Vizzuality/landgriffon

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7fb4538-65d8-4383-8250-a5ba55c0c9da",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "import math\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import rasterio as rio\n",
    "import rioxarray\n",
    "from affine import Affine\n",
    "from h3ronpy import raster\n",
    "from h3ronpy.raster import nearest_h3_resolution, raster_to_dataframe\n",
    "from rasterio.coords import BoundingBox\n",
    "from rasterio.enums import Resampling\n",
    "from rasterio.plot import show\n",
    "from shapely.geometry import Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "24d0584b-cc8e-4258-94e5-0fcd47146ce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "raster?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0cd02f5-9fa9-46de-8d0e-f7a5b46c920a",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_data_path = Path(\"../../h3_data_importer/data/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91df4087-c987-4c51-bd6a-3fb0d1b1b6d4",
   "metadata": {},
   "source": [
    "## Resample woodpulp dataset to match the H3 resolution 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c647d07-e5e4-4bce-b1f8-c56b32017633",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPSG:4326\n"
     ]
    }
   ],
   "source": [
    "wood_pulp_harvest = \"../../h3_data_importer/data/woodpulp/gfw_plantations_woodpulp_harvest_ha.tif\"\n",
    "with rio.open(wood_pulp_harvest) as src:\n",
    "    print(src.crs)\n",
    "    aff = src.transform\n",
    "    bounds = src.bounds\n",
    "    shape = src.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "90684cbc-efc6-4c30-858f-49d5b6267239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rasterio.coords.BoundingBox"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86b312a7-6237-4fb9-ab7d-b4813892c62d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_h3_res_best_fit(transform: Affine, shape: tuple[int, int], bounds: BoundingBox, resolution: int) -> list:\n",
    "    result = []\n",
    "    for scale_factor in (x for x in range(1, 400)):\n",
    "        x_pix_size = transform.a * scale_factor\n",
    "        y_pix_size = transform.e * scale_factor\n",
    "\n",
    "        shape = (int((bounds.right - bounds.left) / x_pix_size), int((bounds.bottom - bounds.top) / y_pix_size))\n",
    "        new_trans = Affine(x_pix_size, transform.b, transform.c, transform.d, y_pix_size, transform.f)\n",
    "\n",
    "        h3_res = nearest_h3_resolution(shape, new_trans, search_mode=\"min_diff\")\n",
    "        result.append((scale_factor, x_pix_size, shape, h3_res))\n",
    "\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f93ea09-06ad-449f-bb3a-7fec7b28afd7",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       " (270, 0.06750000000000043, (355, 511), 6),\n",
       " (271, 0.06775000000000045, (353, 509), 6),\n",
       " (272, 0.06800000000000045, (352, 507), 6),\n",
       " (273, 0.06825000000000045, (351, 505), 6),\n",
       " (274, 0.06850000000000045, (350, 503), 6),\n",
       " (275, 0.06875000000000045, (348, 502), 6),\n",
       " (276, 0.06900000000000045, (347, 500), 6),\n",
       " (277, 0.06925000000000045, (346, 498), 6),\n",
       " (278, 0.06950000000000045, (345, 496), 6),\n",
       " (279, 0.06975000000000045, (343, 494), 6),\n",
       " (280, 0.07000000000000045, (342, 493), 6),\n",
       " (281, 0.07025000000000046, (341, 491), 6),\n",
       " (282, 0.07050000000000047, (340, 489), 6),\n",
       " (283, 0.07075000000000047, (338, 487), 6),\n",
       " (284, 0.07100000000000047, (337, 486), 6),\n",
       " (285, 0.07125000000000047, (336, 484), 6),\n",
       " (286, 0.07150000000000047, (335, 482), 6),\n",
       " (287, 0.07175000000000047, (334, 481), 6),\n",
       " (288, 0.07200000000000047, (333, 479), 6),\n",
       " (289, 0.07225000000000047, (331, 477), 6),\n",
       " (290, 0.07250000000000047, (330, 476), 6),\n",
       " (291, 0.07275000000000048, (329, 474), 6),\n",
       " (292, 0.07300000000000048, (328, 472), 6),\n",
       " (293, 0.07325000000000048, (327, 471), 6),\n",
       " (294, 0.07350000000000048, (326, 469), 6),\n",
       " (295, 0.07375000000000048, (325, 468), 6),\n",
       " (296, 0.07400000000000048, (324, 466), 6),\n",
       " (297, 0.07425000000000048, (322, 464), 6),\n",
       " (298, 0.07450000000000048, (321, 463), 6),\n",
       " (299, 0.07475000000000048, (320, 461), 6),\n",
       " (300, 0.07500000000000048, (319, 460), 6),\n",
       " (301, 0.0752500000000005, (318, 458), 6),\n",
       " (302, 0.0755000000000005, (317, 457), 6),\n",
       " (303, 0.0757500000000005, (316, 455), 6),\n",
       " (304, 0.0760000000000005, (315, 454), 6),\n",
       " (305, 0.0762500000000005, (314, 452), 6),\n",
       " (306, 0.0765000000000005, (313, 451), 6),\n",
       " (307, 0.0767500000000005, (312, 449), 6),\n",
       " (308, 0.0770000000000005, (311, 448), 6),\n",
       " (309, 0.0772500000000005, (310, 446), 6),\n",
       " (310, 0.0775000000000005, (309, 445), 6),\n",
       " (311, 0.07775000000000051, (308, 444), 6),\n",
       " (312, 0.07800000000000051, (307, 442), 6),\n",
       " (313, 0.07825000000000051, (306, 441), 6),\n",
       " (314, 0.07850000000000051, (305, 439), 6),\n",
       " (315, 0.07875000000000051, (304, 438), 6),\n",
       " (316, 0.07900000000000051, (303, 436), 6),\n",
       " (317, 0.07925000000000051, (302, 435), 6),\n",
       " (318, 0.07950000000000051, (301, 434), 6),\n",
       " (319, 0.07975000000000051, (300, 432), 6),\n",
       " (320, 0.08000000000000052, (299, 431), 6),\n",
       " (321, 0.08025000000000053, (298, 430), 6),\n",
       " (322, 0.08050000000000053, (297, 428), 6),\n",
       " (323, 0.08075000000000053, (296, 427), 6),\n",
       " (324, 0.08100000000000053, (296, 426), 6),\n",
       " (325, 0.08125000000000053, (295, 424), 6),\n",
       " (326, 0.08150000000000053, (294, 423), 6),\n",
       " (327, 0.08175000000000053, (293, 422), 6),\n",
       " (328, 0.08200000000000053, (292, 421), 6),\n",
       " (329, 0.08225000000000053, (291, 419), 6),\n",
       " (330, 0.08250000000000055, (290, 418), 6),\n",
       " (331, 0.08275000000000055, (289, 417), 6),\n",
       " (332, 0.08300000000000055, (288, 415), 6),\n",
       " (333, 0.08325000000000055, (288, 414), 6),\n",
       " (334, 0.08350000000000055, (287, 413), 6),\n",
       " (335, 0.08375000000000055, (286, 412), 6),\n",
       " (336, 0.08400000000000055, (285, 410), 6),\n",
       " (337, 0.08425000000000055, (284, 409), 6),\n",
       " (338, 0.08450000000000055, (283, 408), 6),\n",
       " (339, 0.08475000000000055, (282, 407), 6),\n",
       " (340, 0.08500000000000056, (282, 406), 6),\n",
       " (341, 0.08525000000000056, (281, 404), 6),\n",
       " (342, 0.08550000000000056, (280, 403), 6),\n",
       " (343, 0.08575000000000056, (279, 402), 6),\n",
       " (344, 0.08600000000000056, (278, 401), 6),\n",
       " (345, 0.08625000000000056, (278, 400), 6),\n",
       " (346, 0.08650000000000056, (277, 399), 6),\n",
       " (347, 0.08675000000000056, (276, 397), 6),\n",
       " (348, 0.08700000000000056, (275, 396), 6),\n",
       " (349, 0.08725000000000056, (274, 395), 6),\n",
       " (350, 0.08750000000000058, (274, 394), 6),\n",
       " (351, 0.08775000000000058, (273, 393), 6),\n",
       " (352, 0.08800000000000058, (272, 392), 6),\n",
       " (353, 0.08825000000000058, (271, 391), 6),\n",
       " (354, 0.08850000000000058, (270, 390), 6),\n",
       " (355, 0.08875000000000058, (270, 388), 6),\n",
       " (356, 0.08900000000000058, (269, 387), 6),\n",
       " (357, 0.08925000000000058, (268, 386), 6),\n",
       " (358, 0.08950000000000058, (267, 385), 6),\n",
       " (359, 0.08975000000000058, (267, 384), 6),\n",
       " (360, 0.0900000000000006, (266, 383), 6),\n",
       " (361, 0.0902500000000006, (265, 382), 6),\n",
       " (362, 0.0905000000000006, (264, 381), 6),\n",
       " (363, 0.0907500000000006, (264, 380), 6),\n",
       " (364, 0.0910000000000006, (263, 379), 6),\n",
       " (365, 0.0912500000000006, (262, 378), 6),\n",
       " (366, 0.0915000000000006, (262, 377), 6),\n",
       " (367, 0.0917500000000006, (261, 376), 6),\n",
       " (368, 0.0920000000000006, (260, 375), 6),\n",
       " (369, 0.0922500000000006, (259, 374), 6),\n",
       " (370, 0.09250000000000061, (259, 373), 6),\n",
       " (371, 0.09275000000000061, (258, 372), 6),\n",
       " (372, 0.09300000000000061, (257, 371), 6),\n",
       " (373, 0.09325000000000061, (257, 370), 6),\n",
       " (374, 0.09350000000000061, (256, 369), 6),\n",
       " (375, 0.09375000000000061, (255, 368), 6),\n",
       " (376, 0.09400000000000061, (255, 367), 6),\n",
       " (377, 0.09425000000000061, (254, 366), 6),\n",
       " (378, 0.09450000000000061, (253, 365), 6),\n",
       " (379, 0.09475000000000061, (253, 364), 6),\n",
       " (380, 0.09500000000000063, (252, 363), 6),\n",
       " (381, 0.09525000000000063, (251, 362), 6),\n",
       " (382, 0.09550000000000063, (251, 361), 6),\n",
       " (383, 0.09575000000000063, (250, 360), 6),\n",
       " (384, 0.09600000000000063, (249, 359), 6),\n",
       " (385, 0.09625000000000063, (249, 358), 6),\n",
       " (386, 0.09650000000000063, (248, 357), 6),\n",
       " (387, 0.09675000000000063, (247, 356), 6),\n",
       " (388, 0.09700000000000063, (247, 355), 6),\n",
       " (389, 0.09725000000000064, (246, 354), 6),\n",
       " (390, 0.09750000000000064, (245, 354), 6),\n",
       " (391, 0.09775000000000064, (245, 353), 6),\n",
       " (392, 0.09800000000000064, (244, 352), 6),\n",
       " (393, 0.09825000000000064, (244, 351), 6),\n",
       " (394, 0.09850000000000064, (243, 350), 6),\n",
       " (395, 0.09875000000000064, (242, 349), 6),\n",
       " (396, 0.09900000000000064, (242, 348), 6),\n",
       " (397, 0.09925000000000064, (241, 347), 6),\n",
       " (398, 0.09950000000000064, (240, 346), 6),\n",
       " (399, 0.09975000000000066, (240, 346), 6)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_h3_res_best_fit(aff, shape, bounds, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dcbc26a-35ec-422c-9e70-9734f2bd4b7d",
   "metadata": {},
   "source": [
    "So ` 0.04525` deegres will be the smaller pixel size that can contain a res 6 H3 hexagon\n",
    "\n",
    "which is `181` times bigger than the original res of `0.00025`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "158bcd16-09a7-419d-a669-081efcdf113a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0452500000000003"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aff.a * 181"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c2963bee-4bad-4e30-a30a-c98d968c4f03",
   "metadata": {},
   "source": [
    "!gdalwarp -q -s_srs EPSG:4326 -t_srs EPSG:4326 -r sum -tr 0.04525 0.04525 -multi -of GTiff -overwrite\\\n",
    "\t\t\"../../h3_data_importer/data/woodpulp/gfw_plantations_woodpulp_harvest_ha.tif\" \\\n",
    "\t\t\"../../h3_data_importer/data/woodpulp/gfw_plantations_woodpulp_harvest_ha_res.tif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2801f0f1-f5db-450d-ae0f-5349e6b0a66f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with rio.open(\"../../h3_data_importer/data/woodpulp/gfw_plantations_woodpulp_harvest_ha_res.tif\") as src:\n",
    "    show(src, interpolation=\"none\")\n",
    "    df = raster_to_dataframe(\n",
    "        src.read(1), src.transform, h3_resolution=6, nodata_value=src.nodata, compacted=False, geo=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c3f9dd9-ef2b-4cc0-bf9b-b02af926ef30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(column=\"value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5684dcb9-3067-4bc8-8161-da085603685c",
   "metadata": {},
   "source": [
    "looks like successs!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69ed9b41-2942-4106-98d2-9965c48ca1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_file(\"../../h3_data_importer/data/woodpulp/gfw_plantations_woodpulp_harvest_ha_h3.geojson\", driver=\"GeoJSON\")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f40f7854-c52f-4f02-81b3-79dfbf0484f0",
   "metadata": {},
   "source": [
    "!gdalwarp -q -s_srs EPSG:4326 -t_srs EPSG:4326 -r sum -tr 0.04525 0.04525 -multi -of GTiff\\\n",
    "\t\t\"../../h3_data_importer/data/woodpulp/gfw_plantations_woodpulp_prod_t_nd.tif\" \\\n",
    "\t\t\"../../h3_data_importer/data/woodpulp/gfw_plantations_woodpulp_prod_t_nd_res.tif\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3178a64-4365-4953-9866-578aa6a45977",
   "metadata": {},
   "source": [
    "## Sateligence ressampling to a bit smaller resolution\n",
    "\n",
    "### Deforestation Mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f7a094c2-6b0d-4ca8-be18-80f4d81ad6cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "with rio.open(\"../../h3_data_importer/data/satelligence/Deforestation_Masked_2016-2022-10-01.tif\") as src:\n",
    "    target = find_h3_res_best_fit(src.transform, src.shape, src.bounds, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9c6f1a5a-06f6-493d-af46-4d71c60cc65c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 0.0450000000000003, (17, 18), 6)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x for x in target if x[-1] == 6][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2035e0b-a28c-4f6c-9b28-60e5eddc60e4",
   "metadata": {},
   "source": [
    "So te target pix size to get a nice 6 res h3 will be `0.045`\n",
    "\n",
    "But first lets convert the mask to pixel area so we can sum easily during the resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "10f1acdf-7d11-4e2a-9ded-bb155334261c",
   "metadata": {},
   "outputs": [],
   "source": [
    "deforest_mask = rioxarray.open_rasterio(\n",
    "    \"../../h3_data_importer/data/satelligence/Deforestation_Masked_2016-2022-10-01.tif\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b59722ed-01ce-4931-bd90-302cac95ebb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter to get only the 2021 mask\n",
    "deforest_mask.values = (deforest_mask.values == 2021).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad98dac5-76fc-492a-8c69-2ed431af38d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.164188908894495"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deforest_mask_3395 = deforest_mask.rio.reproject(dst_crs=\"epsg:3395\").squeeze(\"band\")\n",
    "pix_size_3395 = deforest_mask_3395.rio.transform().a\n",
    "pix_size_3395"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "50b45171-bee6-4fa1-b321-99a0326affac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixel size in Ha: 0.08505499147136847\n"
     ]
    }
   ],
   "source": [
    "pix_area_ha = pix_size_3395**2 / 10_000\n",
    "print(\"Pixel size in Ha:\", pix_area_ha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4112ed7c-c97a-47a6-aabe-c7793eeb8008",
   "metadata": {},
   "outputs": [],
   "source": [
    "deforest_ha_3395 = deforest_mask_3395.where(~deforest_mask_3395, pix_area_ha)\n",
    "\n",
    "deforest_ha = deforest_ha_3395.rio.reproject(\n",
    "    dst_crs=\"EPSG:4326\", resolution=(0.045, 0.045), resampling=Resampling.sum\n",
    ")\n",
    "\n",
    "deforest_ha.rio.to_raster(\"../../h3_data_importer/data/satelligence/Deforestation_2021_res6_ha.tif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e62ca00-2afe-4f16-9eae-c3be0a64e379",
   "metadata": {},
   "source": [
    "### Deforestation risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c71fc554-e8bb-4287-bf20-e19f724c809f",
   "metadata": {},
   "outputs": [],
   "source": [
    "deforest_risk = rioxarray.open_rasterio(\n",
    "    \"../../h3_data_importer/data/satelligence/DeforestationRisk_2016-2022-10-01.tif\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "13a40cd3-9507-4d25-a1f8-4b0d533a9dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 1, y: 1107, x: 1057)&gt;\n",
       "array([[[-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        ...,\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.]]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * x            (x) float64 -50.89 -50.89 -50.89 -50.88 ... -50.1 -50.1 -50.09\n",
       "  * y            (y) float64 -23.74 -23.74 -23.74 ... -24.56 -24.56 -24.57\n",
       "  * band         (band) int64 1\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    scale_factor:  1.0\n",
       "    add_offset:    0.0\n",
       "    _FillValue:    -9999.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 1107</li><li><span class='xr-has-index'>x</span>: 1057</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-195ee422-8a52-4b08-a5aa-8d35ba0a03b7' class='xr-array-in' type='checkbox' checked><label for='section-195ee422-8a52-4b08-a5aa-8d35ba0a03b7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-9.999e+03 -9.999e+03 -9.999e+03 ... -9.999e+03 -9.999e+03 -9.999e+03</span></div><div class='xr-array-data'><pre>array([[[-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        ...,\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.]]],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-f5070748-1575-46ea-b04c-8f8d8c7633bc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f5070748-1575-46ea-b04c-8f8d8c7633bc' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-50.89 -50.89 ... -50.1 -50.09</div><input id='attrs-814a2b31-0a5f-4228-8911-056524ef85d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-814a2b31-0a5f-4228-8911-056524ef85d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2af9da5-954a-4201-9d38-85d7b64f7e61' class='xr-var-data-in' type='checkbox'><label for='data-f2af9da5-954a-4201-9d38-85d7b64f7e61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-50.886875, -50.886125, -50.885375, ..., -50.096375, -50.095625,\n",
       "       -50.094875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-23.74 -23.74 ... -24.56 -24.57</div><input id='attrs-2f268e24-89d0-49ea-8bae-f3d31be23ea8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f268e24-89d0-49ea-8bae-f3d31be23ea8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-317c21c6-5b8e-42e5-86af-0b265e173096' class='xr-var-data-in' type='checkbox'><label for='data-317c21c6-5b8e-42e5-86af-0b265e173096' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-23.735875, -23.736625, -23.737375, ..., -24.563875, -24.564625,\n",
       "       -24.565375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-b459c1ff-ebe0-473f-bfaf-7f8d7e11470a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b459c1ff-ebe0-473f-bfaf-7f8d7e11470a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93ce9f7d-df65-4876-b8f2-cf16a520ed7e' class='xr-var-data-in' type='checkbox'><label for='data-93ce9f7d-df65-4876-b8f2-cf16a520ed7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-acfff3d5-05f9-4b43-8c69-4ccf603a9531' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acfff3d5-05f9-4b43-8c69-4ccf603a9531' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32bb461c-08a3-40f0-b3cf-90608062785f' class='xr-var-data-in' type='checkbox'><label for='data-32bb461c-08a3-40f0-b3cf-90608062785f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>-50.887249999999916 0.00075 0.0 -23.735500000000073 0.0 -0.00075</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8a0a2527-19a9-4388-87bb-98d50f3ef3fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a0a2527-19a9-4388-87bb-98d50f3ef3fe' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (band: 1, y: 1107, x: 1057)>\n",
       "array([[[-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        ...,\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],\n",
       "        [-9999., -9999., -9999., ..., -9999., -9999., -9999.]]],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * x            (x) float64 -50.89 -50.89 -50.89 -50.88 ... -50.1 -50.1 -50.09\n",
       "  * y            (y) float64 -23.74 -23.74 -23.74 ... -24.56 -24.56 -24.57\n",
       "  * band         (band) int64 1\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    scale_factor:  1.0\n",
       "    add_offset:    0.0\n",
       "    _FillValue:    -9999.0"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deforest_risk = deforest_risk.rio.reproject(\"EPSG:4326\", resolution=(0.00075, 0.00075), resampling=Resampling.sum)\n",
    "deforest_risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "3f0df4dd-4ced-4d4a-8111-9c3e84fa1efa",
   "metadata": {},
   "outputs": [],
   "source": [
    "deforest_risk.rio.to_raster('../../h3_data_importer/data/satelligence/Deforestation_risk.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66023888-5eea-4049-9f96-f959224a9c5c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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