polygon_map .ipynb

polygon_map .ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import ee\nimport geemap\nee.Initialize()",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# create binary layer \ndef isCrop(image):\n  return image.eq(5)",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# create binary layer \ndef cropField(imageColl, countyGeom):\n  # create binary layer \n  historicalCrops = imageColl.map(isCrop)\n  # sum over all years\n  cropFields = historicalCrops.sum()\n  # create mask \n  mask = cropFields.gt(0)\n  # update CDL image \n  cropMask = cropFields.updateMask(mask).clip(counties.geometry())\n  return(cropMask)",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# create an impervious NLCD masking layer \ndef nlcdImperv(image):\n  return image.remap([21,22,23,24],\n                      [0, 0, 0, 0],1)",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# define counties of interest\ncountyList = ['Moniteau']\n# load state boundary collection\ncounties = ee.FeatureCollection(\"TIGER/2018/Counties\") \\\n    .filter(ee.Filter.inList('NAME', countyList))\n",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# load cropland data layer from previous 10 years\nhistoricalCrops = ee.ImageCollection('USDA/NASS/CDL') \\\n                  .filter(ee.Filter.date('2010-01-01', '2019-12-31')) \\\n                  .select('cropland') \\\n                  .filterBounds(counties.geometry())\ncropMap = cropField(historicalCrops, counties)",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#----CLEAN THE LAYER-----------------------------------------------------------------#\n# Unsupervised classification based on training-dataset using a county bounary\nseeds = ee.Algorithms.Image.Segmentation.seedGrid(36)\n# training region is the full county \"cropMap\" layer\ntraining = cropMap.sample(**{\n  'region': counties,\n  'scale': 30,\n  'numPixels': 5000\n})",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# train cluster on a county layer \ncropMap_clusterer = ee.Clusterer.wekaKMeans(15).train(training)\n# cluster the complete county \"cropMap\" layer \ncropMap_cluster = cropMap.cluster(cropMap_clusterer)\n# define the size of a low-pass kernel\nkernel_size = ee.Kernel.square(**{\n  'radius': 3, 'units': 'pixels', 'normalize': True\n})",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# smoothen the \"cropMap\" layer by convolving with the low-pass-kernel\ncropMap_cluster_smooth = cropMap_cluster.convolve(kernel_size)",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# load NLCD dataset\nnlcd = ee.ImageCollection('USGS/NLCD') \\\n                          .filter(ee.Filter.eq('system:index', 'NLCD2016')) \\\n                          .select(0) \nnlcdImperv = nlcd.map(nlcdImperv)",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# clip the nlcdImperv layer \nnlcdImperv_county = ee.Image(nlcdImperv.first()).clip(counties.geometry())\n\n# mask out all impervious layers from the CDL\nimpervMask = nlcdImperv_county.eq(1)\ncropMap_cluster_smooth_imperMasked = cropMap_cluster_smooth.updateMask(impervMask)",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Convert to float\ncropMap_cluster_smooth_imperMasked_Float = cropMap_cluster_smooth_imperMasked.toFloat().selfMask()",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Convert the post-processed to integer\ncropMap_cluster_smooth_imperMasked_Int = cropMap_cluster_smooth_imperMasked.toInt().selfMask()",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# convert to vector format\ncropMap_cluster_smooth_imperMasked_Int_vector = cropMap_cluster_smooth_imperMasked_Int.reduceToVectors(**{\n  'reducer': ee.Reducer.countEvery(),\n  'geometry': counties,\n  'scale': 30,\n  'maxPixels': 1e8\n})",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "cropMap_cluster_smooth_imperMasked_Int_vector2 = ee.FeatureCollection(cropMap_cluster_smooth_imperMasked_Int_vector)",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "cropMap_cluster_smooth_imperMasked_Int_vector2.size().getInfo()",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": "6665"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "polygons = cropMap_cluster_smooth_imperMasked_Int_vector2.map(lambda f: f.set({'area': f.geometry().area(1)}))",
      "execution_count": 21,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "selected_polygons = polygons.filter(ee.Filter.gt('area', 40000))",
      "execution_count": 25,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "selected_polygons.size().getInfo()",
      "execution_count": 26,
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          "execution_count": 26,
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            "text/plain": "1001"
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      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# export the refined map \ngeemap.ee_export_vector(cropMap_cluster_smooth_imperMasked_Int_vector2.union(1), 'C:/Users/Tedros/Desktop/geoJason_test_GEE/test4.geojson')",
      "execution_count": null,
      "outputs": []
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