tassajara_ndvi.ipynb

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        "<a href=\"https://colab.research.google.com/github/skylerlewis/tassajara/blob/main/tassajara_ndvi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# install packages\n",
        "\n",
        "!pip install -q -U PyDrive\n",
        "!pip install -q -U geemap\n",
        "!pip install -q -U geopandas\n",
        "\n",
        "# get files from github\n",
        "\n",
        "!wget https://github.com/skylerlewis/tassajara/raw/main/data/tassajara_perim.geojson\n",
        "!wget https://github.com/skylerlewis/tassajara/raw/main/data/tassajara_perim_50ft.geojson\n",
        "!wget https://github.com/skylerlewis/tassajara/raw/main/data/raster/lidar2021_REM.tif\n",
        "!wget https://github.com/skylerlewis/tassajara/raw/main/data/tassajara_perim_50ft.kml"
      ],
      "metadata": {
        "id": "QQGoqpUrzR_Y",
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      "execution_count": 1,
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          "name": "stdout",
          "text": [
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            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get API key from private google drive\n",
        "\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "def file_from_gdrive(filename, driveid):\n",
        "    downloaded = drive.CreateFile({\"id\": driveid}) \n",
        "    downloaded.GetContentFile(filename)\n",
        "    return filename\n",
        "\n",
        "file_from_gdrive(\"tassajara-creek-e83f6fb39a0f.json\", \"1_BJV5EcePhqi_8Iwrreonng5-Q2JJXpz\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "rx-f-f_Cj_mb",
        "outputId": "551952ed-cdc6-4c2b-8eb0-7dd8be2094c8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'tassajara-creek-e83f6fb39a0f.json'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import packages\n",
        "\n",
        "import ee\n",
        "import geemap\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "U5OLMX0jTz2Y"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Vhcrb5BvNCcn"
      },
      "outputs": [],
      "source": [
        "# authenticate with Google Earth Engine\n",
        "\n",
        "service_account = \"earth-engine@tassajara-creek.iam.gserviceaccount.com\"\n",
        "credentials = ee.ServiceAccountCredentials(service_account, 'tassajara-creek-e83f6fb39a0f.json')\n",
        "ee.Initialize(credentials)\n",
        "\n",
        "# https://colab.research.google.com/github/google/earthengine-api/blob/master/python/examples/ipynb/ee-api-colab-setup.ipynb#scrollTo=v7pD6pDOAhOW\n",
        "# first make a google cloud project with a service account and make sure you have access https://console.cloud.google.com/apis/credentials?project=tassajara-creek\n",
        "# enable the API in cloud via https://console.cloud.google.com/apis/library/earthengine.googleapis.com?project=tassajara-creek\n",
        "# then register the project via https://code.earthengine.google.com/register "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import and map the area of interest\n",
        "\n",
        "tassajara_perim = geemap.geojson_to_ee(\"tassajara_perim.geojson\")\n",
        "m = geemap.Map()\n",
        "m.addLayer(tassajara_perim, {}, \"perimeter\")\n",
        "m.centerObject(tassajara_perim, zoom=15)\n",
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
            "4cbd386bcd0544119d49fd30617abbc2",
            "15b0b0ed4aef4b27b491a16982dd85c0",
            "eb7fc4e7ea594132a96e9767ddd3b825",
            "a20f83a860504d7ca02ef98f89cfa7a6",
            "90cb4af3a432480d8c8097c551835d4b",
            "eab35b171fa5471988e9e8d37ade3265",
            "06d392f8aa8d4bf79e05d6ad1d8b2bcc",
            "25dfae8db4a245fe9e219114ae444ca3",
            "87aa8996ad834528ab0421a201dde856",
            "481254a64f2847b48719db2ca5150bef",
            "fc2d18366dcc413f8019f41c480a42cb",
            "a8c8a1062a6743d9adaa8d560ce89811",
            "9c2bbccbf82c4a4ea456ba828bd8468f",
            "50b2b9900d2142ad8930245dd1abc226",
            "79203b3eacc8462095b9bcf48bf756bd",
            "504a2183d0b4462fa3d4f2406406e25c",
            "ddfcb335597e4f8e96b6fb198685b208",
            "15d356ebee0c444c95be238219f9bffc",
            "8c8e64ee9e094c89961be7d4a4c3bd24",
            "231c8fb6d2554d1e9c3294e2333aa71d",
            "a785c2859f2f4f369ad106d927fd6024",
            "cae694a5833b4c3ea52e8b2de05c334a",
            "09b02de7e0a5497ebc2e48c15ddefb33",
            "da9909a5ce9e40d78c0570fd875fc1af",
            "da88278844104c6ba6d7fb4a34396c3b",
            "b9c4432401294edc9b43b2ebc6544481"
          ]
        },
        "id": "2Pk7AL-NalW6",
        "outputId": "246d3943-cab0-4666-bdab-365eb720a680"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map(center=[37.707227929651516, -121.87901748917385], controls=(WidgetControl(options=['position', 'transparen…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cbd386bcd0544119d49fd30617abbc2"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Landsat Time Series"
      ],
      "metadata": {
        "id": "qJGAZPS_IBAE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# download NDVI data from Landsat 5, 7, and 8 for the area of interest\n",
        "\n",
        "def applyScaleFactorsL5(image):\n",
        "  opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2)\n",
        "  thermalBand = image.select('ST_B6').multiply(0.00341802).add(149.0)\n",
        "  return (image.addBands(opticalBands, None, True)\n",
        "              .addBands(thermalBand, None, True))\n",
        "\n",
        "def applyNdviL5(image):\n",
        "  ndvi = ee.Image(image).normalizedDifference(['SR_B4', 'SR_B3'])\n",
        "  return ee.Image(image).addBands(ndvi)\n",
        "\n",
        "# get Landsat 5 through end of September 2013\n",
        "landsat5 = (ee.ImageCollection(\"LANDSAT/LT05/C02/T1_L2\")\n",
        "    .filterBounds(tassajara_perim)\n",
        "    .filterDate('1984-01-01', '2011-09-30')\n",
        "    .filterMetadata('CLOUD_COVER_LAND', 'less_than', 20)\n",
        "    .map(applyScaleFactorsL5)\n",
        "    .map(applyNdviL5))\n",
        "\n",
        "def applyScaleFactorsL7(image):\n",
        "  opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2)\n",
        "  thermalBand = image.select('ST_B6').multiply(0.00341802).add(149.0)\n",
        "  return (image.addBands(opticalBands, None, True)\n",
        "              .addBands(thermalBand, None, True))\n",
        "\n",
        "# get Landsat 7 for just the interim period\n",
        "landsat7 = (ee.ImageCollection('LANDSAT/LE07/C02/T1_L2')\n",
        "    .filterBounds(tassajara_perim)\n",
        "    .filterDate('2011-10-01', '2013-03-31')\n",
        "    .filterMetadata('CLOUD_COVER_LAND', 'less_than', 20)\n",
        "    .map(applyScaleFactorsL7)\n",
        "    .map(applyNdviL5)) # can use same function as Landsat 5\n",
        "\n",
        "def applyScaleFactorsL8(image):\n",
        "  opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2)\n",
        "  thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0)\n",
        "  return (image.addBands(opticalBands, None, True)\n",
        "              .addBands(thermalBands, None, True))\n",
        "\n",
        "def applyNdviL8(image):\n",
        "  ndvi = ee.Image(image).normalizedDifference(['SR_B5', 'SR_B4'])\n",
        "  return ee.Image(image).addBands(ndvi)\n",
        "\n",
        "# get Landsat 8 starting April 2013\n",
        "landsat8 = (ee.ImageCollection(\"LANDSAT/LC08/C02/T1_L2\")\n",
        "    .filterBounds(tassajara_perim)\n",
        "    .filterDate('2013-04-01', '2023-12-31')\n",
        "    .filterMetadata('CLOUD_COVER_LAND', 'less_than', 20)\n",
        "    .map(applyScaleFactorsL8)\n",
        "    .map(applyNdviL8))\n",
        "\n",
        "# merge all collections together\n",
        "\n",
        "ndvi = landsat5.merge(landsat7).merge(landsat8).select('nd')"
      ],
      "metadata": {
        "id": "-rJvQmUlalbZ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate the average NDVI within the AOI for each image in the collection\n",
        "\n",
        "def setProperty(image):\n",
        "    dict = image.reduceRegion(ee.Reducer.mean(), tassajara_perim)\n",
        "    return image.set(dict)\n",
        "\n",
        "# add the results to image properties\n",
        "withMean = ndvi.map(setProperty)"
      ],
      "metadata": {
        "id": "A80obfkSapyZ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# extract the cloud-calculated NDVI means as a local Python list \n",
        "\n",
        "time = withMean.aggregate_array('system:time_start').getInfo()\n",
        "ndvi = withMean.aggregate_array('nd').getInfo()"
      ],
      "metadata": {
        "id": "y7-9W4NvveNC"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# consolidate as a data frame\n",
        "\n",
        "ndvi_time_series = (\n",
        "    pd.concat([pd.Series(time), pd.Series(ndvi)], axis=1, keys=[\"time\", \"ndvi\"])\n",
        "    .assign(time = lambda df: [pd.to_datetime(x * 1e6) for x in df[\"time\"]])\n",
        "    )\n",
        "ndvi_time_series"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "D6p5MgTZw0T5",
        "outputId": "8a015750-4fdf-4d69-91c3-e1f353a2897e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             time      ndvi\n",
              "0   1984-05-18 18:13:47.194000000  0.364794\n",
              "1   1984-06-03 18:14:10.438000000  0.267640\n",
              "2   1984-07-05 18:14:37.630000000  0.224809\n",
              "3   1984-09-07 18:16:09.720000000  0.219606\n",
              "4   1984-09-23 18:16:11.544000000  0.216027\n",
              "..                            ...       ...\n",
              "448 2022-09-16 18:46:43.008000000  0.476842\n",
              "449 2022-10-02 18:46:42.263000064  0.511270\n",
              "450 2022-11-03 18:46:43.406000128  0.540508\n",
              "451 2022-11-19 18:46:36.198000128  0.499110\n",
              "452 2023-01-22 18:46:25.319000064  0.350330\n",
              "\n",
              "[453 rows x 2 columns]"
            ],
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              "      <td>0.224809</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1984-09-07 18:16:09.720000000</td>\n",
              "      <td>0.219606</td>\n",
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              "    <tr>\n",
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              "      <td>2022-11-03 18:46:43.406000128</td>\n",
              "      <td>0.540508</td>\n",
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              "    <tr>\n",
              "      <th>451</th>\n",
              "      <td>2022-11-19 18:46:36.198000128</td>\n",
              "      <td>0.499110</td>\n",
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              "    <tr>\n",
              "      <th>452</th>\n",
              "      <td>2023-01-22 18:46:25.319000064</td>\n",
              "      <td>0.350330</td>\n",
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    {
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      "source": [
        "# plot the result\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax = sns.lineplot(data = ndvi_time_series, x=\"time\", y=\"ndvi\")\n",
        "fig.suptitle(\"NDVI vs time\")\n",
        "plt.show()"
      ],
      "metadata": {
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          "height": 308
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      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# export the result to CSV\n",
        "\n",
        "ndvi_time_series.to_csv(\"ndvi_time_series.csv\")"
      ],
      "metadata": {
        "id": "m-I0fQRE0PWe"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NAIP Spatial Trend Analysis"
      ],
      "metadata": {
        "id": "hdo24qcuIFo1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#tassajara_perim_50ft = geemap.geojson_to_ee(\"tassajara_perim_50ft.geojson\")\n",
        "geemap.kml_to_shp(\"tassajara_perim_50ft.kml\", \"tassajara_perim_50ft.shp\")\n",
        "tassajara_perim_50ft = geemap.shp_to_ee('tassajara_perim_50ft.shp')\n",
        "\n",
        "def clipToBounds(img):\n",
        "    return img.clip(tassajara_perim_50ft)\n",
        "\n",
        "naip = (ee.ImageCollection('USDA/NAIP/DOQQ')\n",
        "    .filterBounds(tassajara_perim_50ft)\n",
        "    .sort(\"system:time_start\")\n",
        "    .map(clipToBounds))\n",
        "\n",
        "def calcNaipNdvi(img):\n",
        "    return img.addBands(img.normalizedDifference([\"N\", \"R\"]))\n",
        "\n",
        "naipNDVI = (naip\n",
        "    .filterDate(\"2009-01-01\", \"2023-12-31\")\n",
        "    .map(calcNaipNdvi))\n",
        "\n",
        "def createTimeBand(img):\n",
        "    year = img.date().difference(ee.Date('2009-01-01'), 'year')\n",
        "    return ee.Image(year).float().addBands(img)\n",
        "\n",
        "fit = (naipNDVI\n",
        "    .select('nd')\n",
        "    .map(createTimeBand)\n",
        "    .reduce(ee.Reducer.linearFit()))"
      ],
      "metadata": {
        "id": "2RGemLDWII21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0d18271-8219-49de-ac6a-b328d9189b89"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:fiona._env:Normalized/laundered field name: 'Description' to 'Descriptio'\n",
            "WARNING:fiona._env:Value '<html xmlns:fo=\"http://www.w3.org/1999/XSL/Format\" xmlns:msxsl=\"urn:schemas-microsoft-com:xslt\"> <head> <META http-equiv=\"Content-Type\" content=\"text/html\"> <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\"> </head> <body style=\"margin:0px 0px 0px 0px;overflow:auto;background:#FFFFFF;\"> <table style=\"font-family:Arial,Verdana,Times;font-size:12px;text-align:left;width:100%;border-collapse:collapse;padding:3px 3px 3px 3px\"> <tr style=\"text-align:center;font-weight:bold;background:#9CBCE2\"> <td></td> </tr> <tr> <td> <table style=\"font-family:Arial,Verdana,Times;font-size:12px;text-align:left;width:100%;border-spacing:0px; padding:3px 3px 3px 3px\"> <tr> <td>APN</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>APN_SORT</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>BOOK</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>PAGE</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>PARCEL</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>SUB_PARCEL</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>CLCA_CATEGORY</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>COMMENTS</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>DATE_CREATE</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>DATE_UPDATE</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>EDITOR</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>FID_PARCELS</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>CENTROID_X</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>CENTROID_Y</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>SortParcel</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>PrintParcel</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>TRAPrimary</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>TRASecondary</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>SitusStreetNumber</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>SitusStreetName</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>SitusUnit</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>SitusCity</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>SitusZip</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>SitusZip_4</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>Land</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>Imps</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>CLCALand</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>CLCAImps</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>HOEX</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>OTEX</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>TotalNetValue</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>LatestDocument_Prefix</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>LatestDocumentSeries</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>LatestDocumentDate</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>MailingAddressStreet</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>MailingAddressUnit</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>MailingAddressCityState</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>MailingAddressZip</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>MailingAddressZip_4</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>UseCode</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>EconUnit</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>EconomicUnit</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>SitusAddress</td> <td>&lt;Null&gt;</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>MailingAddress</td> <td>&lt;Null&gt;</td> </tr> <tr> <td>BUFF_DIST</td> <td>50</td> </tr> <tr bgcolor=\"#D4E4F3\"> <td>ORIG_FID</td> <td>5</td> </tr> </table> </td> </tr> </table> </body><script type=\"text/javascript\"> function changeImage(attElement, nameElement) { document.getElementById('imageAttachment').src = attElement; document.getElementById('imageName').innerHTML = nameElement;} </script></html>' of field Descriptio has been truncated to 254 characters.  This warning will not be emitted any more for that layer.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m = geemap.Map()\n",
        "m.addLayer(tassajara_perim_50ft, {}, \"perimeter\")\n",
        "m.centerObject(tassajara_perim_50ft, zoom=15)\n",
        "\n",
        "m.addLayer(fit,\n",
        "             {\"min\": -1, \"max\": 1, \"bands\": 'offset', \"palette\": [\"blue\",\"white\",\"green\"]},\n",
        "             \"offset\")\n",
        "m.addLayer(fit,\n",
        "             {\"min\": 0.1, \"max\": 0.1, \"bands\": 'scale', \"palette\": [\"orange\",\"white\",\"green\"]},\n",
        "             \"scale\")\n",
        "m.addLayer(fit, \n",
        "             {\"max\": 0, \"min\": [0.1, -0.1, 0.5], \"bands\": ['scale', 'scale', 'offset']},\n",
        "             \"fit\")\n",
        "\n",
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
            "2a5850a412d5423ca505b4f21d462305",
            "d60d54bdc7b84ff5a6f4e0afa52a4880",
            "94b7f5a23d424ac2a8abe3085e63224b",
            "23af0f6cdb9240039c1fc8829a255341",
            "bdbda160e2684d16aa2e2ac80a55daa5",
            "cb6a14fba8514ad1acdc296bb4fca5d6",
            "defd5868e91e40f192fea9932caa306f",
            "5db6e98833e64f98b4647dbde0b8cea7",
            "7ce688cccc4944ca9322667da266a960",
            "04bf18b8b6e2441f9f1383c1f7e4f59f",
            "775394790a54429684ae08d69ee01065",
            "9de531e218e14dc484bb99b11d349846",
            "9c2bbccbf82c4a4ea456ba828bd8468f",
            "72f8d586d7b848769a9970cd04f6f64e",
            "8545713f807f4552ac884c45d27ba99d",
            "73ed69233e8c447194191aed0a1c25b2",
            "415b88f8681841e8a81bce1ca16ef274",
            "e43d35558f3d4705a7ee29f9e814bbe8",
            "afc3621114e944bda6eb2555ac295b76",
            "c28f915ad8694ec0b92eacab455e654e",
            "24a6b7784c934937a1a2f251dc79033d",
            "eb026b00a8434e4a9ce11099a6b456f7",
            "9aee10810a664de491012aaf6a310498",
            "805d9ebf80154f70aacb6ca0af2a3250",
            "5ac4917b949d4a7bb1e85a2b0b2fecd5",
            "f340aa44196242a385b3a983996600fe",
            "c08f51d5f1064419b89e6a33a31f87c2",
            "f06b4010c8254989b289e422029816ec",
            "3e28f947118c4048a6b66d1062234588"
          ]
        },
        "id": "H0-oIYO0JDOo",
        "outputId": "73759d90-12d8-4b16-b743-85f6e635e8d0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map(center=[37.70714413892085, -121.87902337399693], controls=(WidgetControl(options=['position', 'transparent…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a5850a412d5423ca505b4f21d462305"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fit.select(\"scale\").getDownloadURL({\n",
        "  'scale': 0.3,\n",
        "  'region': tassajara_perim_50ft.geometry(),\n",
        "  'format': 'GEO_TIFF',\n",
        "  'crs': 'EPSG:6318'\n",
        "})"
      ],
      "metadata": {
        "id": "D5qwMdS1Kc_8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "5b4b8967-2fea-4bd1-bd64-17e3f73c4d4b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/thumbnails/ca0e627f74761332e183d05687406fa0-f56831fb11be7dc2b06529cb70a650ba:getPixels'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fit.select(\"offset\").getDownloadURL({\n",
        "  'scale': 0.3,\n",
        "  'region': tassajara_perim_50ft.geometry(),\n",
        "  'format': 'GEO_TIFF',\n",
        "  'crs': 'EPSG:6318'\n",
        "})"
      ],
      "metadata": {
        "id": "oY7-8vHJLdjL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "b068013f-ad17-486e-be0e-1cd51d5d6fc0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/thumbnails/e61a64d58e3af25276415c376013388f-603874cbd944292455fff0dddbbb4729:getPixels'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "histogramDictionary = fit.reduceRegion(**{\n",
        "  'reducer': ee.Reducer.histogram(30),\n",
        "  'geometry': tassajara_perim.geometry(),\n",
        "  'scale': 0.3,\n",
        "  'maxPixels': 1e19\n",
        "})\n",
        "# extract as client-side python dict\n",
        "histogram = histogramDictionary.getInfo()\n",
        "bands = list(histogram.keys())"
      ],
      "metadata": {
        "id": "g6fOYD78Lz4-"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist_scale = pd.DataFrame(histogram['scale'])\n",
        "hist_scale"
      ],
      "metadata": {
        "id": "lR0VxVu6MWbw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 895
        },
        "outputId": "5e6977d0-a7a8-488f-fd06-7d02b73e1dbf"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    bucketMeans  bucketMin  bucketWidth      histogram\n",
              "0     -0.103512  -0.109375     0.007812       3.000000\n",
              "1     -0.096608  -0.109375     0.007812      39.000000\n",
              "2     -0.089255  -0.109375     0.007812     209.000000\n",
              "3     -0.082101  -0.109375     0.007812     256.000000\n",
              "4     -0.073693  -0.109375     0.007812     298.650980\n",
              "5     -0.065763  -0.109375     0.007812     896.733333\n",
              "6     -0.058314  -0.109375     0.007812    2310.780392\n",
              "7     -0.050382  -0.109375     0.007812    3942.023529\n",
              "8     -0.042487  -0.109375     0.007812    7850.615686\n",
              "9     -0.034784  -0.109375     0.007812   14068.050980\n",
              "10    -0.027006  -0.109375     0.007812   25023.341176\n",
              "11    -0.019000  -0.109375     0.007812   47918.345098\n",
              "12    -0.011082  -0.109375     0.007812  118823.211765\n",
              "13    -0.003435  -0.109375     0.007812  316272.200000\n",
              "14     0.003590  -0.109375     0.007812  321334.250980\n",
              "15     0.011415  -0.109375     0.007812  192211.403922\n",
              "16     0.019332  -0.109375     0.007812  127286.713725\n",
              "17     0.027135  -0.109375     0.007812   91149.160784\n",
              "18     0.034894  -0.109375     0.007812   64506.117647\n",
              "19     0.042642  -0.109375     0.007812   42440.341176\n",
              "20     0.050477  -0.109375     0.007812   24904.141176\n",
              "21     0.058108  -0.109375     0.007812   14199.721569\n",
              "22     0.065875  -0.109375     0.007812    7104.282353\n",
              "23     0.073644  -0.109375     0.007812    3040.631373\n",
              "24     0.081072  -0.109375     0.007812     795.250980\n",
              "25     0.087604  -0.109375     0.007812      88.250980\n",
              "26     0.097513  -0.109375     0.007812      15.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-75a54ab0-8b20-4d48-babb-fed8477b681d\">\n",
              "    <div 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>bucketMeans</th>\n",
              "      <th>bucketMin</th>\n",
              "      <th>bucketWidth</th>\n",
              "      <th>histogram</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.103512</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.096608</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>39.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.089255</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>209.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.082101</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>256.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.073693</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>298.650980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-0.065763</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>896.733333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-0.058314</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>2310.780392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-0.050382</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>3942.023529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-0.042487</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>7850.615686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-0.034784</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>14068.050980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>-0.027006</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>25023.341176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>-0.019000</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>47918.345098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>-0.011082</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>118823.211765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>-0.003435</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>316272.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.003590</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>321334.250980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.011415</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>192211.403922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.019332</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>127286.713725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.027135</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>91149.160784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.034894</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>64506.117647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.042642</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>42440.341176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.050477</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>24904.141176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.058108</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>14199.721569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.065875</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>7104.282353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.073644</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>3040.631373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.081072</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>795.250980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.087604</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>88.250980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.097513</td>\n",
              "      <td>-0.109375</td>\n",
              "      <td>0.007812</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75a54ab0-8b20-4d48-babb-fed8477b681d')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-75a54ab0-8b20-4d48-babb-fed8477b681d 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-75a54ab0-8b20-4d48-babb-fed8477b681d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax = sns.barplot(data=hist_scale, x=\"bucketMeans\", y=\"histogram\") #, width=np.mean(hist_scale[\"bucketWidth\"]))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "s1gIFLBsN9d_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "688e2aeb-ee5d-4ae4-d06f-22e11d9cc8ff"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(hist_scale[\"bucketWidth\"])"
      ],
      "metadata": {
        "id": "kbHIgMQNOXMF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "606f35e3-9e38-4a03-aee2-04bccb0df84d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0078125"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to do: import the nDSM and crosstabulate\n",
        "#!wget https://github.com/skylerlewis/tassajara/raw/main/data/raster/lidar2021_REM.tif\n"
      ],
      "metadata": {
        "id": "YWIUbHaQUzCw"
      },
      "execution_count": 27,
      "outputs": []
    }
  ]
}