240726_sentinel2_analysis_valle_nuevo_national_park.ipynb

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        "<a href=\"https://colab.research.google.com/github/osgeokr/kari-sdm/blob/main/240726_sentinel2_analysis_valle_nuevo_national_park.ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "7a0e8ae3-bb8a-4d44-9c90-49d7aa2f3fcf",
      "metadata": {
        "id": "7a0e8ae3-bb8a-4d44-9c90-49d7aa2f3fcf"
      },
      "outputs": [],
      "source": [
        "import ee\n",
        "import geemap\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from ipyleaflet import TileLayer\n",
        "from google.colab import files\n",
        "\n",
        "ee.Authenticate()\n",
        "ee.Initialize(project='ee-foss4g')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "e2b0ce1e-8d42-48b4-a070-ade5330fc507",
      "metadata": {
        "id": "e2b0ce1e-8d42-48b4-a070-ade5330fc507",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2ae6c1b2-5940-43e3-85b4-2bd17516e11b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: Valle Nuevo\n"
          ]
        }
      ],
      "source": [
        "# Valle Nuevo National Park\n",
        "wdpa = ee.FeatureCollection(\"WCMC/WDPA/current/polygons\")\n",
        "wdpa = wdpa.filter(ee.Filter.eq('WDPAID', 478142))\n",
        "\n",
        "wdpa_name = wdpa.first().get('NAME').getInfo()\n",
        "print(\"Name:\", wdpa_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "e0c364d3-0a35-44e9-82b5-20e1370eab8f",
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        {
          "output_type": "display_data",
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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              "            "
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map(center=[18.792416623633116, -70.64776970012024], controls=(WidgetControl(options=['position', 'transparent…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {
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                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "m = geemap.Map(width=\"800px\", height=\"400px\")\n",
        "m.addLayer(wdpa, {'color': 'green'}, wdpa_name)\n",
        "m.centerObject(wdpa, 10)\n",
        "m"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mask_s2_clouds(image):\n",
        "    # Use QA (Quality Assurance) band for cloud masking in Sentinel-2\n",
        "    qa = image.select('QA60')\n",
        "\n",
        "    # Bit 10 is for clouds, Bit 11 is for cirrus clouds\n",
        "    cloud_bit_mask = 1 << 10\n",
        "    cirrus_bit_mask = 1 << 11\n",
        "\n",
        "    # Consider clear if both cloud and cirrus bits are 0\n",
        "    mask = (\n",
        "        qa.bitwiseAnd(cloud_bit_mask)\n",
        "        .eq(0)\n",
        "        .And(qa.bitwiseAnd(cirrus_bit_mask).eq(0))\n",
        "    )\n",
        "\n",
        "    return image.updateMask(mask).divide(10000) # Scaling"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "6cp3aS4bQpIm",
        "outputId": "e4d86f15-a643-4420-9658-84daad6be361"
      },
      "id": "6cp3aS4bQpIm",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "1baeb159-9f41-464d-ab59-df83b3d944c8",
      "metadata": {
        "id": "1baeb159-9f41-464d-ab59-df83b3d944c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d8fb082a-5bea-4c27-c9fd-fa9b7b023bd9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image count: 3\n"
          ]
        }
      ],
      "source": [
        "s2_images = (\n",
        "    ee.ImageCollection(\"COPERNICUS/S2_SR_HARMONIZED\")\n",
        "    .filterDate(\"2023-03-16\", \"2023-03-31\")\n",
        "    .filterBounds(wdpa)\n",
        "    .filter(ee.Filter.lt(\"CLOUDY_PIXEL_PERCENTAGE\", 5))\n",
        "    .map(mask_s2_clouds)\n",
        ")\n",
        "\n",
        "image_count = s2_images.size()\n",
        "\n",
        "print(\"Image count:\", image_count.getInfo())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s2_image = s2_images.median()\n",
        "# s2_image = ee.Image(s2_images.toList(s2_images.size()).get(1))"
      ],
      "metadata": {
        "id": "CTW82bUGH2rU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "51155769-c8e8-4a33-e23f-50fa20475a6c"
      },
      "id": "CTW82bUGH2rU",
      "execution_count": 7,
      "outputs": [
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
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          "metadata": {}
        }
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            "b1a44aaca4db4930bf35adc89b7b635d",
            "6a7de57052434bf5a9c2784e932b6f73"
          ]
        },
        "outputId": "526333a1-7959-4599-8298-c5d40f6e8a66"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map(center=[18.792416623633116, -70.64776970012024], controls=(WidgetControl(options=['position', 'transparent…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "10062cb90f80469cb4e69fe8937ae4af"
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          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "# 1: Pixel value for the outline, 2: Thickness of the outline (in pixels)\n",
        "wdpa_raster = ee.Image().paint(wdpa, 1, 2)\n",
        "\n",
        "# Set visualization parameters\n",
        "wdpa_vis = {\n",
        "    'palette': ['#FFFF00'], # Apply Yellow color\n",
        "    'opacity': 1\n",
        "}\n",
        "\n",
        "# TCC\n",
        "vis_params = {\n",
        "    'min': 0.0,\n",
        "    'max': 0.3,\n",
        "    'bands': ['B4', 'B3', 'B2'],\n",
        "}\n",
        "\n",
        "m = geemap.Map(width=\"800px\", height=\"400px\")\n",
        "m.add_layer(s2_image, vis_params, 'TCC')\n",
        "m.addLayer(wdpa_raster, wdpa_vis, wdpa_name)\n",
        "m.centerObject(wdpa, 10)\n",
        "m"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#FCC\n",
        "vis_params = {\n",
        "    'min': 0.0,\n",
        "    'max': 0.3,\n",
        "    'bands': ['B8', 'B4', 'B3'],\n",
        "}\n",
        "\n",
        "m = geemap.Map(width=\"800px\", height=\"400px\")\n",
        "m.add_layer(s2_image, vis_params, 'FCC')\n",
        "m.addLayer(wdpa_raster, wdpa_vis, wdpa_name)\n",
        "m.centerObject(wdpa, 10)\n",
        "m"
      ],
      "metadata": {
        "id": "34AZOwfWHpkD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
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            "27bbcabafd2e4bee975bf3ff12247d1f",
            "ab2ae5ccb42b454eb450e6b25989c2ba"
          ]
        },
        "outputId": "c99d180e-f276-47a9-c9d2-07ee437c1045"
      },
      "id": "34AZOwfWHpkD",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map(center=[18.792416623633116, -70.64776970012024], controls=(WidgetControl(options=['position', 'transparent…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "752ec0b2aabf4f928f94ebb104defc63"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s2_image = s2_image.clip(wdpa.geometry())"
      ],
      "metadata": {
        "id": "5G6gUT17j9aV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "20bd62bd-1437-414c-f5bb-8b3c86e9941d"
      },
      "id": "5G6gUT17j9aV",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "314da205-feb7-44cb-b6c8-ef249eb3dae4",
      "metadata": {
        "id": "314da205-feb7-44cb-b6c8-ef249eb3dae4"
      },
      "source": [
        "### 3.1.3. 국립공원 경계 내에서 NDVI 계산 및 통계치 산출"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# NDVI 계산: (NIR - Red) / (NIR + Red)\n",
        "ndvi = s2_image.normalizedDifference(['B8', 'B4'])"
      ],
      "metadata": {
        "id": "Kd3J5FYTJhQU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "edf04b31-e383-4afc-c13d-e2b0d7051c09"
      },
      "id": "Kd3J5FYTJhQU",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
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              "\n",
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              "            "
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        }
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      "execution_count": 13,
      "id": "b692709c-6e45-479c-84c2-830e408a2c6f",
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        "id": "b692709c-6e45-479c-84c2-830e408a2c6f",
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          ]
        },
        "outputId": "0baba585-68d3-4f11-c842-2ea90b2ca5d8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map(center=[18.792416623633116, -70.64776970012024], controls=(WidgetControl(options=['position', 'transparent…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d37bbb2c754747669310b0e5e9f363ac"
            }
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          "metadata": {
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              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
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          }
        }
      ],
      "source": [
        "# Define NDVI color palette\n",
        "ndvi_palette = [\n",
        "    'FE8374',  # Low NDVI - Brown\n",
        "    'FED976',  # Low-Medium NDVI - Light Green\n",
        "    'CAE23C',  # Medium NDVI - Green\n",
        "    '98B718',  # Medium-High NDVI - Dark Green\n",
        "    '059033',  # High NDVI - Very Dark Green\n",
        "]\n",
        "\n",
        "m = geemap.Map(width=\"800px\", height=\"400px\")\n",
        "m.add_layer(ndvi, {'min': 0, 'max': 0.5, 'palette': ndvi_palette}, 'NDVI')\n",
        "m.addLayer(wdpa_raster, wdpa_vis, wdpa_name)\n",
        "m.centerObject(wdpa, 10)\n",
        "m"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "b7f10d4c-5d54-49d9-a2fc-0cb74c60e8d3",
      "metadata": {
        "id": "b7f10d4c-5d54-49d9-a2fc-0cb74c60e8d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "6639b272-19af-41c0-b718-8f6e5d1322fd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Min      Mean    Median  Max    StdDev\n",
              "VN -0.701068  0.666756  0.753985    1  0.195951"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2c194f3d-5f0a-4cb1-94c6-fc5eed3d1f09\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        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>Min</th>\n",
              "      <th>Mean</th>\n",
              "      <th>Median</th>\n",
              "      <th>Max</th>\n",
              "      <th>StdDev</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>VN</th>\n",
              "      <td>-0.701068</td>\n",
              "      <td>0.666756</td>\n",
              "      <td>0.753985</td>\n",
              "      <td>1</td>\n",
              "      <td>0.195951</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c194f3d-5f0a-4cb1-94c6-fc5eed3d1f09')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2c194f3d-5f0a-4cb1-94c6-fc5eed3d1f09 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2c194f3d-5f0a-4cb1-94c6-fc5eed3d1f09');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_5f3b0629-1844-40fd-ad42-950411bf2b9f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_stats')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5f3b0629-1844-40fd-ad42-950411bf2b9f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_stats');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_stats",
              "summary": "{\n  \"name\": \"df_stats\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -0.7010676287504977,\n        \"max\": -0.7010676287504977,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -0.7010676287504977\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.6667559977835306,\n        \"max\": 0.6667559977835306,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.6667559977835306\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Median\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.7539854829945052,\n        \"max\": 0.7539854829945052,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.7539854829945052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StdDev\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.19595105476889077,\n        \"max\": 0.19595105476889077,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.19595105476889077\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# NDVI 통계치 계산 (최소값, 평균, 중간값, 최대값, 표준편차)\n",
        "stats = ndvi.reduceRegion(\n",
        "    reducer=ee.Reducer.min()\n",
        "    .combine(reducer2=ee.Reducer.mean(), sharedInputs=True)\n",
        "    .combine(reducer2=ee.Reducer.median(), sharedInputs=True)\n",
        "    .combine(reducer2=ee.Reducer.max(), sharedInputs=True)\n",
        "    .combine(reducer2=ee.Reducer.stdDev(), sharedInputs=True),\n",
        "    geometry=wdpa.geometry(),\n",
        "    scale=10,\n",
        "    maxPixels=1e9,\n",
        ")\n",
        "\n",
        "# 통계치 결과를 DataFrame으로 변환\n",
        "df_stats = pd.DataFrame(\n",
        "    [stats.getInfo()],\n",
        "    columns=[\"nd_min\", \"nd_mean\", \"nd_median\", \"nd_max\", \"nd_stdDev\"],\n",
        "    index=[\"VN\"],\n",
        ")\n",
        "df_stats.columns = [\"Min\", \"Mean\", \"Median\", \"Max\", \"StdDev\"]\n",
        "df_stats"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Hansen Global Forest Change v1.10 (2000-2022) dataset\n",
        "gfc = ee.Image('UMD/hansen/global_forest_change_2022_v1_10')\n",
        "\n",
        "# Visualization parameters for tree cover\n",
        "tree_cover_vis = {\n",
        "    'bands': ['treecover2000'],\n",
        "    'min': 0,\n",
        "    'max': 100,\n",
        "    'palette': ['black', 'green']\n",
        "}\n",
        "\n",
        "# Visualization parameters for forest loss\n",
        "tree_loss_vis = {\n",
        "    'bands': ['lossyear'],\n",
        "    'min': 0,\n",
        "    'max': 22,\n",
        "    'palette': ['yellow', 'red']\n",
        "}"
      ],
      "metadata": {
        "id": "AzZt6IICS-DX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "e54463ea-4a36-4be2-ed97-c577e3397529"
      },
      "id": "AzZt6IICS-DX",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add GFC layers\n",
        "m = geemap.Map(width=\"800px\", height=\"400px\")\n",
        "m.addLayer(gfc, tree_cover_vis, 'tree cover')\n",
        "m.addLayer(gfc, tree_loss_vis, 'tree loss year')\n",
        "m.addLayer(wdpa_raster, wdpa_vis, wdpa_name)\n",
        "m.centerObject(wdpa, 10)\n",
        "m  # Display the map object"
      ],
      "metadata": {
        "id": "DKwE2fWsTp7w",
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      },
      "id": "DKwE2fWsTp7w",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map(center=[18.792416623633116, -70.64776970012024], controls=(WidgetControl(options=['position', 'transparent…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77ad7617deb54292af192f7a8c3f4389"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Forest loss image\n",
        "loss_image = gfc.select(['loss'])\n",
        "area_image = loss_image.multiply(ee.Image.pixelArea())\n",
        "\n",
        "# Sum the forest loss area within Seoraksan BR\n",
        "stats = area_image.reduceRegion(\n",
        "    reducer=ee.Reducer.sum(),\n",
        "    geometry=wdpa.geometry(),\n",
        "    scale=30,\n",
        "    maxPixels=1e9,\n",
        ")\n",
        "\n",
        "# Output the forest loss area\n",
        "loss_area = stats.get('loss').getInfo()\n",
        "print(f\"Forest loss area: {loss_area:.2f}m²\")"
      ],
      "metadata": {
        "id": "KLCFfraMT9cj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5fe569b6-03f1-44cf-836e-16135f1ee89c"
      },
      "id": "KLCFfraMT9cj",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Forest loss area: 52273348.65m²\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Forest loss year image\n",
        "lossyear_image = gfc.select(['lossyear'])\n",
        "\n",
        "# Calculate forest loss area for 2001-2022\n",
        "years = range(1, 23)\n",
        "loss_areas = []\n",
        "\n",
        "for year in years:\n",
        "    # Select forest loss pixels for the given year\n",
        "    yearly_loss = lossyear_image.eq(year)\n",
        "\n",
        "    # Calculate forest loss area\n",
        "    area_image = yearly_loss.multiply(ee.Image.pixelArea())\n",
        "    stats = area_image.reduceRegion(\n",
        "        reducer=ee.Reducer.sum(),\n",
        "        geometry=wdpa.geometry(),\n",
        "        scale=30,\n",
        "        maxPixels=1e9,\n",
        "    )\n",
        "\n",
        "    # Add the loss area for the year to the list (unit: square meters)\n",
        "    loss_area = stats.getInfo()['lossyear']\n",
        "    loss_areas.append(loss_area)\n",
        "\n",
        "# Convert years and loss areas to a DataFrame\n",
        "df = pd.DataFrame({\n",
        "    'Year': range(2001, 2023),\n",
        "    'Loss_Area_m2': loss_areas\n",
        "})\n",
        "\n",
        "df['Loss_Area_ha'] = df['Loss_Area_m2'] / 10000\n",
        "df.head(1)"
      ],
      "metadata": {
        "id": "vGeZJXmEUXyq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "a1b5e770-a762-4bb7-eb3b-d96d8727f6c2"
      },
      "id": "vGeZJXmEUXyq",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  Loss_Area_m2  Loss_Area_ha\n",
              "0  2001  2.114148e+06    211.414826"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c7464392-73ac-4ce6-ac64-92a76ef8eab1\" class=\"colab-df-container\">\n",
              "    <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>Year</th>\n",
              "      <th>Loss_Area_m2</th>\n",
              "      <th>Loss_Area_ha</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2001</td>\n",
              "      <td>2.114148e+06</td>\n",
              "      <td>211.414826</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7464392-73ac-4ce6-ac64-92a76ef8eab1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c7464392-73ac-4ce6-ac64-92a76ef8eab1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c7464392-73ac-4ce6-ac64-92a76ef8eab1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 22,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 2001,\n        \"max\": 2022,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          2001,\n          2014,\n          2009\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loss_Area_m2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3361431.929682909,\n        \"min\": 155102.54913545496,\n        \"max\": 11482436.842317946,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          2114148.2591634113,\n          10905465.369324448,\n          342544.5590997434\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loss_Area_ha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.14319296829086,\n        \"min\": 15.510254913545497,\n        \"max\": 1148.2436842317945,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          211.41482591634113,\n          1090.5465369324447,\n          34.25445590997434\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the new record as a DataFrame\n",
        "new_record = pd.DataFrame({'Year': [2023], 'Loss_Area_m2': [38200000], 'Loss_Area_ha': [3820]})\n",
        "\n",
        "# Concatenate the new record with the original DataFrame\n",
        "df = pd.concat([df, new_record], ignore_index=True)"
      ],
      "metadata": {
        "id": "HEK8BgqXa7of",
        "outputId": "90bbe6bb-03ec-40de-b66d-20fb6a753b48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "id": "HEK8BgqXa7of",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a bar graph for annual forest loss area\n",
        "fig, ax = plt.subplots(figsize=(12, 4))\n",
        "colors = plt.cm.Reds(df['Loss_Area_ha'] / df['Loss_Area_ha'].max())\n",
        "bars = ax.bar(df['Year'], df['Loss_Area_ha'], color=colors)\n",
        "\n",
        "# Display area in hectares on top of each bar\n",
        "for bar, area_ha in zip(bars, df['Loss_Area_ha']):\n",
        "    yval = bar.get_height()\n",
        "    ax.text(bar.get_x() + bar.get_width() / 2, yval, f'{area_ha:.0f}ha', va='bottom', ha='center', color='black')\n",
        "\n",
        "# Set labels\n",
        "ax.set_xlabel('Year')\n",
        "ax.set_ylabel('Forest Loss Area (ha)')\n",
        "ax.set_title('Annual Forest Loss Area')\n",
        "\n",
        "# Save the chart as an image\n",
        "plt.savefig('forest_loss_area_colored.png', dpi=100)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "up00If18U1Ua",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "f085c050-b467-4943-8457-8545209f9935"
      },
      "id": "up00If18U1Ua",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
              "                    --jp-widgets-color: white;\n",
              "                    --jp-widgets-label-color: white;\n",
              "                    --jp-ui-font-color1: white;\n",
              "                    --jp-layout-color2: #454545;\n",
              "                    background-color: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-dark .jupyter-button {\n",
              "                    --jp-layout-color3: #383838;\n",
              "                }\n",
              "\n",
              "                .geemap-colab {\n",
              "                    background-color: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
              "                }\n",
              "            </style>\n",
              "            "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fw7QR2ekLx-A"
      },
      "id": "Fw7QR2ekLx-A",
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