       "0  9673.072153  264.749422     8049.0    10000.0  9673.072153  264.749422   \n",
       "1  9954.327778  104.017425     9213.0    10000.0  9954.327778  104.017425   \n",
       "2  9652.822283  275.581389     8530.0    10000.0  9652.822283  275.581389   \n",
       "3  9715.193219  191.880403     8834.0    10000.0  9715.193219  191.880403   \n",
       "4  9718.401122  240.410580     8530.0    10000.0  9718.401122  240.410580   \n",
       "\n",
       "   swir2_min  swir2_max    ndfi_mean     ndfi_sd  ndfi_min  ndfi_max  \\\n",
       "0     8049.0    10000.0  9673.072153  264.749422    8049.0   10000.0   \n",
       "1     9213.0    10000.0  9954.327778  104.017425    9213.0   10000.0   \n",
       "2     8530.0    10000.0  9652.822283  275.581389    8530.0   10000.0   \n",
       "3     8834.0    10000.0  9715.193219  191.880403    8834.0   10000.0   \n",
       "4     8530.0    10000.0  9718.401122  240.410580    8530.0   10000.0   \n",
       "\n",
       "   brightness_mean  brightness_sd  brightness_min  brightness_max  \\\n",
       "0      9673.072153     264.749422          8049.0         10000.0   \n",
       "1      9954.327778     104.017425          9213.0         10000.0   \n",
       "2      9652.822283     275.581389          8530.0         10000.0   \n",
       "3      9715.193219     191.880403          8834.0         10000.0   \n",
       "4      9718.401122     240.410580          8530.0         10000.0   \n",
       "\n",
       "   greenness_mean  greenness_sd  greenness_min  greenness_max  wetness_mean  \\\n",
       "0     9673.072153    264.749422         8049.0        10000.0   9673.072153   \n",
       "1     9954.327778    104.017425         9213.0        10000.0   9954.327778   \n",
       "2     9652.822283    275.581389         8530.0        10000.0   9652.822283   \n",
       "3     9715.193219    191.880403         8834.0        10000.0   9715.193219   \n",
       "4     9718.401122    240.410580         8530.0        10000.0   9718.401122   \n",
       "\n",
       "   wetness_sd  wetness_min  wetness_max  bs_slope_mean  bs_slope_sd  \\\n",
       "0  264.749422       8049.0      10000.0      44.330723     9.525018   \n",
       "1  104.017425       9213.0      10000.0      -2.921160     3.150850   \n",
       "2  275.581389       8530.0      10000.0       0.325556     9.713268   \n",
       "3  191.880403       8834.0      10000.0      -8.626105     7.777670   \n",
       "4  240.410580       8530.0      10000.0      14.225275     9.960431   \n",
       "\n",
       "   bs_slope_max  bs_slope_min  ccdc_change_date  ccdc_magnitude  aspect  \\\n",
       "0     72.820237     15.758345               0.0             0.0   149.0   \n",
       "1      5.662446    -11.455737               0.0             0.0   358.0   \n",
       "2     41.748108    -29.690243               0.0             0.0   307.0   \n",
       "3     22.566297    -29.511456               0.0             0.0   190.0   \n",
       "4     45.675925    -15.207377               0.0             0.0   106.0   \n",
       "\n",
       "   dw_class_mode  dw_tree_prob__max  dw_tree_prob__min  dw_tree_prob__stdDev  \\\n",
       "0            1.0               76.0                3.0                   8.0   \n",
       "1            1.0               75.0                2.0                  12.0   \n",
       "2            1.0               74.0                2.0                  10.0   \n",
       "3            1.0               74.0               13.0                  10.0   \n",
       "4            1.0               76.0                3.0                  11.0   \n",
       "\n",
       "   dw_tree_prob_mean   elevation  esa_lc20  esa_lc21  esri_lc17  esri_lc18  \\\n",
       "0               71.0  471.539795      10.0      10.0        2.0        2.0   \n",
       "1               70.0  477.131683      10.0      10.0        2.0        2.0   \n",
       "2               70.0  407.467529      10.0      10.0        2.0        2.0   \n",
       "3               70.0  405.362030      10.0      10.0        2.0        2.0   \n",
       "4               70.0  471.921875      10.0      10.0        2.0        2.0   \n",
       "\n",
       "   esri_lc19  esri_lc20  esri_lc21  gfc_gain  gfc_loss  gfc_lossyear  \\\n",
       "0        2.0        2.0        2.0       0.0       0.0           NaN   \n",
       "1        2.0        2.0        2.0       0.0       0.0           NaN   \n",
       "2        2.0        2.0        2.0       0.0       0.0           NaN   \n",
       "3        2.0        2.0        2.0       0.0       0.0           NaN   \n",
       "4        2.0        2.0        2.0       0.0       0.0           NaN   \n",
       "\n",
       "   gfc_tc00  lang_tree_height  potapov_tree_height  slope  tmf_2016  tmf_2017  \\\n",
       "0     100.0              36.0                 35.0    4.0       1.0       1.0   \n",
       "1     100.0              35.0                 33.0    3.0       1.0       1.0   \n",
       "2     100.0              25.0                 35.0   12.0       1.0       1.0   \n",
       "3     100.0              19.0                 27.0    2.0       1.0       1.0   \n",
       "4     100.0              34.0                 30.0    5.0       1.0       1.0   \n",
       "\n",
       "   tmf_2018  tmf_2019  tmf_2020  tmf_defyear  tmf_degyear  tmf_main  tmf_sub  \\\n",
       "0       1.0       1.0       1.0          0.0          0.0      10.0     10.0   \n",
       "1       1.0       1.0       1.0          0.0          0.0      10.0     10.0   \n",
       "2       1.0       1.0       1.0          0.0          0.0      10.0     10.0   \n",
       "3       1.0       1.0       1.0          0.0          0.0      10.0     10.0   \n",
       "4       1.0       1.0       1.0          0.0          0.0      10.0     10.0   \n",
       "\n",
       "                     geometry  point_id  cnc_1520  chg_prob_x  \\\n",
       "0  POINT (21.814596 3.176039)   1786794       NaN    0.037333   \n",
       "1  POINT (21.819697 3.185814)   1786795       NaN    0.061333   \n",
       "2  POINT (21.814248 3.156549)   1786824       NaN    0.019333   \n",
       "3  POINT (21.819349 3.166323)   1786825       NaN    0.019333   \n",
       "4  POINT (21.818997 3.146832)   1786858       NaN    0.003333   \n",
       "\n",
       "   simple_combined_x  stratum_x  kmeans   plotid_x      lon_x     lat_x  \\\n",
       "0           0.037333        1.0     1.0  1786794.0  21.814596  3.176039   \n",
       "1           0.061333        1.0     1.0  1786795.0  21.819697  3.185814   \n",
       "2           0.019333        1.0     1.0  1786824.0  21.814248  3.156549   \n",
       "3           0.019333        1.0     1.0  1786825.0  21.819349  3.166323   \n",
       "4           0.003333        1.0     1.0  1786858.0  21.818997  3.146832   \n",
       "\n",
       "  SCE_SEM    SCE_GEO       MODIF                    ORIGINE          NOM  \\\n",
       "0     INS  UNDP/OCHA  2015-04-28  Numerisation Saint Moulin  Nord-Ubangi   \n",
       "1     INS  UNDP/OCHA  2015-04-28  Numerisation Saint Moulin  Nord-Ubangi   \n",
       "2     INS  UNDP/OCHA  2015-04-28  Numerisation Saint Moulin  Nord-Ubangi   \n",
       "3     INS  UNDP/OCHA  2015-04-28  Numerisation Saint Moulin  Nord-Ubangi   \n",
       "4     INS  UNDP/OCHA  2015-04-28  Numerisation Saint Moulin  Nord-Ubangi   \n",
       "\n",
       "   CODE_INS       SURFACE  CHEF_LIEU  ID_SEPAL  BLABLA  index_right  \\\n",
       "0     405.0  54449.141681  Gbadolite       8.0     0.0        178.0   \n",
       "1     405.0  54449.141681  Gbadolite       8.0     0.0        178.0   \n",
       "2     405.0  54449.141681  Gbadolite       8.0     0.0        178.0   \n",
       "3     405.0  54449.141681  Gbadolite       8.0     0.0        178.0   \n",
       "4     405.0  54449.141681  Gbadolite       8.0     0.0        178.0   \n",
       "\n",
       "        GID_2 GID_0                           COUNTRY     GID_1       NAME_1  \\\n",
       "0  COD.20.2_1   COD  Democratic Republic of the Congo  COD.20_1  Nord-Ubangi   \n",
       "1  COD.20.2_1   COD  Democratic Republic of the Congo  COD.20_1  Nord-Ubangi   \n",
       "2  COD.20.2_1   COD  Democratic Republic of the Congo  COD.20_1  Nord-Ubangi   \n",
       "3  COD.20.2_1   COD  Democratic Republic of the Congo  COD.20_1  Nord-Ubangi   \n",
       "4  COD.20.2_1   COD  Democratic Republic of the Congo  COD.20_1  Nord-Ubangi   \n",
       "\n",
       "   NL_NAME_1   NAME_2  VARNAME_2  NL_NAME_2      TYPE_2  ENGTYPE_2  CC_2  \\\n",
       "0        NaN  Businga        NaN        NaN  Territoire  Territory   NaN   \n",
       "1        NaN  Businga        NaN        NaN  Territoire  Territory   NaN   \n",
       "2        NaN  Businga        NaN        NaN  Territoire  Territory   NaN   \n",
       "3        NaN  Businga        NaN        NaN  Territoire  Territory   NaN   \n",
       "4        NaN  Businga        NaN        NaN  Territoire  Territory   NaN   \n",
       "\n",
       "   HASC_2  plotid_y  sampleid  lon_y  lat_y email flagged collection_time  \\\n",
       "0     NaN       NaN       NaN    NaN    NaN   NaN     NaN             NaN   \n",
       "1     NaN       NaN       NaN    NaN    NaN   NaN     NaN             NaN   \n",
       "2     NaN       NaN       NaN    NaN    NaN   NaN     NaN             NaN   \n",
       "3     NaN       NaN       NaN    NaN    NaN   NaN     NaN             NaN   \n",
       "4     NaN       NaN       NaN    NaN    NaN   NaN     NaN             NaN   \n",
       "\n",
       "  analysis_duration sample_geom  chg_prob_y  simple_combined_y  stratum_y  \\\n",
       "0               NaN         NaN         NaN                NaN        NaN   \n",
       "1               NaN         NaN         NaN                NaN        NaN   \n",
       "2               NaN         NaN         NaN                NaN        NaN   \n",
       "3               NaN         NaN         NaN                NaN        NaN   \n",
       "4               NaN         NaN         NaN                NaN        NaN   \n",
       "\n",
       "  Ref_FNF_2015 Ref_Regeneration Ref_Change_1522 Ref_Change_Type_1522 ArtFor  \\\n",
       "0          NaN              NaN             NaN                  NaN    NaN   \n",
       "1          NaN              NaN             NaN                  NaN    NaN   \n",
       "2          NaN              NaN             NaN                  NaN    NaN   \n",
       "3          NaN              NaN             NaN                  NaN    NaN   \n",
       "4          NaN              NaN             NaN                  NaN    NaN   \n",
       "\n",
       "  ArtMine InfraR  Urb IndFor Other Other_Desc IndMine IndAg ArtAg  Ref_Year  \\\n",
       "0     NaN    NaN  NaN    NaN   NaN        NaN     NaN   NaN   NaN       NaN   \n",
       "1     NaN    NaN  NaN    NaN   NaN        NaN     NaN   NaN   NaN       NaN   \n",
       "2     NaN    NaN  NaN    NaN   NaN        NaN     NaN   NaN   NaN       NaN   \n",
       "3     NaN    NaN  NaN    NaN   NaN        NaN     NaN   NaN   NaN       NaN   \n",
       "4     NaN    NaN  NaN    NaN   NaN        NaN     NaN   NaN   NaN       NaN   \n",
       "\n",
       "  Ref_NFtype Ref_Ftype Commentaires  collection  interpreted Ref_LCtype  \\\n",
       "0        NaN       NaN          NaN         NaN          NaN        NaN   \n",
       "1        NaN       NaN          NaN         NaN          NaN        NaN   \n",
       "2        NaN       NaN          NaN         NaN          NaN        NaN   \n",
       "3        NaN       NaN          NaN         NaN          NaN        NaN   \n",
       "4        NaN       NaN          NaN         NaN          NaN        NaN   \n",
       "\n",
       "  Ref_Change_Year_1522  Def2016  Def2017  Def2018  Def2019  Def2020  Def2021  \\\n",
       "0                  NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1                  NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2                  NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3                  NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4                  NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   Def2022  Deg2016  Deg2017  Deg2018  Deg2019  Deg2020  Deg2021  Deg2022  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "   Defall  Degall  Stable  NF  stratum  \n",
       "0     NaN     NaN     NaN NaN      1.0  \n",
       "1     NaN     NaN     NaN NaN      1.0  \n",
       "2     NaN     NaN     NaN NaN      1.0  \n",
       "3     NaN     NaN     NaN NaN      1.0  \n",
       "4     NaN     NaN     NaN NaN      1.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esbae_ceo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27fdc0d6-d2ff-482b-93e5-736ea6acfd14",
   "metadata": {},
   "source": [
    "#### clean up _x and _y columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2ea74501-e56d-440c-9713-d4a3d134b2da",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['images', 'mon_images', 'bfast_change_date', 'bfast_magnitude', 'bfast_means', 'cusum_change_date', 'cusum_confidence', 'cusum_magnitude', 'red_mean', 'red_sd', 'red_min', 'red_max', 'nir_mean', 'nir_sd', 'nir_min', 'nir_max', 'swir1_mean', 'swir1_sd', 'swir1_min', 'swir1_max', 'swir2_mean', 'swir2_sd', 'swir2_min', 'swir2_max', 'ndfi_mean', 'ndfi_sd', 'ndfi_min', 'ndfi_max', 'brightness_mean', 'brightness_sd', 'brightness_min', 'brightness_max', 'greenness_mean', 'greenness_sd', 'greenness_min', 'greenness_max', 'wetness_mean', 'wetness_sd', 'wetness_min', 'wetness_max', 'bs_slope_mean', 'bs_slope_sd', 'bs_slope_max', 'bs_slope_min', 'ccdc_change_date', 'ccdc_magnitude', 'aspect', 'dw_class_mode', 'dw_tree_prob__max', 'dw_tree_prob__min', 'dw_tree_prob__stdDev', 'dw_tree_prob_mean', 'elevation', 'esa_lc20', 'esa_lc21', 'esri_lc17', 'esri_lc18', 'esri_lc19', 'esri_lc20', 'esri_lc21', 'gfc_gain', 'gfc_loss', 'gfc_lossyear', 'gfc_tc00', 'lang_tree_height', 'potapov_tree_height', 'slope', 'tmf_2016', 'tmf_2017', 'tmf_2018', 'tmf_2019', 'tmf_2020', 'tmf_defyear', 'tmf_degyear', 'tmf_main', 'tmf_sub', 'geometry', 'point_id', 'cnc_1520', 'chg_prob_x', 'simple_combined_x', 'stratum_x', 'kmeans', 'plotid_x', 'lon_x', 'lat_x', 'SCE_SEM', 'SCE_GEO', 'MODIF', 'ORIGINE', 'NOM', 'CODE_INS', 'SURFACE', 'CHEF_LIEU', 'ID_SEPAL', 'BLABLA', 'index_right', 'GID_2', 'GID_0', 'COUNTRY', 'GID_1', 'NAME_1', 'NL_NAME_1', 'NAME_2', 'VARNAME_2', 'NL_NAME_2', 'TYPE_2', 'ENGTYPE_2', 'CC_2', 'HASC_2', 'plotid_y', 'sampleid', 'lon_y', 'lat_y', 'email', 'flagged', 'collection_time', 'analysis_duration', 'sample_geom', 'chg_prob_y', 'simple_combined_y', 'stratum_y', 'Ref_FNF_2015', 'Ref_Regeneration', 'Ref_Change_1522', 'Ref_Change_Type_1522', 'ArtFor', 'ArtMine', 'InfraR', 'Urb', 'IndFor', 'Other', 'Other_Desc', 'IndMine', 'IndAg', 'ArtAg', 'Ref_Year', 'Ref_NFtype', 'Ref_Ftype', 'Commentaires', 'collection', 'interpreted', 'Ref_LCtype', 'Ref_Change_Year_1522', 'Def2016', 'Def2017', 'Def2018', 'Def2019', 'Def2020', 'Def2021', 'Def2022', 'Deg2016', 'Deg2017', 'Deg2018', 'Deg2019', 'Deg2020', 'Deg2021', 'Deg2022', 'Defall', 'Degall', 'Stable', 'NF', 'stratum']\n"
     ]
    }
   ],
   "source": [
    "merged_columns = esbae_ceo.columns.tolist()\n",
    "print(merged_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "615ac4df-3bae-4fcc-9d65-d6b094d5f043",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#drop and columns\n",
    "esbae_ceo = esbae_ceo.drop(['plotid_y', 'lon_y', 'lat_y','chg_prob_y', 'simple_combined_y', 'stratum_y', 'stratum_x'], axis=1)\n",
    "esbae_ceo.columns = esbae_ceo.columns.str.replace('_x', '')\n",
    "esbae_ceo.columns = esbae_ceo.columns.str.replace('_y', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a413d7d4-c52b-4e7b-a830-9d5f2019a41b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['images' 'mon_images' 'bfast_change_date' 'bfast_magnitude' 'bfast_means'\n",
      " 'cusum_change_date' 'cusum_confidence' 'cusum_magnitude' 'red_mean'\n",
      " 'red_sd' 'red_min' 'red_max' 'nir_mean' 'nir_sd' 'nir_min' 'nir_max'\n",
      " 'swir1_mean' 'swir1_sd' 'swir1_min' 'swir1_max' 'swir2_mean' 'swir2_sd'\n",
      " 'swir2_min' 'swir2_max' 'ndfi_mean' 'ndfi_sd' 'ndfi_min' 'ndfi_max'\n",
      " 'brightness_mean' 'brightness_sd' 'brightness_min' 'brightness_max'\n",
      " 'greenness_mean' 'greenness_sd' 'greenness_min' 'greenness_max'\n",
      " 'wetness_mean' 'wetness_sd' 'wetness_min' 'wetness_max' 'bs_slope_mean'\n",
      " 'bs_slope_sd' 'bs_slope_max' 'bs_slope_min' 'ccdc_change_date'\n",
      " 'ccdc_magnitude' 'aspect' 'dw_class_mode' 'dw_tree_prob__max'\n",
      " 'dw_tree_prob__min' 'dw_tree_prob__stdDev' 'dw_tree_prob_mean'\n",
      " 'elevation' 'esa_lc20' 'esa_lc21' 'esri_lc17' 'esri_lc18' 'esri_lc19'\n",
      " 'esri_lc20' 'esri_lc21' 'gfc_gain' 'gfc_loss' 'gfc_lossyear' 'gfc_tc00'\n",
      " 'lang_tree_height' 'potapov_tree_height' 'slope' 'tmf_2016' 'tmf_2017'\n",
      " 'tmf_2018' 'tmf_2019' 'tmf_2020' 'tmf_defyear' 'tmf_degyear' 'tmf_main'\n",
      " 'tmf_sub' 'geometry' 'point_id' 'cnc_1520' 'chg_prob' 'simple_combined'\n",
      " 'kmeans' 'plotid' 'lon' 'lat' 'SCE_SEM' 'SCE_GEO' 'MODIF' 'ORIGINE' 'NOM'\n",
      " 'CODE_INS' 'SURFACE' 'CHEF_LIEU' 'ID_SEPAL' 'BLABLA' 'index_right'\n",
      " 'GID_2' 'GID_0' 'COUNTRY' 'GID_1' 'NAME_1' 'NL_NAME_1' 'NAME_2'\n",
      " 'VARNAME_2' 'NL_NAME_2' 'TYPE_2' 'ENGTYPE_2' 'CC_2' 'HASC_2' 'sampleid'\n",
      " 'email' 'flagged' 'collection_time' 'analysis_duration' 'sample_geom'\n",
      " 'Ref_FNF_2015' 'Ref_Regeneration' 'Ref_Change_1522'\n",
      " 'Ref_Change_Type_1522' 'ArtFor' 'ArtMine' 'InfraR' 'Urb' 'IndFor' 'Other'\n",
      " 'Other_Desc' 'IndMine' 'IndAg' 'ArtAg' 'Ref_Year' 'Ref_NFtype'\n",
      " 'Ref_Ftype' 'Commentaires' 'collection' 'interpreted' 'Ref_LCtype'\n",
      " 'Ref_Change_Year_1522' 'Def2016' 'Def2017' 'Def2018' 'Def2019' 'Def2020'\n",
      " 'Def2021' 'Def2022' 'Deg2016' 'Deg2017' 'Deg2018' 'Deg2019' 'Deg2020'\n",
      " 'Deg2021' 'Deg2022' 'Defall' 'Degall' 'Stable' 'NF' 'stratum']\n"
     ]
    }
   ],
   "source": [
    "print(esbae_ceo.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ef5a019b-fce8-474e-a75c-4d442a303f69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1539678\n",
       "1      10430\n",
       "Name: interpreted, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esbae_ceo['interpreted'] = esbae_ceo['interpreted'].fillna(0)\n",
    "esbae_ceo['interpreted'] = esbae_ceo['interpreted'].astype(int)\n",
    "esbae_ceo['interpreted'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dbc6a9bd-8fd3-49e9-8d56-e1420024ecc7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN        1539678\n",
       "NF            5679\n",
       "Stable        3808\n",
       "Def2019        136\n",
       "Def2018        127\n",
       "Def2020        124\n",
       "Def2021        107\n",
       "Def2017        107\n",
       "Def2022         71\n",
       "Def2016         71\n",
       "Deg2016         54\n",
       "Deg2020         33\n",
       "Deg2019         33\n",
       "Deg2018         24\n",
       "Deg2021         21\n",
       "Deg2017         18\n",
       "Deg2022         17\n",
       "Name: Ref_Change_Year_1522, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esbae_ceo['Ref_Change_Year_1522'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7aa39336-119b-438e-8925-b84ad0b9afcb",
   "metadata": {
    "tags": []
   },
   "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>stratum</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ref_Change_Year_1522</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Def2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Def2017</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Def2018</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Def2019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Def2020</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Def2021</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Def2022</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deg2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deg2017</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deg2018</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deg2019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deg2020</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deg2021</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deg2022</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NF</th>\n",
       "      <td>19.0</td>\n",
       "      <td>3134.0</td>\n",
       "      <td>2526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stable</th>\n",
       "      <td>145.0</td>\n",
       "      <td>2212.0</td>\n",
       "      <td>1451.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stratum                 1.0     2.0     3.0\n",
       "Ref_Change_Year_1522                       \n",
       "Def2016                 NaN    10.0    61.0\n",
       "Def2017                 NaN    14.0    93.0\n",
       "Def2018                 NaN    12.0   115.0\n",
       "Def2019                 NaN    20.0   116.0\n",
       "Def2020                 NaN    22.0   102.0\n",
       "Def2021                 NaN    31.0    76.0\n",
       "Def2022                 NaN    32.0    39.0\n",
       "Deg2016                 NaN     6.0    48.0\n",
       "Deg2017                 NaN     6.0    12.0\n",
       "Deg2018                 NaN     7.0    17.0\n",
       "Deg2019                 NaN    13.0    20.0\n",
       "Deg2020                 1.0    10.0    22.0\n",
       "Deg2021                 NaN    10.0    11.0\n",
       "Deg2022                 NaN     8.0     9.0\n",
       "NF                     19.0  3134.0  2526.0\n",
       "Stable                145.0  2212.0  1451.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check points by change and stratum\n",
    "points_strata = pd.pivot_table(esbae_ceo, values = 'point_id', index = 'Ref_Change_Year_1522',columns = 'stratum', aggfunc =  \"count\")\n",
    "points_strata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "395abe74-816c-4574-b9c1-723ad8847395",
   "metadata": {
    "tags": []
   },
   "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>stratum</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>165.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>3166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1948.0</td>\n",
       "      <td>1394.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "stratum       1.0     2.0     3.0\n",
       "collection                       \n",
       "1.0         165.0   175.0   158.0\n",
       "2.0           NaN  3424.0  3166.0\n",
       "3.0           NaN  1948.0  1394.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check points by change and stratum\n",
    "ceo_validated = esbae_ceo[esbae_ceo['interpreted'] == 1]\n",
    "ceo_strata = pd.pivot_table(ceo_validated, values = 'point_id', index = 'collection',columns = 'stratum', aggfunc =  \"count\")\n",
    "ceo_strata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "227e4a9b-f5d9-46fe-b580-22032d1c8a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    845486\n",
       "2.0    526569\n",
       "3.0    178053\n",
       "Name: stratum, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esbae_ceo['stratum'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337f6b9d-9d22-478f-a241-82cb5e605fb8",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a12dbf8a-baa2-472d-ad75-bc528465ec2f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'images': 1743, 'mon_images': 1743, 'bfast_change_date': 1743, 'bfast_magnitude': 1743, 'bfast_means': 1743, 'cusum_change_date': 1743, 'cusum_confidence': 1743, 'cusum_magnitude': 1743, 'red_mean': 1743, 'red_sd': 1743, 'red_min': 1743, 'red_max': 1743, 'nir_mean': 1743, 'nir_sd': 1743, 'nir_min': 1743, 'nir_max': 1743, 'swir1_mean': 1743, 'swir1_sd': 1743, 'swir1_min': 1743, 'swir1_max': 1743, 'swir2_mean': 1743, 'swir2_sd': 1743, 'swir2_min': 1743, 'swir2_max': 1743, 'ndfi_mean': 1743, 'ndfi_sd': 1743, 'ndfi_min': 1743, 'ndfi_max': 1743, 'brightness_mean': 1743, 'brightness_sd': 1743, 'brightness_min': 1743, 'brightness_max': 1743, 'greenness_mean': 1743, 'greenness_sd': 1743, 'greenness_min': 1743, 'greenness_max': 1743, 'wetness_mean': 1743, 'wetness_sd': 1743, 'wetness_min': 1743, 'wetness_max': 1743, 'bs_slope_mean': 1743, 'bs_slope_sd': 1743, 'bs_slope_max': 1743, 'bs_slope_min': 1743, 'ccdc_change_date': 1743, 'ccdc_magnitude': 1743, 'aspect': 27763, 'dw_class_mode': 27787, 'dw_tree_prob__max': 27787, 'dw_tree_prob__min': 27787, 'dw_tree_prob__stdDev': 27787, 'dw_tree_prob_mean': 27787, 'elevation': 27763, 'esa_lc20': 27763, 'esa_lc21': 27763, 'esri_lc17': 27763, 'esri_lc18': 27763, 'esri_lc19': 27763, 'esri_lc20': 27763, 'esri_lc21': 27763, 'gfc_gain': 27763, 'gfc_loss': 27763, 'gfc_lossyear': 1422808, 'gfc_tc00': 27763, 'lang_tree_height': 29238, 'potapov_tree_height': 27763, 'slope': 27763, 'tmf_2016': 27763, 'tmf_2017': 27763, 'tmf_2018': 27763, 'tmf_2019': 27763, 'tmf_2020': 27763, 'tmf_defyear': 27763, 'tmf_degyear': 27763, 'tmf_main': 27763, 'tmf_sub': 27763, 'geometry': 1743, 'point_id': 0, 'cnc_1520': 1550108, 'chg_prob': 1743, 'simple_combined': 1743, 'stratum': 0, 'kmeans': 1743, 'plotid': 1743, 'lon': 1743, 'lat': 1743, 'SCE_SEM': 3090, 'SCE_GEO': 3090, 'MODIF': 3090, 'ORIGINE': 3090, 'NOM': 3090, 'CODE_INS': 3090, 'SURFACE': 3090, 'CHEF_LIEU': 3090, 'ID_SEPAL': 3090, 'BLABLA': 3090, 'index_right': 3030, 'GID_2': 3030, 'GID_0': 3030, 'COUNTRY': 3030, 'GID_1': 3030, 'NAME_1': 3030, 'NL_NAME_1': 1550108, 'NAME_2': 3030, 'VARNAME_2': 1550108, 'NL_NAME_2': 1550108, 'TYPE_2': 3030, 'ENGTYPE_2': 3030, 'CC_2': 1550108, 'HASC_2': 1550108}\n"
     ]
    }
   ],
   "source": [
    "# Initialize a dictionary to store the counts by column\n",
    "missing_counts = {}\n",
    "\n",
    "# Count missing values and sum the counts by column\n",
    "for column in eSBAE_cols:\n",
    "    missing_counts[column] = esbae_ceo[column].isna().sum()\n",
    "\n",
    "# Print the counts\n",
    "print(missing_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cf541d-ffb9-4489-b03a-ba2bcb7b5878",
   "metadata": {},
   "source": [
    "##### plot deforestation points by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f1c32086-c2e8-41ca-b034-fc54d94edeeb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#select the values you want to plot\n",
    "values_to_plot = ['Def2016','Def2017','Def2018','Def2019','Def2020','Def2021','Def2022']\n",
    "\n",
    "# Filter the DataFrame to include only the selected values\n",
    "filtered_df = esbae_ceo[esbae_ceo['Ref_Change_Year_1522'].isin(values_to_plot)]\n",
    "\n",
    "# Get the value counts and sort by index (alphabetically)\n",
    "defvalue_counts = filtered_df['Ref_Change_Year_1522'].value_counts().sort_index()\n",
    "\n",
    "# Plot the value counts as a bar graph\n",
    "plt.figure(figsize=(8, 6))  # Optional: Set the figure size\n",
    "defvalue_counts.plot(kind='bar', color='skyblue')\n",
    "\n",
    "\n",
    "# Set plot labels and title\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Count')\n",
    "plt.title('# of def points per year')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c7b947-6152-49cb-90cd-a54b7f7d0e3c",
   "metadata": {},
   "source": [
    "##### plot degradation points by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a0bd70ef-914e-4c48-a377-4ee31526a640",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot DEG \n",
    "#select the values you want to plot\n",
    "values_to_plot = ['Deg2016','Deg2017','Deg2018','Deg2019','Deg2020','Deg2021','Deg2022']\n",
    "\n",
    "# Filter the DataFrame to include only the selected values\n",
    "filtered_df = esbae_ceo[esbae_ceo['Ref_Change_Year_1522'].isin(values_to_plot)]\n",
    "\n",
    "# Get the value counts and sort by index (alphabetically)\n",
    "degvalue_counts = filtered_df['Ref_Change_Year_1522'].value_counts().sort_index()\n",
    "\n",
    "# Plot the value counts as a bar graph\n",
    "plt.figure(figsize=(8, 6))  # Optional: Set the figure size\n",
    "degvalue_counts.plot(kind='bar', color='skyblue')\n",
    "\n",
    "\n",
    "# Set plot labels and title\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Count')\n",
    "plt.title('# of deg points per year')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "60b15ae2-0dc1-4edc-896d-028c731faff6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['images' 'mon_images' 'bfast_change_date' 'bfast_magnitude' 'bfast_means'\n",
      " 'cusum_change_date' 'cusum_confidence' 'cusum_magnitude' 'red_mean'\n",
      " 'red_sd' 'red_min' 'red_max' 'nir_mean' 'nir_sd' 'nir_min' 'nir_max'\n",
      " 'swir1_mean' 'swir1_sd' 'swir1_min' 'swir1_max' 'swir2_mean' 'swir2_sd'\n",
      " 'swir2_min' 'swir2_max' 'ndfi_mean' 'ndfi_sd' 'ndfi_min' 'ndfi_max'\n",
      " 'brightness_mean' 'brightness_sd' 'brightness_min' 'brightness_max'\n",
      " 'greenness_mean' 'greenness_sd' 'greenness_min' 'greenness_max'\n",
      " 'wetness_mean' 'wetness_sd' 'wetness_min' 'wetness_max' 'bs_slope_mean'\n",
      " 'bs_slope_sd' 'bs_slope_max' 'bs_slope_min' 'ccdc_change_date'\n",
      " 'ccdc_magnitude' 'aspect' 'dw_class_mode' 'dw_tree_prob__max'\n",
      " 'dw_tree_prob__min' 'dw_tree_prob__stdDev' 'dw_tree_prob_mean'\n",
      " 'elevation' 'esa_lc20' 'esa_lc21' 'esri_lc17' 'esri_lc18' 'esri_lc19'\n",
      " 'esri_lc20' 'esri_lc21' 'gfc_gain' 'gfc_loss' 'gfc_lossyear' 'gfc_tc00'\n",
      " 'lang_tree_height' 'potapov_tree_height' 'slope' 'tmf_2016' 'tmf_2017'\n",
      " 'tmf_2018' 'tmf_2019' 'tmf_2020' 'tmf_defyear' 'tmf_degyear' 'tmf_main'\n",
      " 'tmf_sub' 'geometry' 'point_id' 'cnc_1520' 'CAFI_LC_2015' 'chg_prob'\n",
      " 'simple_combined' 'kmeans' 'plotid' 'lon' 'lat' 'sampleid' 'email'\n",
      " 'flagged' 'collection_time' 'analysis_duration' 'sample_geom'\n",
      " 'Ref_FNF_2015' 'Ref_Regeneration' 'Ref_Change_1522'\n",
      " 'Ref_Change_Type_1522' 'ArtFor' 'ArtMine' 'InfraR' 'Urb' 'IndFor' 'Other'\n",
      " 'Other_Desc' 'IndMine' 'IndAg' 'ArtAg' 'Ref_Year' 'Ref_NFType'\n",
      " 'Ref_FType' 'Commentaires' 'collection' 'interpreted'\n",
      " 'Ref_Change_Year_1522' 'Def2016' 'Def2017' 'Def2018' 'Def2019' 'Def2020'\n",
      " 'Def2021' 'Def2022' 'Deg2016' 'Deg2017' 'Deg2018' 'Deg2019' 'Deg2020'\n",
      " 'Deg2021' 'Deg2022' 'Defall' 'Degall' 'Stable' 'NF' 'stratum']\n"
     ]
    }
   ],
   "source": [
    "print(esbae_ceo.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b11d713f-fc8b-4fb3-92b8-d8070726fde3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Def2016      21\n",
       "Def2017      13\n",
       "Def2018       7\n",
       "Def2019       2\n",
       "Def2020       6\n",
       "Def2021       7\n",
       "Def2022      13\n",
       "Deg2016      14\n",
       "Deg2017      15\n",
       "Deg2018      25\n",
       "Deg2019      16\n",
       "Deg2020      28\n",
       "Deg2021      26\n",
       "Deg2022      38\n",
       "NF         1238\n",
       "Stable     5070\n",
       "Name: Ref_Change_Year_1522, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_annual_counts = esbae_ceo['Ref_Change_Year_1522'].value_counts()\n",
    "merged_annual_counts = merged_annual_counts.sort_index()\n",
    "merged_annual_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "28ba55f8-c672-43c9-bff1-667f09029210",
   "metadata": {},
   "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>images</th>\n",
       "      <th>mon_images</th>\n",
       "      <th>bfast_change_date</th>\n",
       "      <th>bfast_magnitude</th>\n",
       "      <th>bfast_means</th>\n",
       "      <th>cusum_change_date</th>\n",
       "      <th>cusum_confidence</th>\n",
       "      <th>cusum_magnitude</th>\n",
       "      <th>red_mean</th>\n",
       "      <th>red_sd</th>\n",
       "      <th>red_min</th>\n",
       "      <th>red_max</th>\n",
       "      <th>nir_mean</th>\n",
       "      <th>nir_sd</th>\n",
       "      <th>nir_min</th>\n",
       "      <th>nir_max</th>\n",
       "      <th>swir1_mean</th>\n",
       "      <th>swir1_sd</th>\n",
       "      <th>swir1_min</th>\n",
       "      <th>swir1_max</th>\n",
       "      <th>swir2_mean</th>\n",
       "      <th>swir2_sd</th>\n",
       "      <th>swir2_min</th>\n",
       "      <th>swir2_max</th>\n",
       "      <th>ndfi_mean</th>\n",
       "      <th>ndfi_sd</th>\n",
       "      <th>ndfi_min</th>\n",
       "      <th>ndfi_max</th>\n",
       "      <th>brightness_mean</th>\n",
       "      <th>brightness_sd</th>\n",
       "      <th>brightness_min</th>\n",
       "      <th>brightness_max</th>\n",
       "      <th>greenness_mean</th>\n",
       "      <th>greenness_sd</th>\n",
       "      <th>greenness_min</th>\n",
       "      <th>greenness_max</th>\n",
       "      <th>wetness_mean</th>\n",
       "      <th>wetness_sd</th>\n",
       "      <th>wetness_min</th>\n",
       "      <th>wetness_max</th>\n",
       "      <th>bs_slope_mean</th>\n",
       "      <th>bs_slope_sd</th>\n",
       "      <th>bs_slope_max</th>\n",
       "      <th>bs_slope_min</th>\n",
       "      <th>ccdc_change_date</th>\n",
       "      <th>ccdc_magnitude</th>\n",
       "      <th>aspect</th>\n",
       "      <th>dw_class_mode</th>\n",
       "      <th>dw_tree_prob__max</th>\n",
       "      <th>dw_tree_prob__min</th>\n",
       "      <th>dw_tree_prob__stdDev</th>\n",
       "      <th>dw_tree_prob_mean</th>\n",
       "      <th>elevation</th>\n",
       "      <th>esa_lc20</th>\n",
       "      <th>esa_lc21</th>\n",
       "      <th>esri_lc17</th>\n",
       "      <th>esri_lc18</th>\n",
       "      <th>esri_lc19</th>\n",
       "      <th>esri_lc20</th>\n",
       "      <th>esri_lc21</th>\n",
       "      <th>gfc_gain</th>\n",
       "      <th>gfc_loss</th>\n",
       "      <th>gfc_lossyear</th>\n",
       "      <th>gfc_tc00</th>\n",
       "      <th>lang_tree_height</th>\n",
       "      <th>potapov_tree_height</th>\n",
       "      <th>slope</th>\n",
       "      <th>tmf_2016</th>\n",
       "      <th>tmf_2017</th>\n",
       "      <th>tmf_2018</th>\n",
       "      <th>tmf_2019</th>\n",
       "      <th>tmf_2020</th>\n",
       "      <th>tmf_defyear</th>\n",
       "      <th>tmf_degyear</th>\n",
       "      <th>tmf_main</th>\n",
       "      <th>tmf_sub</th>\n",
       "      <th>point_id</th>\n",
       "      <th>cnc_1520</th>\n",
       "      <th>CAFI_LC_2015</th>\n",
       "      <th>chg_prob</th>\n",
       "      <th>simple_combined</th>\n",
       "      <th>kmeans</th>\n",
       "      <th>plotid</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>sampleid</th>\n",
       "      <th>Ref_Year</th>\n",
       "      <th>collection</th>\n",
       "      <th>interpreted</th>\n",
       "      <th>Def2016</th>\n",
       "      <th>Def2017</th>\n",
       "      <th>Def2018</th>\n",
       "      <th>Def2019</th>\n",
       "      <th>Def2020</th>\n",
       "      <th>Def2021</th>\n",
       "      <th>Def2022</th>\n",
       "      <th>Deg2016</th>\n",
       "      <th>Deg2017</th>\n",
       "      <th>Deg2018</th>\n",
       "      <th>Deg2019</th>\n",
       "      <th>Deg2020</th>\n",
       "      <th>Deg2021</th>\n",
       "      <th>Deg2022</th>\n",
       "      <th>Defall</th>\n",
       "      <th>Degall</th>\n",
       "      <th>Stable</th>\n",
       "      <th>NF</th>\n",
       "      <th>stratum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>2.228780e+05</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.00000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.00000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.00000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.00000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.00000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.00000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.00000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.00000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222523.000000</td>\n",
       "      <td>222523.000000</td>\n",
       "      <td>222523.000000</td>\n",
       "      <td>222523.000000</td>\n",
       "      <td>222523.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>220564.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>8594.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>216896.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>6539.000000</td>\n",
       "      <td>222878.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.770480</td>\n",
       "      <td>36.414254</td>\n",
       "      <td>1620.747038</td>\n",
       "      <td>-3.936606</td>\n",
       "      <td>-2.418103e+07</td>\n",
       "      <td>2019.863104</td>\n",
       "      <td>0.180397</td>\n",
       "      <td>7555.439516</td>\n",
       "      <td>8982.382143</td>\n",
       "      <td>878.493371</td>\n",
       "      <td>6002.95281</td>\n",
       "      <td>9848.048646</td>\n",
       "      <td>8982.382143</td>\n",
       "      <td>878.493371</td>\n",
       "      <td>6002.95281</td>\n",
       "      <td>9848.048646</td>\n",
       "      <td>8982.382143</td>\n",
       "      <td>878.493371</td>\n",
       "      <td>6002.95281</td>\n",
       "      <td>9848.048646</td>\n",
       "      <td>8982.382143</td>\n",
       "      <td>878.493371</td>\n",
       "      <td>6002.95281</td>\n",
       "      <td>9848.048646</td>\n",
       "      <td>8982.382143</td>\n",
       "      <td>878.493371</td>\n",
       "      <td>6002.95281</td>\n",
       "      <td>9848.048646</td>\n",
       "      <td>8982.382143</td>\n",
       "      <td>878.493371</td>\n",
       "      <td>6002.95281</td>\n",
       "      <td>9848.048646</td>\n",
       "      <td>8982.382143</td>\n",
       "      <td>878.493371</td>\n",
       "      <td>6002.95281</td>\n",
       "      <td>9848.048646</td>\n",
       "      <td>8982.382143</td>\n",
       "      <td>878.493371</td>\n",
       "      <td>6002.95281</td>\n",
       "      <td>9848.048646</td>\n",
       "      <td>24.261766</td>\n",
       "      <td>62.404433</td>\n",
       "      <td>216.740851</td>\n",
       "      <td>-166.934007</td>\n",
       "      <td>357.346060</td>\n",
       "      <td>-139.212613</td>\n",
       "      <td>179.698903</td>\n",
       "      <td>1.098363</td>\n",
       "      <td>70.977863</td>\n",
       "      <td>27.035601</td>\n",
       "      <td>12.430212</td>\n",
       "      <td>61.805584</td>\n",
       "      <td>377.500329</td>\n",
       "      <td>12.975036</td>\n",
       "      <td>12.785964</td>\n",
       "      <td>2.726038</td>\n",
       "      <td>2.683446</td>\n",
       "      <td>2.651670</td>\n",
       "      <td>2.658226</td>\n",
       "      <td>2.805216</td>\n",
       "      <td>0.000960</td>\n",
       "      <td>0.020352</td>\n",
       "      <td>12.204212</td>\n",
       "      <td>84.136528</td>\n",
       "      <td>30.810596</td>\n",
       "      <td>25.527163</td>\n",
       "      <td>6.961732</td>\n",
       "      <td>1.444503</td>\n",
       "      <td>1.446899</td>\n",
       "      <td>1.449232</td>\n",
       "      <td>1.451543</td>\n",
       "      <td>1.452669</td>\n",
       "      <td>11.141166</td>\n",
       "      <td>44.216332</td>\n",
       "      <td>15.441793</td>\n",
       "      <td>17.291213</td>\n",
       "      <td>112590.241756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.655758</td>\n",
       "      <td>0.084998</td>\n",
       "      <td>0.084998</td>\n",
       "      <td>1.272678</td>\n",
       "      <td>112590.241756</td>\n",
       "      <td>11.789000</td>\n",
       "      <td>-0.605674</td>\n",
       "      <td>70303.029362</td>\n",
       "      <td>2019.259740</td>\n",
       "      <td>2.964215</td>\n",
       "      <td>0.029339</td>\n",
       "      <td>0.003212</td>\n",
       "      <td>0.001988</td>\n",
       "      <td>0.001071</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.000918</td>\n",
       "      <td>0.001071</td>\n",
       "      <td>0.001988</td>\n",
       "      <td>0.002141</td>\n",
       "      <td>0.002294</td>\n",
       "      <td>0.003823</td>\n",
       "      <td>0.002447</td>\n",
       "      <td>0.004282</td>\n",
       "      <td>0.003976</td>\n",
       "      <td>0.005811</td>\n",
       "      <td>0.010552</td>\n",
       "      <td>0.024774</td>\n",
       "      <td>0.775348</td>\n",
       "      <td>0.189326</td>\n",
       "      <td>1.282971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.277351</td>\n",
       "      <td>15.406316</td>\n",
       "      <td>802.964585</td>\n",
       "      <td>3507.699885</td>\n",
       "      <td>1.179744e+09</td>\n",
       "      <td>2.171179</td>\n",
       "      <td>0.156176</td>\n",
       "      <td>9692.568721</td>\n",
       "      <td>1892.420847</td>\n",
       "      <td>830.488473</td>\n",
       "      <td>4035.28847</td>\n",
       "      <td>825.362366</td>\n",
       "      <td>1892.420847</td>\n",
       "      <td>830.488473</td>\n",
       "      <td>4035.28847</td>\n",
       "      <td>825.362366</td>\n",
       "      <td>1892.420847</td>\n",
       "      <td>830.488473</td>\n",
       "      <td>4035.28847</td>\n",
       "      <td>825.362366</td>\n",
       "      <td>1892.420847</td>\n",
       "      <td>830.488473</td>\n",
       "      <td>4035.28847</td>\n",
       "      <td>825.362366</td>\n",
       "      <td>1892.420847</td>\n",
       "      <td>830.488473</td>\n",
       "      <td>4035.28847</td>\n",
       "      <td>825.362366</td>\n",
       "      <td>1892.420847</td>\n",
       "      <td>830.488473</td>\n",
       "      <td>4035.28847</td>\n",
       "      <td>825.362366</td>\n",
       "      <td>1892.420847</td>\n",
       "      <td>830.488473</td>\n",
       "      <td>4035.28847</td>\n",
       "      <td>825.362366</td>\n",
       "      <td>1892.420847</td>\n",
       "      <td>830.488473</td>\n",
       "      <td>4035.28847</td>\n",
       "      <td>825.362366</td>\n",
       "      <td>151.979665</td>\n",
       "      <td>78.898918</td>\n",
       "      <td>281.611144</td>\n",
       "      <td>303.847077</td>\n",
       "      <td>771.070473</td>\n",
       "      <td>982.674412</td>\n",
       "      <td>103.261340</td>\n",
       "      <td>0.596971</td>\n",
       "      <td>11.734852</td>\n",
       "      <td>25.428479</td>\n",
       "      <td>8.237937</td>\n",
       "      <td>15.464620</td>\n",
       "      <td>218.615187</td>\n",
       "      <td>12.043425</td>\n",
       "      <td>11.554925</td>\n",
       "      <td>2.424515</td>\n",
       "      <td>2.369229</td>\n",
       "      <td>2.323989</td>\n",
       "      <td>2.329031</td>\n",
       "      <td>2.523019</td>\n",
       "      <td>0.030972</td>\n",
       "      <td>0.141201</td>\n",
       "      <td>5.894912</td>\n",
       "      <td>20.530808</td>\n",
       "      <td>9.383605</td>\n",
       "      <td>9.791855</td>\n",
       "      <td>5.545324</td>\n",
       "      <td>1.347265</td>\n",
       "      <td>1.348230</td>\n",
       "      <td>1.349405</td>\n",
       "      <td>1.350114</td>\n",
       "      <td>1.350281</td>\n",
       "      <td>116.891132</td>\n",
       "      <td>224.150943</td>\n",
       "      <td>16.224859</td>\n",
       "      <td>21.811836</td>\n",
       "      <td>64745.112261</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.149875</td>\n",
       "      <td>0.104789</td>\n",
       "      <td>0.104789</td>\n",
       "      <td>0.532273</td>\n",
       "      <td>64745.112261</td>\n",
       "      <td>1.379958</td>\n",
       "      <td>1.364103</td>\n",
       "      <td>77664.715179</td>\n",
       "      <td>2.155292</td>\n",
       "      <td>1.465501</td>\n",
       "      <td>0.168755</td>\n",
       "      <td>0.056583</td>\n",
       "      <td>0.044547</td>\n",
       "      <td>0.032703</td>\n",
       "      <td>0.017487</td>\n",
       "      <td>0.030280</td>\n",
       "      <td>0.032703</td>\n",
       "      <td>0.044547</td>\n",
       "      <td>0.046225</td>\n",
       "      <td>0.047844</td>\n",
       "      <td>0.061719</td>\n",
       "      <td>0.049409</td>\n",
       "      <td>0.065302</td>\n",
       "      <td>0.062936</td>\n",
       "      <td>0.076016</td>\n",
       "      <td>0.102188</td>\n",
       "      <td>0.155449</td>\n",
       "      <td>0.417385</td>\n",
       "      <td>0.391797</td>\n",
       "      <td>0.540858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-961337.625000</td>\n",
       "      <td>-2.048748e+11</td>\n",
       "      <td>2016.016000</td>\n",
       "      <td>-0.076809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9985.763158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.00000</td>\n",
       "      <td>-9459.000000</td>\n",
       "      <td>-9985.763158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.00000</td>\n",
       "      <td>-9459.000000</td>\n",
       "      <td>-9985.763158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.00000</td>\n",
       "      <td>-9459.000000</td>\n",
       "      <td>-9985.763158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.00000</td>\n",
       "      <td>-9459.000000</td>\n",
       "      <td>-9985.763158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.00000</td>\n",
       "      <td>-9459.000000</td>\n",
       "      <td>-9985.763158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.00000</td>\n",
       "      <td>-9459.000000</td>\n",
       "      <td>-9985.763158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.00000</td>\n",
       "      <td>-9459.000000</td>\n",
       "      <td>-9985.763158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10000.00000</td>\n",
       "      <td>-9459.000000</td>\n",
       "      <td>-5026.991194</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2631.695709</td>\n",
       "      <td>-45852.752722</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-22890.937460</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-0.499672</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.699735</td>\n",
       "      <td>-3.951404</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2016.104000</td>\n",
       "      <td>-340.556984</td>\n",
       "      <td>-1.414579e+00</td>\n",
       "      <td>2017.948000</td>\n",
       "      <td>0.036566</td>\n",
       "      <td>2722.856481</td>\n",
       "      <td>9246.239451</td>\n",
       "      <td>377.386501</td>\n",
       "      <td>5370.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9246.239451</td>\n",
       "      <td>377.386501</td>\n",
       "      <td>5370.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9246.239451</td>\n",
       "      <td>377.386501</td>\n",
       "      <td>5370.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9246.239451</td>\n",
       "      <td>377.386501</td>\n",
       "      <td>5370.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9246.239451</td>\n",
       "      <td>377.386501</td>\n",
       "      <td>5370.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9246.239451</td>\n",
       "      <td>377.386501</td>\n",
       "      <td>5370.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9246.239451</td>\n",
       "      <td>377.386501</td>\n",
       "      <td>5370.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9246.239451</td>\n",
       "      <td>377.386501</td>\n",
       "      <td>5370.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>-30.268011</td>\n",
       "      <td>23.757547</td>\n",
       "      <td>68.084363</td>\n",
       "      <td>-201.326899</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>170.957882</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>56616.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>56616.250000</td>\n",
       "      <td>10.696994</td>\n",
       "      <td>-1.631947</td>\n",
       "      <td>1638.500000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>2017.068000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2020.181000</td>\n",
       "      <td>0.174683</td>\n",
       "      <td>4589.278825</td>\n",
       "      <td>9512.848238</td>\n",
       "      <td>596.381227</td>\n",
       "      <td>7362.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9512.848238</td>\n",
       "      <td>596.381227</td>\n",
       "      <td>7362.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9512.848238</td>\n",
       "      <td>596.381227</td>\n",
       "      <td>7362.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9512.848238</td>\n",
       "      <td>596.381227</td>\n",
       "      <td>7362.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9512.848238</td>\n",
       "      <td>596.381227</td>\n",
       "      <td>7362.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9512.848238</td>\n",
       "      <td>596.381227</td>\n",
       "      <td>7362.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9512.848238</td>\n",
       "      <td>596.381227</td>\n",
       "      <td>7362.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9512.848238</td>\n",
       "      <td>596.381227</td>\n",
       "      <td>7362.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>18.109070</td>\n",
       "      <td>39.805370</td>\n",
       "      <td>132.282976</td>\n",
       "      <td>-88.440657</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>432.239380</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>112471.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.055333</td>\n",
       "      <td>0.055333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>112471.500000</td>\n",
       "      <td>11.745932</td>\n",
       "      <td>-0.657398</td>\n",
       "      <td>33532.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2018.997000</td>\n",
       "      <td>361.663849</td>\n",
       "      <td>8.544898e-01</td>\n",
       "      <td>2021.847000</td>\n",
       "      <td>0.304766</td>\n",
       "      <td>7987.790121</td>\n",
       "      <td>9683.111929</td>\n",
       "      <td>1017.055567</td>\n",
       "      <td>8436.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9683.111929</td>\n",
       "      <td>1017.055567</td>\n",
       "      <td>8436.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9683.111929</td>\n",
       "      <td>1017.055567</td>\n",
       "      <td>8436.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9683.111929</td>\n",
       "      <td>1017.055567</td>\n",
       "      <td>8436.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9683.111929</td>\n",
       "      <td>1017.055567</td>\n",
       "      <td>8436.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9683.111929</td>\n",
       "      <td>1017.055567</td>\n",
       "      <td>8436.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9683.111929</td>\n",
       "      <td>1017.055567</td>\n",
       "      <td>8436.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9683.111929</td>\n",
       "      <td>1017.055567</td>\n",
       "      <td>8436.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>73.047596</td>\n",
       "      <td>75.077082</td>\n",
       "      <td>258.331272</td>\n",
       "      <td>-30.640301</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>268.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>543.681046</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>168541.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.098667</td>\n",
       "      <td>0.098667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>168541.750000</td>\n",
       "      <td>12.918008</td>\n",
       "      <td>0.473475</td>\n",
       "      <td>137022.500000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>164.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>2022.989000</td>\n",
       "      <td>265225.687500</td>\n",
       "      <td>1.060526e+09</td>\n",
       "      <td>2022.989000</td>\n",
       "      <td>0.681502</td>\n",
       "      <td>174685.312500</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9428.090416</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9428.090416</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9428.090416</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9428.090416</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9428.090416</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9428.090416</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9428.090416</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>9428.090416</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>3045.277260</td>\n",
       "      <td>10133.972126</td>\n",
       "      <td>14428.092362</td>\n",
       "      <td>1273.035021</td>\n",
       "      <td>2022.989000</td>\n",
       "      <td>17137.596034</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1008.222961</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>225179.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.967333</td>\n",
       "      <td>0.967333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>225179.000000</td>\n",
       "      <td>14.524399</td>\n",
       "      <td>2.315793</td>\n",
       "      <td>225179.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              images     mon_images  bfast_change_date  bfast_magnitude  \\\n",
       "count  222878.000000  222878.000000      222878.000000    222878.000000   \n",
       "mean       54.770480      36.414254        1620.747038        -3.936606   \n",
       "std        22.277351      15.406316         802.964585      3507.699885   \n",
       "min         5.000000       2.000000          -2.000000   -961337.625000   \n",
       "25%        38.000000      25.000000        2016.104000      -340.556984   \n",
       "50%        52.000000      34.000000        2017.068000         0.000000   \n",
       "75%        67.000000      45.000000        2018.997000       361.663849   \n",
       "max       164.000000     111.000000        2022.989000    265225.687500   \n",
       "\n",
       "        bfast_means  cusum_change_date  cusum_confidence  cusum_magnitude  \\\n",
       "count  2.228780e+05      222878.000000     222878.000000    222878.000000   \n",
       "mean  -2.418103e+07        2019.863104          0.180397      7555.439516   \n",
       "std    1.179744e+09           2.171179          0.156176      9692.568721   \n",
       "min   -2.048748e+11        2016.016000         -0.076809         0.000000   \n",
       "25%   -1.414579e+00        2017.948000          0.036566      2722.856481   \n",
       "50%    0.000000e+00        2020.181000          0.174683      4589.278825   \n",
       "75%    8.544898e-01        2021.847000          0.304766      7987.790121   \n",
       "max    1.060526e+09        2022.989000          0.681502    174685.312500   \n",
       "\n",
       "            red_mean         red_sd       red_min        red_max  \\\n",
       "count  222878.000000  222878.000000  222878.00000  222878.000000   \n",
       "mean     8982.382143     878.493371    6002.95281    9848.048646   \n",
       "std      1892.420847     830.488473    4035.28847     825.362366   \n",
       "min     -9985.763158       0.000000  -10000.00000   -9459.000000   \n",
       "25%      9246.239451     377.386501    5370.00000   10000.000000   \n",
       "50%      9512.848238     596.381227    7362.00000   10000.000000   \n",
       "75%      9683.111929    1017.055567    8436.00000   10000.000000   \n",
       "max     10000.000000    9428.090416   10000.00000   10000.000000   \n",
       "\n",
       "            nir_mean         nir_sd       nir_min        nir_max  \\\n",
       "count  222878.000000  222878.000000  222878.00000  222878.000000   \n",
       "mean     8982.382143     878.493371    6002.95281    9848.048646   \n",
       "std      1892.420847     830.488473    4035.28847     825.362366   \n",
       "min     -9985.763158       0.000000  -10000.00000   -9459.000000   \n",
       "25%      9246.239451     377.386501    5370.00000   10000.000000   \n",
       "50%      9512.848238     596.381227    7362.00000   10000.000000   \n",
       "75%      9683.111929    1017.055567    8436.00000   10000.000000   \n",
       "max     10000.000000    9428.090416   10000.00000   10000.000000   \n",
       "\n",
       "          swir1_mean       swir1_sd     swir1_min      swir1_max  \\\n",
       "count  222878.000000  222878.000000  222878.00000  222878.000000   \n",
       "mean     8982.382143     878.493371    6002.95281    9848.048646   \n",
       "std      1892.420847     830.488473    4035.28847     825.362366   \n",
       "min     -9985.763158       0.000000  -10000.00000   -9459.000000   \n",
       "25%      9246.239451     377.386501    5370.00000   10000.000000   \n",
       "50%      9512.848238     596.381227    7362.00000   10000.000000   \n",
       "75%      9683.111929    1017.055567    8436.00000   10000.000000   \n",
       "max     10000.000000    9428.090416   10000.00000   10000.000000   \n",
       "\n",
       "          swir2_mean       swir2_sd     swir2_min      swir2_max  \\\n",
       "count  222878.000000  222878.000000  222878.00000  222878.000000   \n",
       "mean     8982.382143     878.493371    6002.95281    9848.048646   \n",
       "std      1892.420847     830.488473    4035.28847     825.362366   \n",
       "min     -9985.763158       0.000000  -10000.00000   -9459.000000   \n",
       "25%      9246.239451     377.386501    5370.00000   10000.000000   \n",
       "50%      9512.848238     596.381227    7362.00000   10000.000000   \n",
       "75%      9683.111929    1017.055567    8436.00000   10000.000000   \n",
       "max     10000.000000    9428.090416   10000.00000   10000.000000   \n",
       "\n",
       "           ndfi_mean        ndfi_sd      ndfi_min       ndfi_max  \\\n",
       "count  222878.000000  222878.000000  222878.00000  222878.000000   \n",
       "mean     8982.382143     878.493371    6002.95281    9848.048646   \n",
       "std      1892.420847     830.488473    4035.28847     825.362366   \n",
       "min     -9985.763158       0.000000  -10000.00000   -9459.000000   \n",
       "25%      9246.239451     377.386501    5370.00000   10000.000000   \n",
       "50%      9512.848238     596.381227    7362.00000   10000.000000   \n",
       "75%      9683.111929    1017.055567    8436.00000   10000.000000   \n",
       "max     10000.000000    9428.090416   10000.00000   10000.000000   \n",
       "\n",
       "       brightness_mean  brightness_sd  brightness_min  brightness_max  \\\n",
       "count    222878.000000  222878.000000    222878.00000   222878.000000   \n",
       "mean       8982.382143     878.493371      6002.95281     9848.048646   \n",
       "std        1892.420847     830.488473      4035.28847      825.362366   \n",
       "min       -9985.763158       0.000000    -10000.00000    -9459.000000   \n",
       "25%        9246.239451     377.386501      5370.00000    10000.000000   \n",
       "50%        9512.848238     596.381227      7362.00000    10000.000000   \n",
       "75%        9683.111929    1017.055567      8436.00000    10000.000000   \n",
       "max       10000.000000    9428.090416     10000.00000    10000.000000   \n",
       "\n",
       "       greenness_mean   greenness_sd  greenness_min  greenness_max  \\\n",
       "count   222878.000000  222878.000000   222878.00000  222878.000000   \n",
       "mean      8982.382143     878.493371     6002.95281    9848.048646   \n",
       "std       1892.420847     830.488473     4035.28847     825.362366   \n",
       "min      -9985.763158       0.000000   -10000.00000   -9459.000000   \n",
       "25%       9246.239451     377.386501     5370.00000   10000.000000   \n",
       "50%       9512.848238     596.381227     7362.00000   10000.000000   \n",
       "75%       9683.111929    1017.055567     8436.00000   10000.000000   \n",
       "max      10000.000000    9428.090416    10000.00000   10000.000000   \n",
       "\n",
       "        wetness_mean     wetness_sd   wetness_min    wetness_max  \\\n",
       "count  222878.000000  222878.000000  222878.00000  222878.000000   \n",
       "mean     8982.382143     878.493371    6002.95281    9848.048646   \n",
       "std      1892.420847     830.488473    4035.28847     825.362366   \n",
       "min     -9985.763158       0.000000  -10000.00000   -9459.000000   \n",
       "25%      9246.239451     377.386501    5370.00000   10000.000000   \n",
       "50%      9512.848238     596.381227    7362.00000   10000.000000   \n",
       "75%      9683.111929    1017.055567    8436.00000   10000.000000   \n",
       "max     10000.000000    9428.090416   10000.00000   10000.000000   \n",
       "\n",
       "       bs_slope_mean    bs_slope_sd   bs_slope_max   bs_slope_min  \\\n",
       "count  222878.000000  222878.000000  222878.000000  222878.000000   \n",
       "mean       24.261766      62.404433     216.740851    -166.934007   \n",
       "std       151.979665      78.898918     281.611144     303.847077   \n",
       "min     -5026.991194       0.000000   -2631.695709  -45852.752722   \n",
       "25%       -30.268011      23.757547      68.084363    -201.326899   \n",
       "50%        18.109070      39.805370     132.282976     -88.440657   \n",
       "75%        73.047596      75.077082     258.331272     -30.640301   \n",
       "max      3045.277260   10133.972126   14428.092362    1273.035021   \n",
       "\n",
       "       ccdc_change_date  ccdc_magnitude         aspect  dw_class_mode  \\\n",
       "count     222878.000000   222878.000000  222878.000000  222523.000000   \n",
       "mean         357.346060     -139.212613     179.698903       1.098363   \n",
       "std          771.070473      982.674412     103.261340       0.596971   \n",
       "min            0.000000   -22890.937460       0.000000       0.000000   \n",
       "25%            0.000000        0.000000      90.000000       1.000000   \n",
       "50%            0.000000        0.000000     181.000000       1.000000   \n",
       "75%            0.000000        0.000000     268.000000       1.000000   \n",
       "max         2022.989000    17137.596034     359.000000       8.000000   \n",
       "\n",
       "       dw_tree_prob__max  dw_tree_prob__min  dw_tree_prob__stdDev  \\\n",
       "count      222523.000000      222523.000000         222523.000000   \n",
       "mean           70.977863          27.035601             12.430212   \n",
       "std            11.734852          25.428479              8.237937   \n",
       "min             2.000000           1.000000              0.000000   \n",
       "25%            73.000000           4.000000              5.000000   \n",
       "50%            74.000000          15.000000             13.000000   \n",
       "75%            75.000000          51.000000             19.000000   \n",
       "max            79.000000          77.000000             36.000000   \n",
       "\n",
       "       dw_tree_prob_mean      elevation       esa_lc20       esa_lc21  \\\n",
       "count      222523.000000  222878.000000  222878.000000  222878.000000   \n",
       "mean           61.805584     377.500329      12.975036      12.785964   \n",
       "std            15.464620     218.615187      12.043425      11.554925   \n",
       "min             2.000000      -0.499672      10.000000      10.000000   \n",
       "25%            61.000000     170.957882      10.000000      10.000000   \n",
       "50%            67.000000     432.239380      10.000000      10.000000   \n",
       "75%            70.000000     543.681046      10.000000      10.000000   \n",
       "max            77.000000    1008.222961      95.000000      95.000000   \n",
       "\n",
       "           esri_lc17      esri_lc18      esri_lc19      esri_lc20  \\\n",
       "count  222878.000000  222878.000000  222878.000000  220564.000000   \n",
       "mean        2.726038       2.683446       2.651670       2.658226   \n",
       "std         2.424515       2.369229       2.323989       2.329031   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%         2.000000       2.000000       2.000000       2.000000   \n",
       "50%         2.000000       2.000000       2.000000       2.000000   \n",
       "75%         2.000000       2.000000       2.000000       2.000000   \n",
       "max        11.000000      11.000000      11.000000      11.000000   \n",
       "\n",
       "           esri_lc21       gfc_gain       gfc_loss  gfc_lossyear  \\\n",
       "count  222878.000000  222878.000000  222878.000000   8594.000000   \n",
       "mean        2.805216       0.000960       0.020352     12.204212   \n",
       "std         2.523019       0.030972       0.141201      5.894912   \n",
       "min         1.000000       0.000000       0.000000      1.000000   \n",
       "25%         2.000000       0.000000       0.000000      7.000000   \n",
       "50%         2.000000       0.000000       0.000000     13.000000   \n",
       "75%         2.000000       0.000000       0.000000     17.000000   \n",
       "max        11.000000       1.000000       1.000000     22.000000   \n",
       "\n",
       "            gfc_tc00  lang_tree_height  potapov_tree_height          slope  \\\n",
       "count  222878.000000     216896.000000        222878.000000  222878.000000   \n",
       "mean       84.136528         30.810596            25.527163       6.961732   \n",
       "std        20.530808          9.383605             9.791855       5.545324   \n",
       "min         0.000000          0.000000             0.000000       0.000000   \n",
       "25%        90.000000         29.000000            25.000000       3.000000   \n",
       "50%        90.000000         34.000000            29.000000       6.000000   \n",
       "75%        91.000000         37.000000            31.000000      10.000000   \n",
       "max       100.000000         52.000000            41.000000      50.000000   \n",
       "\n",
       "            tmf_2016       tmf_2017       tmf_2018       tmf_2019  \\\n",
       "count  222878.000000  222878.000000  222878.000000  222878.000000   \n",
       "mean        1.444503       1.446899       1.449232       1.451543   \n",
       "std         1.347265       1.348230       1.349405       1.350114   \n",
       "min         1.000000       1.000000       1.000000       1.000000   \n",
       "25%         1.000000       1.000000       1.000000       1.000000   \n",
       "50%         1.000000       1.000000       1.000000       1.000000   \n",
       "75%         1.000000       1.000000       1.000000       1.000000   \n",
       "max         6.000000       6.000000       6.000000       6.000000   \n",
       "\n",
       "            tmf_2020    tmf_defyear    tmf_degyear       tmf_main  \\\n",
       "count  222878.000000  222878.000000  222878.000000  222878.000000   \n",
       "mean        1.452669      11.141166      44.216332      15.441793   \n",
       "std         1.350281     116.891132     224.150943      16.224859   \n",
       "min         1.000000       0.000000       0.000000      10.000000   \n",
       "25%         1.000000       0.000000       0.000000      10.000000   \n",
       "50%         1.000000       0.000000       0.000000      10.000000   \n",
       "75%         1.000000       0.000000       0.000000      10.000000   \n",
       "max         6.000000    2020.000000    2020.000000      70.000000   \n",
       "\n",
       "             tmf_sub       point_id  cnc_1520   CAFI_LC_2015       chg_prob  \\\n",
       "count  222878.000000  222878.000000       0.0  222878.000000  222878.000000   \n",
       "mean       17.291213  112590.241756       NaN       2.655758       0.084998   \n",
       "std        21.811836   64745.112261       NaN       4.149875       0.104789   \n",
       "min        10.000000       5.000000       NaN       0.000000       0.000000   \n",
       "25%        10.000000   56616.250000       NaN       1.000000       0.024000   \n",
       "50%        10.000000  112471.500000       NaN       1.000000       0.055333   \n",
       "75%        10.000000  168541.750000       NaN       1.000000       0.098667   \n",
       "max        93.000000  225179.000000       NaN      19.000000       0.967333   \n",
       "\n",
       "       simple_combined         kmeans         plotid            lon  \\\n",
       "count    222878.000000  222878.000000  222878.000000  222878.000000   \n",
       "mean          0.084998       1.272678  112590.241756      11.789000   \n",
       "std           0.104789       0.532273   64745.112261       1.379958   \n",
       "min           0.000000       1.000000       5.000000       8.699735   \n",
       "25%           0.024000       1.000000   56616.250000      10.696994   \n",
       "50%           0.055333       1.000000  112471.500000      11.745932   \n",
       "75%           0.098667       1.000000  168541.750000      12.918008   \n",
       "max           0.967333       3.000000  225179.000000      14.524399   \n",
       "\n",
       "                 lat       sampleid     Ref_Year   collection    interpreted  \\\n",
       "count  222878.000000    6539.000000   231.000000  6539.000000  222878.000000   \n",
       "mean       -0.605674   70303.029362  2019.259740     2.964215       0.029339   \n",
       "std         1.364103   77664.715179     2.155292     1.465501       0.168755   \n",
       "min        -3.951404       1.000000  2016.000000     1.000000       0.000000   \n",
       "25%        -1.631947    1638.500000  2017.000000     2.000000       0.000000   \n",
       "50%        -0.657398   33532.000000  2020.000000     2.000000       0.000000   \n",
       "75%         0.473475  137022.500000  2021.000000     5.000000       0.000000   \n",
       "max         2.315793  225179.000000  2022.000000     5.000000       1.000000   \n",
       "\n",
       "           Def2016      Def2017      Def2018      Def2019      Def2020  \\\n",
       "count  6539.000000  6539.000000  6539.000000  6539.000000  6539.000000   \n",
       "mean      0.003212     0.001988     0.001071     0.000306     0.000918   \n",
       "std       0.056583     0.044547     0.032703     0.017487     0.030280   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "           Def2021      Def2022      Deg2016      Deg2017      Deg2018  \\\n",
       "count  6539.000000  6539.000000  6539.000000  6539.000000  6539.000000   \n",
       "mean      0.001071     0.001988     0.002141     0.002294     0.003823   \n",
       "std       0.032703     0.044547     0.046225     0.047844     0.061719   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "           Deg2019      Deg2020      Deg2021      Deg2022       Defall  \\\n",
       "count  6539.000000  6539.000000  6539.000000  6539.000000  6539.000000   \n",
       "mean      0.002447     0.004282     0.003976     0.005811     0.010552   \n",
       "std       0.049409     0.065302     0.062936     0.076016     0.102188   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "            Degall       Stable           NF        stratum  \n",
       "count  6539.000000  6539.000000  6539.000000  222878.000000  \n",
       "mean      0.024774     0.775348     0.189326       1.282971  \n",
       "std       0.155449     0.417385     0.391797       0.540858  \n",
       "min       0.000000     0.000000     0.000000       1.000000  \n",
       "25%       0.000000     1.000000     0.000000       1.000000  \n",
       "50%       0.000000     1.000000     0.000000       1.000000  \n",
       "75%       0.000000     1.000000     0.000000       1.000000  \n",
       "max       1.000000     1.000000     1.000000       3.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esbae_ceo.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "14fcaa3e-1f21-43b1-a4ec-671a1950134c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images                    int64\n",
      "mon_images                int64\n",
      "bfast_change_date       float64\n",
      "bfast_magnitude         float64\n",
      "bfast_means             float64\n",
      "cusum_change_date       float64\n",
      "cusum_confidence        float64\n",
      "cusum_magnitude         float64\n",
      "red_mean                float64\n",
      "red_sd                  float64\n",
      "red_min                 float64\n",
      "red_max                 float64\n",
      "nir_mean                float64\n",
      "nir_sd                  float64\n",
      "nir_min                 float64\n",
      "nir_max                 float64\n",
      "swir1_mean              float64\n",
      "swir1_sd                float64\n",
      "swir1_min               float64\n",
      "swir1_max               float64\n",
      "swir2_mean              float64\n",
      "swir2_sd                float64\n",
      "swir2_min               float64\n",
      "swir2_max               float64\n",
      "ndfi_mean               float64\n",
      "ndfi_sd                 float64\n",
      "ndfi_min                float64\n",
      "ndfi_max                float64\n",
      "brightness_mean         float64\n",
      "brightness_sd           float64\n",
      "brightness_min          float64\n",
      "brightness_max          float64\n",
      "greenness_mean          float64\n",
      "greenness_sd            float64\n",
      "greenness_min           float64\n",
      "greenness_max           float64\n",
      "wetness_mean            float64\n",
      "wetness_sd              float64\n",
      "wetness_min             float64\n",
      "wetness_max             float64\n",
      "bs_slope_mean           float64\n",
      "bs_slope_sd             float64\n",
      "bs_slope_max            float64\n",
      "bs_slope_min            float64\n",
      "ccdc_change_date        float64\n",
      "ccdc_magnitude          float64\n",
      "aspect                  float64\n",
      "dw_class_mode           float64\n",
      "dw_tree_prob__max       float64\n",
      "dw_tree_prob__min       float64\n",
      "dw_tree_prob__stdDev    float64\n",
      "dw_tree_prob_mean       float64\n",
      "elevation               float64\n",
      "esa_lc20                float64\n",
      "esa_lc21                float64\n",
      "esri_lc17               float64\n",
      "esri_lc18               float64\n",
      "esri_lc19               float64\n",
      "esri_lc20               float64\n",
      "esri_lc21               float64\n",
      "gfc_gain                float64\n",
      "gfc_loss                float64\n",
      "gfc_lossyear            float64\n",
      "gfc_tc00                float64\n",
      "lang_tree_height        float64\n",
      "potapov_tree_height     float64\n",
      "slope                   float64\n",
      "tmf_2016                float64\n",
      "tmf_2017                float64\n",
      "tmf_2018                float64\n",
      "tmf_2019                float64\n",
      "tmf_2020                float64\n",
      "tmf_defyear             float64\n",
      "tmf_degyear             float64\n",
      "tmf_main                float64\n",
      "tmf_sub                 float64\n",
      "geometry                 object\n",
      "point_id                  int64\n",
      "cnc_1520                float64\n",
      "CAFI_LC_2015              int64\n",
      "chg_prob                float64\n",
      "simple_combined         float64\n",
      "kmeans                    int64\n",
      "plotid                    int64\n",
      "lon                     float64\n",
      "lat                     float64\n",
      "sampleid                float64\n",
      "email                    object\n",
      "flagged                  object\n",
      "collection_time          object\n",
      "analysis_duration        object\n",
      "sample_geom              object\n",
      "Ref_FNF_2015             object\n",
      "Ref_Regeneration         object\n",
      "Ref_Change_1522          object\n",
      "Ref_Change_Type_1522     object\n",
      "ArtFor                   object\n",
      "ArtMine                  object\n",
      "InfraR                   object\n",
      "Urb                      object\n",
      "IndFor                   object\n",
      "Other                    object\n",
      "Other_Desc               object\n",
      "IndMine                  object\n",
      "IndAg                    object\n",
      "ArtAg                    object\n",
      "Ref_Year                float64\n",
      "Ref_NFType               object\n",
      "Ref_FType                object\n",
      "Commentaires             object\n",
      "collection              float64\n",
      "interpreted               int64\n",
      "Ref_Change_Year_1522     object\n",
      "Def2016                 float64\n",
      "Def2017                 float64\n",
      "Def2018                 float64\n",
      "Def2019                 float64\n",
      "Def2020                 float64\n",
      "Def2021                 float64\n",
      "Def2022                 float64\n",
      "Deg2016                 float64\n",
      "Deg2017                 float64\n",
      "Deg2018                 float64\n",
      "Deg2019                 float64\n",
      "Deg2020                 float64\n",
      "Deg2021                 float64\n",
      "Deg2022                 float64\n",
      "Defall                  float64\n",
      "Degall                  float64\n",
      "Stable                  float64\n",
      "NF                      float64\n",
      "stratum                 float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(esbae_ceo.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60bd0119-9d19-461b-854a-7d1345b01984",
   "metadata": {},
   "source": [
    "##### export merged esbae and ceo data to one csv file for next step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "44453121-cc91-406e-9f6b-e4d78bb40ef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you want to remove all the eSBAE variables\n",
    "#DRC\n",
    "#esbae = esbae[['point_id', 'PLOTID', 'LAT','LON','stratum', 'chg_prob', 'simple_combined','NOM']]\n",
    "#GAB\n",
    "#esbae = esbae[['point_id', 'PLOTID', 'LAT','LON','stratum', 'chg_prob', 'simple_combined']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3199a69-7b32-4d91-9e59-f2daa370a714",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "esbae_ceo_data_out =  '/home/sepal-user/module_results/esbae/DRC/DRC_esbae_ceo_merge.csv'\n",
    "esbae_ceo.to_csv(esbae_ceo_data_out,index=False)\n",
    "#OR\n",
    "#esbae_ceo[esbae_ceo_columns].to_csv(data_out,index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6930c9dc-3ca9-40f8-a2d0-e8d46edbe4dc",
   "metadata": {},
   "source": [
    "#### now move on to calculation of areas - script 6b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "718daa5d-6ea8-45eb-a6a9-37f29bc08f03",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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