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        "<a href=\"https://colab.research.google.com/github/osgeokr/GEE-PAM-Book/blob/main/Google_Earth_Engine_Maxent%EB%A5%BC_%EC%9D%B4%EC%9A%A9%ED%95%9C_%EC%A2%85_%EB%B6%84%ED%8F%AC_%EB%AA%A8%EB%8D%B8%EB%A7%81.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#Google Earth Engine: Maxent를 이용한 종 분포 모델링\n",
        "\n",
        "안녕하세요? 이번 글은 Google Earth Engine의 **Maxent** 소프트웨어 구현 기능을 간략히 소개해 보겠습니다. [Maxent](https://biodiversityinformatics.amnh.org/open_source/maxent/)는 알려진 출현 위치와 대규모 '배경' 위치의 환경 데이터를 사용하여 종 분포 확률을 모델링하는 데 사용됩니다. 자세한 정보와 인용은 다음을 참조하시면 되겠습니다:\n",
        "  > Phillips, et. al., 2004 A maximum entropy approach to species distribution modeling, Proceedings of the Twenty-First International Conference on Machine Learning\n",
        "\n",
        "Google Earth Engine에서 [ee.Classifier.amnhMaxent](https://developers.google.com/earth-engine/apidocs/ee-classifier-amnhmaxent) 메소드는 Maximum Entropy(최대 엔트로피) 분류기를 생성합니다. 여기서 AMNH는 미국 자연사박물관(American Museum of Natural History)의 약어입니다. Maxent의 아이디어는 미국 자연사박물관 내 생물다양성보전센터(Center for Biodiversity and Conservation)에서 AMNH와 AT&T-Research 간의 공공-민간 파트너십을 통해 처음 구상되었습니다.\n",
        "\n",
        "출력은 `probability`라는 이름의 단일 밴드로 모델링된 확률을 포함하며, `writeClampGrid` 인수가 참일 경우 `clamp`라는 추가 밴드가 포함됩니다."
      ],
      "metadata": {
        "id": "1zmOgAYIvkYF"
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    },
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      "cell_type": "markdown",
      "source": [
        "Google Earth Engine에서 종 분포 확률을 모델링하는 방법은 제가 집필한 [Earth Engine Community Tutorial - Species Distribution Modeling](https://developers.google.com/earth-engine/tutorials/community/species-distribution-modeling)에 상세히 소개되어 있습니다. 따라서 이 글은 Maxent 구현 기능 예제를 중심으로 정리하겠습니다. 먼저 Earth Engine을 인증/초기화 합니다."
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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",
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              "\n",
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              "                }\n",
              "            </style>\n",
              "            "
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      ],
      "source": [
        "import ee\n",
        "import geemap\n",
        "import geemap.colormaps as cm\n",
        "\n",
        "# Earth Engine 인증\n",
        "ee.Authenticate()\n",
        "\n",
        "# Earth Engine 초기화\n",
        "ee.Initialize(project='ee-foss4g')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "일단, 출현/부재 훈련 데이터를 임의 생성합니다. 아래 코드는 ee.Classifier.amnhMaxent 메소드의 예제 코드를 그대로 차용했습니다."
      ],
      "metadata": {
        "id": "Z1KI0ocH1jqN"
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    },
    {
      "cell_type": "code",
      "source": [
        "# 출현/부재 훈련 데이터 생성\n",
        "training_data = ee.FeatureCollection([\n",
        "    # 종 출현 지점\n",
        "    ee.Feature(ee.Geometry.Point([-122.39567, 38.02740]), {'presence': 1}),\n",
        "    ee.Feature(ee.Geometry.Point([-122.68560, 37.83690]), {'presence': 1}),\n",
        "    # 종 부재 지점\n",
        "    ee.Feature(ee.Geometry.Point([-122.59755, 37.92402]), {'presence': 0}),\n",
        "    ee.Feature(ee.Geometry.Point([-122.47137, 37.99291]), {'presence': 0}),\n",
        "    ee.Feature(ee.Geometry.Point([-122.52905, 37.85642]), {'presence': 0}),\n",
        "    ee.Feature(ee.Geometry.Point([-122.03010, 37.66660]), {'presence': 0})\n",
        "])"
      ],
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      },
      "execution_count": 32,
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              "\n",
              "            <style>\n",
              "                .geemap-dark {\n",
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              "                    --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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    },
    {
      "cell_type": "markdown",
      "source": [
        "환경 조건으로 Landsat 8호 이미지를 가져와 반사율 밴드를 선택해 보겠습니다. Earth Engine Snippet에서 각각의 약어는 다음을 지칭합니다.\n",
        "- `C02`: Collection 2\n",
        "- `T1`: Tier 1\n",
        "- `L2`: Level 2\n",
        "- `044034`: `044`는 경로 번호(Path), `034`는 열 번호(Row)\n",
        "- `20200606`: 이미지 촬영 날짜"
      ],
      "metadata": {
        "id": "1vD5WDNI2Ts9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Landsat 8호 이미지를 가져와 반사율 밴드 선택\n",
        "image = (ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20200606')\n",
        "         .select(['SR_B[1-7]'])\n",
        "         .multiply(0.0000275).add(-0.2))  # 축척 계수 적용"
      ],
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        "colab": {
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          "height": 17
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        "id": "5FgSrNdczUhd",
        "outputId": "bbf8d941-38ea-4c0b-ca0e-2aa746281e5b"
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      "execution_count": 33,
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              "\n",
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              "                    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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              "            </style>\n",
              "            "
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Landsat 8호 이미지 밴드 객체 내에서 특정 종의 출현/부재 위치에 해당하는 데이터 샘플을 추출합니다. 여기서 `scale` 매개변수는 샘플링의 공간 해상도를 의미합니다."
      ],
      "metadata": {
        "id": "gu0bBaCr4-tG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 종 출현/부재 위치에서 이미지 샘플링\n",
        "training = image.sampleRegions(**{\n",
        "    'collection': training_data,\n",
        "    'scale': 30\n",
        "})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "JQ9wXQbwz08U",
        "outputId": "c3dfb3f1-484b-477d-8d94-a946f2152a6e"
      },
      "execution_count": 22,
      "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",
      "source": [
        "이제 Google Earth Engine의 Maxent 분류기를 통해, 종 분포 모델을 정의하고 훈련하는 과정을 실행합니다. 훈련 데이터 내에서 예측하고자 하는 대상 속성(`presence`)을 지정하고, 이미지의 모든 분광 정보를 활용하여 종이 존재할 가능성이 있는 지역을 예측합니다."
      ],
      "metadata": {
        "id": "7NU-UWVC52kd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Maxent 분류기 정의 및 훈련\n",
        "classifier = ee.Classifier.amnhMaxent().train(**{\n",
        "    'features': training,\n",
        "    'classProperty': 'presence',\n",
        "    'inputProperties': image.bandNames()\n",
        "})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "OAzIrvUo0Nln",
        "outputId": "73b6f871-0d20-4395-e122-e508dd3ad91d"
      },
      "execution_count": 34,
      "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",
      "source": [
        "훈련된 Maxent 분류기를 이용하여 위성 이미지로 종의 분포를 예측합니다."
      ],
      "metadata": {
        "id": "Nm1bP9Vy8exR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Maxent 분류기를 사용하여 이미지 분류\n",
        "image_classified = image.classify(classifier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "vnRuKWtY0uHO",
        "outputId": "0fa445f2-ae50-4130-fc43-32fa7393081f"
      },
      "execution_count": 24,
      "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",
      "source": [
        "원본 이미지, 종 분포 확률, 훈련 데이터 레이어를 시각화해 봅니다. 종 출현 위치는 파란색, 종 부재 위치는 빨간색으로 표시하였습니다."
      ],
      "metadata": {
        "id": "3S6A1Yyl9VYv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 지도 생성\n",
        "m = geemap.Map(layout={'height':'400px', 'width':'800px'})\n",
        "\n",
        "# 원본 이미지 레이어\n",
        "m.addLayer(\n",
        "    image.select(['SR_B4', 'SR_B3', 'SR_B2']),\n",
        "    {'min': 0, 'max': 0.3}, 'Image'\n",
        ")\n",
        "\n",
        "# 분류된 확률 레이어\n",
        "m.addLayer(\n",
        "    image_classified.select('probability'),\n",
        "     {'min': 0, 'max': 1, 'palette': cm.palettes.viridis_r}, 'Probability'\n",
        ")\n",
        "\n",
        "# 훈련 데이터 레이어\n",
        "m.addLayer(\n",
        "    training_data.filter(ee.Filter.eq('presence', 0)), {'color': 'red'},\n",
        "    '종 부재'\n",
        ")\n",
        "m.addLayer(\n",
        "    training_data.filter(ee.Filter.eq('presence', 1)), {'color': 'blue'},\n",
        "    '종 출현'\n",
        ")\n",
        "\n",
        "# 지도 표시\n",
        "m.centerObject(training_data, 10)\n",
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421,
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            "4e6558450bfe4ca385a6c904fd266706"
          ]
        },
        "id": "G3jUPq9k078g",
        "outputId": "11f1fd89-aa65-4119-d416-ab9e835c190e"
      },
      "execution_count": 38,
      "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=[37.88422765713263, -122.45138012292709], controls=(WidgetControl(options=['position', 'transparent…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "103cbd67a7ea4b21958f78349cef088d"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
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                "custom_widget_manager": {
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      "cell_type": "code",
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      "metadata": {
        "id": "kWtEI59P1q5j"
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      "execution_count": null,
      "outputs": []
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}