Google_Earth_Engine을_활용한_산림_변화_데이터_분석.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/osgeokr/GEE-PAM-Book/blob/main/Google_Earth_Engine%EC%9D%84_%ED%99%9C%EC%9A%A9%ED%95%9C_%EC%82%B0%EB%A6%BC_%EB%B3%80%ED%99%94_%EB%8D%B0%EC%9D%B4%ED%84%B0_%EB%B6%84%EC%84%9D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c98405ab-ccc0-4f02-8154-2149a5e4e63e",
      "metadata": {
        "id": "c98405ab-ccc0-4f02-8154-2149a5e4e63e"
      },
      "source": [
        "# Google Earth Engine을 활용한 산림 변화 데이터 분석\n",
        "\n",
        "안녕하세요? 이번 코드 실습은 Google Earth Engine을 활용한 산림 변화 데이터 분석을 진행해 보겠습니다. 실습 내용은 다음과 같습니다:\n",
        "1. GEE를 사용하여 설악산 생물권보전지역의 경계를 추출합니다.\n",
        "2. Global Forest Change 데이터셋에 관해 학습해 봅니다.\n",
        "3. 2001년부터 2022년까지의 기간 동안 산림손실 면적을 계산하고, 그 결과를 그래프로 시각화해봅니다.\n",
        "\n",
        "먼저 GEE Python API를 설치하고 인증해야 합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7a0e8ae3-bb8a-4d44-9c90-49d7aa2f3fcf",
      "metadata": {
        "id": "7a0e8ae3-bb8a-4d44-9c90-49d7aa2f3fcf"
      },
      "outputs": [],
      "source": [
        "import ee\n",
        "import geemap\n",
        "from ipyleaflet import TileLayer\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Earth Engine 인증\n",
        "ee.Authenticate()\n",
        "\n",
        "# Earth Engine 초기화\n",
        "ee.Initialize(project='my-project')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2f4dad2c-01ce-48ff-ad50-70f22ce724e2",
      "metadata": {
        "id": "2f4dad2c-01ce-48ff-ad50-70f22ce724e2"
      },
      "source": [
        "## 설악산 생물권보전지역 경계 추출하기\n",
        "\n",
        "세계 보호지역 데이터베이스(WDPA: World Database on Protected Areas)는 매월 업데이트되는 보호지역에 대한 가장 최신이며 완전한 정보의 원천입니다. 이는 유엔 환경 프로그램의 세계 보전 모니터링 센터(UNEP-WCMC: United Nations Environment Programme's World Conservation Monitoring Centre)가 IUCN 및 세계보호지역위원회(WCPA: World Commission on Protected Areas)의 지원을 받아 관리하고 있습니다.\n",
        "\n",
        "GEE에서 WDPA 폴리곤은 [FeatureCollection](https://developers.google.com/earth-engine/datasets/catalog/WCMC_WDPA_current_polygons) 데이터 구조로 접근 가능하며, 설악산 생물권보전지역의 WDPA ID는 [5085](https://www.protectedplanet.net/5085)입니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e2b0ce1e-8d42-48b4-a070-ade5330fc507",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "e2b0ce1e-8d42-48b4-a070-ade5330fc507",
        "outputId": "c6ea7e34-06ef-49e7-d0dd-baed1875a38b"
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      "outputs": [
        {
          "data": {
            "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",
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              "\n",
              "                .geemap-colab .jupyter-button {\n",
              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Name: Mount Sorak\n"
          ]
        }
      ],
      "source": [
        "# 설악산 생물권보전지역 경계 추출\n",
        "wdpa = ee.FeatureCollection(\"WCMC/WDPA/current/polygons\")\n",
        "seorak = wdpa.filter(ee.Filter.eq('WDPAID', 5085))\n",
        "\n",
        "# 선택된 보호지역의 이름 확인\n",
        "wdpa_name = seorak.first().get('NAME').getInfo()\n",
        "print(\"Name:\", wdpa_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9bc8de97-1c67-475d-9ecc-ee5f6da031a1",
      "metadata": {
        "id": "9bc8de97-1c67-475d-9ecc-ee5f6da031a1"
      },
      "source": [
        "Vworld 영상지도 위에 설악산 생물권보전지역의 외곽선을 표현해 봅니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ba5437cc-d77a-4adb-b70a-e9e65328667f",
      "metadata": {
        "id": "ba5437cc-d77a-4adb-b70a-e9e65328667f",
        "outputId": "00c2b180-a5f8-4f8b-c060-dd40502b618d",
        "colab": {
          "referenced_widgets": [
            "2331c5768da645cc99353a8f386b922f"
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      "outputs": [
        {
          "data": {
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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",
              "            "
            ],
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              "<IPython.core.display.HTML object>"
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              "model_id": "2331c5768da645cc99353a8f386b922f",
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              "version_minor": 0
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            "text/plain": [
              "Map(center=[38.121201950409365, 128.4049622752154], controls=(WidgetControl(options=['position', 'transparent_…"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Vworld 영상지도 객체\n",
        "vworld_satellite = TileLayer(\n",
        "    url='https://xdworld.vworld.kr/2d/Satellite/service/{z}/{x}/{y}.jpeg',\n",
        "    name='Vworld Satellite',\n",
        "    attribution='Vworld'\n",
        ")\n",
        "\n",
        "# 1: 외곽선의 픽셀 값, 2: 외곽선의 두께(픽셀 단위)\n",
        "wdpa_raster = ee.Image().paint(seorak, 1, 2)\n",
        "\n",
        "# 시각화 파라미터 설정\n",
        "wdpa_vis = {\n",
        "    'palette': ['#FFFF00'], # Yellow 색상 적용\n",
        "    'opacity': 1\n",
        "}\n",
        "\n",
        "# 설악산 생물권보전지역 경계 가시화\n",
        "m = geemap.Map(width=\"800px\", height=\"500px\")\n",
        "m.add_layer(vworld_satellite)\n",
        "m.addLayer(wdpa_raster, wdpa_vis, wdpa_name) # 레이어 추가\n",
        "m.centerObject(seorak, 10) # 지도의 중심 설정\n",
        "m # 지도 객체 출력"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5adc2e2d-b995-462b-85d1-2079671efd1f",
      "metadata": {
        "id": "5adc2e2d-b995-462b-85d1-2079671efd1f"
      },
      "source": [
        "## Hansen Global Forest Change v1.10 (2000-2022) 데이터셋\n",
        "\n",
        "[Hansen Global Forest Change v1.10 (2000-2022)](https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2022_v1_10) 데이터셋은 Landsat 이미지 시계열 분석 결과를 통해 전 세계 산림 범위와 변화를 특성화합니다."
      ]
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      "id": "50c0bdae-6230-4168-9d51-c2b2d43db5d0",
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        "outputId": "b868d394-0e76-474c-f984-ceab1497d0ef"
      },
      "outputs": [
        {
          "data": {
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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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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
          "output_type": "display_data"
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      ],
      "source": [
        "# Hansen Global Forest Change v1.10 (2000-2022) 데이터셋\n",
        "gfc = ee.Image('UMD/hansen/global_forest_change_2022_v1_10')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3b17d2da-ceaa-47b1-afcf-5e882b12590b",
      "metadata": {
        "id": "3b17d2da-ceaa-47b1-afcf-5e882b12590b"
      },
      "source": [
        "GFC 데이터셋에서 `treecover2000`과 `lossyear` 밴드를 시각화 해보겠습니다. 각 밴드의 의미는 다음과 같습니다:\n",
        "\n",
        "- `treecover2000`: 2000년도의 수관피복율(tree canopy cover, 0-100%)로, 5미터 이상 높이의 모든 식생에 대한 수관울폐도(canopy closure)를 의미합니다.\n",
        "  > 수관울폐도는 특정 지역에서 지상을 덮고 있는 나무 수관(나뭇가지와 잎으로 이루어진 상층부)의 면적 비율을 나타냅니다. 다시 말해, 어떤 지역을 위에서 내려다 보았을 때, 그 지역 중 얼마나 많은 부분이 나무의 수관에 의해 가려지는지를 백분율로 표현한 것입니다.\n",
        "- `lossyear`: 연구 기간 동안의 산림 손실(forest loss)로, 산림에서 비산림 상태로의 변화, 입지-대체 교란(stand-replacement disturbance)를 정의합니다.\n",
        "  > 0(손실 없음) 또는 1-22 범위의 값으로 표현되며, 각각 2001년부터 2022년까지 주로 감지된 손실을 나타냅니다."
      ]
    },
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        "outputId": "db109e83-d43e-4cfc-cc1c-c5200c75639b"
      },
      "outputs": [
        {
          "data": {
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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",
              "            "
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              "<IPython.core.display.HTML object>"
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            "text/plain": [
              "Map(center=[38.121201950409365, 128.4049622752154], controls=(WidgetControl(options=['position', 'transparent_…"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 수관피복 시각화 파라미터\n",
        "tree_cover_vis = {\n",
        "    'bands': ['treecover2000'],\n",
        "    'min': 0,\n",
        "    'max': 100,\n",
        "    'palette': ['black', 'green']\n",
        "}\n",
        "\n",
        "# 산림손실 시각화 파라미터\n",
        "tree_loss_vis = {\n",
        "    'bands': ['lossyear'],\n",
        "    'min': 0,\n",
        "    'max': 22,\n",
        "    'palette': ['yellow', 'red']\n",
        "}\n",
        "\n",
        "# Vworld 하이브리드지도 객체\n",
        "vworld_hybrid = TileLayer(\n",
        "    url='https://xdworld.vworld.kr/2d/Hybrid/service/{z}/{x}/{y}.png',\n",
        "    name='Vworld Hybrid',\n",
        "    attribution='Vworld'\n",
        ")\n",
        "\n",
        "# gfc 레이어 추가\n",
        "m = geemap.Map(width=\"800px\", height=\"500px\")\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.add_layer(vworld_hybrid)\n",
        "m.centerObject(seorak, 10) # 지도의 중심 설정\n",
        "m # 지도 객체 출력"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "07b9006a-28e0-414a-9695-e8566a19b021",
      "metadata": {
        "id": "07b9006a-28e0-414a-9695-e8566a19b021"
      },
      "source": [
        "GFC 데이터셋은 2000년과 2022년의 구름 제거 이미지 합성 밴드들을 제공하고 있습니다. 지도 스와이프 기능을 활용해 동일지역 내 두 시기 이미지를 비교해 보겠습니다. 왼쪽 레이어는 2000년, 오른쪽 레이어는 2022년을 지정한 결과입니다.\n",
        "\n",
        "- 2000년 Landsat 7 구름 제거 이미지 합성 (first)\n",
        "  > `first_b30` (0.63-0.69µm, Red), `first_b40` (0.77-0.90µm, NIR), `first_b50` (1.55-1.75µm, SWIR), `first_b70` (2.09-2.35µm, SWIR)\n",
        "\n",
        "- 2022년 Landsat 구름 제거 이미지 합성 (last)\n",
        "  > `last_b30` (0.63-0.69µm, Red), `last_b40` (0.77-0.90µm, NIR), `last_b50` (1.55-1.75µm, SWIR), `last_b70` (2.09-2.35µm, SWIR)"
      ]
    },
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      "id": "cfa216a9-69b7-44b1-8bfd-6ab1c2ed3bdc",
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        {
          "data": {
            "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",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2c0c9916b250453f96318b03a958fa61",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map(center=[38.121201950409365, 128.4049622752154], controls=(ZoomControl(options=['position', 'zoom_in_text',…"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# first 시각화 파라미터\n",
        "first_vis = {\n",
        "    'bands': ['first_b50', 'first_b40', 'first_b30']\n",
        "}\n",
        "\n",
        "# last 시각화 파라미터\n",
        "last_vis = {\n",
        "    'bands': ['last_b50', 'last_b40', 'last_b30']\n",
        "}\n",
        "\n",
        "# 분할-패널 지도(Split-panel map)\n",
        "m = geemap.Map(width=\"800px\", height=\"500px\")\n",
        "\n",
        "left_layer = geemap.ee_tile_layer(gfc, first_vis, \"first\")\n",
        "right_layer = geemap.ee_tile_layer(gfc, last_vis, \"last\")\n",
        "m.split_map(left_layer, right_layer)\n",
        "m.centerObject(seorak, 11) # 지도의 중심 설정\n",
        "m # 지도 객체 출력"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fff54ea9-9f77-4425-8ea2-bfe7b1a64c51",
      "metadata": {
        "id": "fff54ea9-9f77-4425-8ea2-bfe7b1a64c51"
      },
      "source": [
        "## 산림손실 면적 계산 및 시각화\n",
        "\n",
        "GFC 데이터셋의 `loss`와 `lossyear` 밴드를 사용하여 관심지역 내 산림손실 면적을 계산하고, 이를 시각화해 보겠습니다. 먼저 `loss` 밴드로부터 설악산 BR 내 산림손실 면적 총계를 계산해 봅니다.\n",
        "\n",
        "- `loss`: 연구 기간 동안의 산림 손실(forest loss)로, 산림에서 비산림 상태로의 변화, 입지-대체 교란(stand-replacement disturbance)를 정의합니다.\n",
        "  > 0(손실 없음) 또는 1(손실) 값으로 표현됩니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e0a6006a-af43-40fb-befe-f223225a40b2",
      "metadata": {
        "id": "e0a6006a-af43-40fb-befe-f223225a40b2",
        "outputId": "b950bd46-2dc2-4d83-ffa7-36fd83cb2f74"
      },
      "outputs": [
        {
          "data": {
            "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",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "산림손실 면적: 17368422.44m²\n"
          ]
        }
      ],
      "source": [
        "# 산림손실 이미지\n",
        "loss_image = gfc.select(['loss'])\n",
        "area_image = loss_image.multiply(ee.Image.pixelArea())\n",
        "\n",
        "# 설악산 BR 내 산림손실 면적 합산\n",
        "stats = area_image.reduceRegion(\n",
        "    reducer=ee.Reducer.sum(),\n",
        "    geometry=seorak.geometry(),\n",
        "    scale=30,\n",
        "    maxPixels=1e9,\n",
        ")\n",
        "\n",
        "# 산림손실 면적 출력\n",
        "loss_area = stats.get('loss').getInfo()\n",
        "print(f\"산림손실 면적: {loss_area:.2f}m²\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f2bece50-8bfe-4873-a658-14fade16903f",
      "metadata": {
        "id": "f2bece50-8bfe-4873-a658-14fade16903f"
      },
      "source": [
        "이번에는 `lossyear` 밴드로부터 설악산 BR 내 연도별 손실 면적을 계산해 봅니다. 결과값은 데이터프레임으로 변환하겠습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e87dfa43-f84e-40c9-9430-555ac52bc431",
      "metadata": {
        "id": "e87dfa43-f84e-40c9-9430-555ac52bc431",
        "outputId": "64809241-28e7-479c-b4c6-c66da940127b"
      },
      "outputs": [
        {
          "data": {
            "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",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>200388.253457</td>\n",
              "      <td>20.038825</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year   Loss_Area_m2  Loss_Area_ha\n",
              "0  2001  200388.253457     20.038825"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 산림손실연도 이미지\n",
        "lossyear_image = gfc.select(['lossyear'])\n",
        "\n",
        "# 2001-2022년 산림손실 면적 계산\n",
        "years = range(1, 23)\n",
        "loss_areas = []\n",
        "\n",
        "for year in years:\n",
        "    # 해당 연도의 산림손실 픽셀 선택\n",
        "    yearly_loss = lossyear_image.eq(year)\n",
        "\n",
        "    # 산림손실 면적 계산\n",
        "    area_image = yearly_loss.multiply(ee.Image.pixelArea())\n",
        "    stats = area_image.reduceRegion(\n",
        "        reducer=ee.Reducer.sum(),\n",
        "        geometry=seorak.geometry(),\n",
        "        scale=30,\n",
        "        maxPixels=1e9,\n",
        "    )\n",
        "\n",
        "    # 연도별 손실 면적을 리스트에 추가 (단위: 제곱미터)\n",
        "    loss_area = stats.getInfo()['lossyear']\n",
        "    loss_areas.append(loss_area)\n",
        "\n",
        "# 연도와 손실 면적을 데이터프레임으로 변환\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)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7f0b545d-3636-40b7-8497-8b0211d25271",
      "metadata": {
        "id": "7f0b545d-3636-40b7-8497-8b0211d25271"
      },
      "source": [
        "끝으로, 연도별 산림손실 면적을 막대 그래프로 시각화합니다. Google Earth Engine의 가장 큰 장점 중 하나는 바로 클라우드 기반의 풍부한 데이터셋과 강력한 컴퓨팅 자원을 손쉽게 통합하여 제공한다는 점입니다. 만약 이러한 작업을 로컬 환경에서 진행했다면, 데이터셋의 다운로드로부터 지역별 통계 연산에 이르기까지 상당한 시간이 소요되었을 것입니다. 이번 실습을 통해, GEE가 지리공간 데이터 분석과 환경 모니터링 작업에 강력한 도구임을 실감할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a04c2dc7-a72f-4a8a-a53f-935ca018f553",
      "metadata": {
        "id": "a04c2dc7-a72f-4a8a-a53f-935ca018f553",
        "outputId": "e6092ffc-2149-4d90-91d3-72e522fa699d"
      },
      "outputs": [
        {
          "data": {
            "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",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 연도별 산림손실 면적 막대 그래프 생성\n",
        "fig, ax = plt.subplots(figsize=(12, 5))\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",
        "# 막대 위에 헥타르 단위로 면적 표시\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",
        "# 레이블 설정\n",
        "ax.set_xlabel('Year')\n",
        "ax.set_ylabel('Forest Loss Area (ha)')\n",
        "ax.set_title('Annual Forest Loss Area')\n",
        "\n",
        "# 차트를 이미지로 저장\n",
        "plt.savefig('forest_loss_area_colored.png', dpi=100)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c3abab6-8997-41d6-8582-b7fad3d1e5f7",
      "metadata": {
        "id": "0c3abab6-8997-41d6-8582-b7fad3d1e5f7"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.11"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "09791b015d384738866e0a13a6f3d147": {
          "model_module": "jupyter-leaflet",
          "model_module_version": "^0.18",
          "model_name": "LeafletFullScreenControlModel",
          "state": {
            "_model_module": "jupyter-leaflet",
            "_model_module_version": "^0.18",
            "_model_name": "LeafletFullScreenControlModel",
            "_view_count": null,
            "_view_module": "jupyter-leaflet",
            "_view_module_version": "^0.18",
            "_view_name": "LeafletFullScreenControlView",
            "options": [
              "position"
            ],
            "position": "topleft"
          }
        },
        "0bba004e4c5d4d1eac82791953f8cce6": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
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