ee_landtrendr_example.ipynb

ee_landtrendr_example.ipynb

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        "# Copyright 2022 The Earth Engine Community Authors\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\")\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "\n",
        "\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n",
        "import google\n",
        "SCOPES = ['https://www.googleapis.com/auth/cloud-platform', 'https://www.googleapis.com/auth/earthengine']\n",
        "CREDENTIALS, project_id = google.auth.default(default_scopes=SCOPES)\n",
        "\n",
        "import ee\n",
        "ee.Initialize(CREDENTIALS, project='my-project (edit this string)')\n",
        "\n",
        "import folium\n",
        "import pprint\n",
        "\n",
        "\n",
        "# A MODIS collection filtered by year and month, non-QA bands selected.\n",
        "col = (ee.ImageCollection('MODIS/061/MOD13Q1')\n",
        "  .filterDate('2000', '2021')\n",
        "  .filter(ee.Filter.calendarRange(7, 8, 'month'))\n",
        "  .select('NDVI|EVI|sur_refl.*'))\n",
        "\n",
        "# Add year property to all images in the collection.\n",
        "col = col.map(lambda img: img.set('year', img.date().get('year')))\n",
        "\n",
        "# Join all images from the same year into lists.\n",
        "distinctCol = col.distinct(['year'])\n",
        "joinFilter = ee.Filter.equals(**{'leftField': 'year', 'rightField': 'year'})\n",
        "joinCol = ee.Join.saveAll('same_year').apply(distinctCol, col, joinFilter)\n",
        "\n",
        "# Composite the images from each unique year by median.\n",
        "def annualComposite(img):\n",
        "  img = ee.Image(img)\n",
        "  # Calculate median per-pixel annual July-August composite.\n",
        "  median = ee.ImageCollection.fromImages(img.get('same_year')).median()\n",
        "  # Construct an image for LandTrendr to base segmentation on. The algorithm\n",
        "  # expects that vegetation loss for a given spectral index is a positive\n",
        "  # delta, so here we invert the EVI band.\n",
        "  segBand = median.select(['EVI']).multiply(-1).rename('EVI_lt_seg')\n",
        "  # LandTrendr uses the first band of an image for segmentation and then\n",
        "  # applies vertex-anchored interpolation to subsequent bands' time series, so\n",
        "  # here we prepend the constructed segmentation band to the original median\n",
        "  # image composite. \n",
        "  ltImage = segBand.addBands(median)\n",
        "  # Transfer date metadata to the new image.\n",
        "  year = img.getNumber('year')\n",
        "  millis = ee.Date.fromYMD(year, 8, 1).millis()  # Unix timestamp required\n",
        "  return ltImage.set({'year': year, 'system:time_start': millis})\n",
        "\n",
        "annualCol = joinCol.map(annualComposite)\n",
        "\n",
        "# Set LandTrendr segmentation parameters. Note that the timeSeries argument\n",
        "# is the annual composite from above.\n",
        "ltParams = {\n",
        "  'timeSeries': ee.ImageCollection(annualCol),\n",
        "  'maxSegments': 7,\n",
        "  'spikeThreshold': 0.9,\n",
        "  'vertexCountOvershoot': 3,\n",
        "  'preventOneYearRecovery': False,\n",
        "  'recoveryThreshold': 0.25,\n",
        "  'pvalThreshold': 0.1,\n",
        "  'bestModelProportion': 0.75,\n",
        "  'minObservationsNeeded': 6\n",
        "}\n",
        "\n",
        "# Run the LandTrendr algorithm.\n",
        "lt = ee.Algorithms.TemporalSegmentation.LandTrendr(**ltParams)\n",
        "print('LandTrendr results structure')\n",
        "pprint.pprint(lt.getInfo)\n",
        "\n",
        "# Subset an example fit-to-vertices (FTV) index array, EVI here.\n",
        "eviFtvArray = lt.select('EVI_fit')\n",
        "\n",
        "# Convert the image array to an image with bands labeled by year.\n",
        "years = (distinctCol.aggregate_array('year')\n",
        "  .sort().map(lambda i: ee.Number(i).format()))\n",
        "eviFtvImg = eviFtvArray.arrayFlatten([years])\n",
        "\n",
        "# Display the FTV image to the map using write function memory insertion\n",
        "# to highlight change between three time slices. Colored pixels indicate\n",
        "# change, grey scale indicates no change.\n",
        "visParams = {'bands': ['2000', '2010', '2020'], 'min': 1400, 'max': 5800}\n",
        "aoi = ee.Geometry.BBox(-124.67, 41.89, -121.10, 46.29)\n",
        "map = folium.Map(\n",
        "    location=[44.12, -122.67],\n",
        "    zoom_start=7,\n",
        "    tiles=eviFtvImg.clip(aoi).getMapId(visParams)['tile_fetcher'].url_format,\n",
        "    attr='Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n",
        ")\n",
        "display(map)\n",
        "\n",
        "# For more information on working with LandTrendr outputs, please see:\n",
        "# https:#emapr.github.io/LT-GEE/working-with-outputs.html"
      ]
    }
  ]
}