4_Supervised_Classification_and_Change_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "47c2c44a",
   "metadata": {},
   "source": [
    "# 4: Supervised Classification and Change Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6925a9c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5ce64d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc4b5b83644a43d69bad76e0d0a7d48d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[40, -100], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children=(T…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ee\n",
    "import geemap\n",
    "m = geemap.Map()\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60132150",
   "metadata": {},
   "source": [
    "## 01. Basic Supervised Classification\n",
    "\n",
    "We will learn how to do a basic **land cover classification** using training samples collected from the Code Editor using the High Resolution basemap imagery provided by Google Maps. \n",
    "\n",
    "This method requires no prior training data and is quite effective to generate high quality classification samples anywhere in the world. \n",
    "\n",
    "The goal is to classify each source pixel into one of the following classes:\n",
    "- Urban\n",
    "- Bare\n",
    "- Water\n",
    "- Vegetation\n",
    "\n",
    "Using the drawing tools in the map, you will create 4 new feature collections with points representing pixels of that class. Each feature collection has a property called landcover with values of `0`, `1`, `2` or `3` indicating whether the feature collection represents `urban`, `bare`, `water` or `vegetation` respectively. \n",
    "\n",
    "We then train a Random Forest classifier using these training set to build a model and apply it to all the pixels of the image to create a 4 class image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f4e10a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "bangalore = ee.FeatureCollection(\"users/ujavalgandhi/public/bangalore_boundary\")\n",
    "s2 = ee.ImageCollection(\"COPERNICUS/S2_SR\")\n",
    "\n",
    "# The following collections were created using the \n",
    "# Drawing Tools in the code editor \n",
    "urban = ee.FeatureCollection(\"users/ujavalgandhi/e2e/urban_gcps\")\n",
    "bare = ee.FeatureCollection(\"users/ujavalgandhi/e2e/bare_gcps\")\n",
    "water = ee.FeatureCollection(\"users/ujavalgandhi/e2e/water_gcps\")\n",
    "vegetation = ee.FeatureCollection(\"users/ujavalgandhi/e2e/vegetation_gcps\")\n",
    "\n",
    "filtered = s2\\\n",
    "    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-12-31'))\\\n",
    "    .filter(ee.Filter.geometry(bangalore))\\\n",
    "    .select('B.*')\n",
    "\n",
    "composite = filtered.median().clip(bangalore) \n",
    "\n",
    "# Display the input composite.\n",
    "rgbVis = {\n",
    "  \"min\": 0.0,\n",
    "  \"max\": 3000,\n",
    "  \"bands\": ['B4', 'B3', 'B2'],\n",
    "}\n",
    "m.addLayer(composite, rgbVis, \"image\")\n",
    "m.addLayer(urban, {}, \"urban\")\n",
    "m.addLayer(bare, {}, \"bare\")\n",
    "m.addLayer(water, {}, \"water\")\n",
    "m.addLayer(vegetation, {}, \"vegetation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "323dca2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "gcps = urban.merge(bare).merge(water).merge(vegetation)\n",
    "\n",
    "# Overlay the point on the image to get training data.\n",
    "training = composite.sampleRegions(**{\n",
    "  \"collection\": gcps,\n",
    "  \"properties\": ['landcover'], \n",
    "  \"scale\": 10\n",
    "})\n",
    "\n",
    "\n",
    "# Train a classifier.\n",
    "classifier = ee.Classifier.smileRandomForest(50).train(**{\n",
    "  \"features\": training,  \n",
    "  \"classProperty\": 'landcover', \n",
    "  \"inputProperties\": composite.bandNames()\n",
    "})\n",
    "\n",
    "# Classify the image.\n",
    "classified = composite.classify(classifier)\n",
    "m.addLayer(classified, {\"min\": 0, \"max\": 3, \"palette\": [\"gray\", \"brown\", \"blue\", \"green\"]}, \"2019\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5fe32890",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display the collected points\n",
    "# We use the style() function to style the GCPs\n",
    "palette = ee.List(['gray','brown','blue','green'])\n",
    "landcover = ee.List([0, 1, 2, 3])\n",
    "\n",
    "def fun(lc):\n",
    "    color = palette.get(landcover.indexOf(lc))\n",
    "    markerStyle = {\"color\": \"white\", \"pointShape\": \"diamond\", \"pointSize\": 4, \"width\": 1, \"fillColor\": color}\n",
    "    return gcps.filter(ee.Filter.eq(\"landcover\", lc)).map(lambda point: point.set(\"style\", markerStyle))\n",
    "\n",
    "gcpsStyled = ee.FeatureCollection(landcover.map(lambda lc: fun(lc))).flatten()\n",
    "      \n",
    "m.addLayer(gcpsStyled.style(**{\"styleProperty\":\"style\"}), {}, 'GCPs')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98eaa3c",
   "metadata": {},
   "source": [
    "### Exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b334e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "world = ee.FeatureCollection('FAO/GAUL/2015/level2')\n",
    "tetouan = world.filter(ee.Filter.eq(\"ADM2_CODE\", 21817))\n",
    "m.addLayer(tetouan, {}, \"Tetouan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "536be3ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = ee.ImageCollection(\"COPERNICUS/S2_SR\")\n",
    "\n",
    "# Perform supervised classification for your city\n",
    "# Find the feature id by adding the layer to the map and using Inspector.\n",
    "geometry = tetouan.geometry()\n",
    "m.centerObject(geometry)\n",
    "\n",
    "filtered = s2\\\n",
    "    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-12-31'))\\\n",
    "    .filter(ee.Filter.geometry(geometry))\\\n",
    "    .select('B.*')\n",
    "\n",
    "composite = filtered.median().clip(geometry) \n",
    "\n",
    "# Display the input composite.\n",
    "rgbVis = {\"min\": 0.0, \"max\": 3000, \"bands\": ['B4', 'B3', 'B2']}\n",
    "m.addLayer(composite, rgbVis, \"tetouan\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddddd55a",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Add training points for the 4 classes using the map's \"draw a marker\" tool:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e40c751a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# urban: 0\n",
    "urban = ee.FeatureCollection(m.draw_features).map(lambda f: f.set(\"class\", 0))\n",
    "m.addLayer(urban, {}, \"urban\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2e24f95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# water: 2\n",
    "water = ee.FeatureCollection(m.draw_features).map(lambda f: f.set(\"class\", 2))\n",
    "m.addLayer(water, {}, \"water\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "22dbb817",
   "metadata": {},
   "outputs": [],
   "source": [
    "# vegetation: 3\n",
    "veg = ee.FeatureCollection(m.draw_features).map(lambda f: f.set(\"class\", 3))\n",
    "m.addLayer(veg, {}, \"vegetation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b2069c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bare: 1\n",
    "bare = ee.FeatureCollection(m.draw_features).map(lambda f: f.set(\"class\", 1))\n",
    "m.addLayer(bare, {}, \"bare\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ca6955b2",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'type': 'FeatureCollection', 'columns': {}, 'properties': {'band_order': ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B11', 'B12']}, 'features': [{'type': 'Feature', 'geometry': None, 'id': '1_1_1_0_0', 'properties': {'B1': 993.75, 'B11': 2636, 'B12': 2278.25, 'B2': 1108.5, 'B3': 1365.2, 'B4': 1637.2857142857142, 'B5': 1898.8, 'B6': 2154.2, 'B7': 2277.6666666666665, 'B8': 2293.5714285714284, 'B8A': 2339.1666666666665, 'B9': 2154.4, 'class': 0}}, {'type': 'Feature', 'geometry': None, 'id': '1_1_1_1_0', 'properties': {'B1': 1702.4545454545455, 'B11': 3336.5, 'B12': 3085.6666666666665, 'B2': 1709.8, 'B3': 1974, 'B4': 2464.125, 'B5': 2656.6666666666665, 'B6': 2720.8, 'B7': 2813, 'B8': 2785.75, 'B8A': 2788.5, 'B9': 3036.4, 'class': 0}}, {'type': 'Feature', 'geometry': None, 'id': '1_1_1_2_0', 'properties': {'B1': 1176.8, 'B11': 3140, 'B12': 2890.5, 'B2': 1515, 'B3': 1844.3333333333333, 'B4': 2263.5, 'B5': 2471.8333333333335, 'B6': 2540.1428571428573, 'B7': 2643.5, 'B8': 2652.75, 'B8A': 2651, 'B9': 2649.4, 'class': 0}}, {'type': 'Feature', 'geometry': None, 'id': '1_1_1_3_0', 'properties': {'B1': 1187.6, 'B11': 3410.5, 'B12': 3039, 'B2': 1451.75, 'B3': 1819.8, 'B4': 2324.4, 'B5': 2659, 'B6': 2861.3333333333335, 'B7': 2932.5, 'B8': 2978.25, 'B8A': 2964.6666666666665, 'B9': 2926.75, 'class': 0}}, {'type': 'Feature', 'geometry': None, 'id': '1_1_1_4_0', 'properties': {'B1': 1181.75, 'B11': 3200, 'B12': 2919, 'B2': 1504.2857142857142, 'B3': 1834.75, 'B4': 2333.4, 'B5': 2532, 'B6': 2623, 'B7': 2636.5, 'B8': 2679.5, 'B8A': 2662.5, 'B9': 2703.1666666666665, 'class': 0}}, {'type': 'Feature', 'geometry': None, 'id': '1_1_1_5_0', 'properties': {'B1': 1315.5, 'B11': 3502.75, 'B12': 3023, 'B2': 1506.4285714285713, 'B3': 1890.6666666666667, 'B4': 2261, 'B5': 2598.1666666666665, 'B6': 2844.8, 'B7': 2968.1666666666665, 'B8': 2988, 'B8A': 3039.375, 'B9': 3298.3076923076924, 'class': 0}}, {'type': 'Feature', 'geometry': None, 'id': '1_1_1_6_0', 'properties': 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'geometry': None, 'id': '1_2_6_0', 'properties': {'B1': 295.3333333333333, 'B11': 587.875, 'B12': 493.5, 'B2': 416.09090909090907, 'B3': 798, 'B4': 529.75, 'B5': 927.8333333333334, 'B6': 668.7272727272727, 'B7': 677.5454545454545, 'B8': 659.5714285714286, 'B8A': 597.75, 'B9': 342.55555555555554, 'class': 2}}, {'type': 'Feature', 'geometry': None, 'id': '1_2_7_0', 'properties': {'B1': 677.75, 'B11': 1267, 'B12': 1126.3333333333333, 'B2': 540.7272727272727, 'B3': 737.8125, 'B4': 743.9, 'B5': 933.125, 'B6': 931.75, 'B7': 976, 'B8': 986, 'B8A': 997, 'B9': 2415, 'class': 2}}, {'type': 'Feature', 'geometry': None, 'id': '1_2_8_0', 'properties': {'B1': 408.5, 'B11': 1173, 'B12': 945, 'B2': 536.1666666666666, 'B3': 930, 'B4': 793.8333333333334, 'B5': 1211, 'B6': 1058.4, 'B7': 1114.875, 'B8': 1060, 'B8A': 994.0833333333334, 'B9': 1314.7142857142858, 'class': 2}}, {'type': 'Feature', 'geometry': None, 'id': '1_2_9_0', 'properties': {'B1': 270, 'B11': 121.25, 'B12': 104.25, 'B2': 408, 'B3': 532.5, 'B4': 326.5, 'B5': 246.75, 'B6': 105.66666666666667, 'B7': 102, 'B8': 100.75, 'B8A': 88.8, 'B9': 42.5, 'class': 2}}]}\n"
     ]
    }
   ],
   "source": [
    "gcps = urban.merge(bare).merge(water).merge(vegetation)\n",
    "\n",
    "# Overlay the point on the image to get training data.\n",
    "training = composite.sampleRegions(**{\n",
    "    \"collection\": gcps, \n",
    "    \"properties\": [\"class\"], \n",
    "    \"scale\": 200,\n",
    "    \"tileScale\": 16\n",
    "})\n",
    "print(training.getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "620a502b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train a classifier.\n",
    "classifier = ee.Classifier.smileRandomForest(50).train(**{\n",
    "    \"features\": training,  \n",
    "    \"classProperty\": 'class', \n",
    "    \"inputProperties\": composite.bandNames()\n",
    "})\n",
    "\n",
    "# Classify the image.\n",
    "classified = composite.classify(classifier)\n",
    "m.addLayer(classified, {\"min\": 0, \"max\": 3, \"palette\": ['gray', 'brown', 'blue', 'green']}, '2019')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b149fd5e",
   "metadata": {},
   "source": [
    "## 02. Accuracy Assessment\n",
    "\n",
    "It is important to get a quantitative estimate of the accuracy of the classification. To do this, a common strategy is to divide your training samples into 2 random fractions: one used for training the model and the other for validation of the predictions. \n",
    "\n",
    "Once a classifier is trained, it can be used to classify the entire image. We can then compare the classified values with the ones in the validation fraction. We can use the `ee.Classifier.confusionMatrix()` method to calculate a Confusion Matrix representing expected accuracy.\n",
    "\n",
    "Don't get carried away tweaking your model to give you the highest validation accuracy. You must use both qualitative measures (such as visual inspection of results) along with quantitative measures to assess the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7edb4700",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = ee.ImageCollection(\"COPERNICUS/S2_SR\")\n",
    "basin = ee.FeatureCollection(\"WWF/HydroSHEDS/v1/Basins/hybas_7\")\n",
    "gcp = ee.FeatureCollection(\"users/ujavalgandhi/e2e/arkavathy_gcps\")\n",
    "    \n",
    "arkavathy = basin.filter(ee.Filter.eq('HYBAS_ID', 4071139640))\n",
    "boundary = arkavathy.geometry()\n",
    "rgbVis = {\n",
    "  \"min\": 0.0,\n",
    "  \"max\": 3000,\n",
    "  \"bands\": ['B4', 'B3', 'B2'],\n",
    "}\n",
    " \n",
    "filtered = s2\\\n",
    "    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-12-31'))\\\n",
    "    .filter(ee.Filter.geometry(boundary))\\\n",
    "    .select('B.*')\n",
    "\n",
    "composite = filtered.median().clip(boundary) \n",
    "\n",
    "# Display the input composite.\n",
    "m.addLayer(composite, rgbVis, 'image');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4fc8e235",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a random column and split the GCPs into training and validation set\n",
    "gcp = gcp.randomColumn()\n",
    "\n",
    "# This being a simpler classification, we take 60% points\n",
    "# for validation. Normal recommended ratio is\n",
    "# 70% training, 30% validation\n",
    "trainingGcp = gcp.filter(ee.Filter.lt('random', 0.6))\n",
    "validationGcp = gcp.filter(ee.Filter.gte('random', 0.6))\n",
    "\n",
    "# Overlay the points on the image to get training data.\n",
    "training = composite.sampleRegions(**{\n",
    "  \"collection\": trainingGcp,\n",
    "  \"properties\": ['landcover'],\n",
    "  \"scale\": 10,\n",
    "  \"tileScale\": 16\n",
    "})\n",
    "\n",
    "# Train a classifier.\n",
    "classifier = ee.Classifier.smileRandomForest(50)\\\n",
    "    .train(**{\n",
    "        \"features\": training,  \n",
    "        \"classProperty\": 'landcover',\n",
    "        \"inputProperties\": composite.bandNames()\n",
    "})\n",
    "\n",
    "# Classify the image\n",
    "classified = composite.classify(classifier)\n",
    "\n",
    "m.addLayer(classified, {\"min\": 0, \"max\": 3, \"palette\": [\"gray\", \"brown\", \"blue\", \"green\"]}, \"2019\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acc40bda",
   "metadata": {},
   "source": [
    "### Accuracy Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "972f0aba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix [[41, 4, 0, 0], [3, 43, 0, 0], [0, 0, 48, 3], [0, 0, 0, 37]]\n",
      "Test Accuracy 0.9441340782122905\n"
     ]
    }
   ],
   "source": [
    "# Use classification map to assess accuracy using the validation fraction\n",
    "# of the overall training set created above.\n",
    "test = classified.sampleRegions(**{\n",
    "  \"collection\": validationGcp,\n",
    "  \"properties\": ['landcover'],\n",
    "  \"tileScale\": 16,\n",
    "  \"scale\": 10,\n",
    "})\n",
    "\n",
    "testConfusionMatrix = test.errorMatrix('landcover', 'classification')\n",
    "\n",
    "# Printing of confusion matrix may time out. Alternatively, you can export it as CSV\n",
    "print('Confusion Matrix', testConfusionMatrix.getInfo())\n",
    "print('Test Accuracy', testConfusionMatrix.accuracy().getInfo())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5681fb1e",
   "metadata": {},
   "source": [
    "## 03. Improving the Classification\n",
    "\n",
    "The Earth Engine data-model is especially well suited for ML tasks because of its ability to easily incorporate data sources of different spatial resolutions, projections and data types together By giving additional information to the classifier, it is able to separate different classes easily. \n",
    "\n",
    "Here we take the same example and augment it with different spectral indices such as - `NDVI`, `NDBI`, `MNDWI` and `BSI`. We also add `slope` and `elevation` bands from the `ALOS DEM`. \n",
    "\n",
    "The result is a much improve the classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b8d95c51",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = ee.ImageCollection(\"COPERNICUS/S2_SR\")\n",
    "basin = ee.FeatureCollection(\"WWF/HydroSHEDS/v1/Basins/hybas_7\")\n",
    "gcp = ee.FeatureCollection(\"users/ujavalgandhi/e2e/arkavathy_gcps\")\n",
    "alos = ee.Image(\"JAXA/ALOS/AW3D30/V2_2\")\n",
    "\n",
    "arkavathy = basin.filter(ee.Filter.eq('HYBAS_ID', 4071139640))\n",
    "boundary = arkavathy.geometry()\n",
    "rgbVis = {\n",
    "  \"min\": 0.0,\n",
    "  \"max\": 3000,\n",
    "  \"bands\": ['B4', 'B3', 'B2'],\n",
    "}\n",
    "\n",
    "def maskCloudAndShadowsSR(img):\n",
    "    \"\"\"Removes cloud and snow pixels from Sentinel-2 SR image.\"\"\"\n",
    "    cloudProb = img.select('MSK_CLDPRB')\n",
    "    snowProb = img.select('MSK_SNWPRB')\n",
    "    cloud = cloudProb.lt(10)\n",
    "    scl = img.select('SCL') \n",
    "    shadow = scl.eq(3)\n",
    "    cirrus = scl.eq(10)\n",
    "    \n",
    "    # Cloud probability less than 10% or cloud shadow classification\n",
    "    mask = cloud.And(cirrus.neq(1)).And(shadow.neq(1))\n",
    "    return img.updateMask(mask).divide(10000)\n",
    "\n",
    "\n",
    "filtered = s2\\\n",
    "    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 30))\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-12-31'))\\\n",
    "    .filter(ee.Filter.geometry(boundary))\\\n",
    "    .map(maskCloudAndShadowsSR)\\\n",
    "    .select('B.*')\n",
    "\n",
    "composite = filtered.median().clip(boundary) \n",
    "\n",
    "def addIndices(img):\n",
    "    ndvi = img.normalizedDifference(['B8', 'B4']).rename(['ndvi'])\n",
    "    ndbi = img.normalizedDifference(['B11', 'B8']).rename(['ndbi'])\n",
    "    mndwi = img.normalizedDifference(['B3', 'B11']).rename(['mndwi'])\n",
    "    bsi = img.expression(\n",
    "      \"(( X + Y ) - (A + B)) /(( X + Y ) + (A + B))\", {\n",
    "        \"X\": img.select(\"B11\"),\n",
    "        \"Y\": img.select(\"B4\"),\n",
    "        \"A\": img.select(\"B8\"),\n",
    "        \"B\": img.select(\"B2\")\n",
    "      }).rename(\"bsi\")\n",
    "    return img.addBands(ndvi).addBands(ndbi).addBands(mndwi).addBands(bsi)\n",
    "\n",
    "composite = addIndices(composite)\n",
    "\n",
    "elev = alos.select('AVE_DSM').divide(2000).rename('elev')\n",
    "slope = ee.Terrain.slope(alos.select('AVE_DSM')).divide(30).rename('slope')\n",
    "\n",
    "composite = composite.addBands(elev).addBands(slope)\n",
    "\n",
    "visParams = {\"bands\": ['B4', 'B3', 'B2'], \"min\": 0, \"max\": 0.3, \"gamma\": 1.2}\n",
    "m.addLayer(composite, visParams, 'RGB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e2c86b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a random column and split the GCPs into training and validation set\n",
    "gcp = gcp.randomColumn()\n",
    "\n",
    "# This being a simpler classification, we take 60% points\n",
    "# for validation. Normal recommended ratio is\n",
    "# 70% training, 30% validation\n",
    "trainingGcp = gcp.filter(ee.Filter.lt('random', 0.6))\n",
    "validationGcp = gcp.filter(ee.Filter.gte('random', 0.6))\n",
    "m.addLayer(validationGcp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "484a4f96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'B5': 0.13570000231266022, 'B6': 0.35339999198913574, 'B7': 0.4203999936580658, 'B8': 0.43320000171661377, 'B8A': 0.4530999958515167, 'B9': 0.44110000133514404, 'bsi': -0.22449497878551483, 'elev': 0.4320000112056732, 'landcover': 3, 'mndwi': -0.4754292368888855, 'ndbi': -0.2696365714073181, 'ndvi': 0.7710548043251038, 'slope': 0.03174785524606705}}, {'type': 'Feature', 'geometry': None, 'id': '000000000000000000ae_0', 'properties': {'B1': 0.04535000026226044, 'B11': 0.2833999991416931, 'B12': 0.15029999613761902, 'B2': 0.0494999997317791, 'B3': 0.08895000070333481, 'B4': 0.07010000199079514, 'B5': 0.1391499936580658, 'B6': 0.2815999984741211, 'B7': 0.3215999901294708, 'B8': 0.33219999074935913, 'B8A': 0.35074999928474426, 'B9': 0.3285500109195709, 'bsi': -0.03835689648985863, 'elev': 0.4415000081062317, 'landcover': 3, 'mndwi': -0.522223711013794, 'ndbi': -0.07927224040031433, 'ndvi': 0.65150386095047, 'slope': 0.06947705149650574}}]}\n"
     ]
    }
   ],
   "source": [
    "# Overlay the point on the image to get training data.\n",
    "training = composite.sampleRegions(**{\n",
    "  \"collection\": trainingGcp,\n",
    "  \"properties\": ['landcover'],\n",
    "  \"scale\": 10,\n",
    "  \"tileScale\": 16\n",
    "})\n",
    "print(training.getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6c951aba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train a classifier.\n",
    "classifier = ee.Classifier.smileRandomForest(50)\\\n",
    "    .train(**{\n",
    "        \"features\": training,  \n",
    "        \"classProperty\": 'landcover',\n",
    "        \"inputProperties\": composite.bandNames()\n",
    "    })\n",
    "\n",
    "# Classify the image.\n",
    "classified = composite.classify(classifier)\n",
    "\n",
    "m.addLayer(classified, {\"min\": 0, \"max\": 3, \"palette\": ['gray', 'brown', 'blue', 'green']}, '2019')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c929e541",
   "metadata": {},
   "source": [
    "### Accuracy Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6200782a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix [[41, 3, 0, 0], [3, 43, 0, 0], [0, 0, 49, 2], [0, 0, 0, 37]]\n",
      "Test Accuracy 0.9550561797752809\n"
     ]
    }
   ],
   "source": [
    "# Use classification map to assess accuracy using the validation fraction\n",
    "# of the overall training set created above.\n",
    "test = classified.sampleRegions(**{\n",
    "  \"collection\": validationGcp,\n",
    "  \"properties\": ['landcover'],\n",
    "  \"scale\": 10,\n",
    "  \"tileScale\": 16\n",
    "})\n",
    "\n",
    "testConfusionMatrix = test.errorMatrix(\"landcover\", \"classification\")\n",
    "\n",
    "# Printing of confusion matrix may time out. Alternatively, you can export it as CSV\n",
    "print('Confusion Matrix', testConfusionMatrix.getInfo())\n",
    "print('Test Accuracy', testConfusionMatrix.accuracy().getInfo())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d860760",
   "metadata": {},
   "source": [
    "## 04. Hyperparameter Tuning\n",
    "\n",
    "A recommended best practice for improving the accuracy of your ML model is to tune different parameters. \n",
    "\n",
    "For example, when using the `ee.Classifier.smileRandomForest()` classifier, we must specify the Number of Trees. We know that higher number of trees result in more computation requirement, but it doesn't necessarily result in better results. \n",
    "\n",
    "Instead of guessing, we programmatically try a range of values and choose the smallest value possible that results in the highest accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4aaf1f45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'classes': [0, 1, 2, 3], 'importance': {'B1': 4.498648322035025, 'B11': 7.914636818057613, 'B12': 8.432450401304282, 'B2': 7.451231643201632, 'B3': 6.094622597080443, 'B4': 6.752217767259128, 'B5': 7.272885269101535, 'B6': 6.823990274482156, 'B7': 7.816408063518979, 'B8': 7.3799454269181455, 'B8A': 9.812447865435246, 'B9': 5.431289603017796, 'bsi': 11.337134735191679, 'elev': 4.074544352466667, 'mndwi': 9.366364820354004, 'ndbi': 3.7615252728407387, 'ndvi': 8.629351259664169, 'slope': 6.346822599901917}, 'numberOfTrees': 50, 'outOfBagErrorEstimate': 0.05303030303030303, 'trees': ['n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) ndvi<=0.369832 96 63.979 2 (0.32000 0.33000 0.34000 0.010000)\\n  4) B7<=0.115400 33 0.0000 2 (0.027027 0.027027 0.91892 0.027027) *\\n  5) B7>0.115400 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   10) B11<=0.393150 30 6.9333 0 (0.79412 0.14706 0.029412 0.029412)\\n    20) bsi<=0.189895 26 0.0000 0 (0.90000 0.033333 0.033333 0.033333) *\\n    21) bsi>0.189895 4 0.0000 1 (0.12500 0.62500 0.12500 0.12500) *\\n   11) B11>0.393150 33 8.4848 1 (0.16216 0.78378 0.027027 0.027027)\\n    22) ndvi<=0.0476836 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n    23) ndvi>0.0476836 28 0.0000 1 (0.031250 0.90625 0.031250 0.031250) *\\n 3) ndvi>0.369832 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B5<=0.151300 71 36.366 3 (0.026667 0.013333 0.45333 0.50667)\\n  4) B8<=0.0886500 33 1.9394 2 (0.027027 0.027027 0.89189 0.054054)\\n   8) B1<=0.0458500 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n   9) B1>0.0458500 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n    18) B7<=0.0600000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    19) B7>0.0600000 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) B8>0.0886500 38 3.8421 3 (0.047619 0.023810 0.047619 0.88095)\\n   10) ndbi<=0.135365 37 1.9459 3 (0.024390 0.024390 0.048780 0.90244)\\n    20) B2<=0.0326250 5 1.6000 3 (0.11111 0.11111 0.22222 0.55556)\\n     40) B6<=0.202150 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n     41) B6>0.202150 4 0.0000 3 (0.12500 0.12500 0.12500 0.62500) *\\n    21) B2>0.0326250 32 0.0000 3 (0.027778 0.027778 0.027778 0.91667) *\\n   11) ndbi>0.135365 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n 3) B5>0.151300 62 30.968 1 (0.46970 0.50000 0.015152 0.015152)\\n  6) bsi<=0.203041 33 7.0303 0 (0.81081 0.13514 0.027027 0.027027)\\n   12) B8A<=0.304250 24 1.9167 0 (0.85714 0.071429 0.035714 0.035714)\\n    24) B5<=0.217850 17 0.0000 0 (0.85714 0.047619 0.047619 0.047619) *\\n    25) B5>0.217850 7 1.7143 0 (0.63636 0.18182 0.090909 0.090909)\\n     50) B6<=0.246450 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     51) B6>0.246450 6 0.0000 0 (0.70000 0.10000 0.10000 0.10000) *\\n   13) B8A>0.304250 9 4.0000 0 (0.53846 0.30769 0.076923 0.076923)\\n    26) B11<=0.461050 4 1.5000 1 (0.25000 0.50000 0.12500 0.12500)\\n     52) B1<=0.153650 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n     53) B1>0.153650 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    27) B11>0.461050 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n  7) bsi>0.203041 29 1.9310 1 (0.060606 0.87879 0.030303 0.030303)\\n   14) bsi<=0.292645 28 0.0000 1 (0.031250 0.90625 0.031250 0.031250) *\\n   15) bsi>0.292645 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) ndvi<=0.253517 95 63.979 1 (0.32323 0.33333 0.32323 0.020202)\\n  4) B8A<=0.109050 32 1.9375 2 (0.027778 0.027778 0.88889 0.055556)\\n   8) B7<=0.0756000 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n   9) B7>0.0756000 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) B8A>0.109050 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   10) ndvi<=0.110388 28 5.3571 0 (0.81250 0.12500 0.031250 0.031250)\\n    20) bsi<=0.219770 27 3.7037 0 (0.83871 0.096774 0.032258 0.032258)\\n     40) B8<=0.294475 17 0.0000 0 (0.85714 0.047619 0.047619 0.047619) *\\n     41) B8>0.294475 10 3.2000 0 (0.64286 0.21429 0.071429 0.071429)\\n      82) mndwi<=-0.243903 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n       164) ndvi<=0.0372859 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n       165) ndvi>0.0372859 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n      83) mndwi>-0.243903 7 0.0000 0 (0.72727 0.090909 0.090909 0.090909) *\\n    21) bsi>0.219770 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n   11) ndvi>0.110388 35 9.9429 1 (0.17949 0.76923 0.025641 0.025641)\\n    22) B8A<=0.270275 6 2.6667 0 (0.50000 0.30000 0.10000 0.10000)\\n     44) B7<=0.190650 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     45) B7>0.190650 5 1.6000 0 (0.55556 0.22222 0.11111 0.11111)\\n      90) B1<=0.0666750 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n      91) B1>0.0666750 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n    23) B8A>0.270275 29 3.7241 1 (0.090909 0.84848 0.030303 0.030303)\\n     46) B12<=0.298250 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     47) B12>0.298250 28 1.9286 1 (0.062500 0.87500 0.031250 0.031250)\\n      94) bsi<=0.296587 27 0.0000 1 (0.032258 0.90323 0.032258 0.032258) *\\n      95) bsi>0.296587 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n 3) ndvi>0.253517 38 3.7895 3 (0.023810 0.023810 0.071429 0.88095)\\n  6) B5<=0.0812500 2 0.0000 2 (0.16667 0.16667 0.50000 0.16667) *\\n  7) B5>0.0812500 36 0.0000 3 (0.025000 0.025000 0.025000 0.92500) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B5<=0.147450 73 39.205 3 (0.051948 0.012987 0.44156 0.49351)\\n  4) B8<=0.0886500 33 1.9394 2 (0.027027 0.027027 0.89189 0.054054)\\n   8) ndbi<=-0.443349 4 1.5000 2 (0.12500 0.12500 0.50000 0.25000)\\n    16) B12<=0.0211000 3 0.0000 2 (0.14286 0.14286 0.57143 0.14286) *\\n    17) B12>0.0211000 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n   9) ndbi>-0.443349 29 0.0000 2 (0.030303 0.030303 0.90909 0.030303) *\\n  5) B8>0.0886500 40 7.3500 3 (0.090909 0.022727 0.045455 0.84091)\\n   10) B2<=0.0756500 37 1.9459 3 (0.024390 0.024390 0.048780 0.90244)\\n    20) B2<=0.0298000 3 1.3333 3 (0.14286 0.14286 0.28571 0.42857)\\n     40) ndvi<=0.757236 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n     41) ndvi>0.757236 2 0.0000 3 (0.16667 0.16667 0.16667 0.50000) *\\n    21) B2>0.0298000 34 0.0000 3 (0.026316 0.026316 0.026316 0.92105) *\\n   11) B2>0.0756500 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n 3) B5>0.147450 60 29.867 1 (0.45313 0.51563 0.015625 0.015625)\\n  6) B11<=0.393150 23 1.9130 0 (0.85185 0.074074 0.037037 0.037037)\\n   12) B2<=0.0905500 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n    24) slope<=0.375284 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    25) slope>0.375284 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n   13) B2>0.0905500 21 0.0000 0 (0.88000 0.040000 0.040000 0.040000) *\\n  7) B11>0.393150 37 10.054 1 (0.17073 0.78049 0.024390 0.024390)\\n   14) ndvi<=0.0436442 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n   15) ndvi>0.0436442 32 1.9375 1 (0.055556 0.88889 0.027778 0.027778)\\n    30) B5<=0.198675 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    31) B5>0.198675 31 0.0000 1 (0.028571 0.91429 0.028571 0.028571) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B2<=0.0679500 69 34.319 3 (0.013699 0.013699 0.45205 0.52055)\\n  4) B7<=0.149200 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n  5) B7>0.149200 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n   10) B4<=0.0371750 3 1.3333 3 (0.14286 0.14286 0.28571 0.42857)\\n    20) B3<=0.0477500 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    21) B3>0.0477500 2 0.0000 3 (0.16667 0.16667 0.16667 0.50000) *\\n   11) B4>0.0371750 35 0.0000 3 (0.025641 0.025641 0.025641 0.92308) *\\n 3) B2>0.0679500 64 32.969 1 (0.47059 0.48529 0.029412 0.014706)\\n  6) mndwi<=-0.364229 35 7.0857 1 (0.12821 0.82051 0.025641 0.025641)\\n   12) B6<=0.222700 4 1.5000 0 (0.50000 0.25000 0.12500 0.12500)\\n    24) B2<=0.0905500 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    25) B2>0.0905500 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n   13) B6>0.222700 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n    26) bsi<=0.296587 30 0.0000 1 (0.029412 0.91176 0.029412 0.029412) *\\n    27) bsi>0.296587 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n  7) mndwi>-0.364229 29 3.7931 0 (0.84848 0.060606 0.060606 0.030303)\\n   14) B9<=0.105200 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   15) B9>0.105200 28 1.9286 0 (0.87500 0.062500 0.031250 0.031250)\\n    30) B11<=0.389775 19 0.0000 0 (0.86957 0.043478 0.043478 0.043478) *\\n    31) B11>0.389775 9 1.7778 0 (0.69231 0.15385 0.076923 0.076923)\\n     62) B3<=0.246700 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     63) B3>0.246700 8 0.0000 0 (0.75000 0.083333 0.083333 0.083333) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B4<=0.104950 68 33.735 3 (0.013889 0.013889 0.44444 0.52778)\\n  4) B11<=0.146425 31 1.9355 2 (0.028571 0.028571 0.88571 0.057143)\\n   8) B3<=0.0930500 30 0.0000 2 (0.029412 0.029412 0.91176 0.029412) *\\n   9) B3>0.0930500 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) B11>0.146425 37 1.9459 3 (0.024390 0.024390 0.048780 0.90244)\\n   10) slope<=0.256631 35 0.0000 3 (0.025641 0.025641 0.025641 0.92308) *\\n   11) slope>0.256631 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n    22) mndwi<=-0.401286 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    23) mndwi>-0.401286 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n 3) B4>0.104950 65 34.400 1 (0.46377 0.47826 0.043478 0.014493)\\n  6) B11<=0.395875 28 7.1429 0 (0.78125 0.093750 0.093750 0.031250)\\n   12) B8A<=0.0915250 2 0.0000 2 (0.16667 0.16667 0.50000 0.16667) *\\n   13) B8A>0.0915250 26 3.6923 0 (0.83333 0.10000 0.033333 0.033333)\\n    26) B1<=0.0607000 4 2.0000 0 (0.37500 0.37500 0.12500 0.12500)\\n     52) B9<=0.281450 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n     53) B9>0.281450 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n    27) B1>0.0607000 22 0.0000 0 (0.88462 0.038462 0.038462 0.038462) *\\n  7) B11>0.395875 37 11.351 1 (0.19512 0.75610 0.024390 0.024390)\\n   14) mndwi<=-0.242182 33 5.4545 1 (0.10811 0.83784 0.027027 0.027027)\\n    28) B2<=0.165700 29 1.9310 1 (0.060606 0.87879 0.030303 0.030303)\\n     56) B2<=0.0794250 5 1.6000 1 (0.22222 0.55556 0.11111 0.11111)\\n      112) B12<=0.391550 4 0.0000 1 (0.12500 0.62500 0.12500 0.12500) *\\n      113) B12>0.391550 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     57) B2>0.0794250 24 0.0000 1 (0.035714 0.89286 0.035714 0.035714) *\\n    29) B2>0.165700 4 2.0000 0 (0.37500 0.37500 0.12500 0.12500)\\n     58) B3<=0.221550 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n     59) B3>0.221550 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n   15) mndwi>-0.242182 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B3<=0.0999500 69 35.362 3 (0.027397 0.013699 0.45205 0.50685)\\n  4) ndbi<=-0.160447 35 7.0857 3 (0.025641 0.025641 0.12821 0.82051)\\n   8) B8<=0.245250 4 1.5000 2 (0.12500 0.12500 0.50000 0.25000)\\n    16) elev<=0.329750 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    17) elev>0.329750 3 0.0000 2 (0.14286 0.14286 0.57143 0.14286) *\\n   9) B8>0.245250 31 1.9355 3 (0.028571 0.028571 0.057143 0.88571)\\n    18) slope<=0.0313308 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n     36) B5<=0.111550 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n     37) B5>0.111550 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    19) slope>0.0313308 29 0.0000 3 (0.030303 0.030303 0.030303 0.90909) *\\n  5) ndbi>-0.160447 34 10.176 2 (0.052632 0.026316 0.76316 0.15789)\\n   10) B5<=0.100175 28 0.0000 2 (0.031250 0.031250 0.90625 0.031250) *\\n   11) B5>0.100175 6 1.6667 3 (0.20000 0.10000 0.10000 0.60000)\\n    22) B8A<=0.175725 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    23) B8A>0.175725 5 0.0000 3 (0.11111 0.11111 0.11111 0.66667) *\\n 3) B3>0.0999500 64 33.906 1 (0.45588 0.48529 0.029412 0.029412)\\n  6) mndwi<=-0.377484 29 3.7241 1 (0.090909 0.84848 0.030303 0.030303)\\n   12) B1<=0.128550 28 1.9286 1 (0.062500 0.87500 0.031250 0.031250)\\n    24) B9<=0.343575 26 0.0000 1 (0.033333 0.90000 0.033333 0.033333) *\\n    25) B9>0.343575 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n     50) B6<=0.315575 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     51) B6>0.315575 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n   13) B1>0.128550 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n  7) mndwi>-0.377484 35 11.829 0 (0.74359 0.15385 0.051282 0.051282)\\n   14) bsi<=0.0471978 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n    28) B9<=0.0178500 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    29) B9>0.0178500 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n   15) bsi>0.0471978 33 8.4848 0 (0.78378 0.16216 0.027027 0.027027)\\n    30) B12<=0.363875 18 0.0000 0 (0.86364 0.045455 0.045455 0.045455) *\\n    31) B12>0.363875 15 6.6667 0 (0.57895 0.31579 0.052632 0.052632)\\n     62) elev<=0.413500 7 2.8571 1 (0.27273 0.54545 0.090909 0.090909)\\n      124) B5<=0.256450 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      125) B5>0.256450 6 1.6667 1 (0.20000 0.60000 0.10000 0.10000)\\n       250) B6<=0.395575 5 0.0000 1 (0.11111 0.66667 0.11111 0.11111) *\\n       251) B6>0.395575 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     63) elev>0.413500 8 0.0000 0 (0.75000 0.083333 0.083333 0.083333) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B1<=0.0507000 67 33.433 3 (0.014085 0.014085 0.46479 0.50704)\\n  4) B4<=0.0379750 19 0.0000 2 (0.043478 0.043478 0.86957 0.043478) *\\n  5) B4>0.0379750 48 18.958 3 (0.019231 0.019231 0.26923 0.69231)\\n   10) B12<=0.0541750 14 3.4286 2 (0.055556 0.055556 0.72222 0.16667)\\n    20) ndvi<=0.369832 13 1.8462 2 (0.058824 0.058824 0.76471 0.11765)\\n     40) B7<=0.0756000 11 0.0000 2 (0.066667 0.066667 0.80000 0.066667) *\\n     41) B7>0.0756000 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n      82) elev<=0.390250 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n      83) elev>0.390250 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    21) ndvi>0.369832 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n   11) B12>0.0541750 34 1.9412 3 (0.026316 0.026316 0.052632 0.89474)\\n    22) B3<=0.0525000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    23) B3>0.0525000 33 0.0000 3 (0.027027 0.027027 0.027027 0.91892) *\\n 3) B1>0.0507000 66 35.848 1 (0.45714 0.47143 0.028571 0.042857)\\n  6) B11<=0.393150 32 10.438 0 (0.75000 0.11111 0.055556 0.083333)\\n   12) B2<=0.0862000 5 3.2000 1 (0.11111 0.33333 0.22222 0.33333)\\n    24) B7<=0.268175 3 1.3333 1 (0.14286 0.42857 0.28571 0.14286)\\n     48) B8<=0.128825 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n     49) B8>0.128825 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n    25) B7>0.268175 2 0.0000 3 (0.16667 0.16667 0.16667 0.50000) *\\n   13) B2>0.0862000 27 1.9259 0 (0.87097 0.064516 0.032258 0.032258)\\n    26) ndvi<=0.195874 23 0.0000 0 (0.88889 0.037037 0.037037 0.037037) *\\n    27) ndvi>0.195874 4 1.5000 0 (0.50000 0.25000 0.12500 0.12500)\\n     54) slope<=0.438669 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n     55) slope>0.438669 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n  7) B11>0.393150 34 8.5294 1 (0.15789 0.78947 0.026316 0.026316)\\n   14) B3<=0.248750 28 0.0000 1 (0.031250 0.90625 0.031250 0.031250) *\\n   15) B3>0.248750 6 1.6667 0 (0.60000 0.20000 0.10000 0.10000)\\n    30) B1<=0.197925 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n    31) B1>0.197925 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B4<=0.104950 68 33.735 3 (0.013889 0.013889 0.44444 0.52778)\\n  4) B7<=0.0722500 28 0.0000 2 (0.031250 0.031250 0.90625 0.031250) *\\n  5) B7>0.0722500 40 5.5500 3 (0.022727 0.022727 0.090909 0.86364)\\n   10) ndbi<=-0.531108 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) ndbi>-0.531108 39 3.7949 3 (0.023256 0.023256 0.069767 0.88372)\\n    22) B1<=0.0220750 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    23) B1>0.0220750 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n     46) B3<=0.0477500 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n     47) B3>0.0477500 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n 3) B4>0.104950 65 34.400 1 (0.46377 0.47826 0.043478 0.014493)\\n  6) mndwi<=-0.379517 31 3.7419 1 (0.085714 0.85714 0.028571 0.028571)\\n   12) ndbi<=0.131802 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n   13) ndbi>0.131802 29 0.0000 1 (0.030303 0.90909 0.030303 0.030303) *\\n  7) mndwi>-0.379517 34 8.8824 0 (0.78947 0.10526 0.078947 0.026316)\\n   14) bsi<=0.207147 30 3.7333 0 (0.85294 0.029412 0.088235 0.029412)\\n    28) ndbi<=-0.254262 2 0.0000 2 (0.16667 0.16667 0.50000 0.16667) *\\n    29) ndbi>-0.254262 28 0.0000 0 (0.90625 0.031250 0.031250 0.031250) *\\n   15) bsi>0.207147 4 1.5000 1 (0.25000 0.50000 0.12500 0.12500)\\n    30) B1<=0.113225 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n    31) B1>0.113225 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B2<=0.0679500 70 34.886 3 (0.013514 0.013514 0.45946 0.51351)\\n  4) B6<=0.103675 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n  5) B6>0.103675 39 3.7949 3 (0.023256 0.023256 0.069767 0.88372)\\n   10) mndwi<=-0.574408 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) mndwi>-0.574408 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n    22) B5<=0.0778000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    23) B5>0.0778000 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n 3) B2>0.0679500 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n  6) bsi<=0.177232 28 1.9286 0 (0.87500 0.062500 0.031250 0.031250)\\n   12) B11<=0.468000 23 0.0000 0 (0.88889 0.037037 0.037037 0.037037) *\\n   13) B11>0.468000 5 1.6000 0 (0.55556 0.22222 0.11111 0.11111)\\n    26) B5<=0.364550 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    27) B5>0.364550 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n  7) bsi>0.177232 35 7.0857 1 (0.12821 0.82051 0.025641 0.025641)\\n   14) mndwi<=-0.368483 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n    28) B6<=0.159175 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    29) B6>0.159175 30 0.0000 1 (0.029412 0.91176 0.029412 0.029412) *\\n   15) mndwi>-0.368483 4 1.5000 0 (0.50000 0.25000 0.12500 0.12500)\\n    30) B8<=0.336575 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n    31) B8>0.336575 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B4<=0.104950 69 34.319 3 (0.013699 0.013699 0.45205 0.52055)\\n  4) B8A<=0.151750 32 1.9375 2 (0.027778 0.027778 0.88889 0.055556)\\n   8) B5<=0.113000 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n   9) B5>0.113000 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) B8A>0.151750 37 1.9459 3 (0.024390 0.024390 0.048780 0.90244)\\n   10) B5<=0.0799000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) B5>0.0799000 36 0.0000 3 (0.025000 0.025000 0.025000 0.92500) *\\n 3) B4>0.104950 64 32.969 1 (0.47059 0.48529 0.029412 0.014706)\\n  6) ndbi<=0.138297 24 5.4167 0 (0.78571 0.10714 0.071429 0.035714)\\n   12) B11<=0.448425 21 1.9048 0 (0.84000 0.040000 0.080000 0.040000)\\n    24) B4<=0.121500 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    25) B4>0.121500 20 0.0000 0 (0.87500 0.041667 0.041667 0.041667) *\\n   13) B11>0.448425 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n    26) mndwi<=-0.206560 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n    27) mndwi>-0.206560 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n  7) ndbi>0.138297 40 15.000 1 (0.25000 0.70455 0.022727 0.022727)\\n   14) B1<=0.0956000 28 3.7143 1 (0.093750 0.84375 0.031250 0.031250)\\n    28) mndwi<=-0.583789 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    29) mndwi>-0.583789 27 1.9259 1 (0.064516 0.87097 0.032258 0.032258)\\n     58) B12<=0.228300 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     59) B12>0.228300 26 0.0000 1 (0.033333 0.90000 0.033333 0.033333) *\\n   15) B1>0.0956000 12 5.3333 0 (0.56250 0.31250 0.062500 0.062500)\\n    30) elev<=0.417000 6 2.6667 1 (0.30000 0.50000 0.10000 0.10000)\\n     60) B5<=0.254350 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n     61) B5>0.254350 4 0.0000 1 (0.12500 0.62500 0.12500 0.12500) *\\n    31) elev>0.417000 6 0.0000 0 (0.70000 0.10000 0.10000 0.10000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B8A<=0.103300 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n 3) B8A>0.103300 101 67.782 3 (0.30476 0.31429 0.019048 0.36190)\\n  6) B5<=0.147525 42 9.0000 3 (0.10870 0.021739 0.043478 0.82609)\\n   12) ndvi<=0.262702 5 1.6000 0 (0.55556 0.11111 0.22222 0.11111)\\n    24) B2<=0.0689000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    25) B2>0.0689000 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n   13) ndvi>0.262702 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n  7) B5>0.147525 59 29.288 1 (0.44444 0.52381 0.015873 0.015873)\\n   14) B12<=0.298250 19 1.8947 0 (0.82609 0.086957 0.043478 0.043478)\\n    28) B2<=0.0885000 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    29) B2>0.0885000 18 0.0000 0 (0.86364 0.045455 0.045455 0.045455) *\\n   15) B12>0.298250 40 13.950 1 (0.22727 0.72727 0.022727 0.022727)\\n    30) mndwi<=-0.346568 30 1.9333 1 (0.058824 0.88235 0.029412 0.029412)\\n     60) B2<=0.197500 29 0.0000 1 (0.030303 0.90909 0.030303 0.030303) *\\n     61) B2>0.197500 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    31) mndwi>-0.346568 10 3.2000 0 (0.64286 0.21429 0.071429 0.071429)\\n     62) ndvi<=0.0476836 6 0.0000 0 (0.70000 0.10000 0.10000 0.10000) *\\n     63) ndvi>0.0476836 4 2.0000 0 (0.37500 0.37500 0.12500 0.12500)\\n      126) B12<=0.404200 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n      127) B12>0.404200 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B8A<=0.105925 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n 3) B8A>0.105925 102 69.078 3 (0.30189 0.31132 0.028302 0.35849)\\n  6) B3<=0.0999500 39 3.7949 3 (0.023256 0.023256 0.069767 0.88372)\\n   12) B5<=0.0778000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   13) B5>0.0778000 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n    26) B2<=0.0299500 4 1.5000 3 (0.12500 0.12500 0.25000 0.50000)\\n     52) mndwi<=-0.543569 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n     53) mndwi>-0.543569 3 0.0000 3 (0.14286 0.14286 0.14286 0.57143) *\\n    27) B2>0.0299500 34 0.0000 3 (0.026316 0.026316 0.026316 0.92105) *\\n  7) B3>0.0999500 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   14) bsi<=0.174408 29 1.9310 0 (0.87879 0.060606 0.030303 0.030303)\\n    28) B9<=0.320300 26 0.0000 0 (0.90000 0.033333 0.033333 0.033333) *\\n    29) B9>0.320300 3 1.3333 0 (0.42857 0.28571 0.14286 0.14286)\\n     58) ndbi<=0.126628 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n     59) ndbi>0.126628 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n   15) bsi>0.174408 34 5.4706 1 (0.10526 0.84211 0.026316 0.026316)\\n    30) B8<=0.184250 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    31) B8>0.184250 33 3.7576 1 (0.081081 0.86486 0.027027 0.027027)\\n     62) bsi<=0.291684 32 1.9375 1 (0.055556 0.88889 0.027778 0.027778)\\n      124) elev<=0.344750 5 1.6000 1 (0.22222 0.55556 0.11111 0.11111)\\n       248) B8<=0.285225 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n        496) B11<=0.387000 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n        497) B11>0.387000 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n       249) B8>0.285225 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n      125) elev>0.344750 27 0.0000 1 (0.032258 0.90323 0.032258 0.032258) *\\n     63) bsi>0.291684 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B2<=0.0634500 68 33.735 3 (0.013889 0.013889 0.44444 0.52778)\\n  4) ndvi<=0.411255 30 1.9333 2 (0.029412 0.029412 0.88235 0.058824)\\n   8) B4<=0.0627250 29 0.0000 2 (0.030303 0.030303 0.90909 0.030303) *\\n   9) B4>0.0627250 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) ndvi>0.411255 38 3.7895 3 (0.023810 0.023810 0.071429 0.88095)\\n   10) B3<=0.0497000 3 1.3333 2 (0.14286 0.14286 0.42857 0.28571)\\n    20) ndvi<=0.782075 2 0.0000 2 (0.16667 0.16667 0.50000 0.16667) *\\n    21) ndvi>0.782075 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n   11) B3>0.0497000 35 0.0000 3 (0.025641 0.025641 0.025641 0.92308) *\\n 3) B2>0.0634500 65 34.400 1 (0.46377 0.47826 0.043478 0.014493)\\n  6) B8A<=0.275400 24 7.0000 0 (0.75000 0.10714 0.10714 0.035714)\\n   12) B9<=0.105875 2 0.0000 2 (0.16667 0.16667 0.50000 0.16667) *\\n   13) B9>0.105875 22 3.6364 0 (0.80769 0.11538 0.038462 0.038462)\\n    26) elev<=0.335250 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    27) elev>0.335250 21 1.9048 0 (0.84000 0.080000 0.040000 0.040000)\\n     54) B7<=0.194275 5 1.6000 0 (0.55556 0.22222 0.11111 0.11111)\\n      108) B2<=0.0875500 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n      109) B2>0.0875500 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n     55) B7>0.194275 16 0.0000 0 (0.85000 0.050000 0.050000 0.050000) *\\n  7) B8A>0.275400 41 16.098 1 (0.26667 0.68889 0.022222 0.022222)\\n   14) mndwi<=-0.417357 28 1.9286 1 (0.062500 0.87500 0.031250 0.031250)\\n    28) bsi<=0.296587 27 0.0000 1 (0.032258 0.90323 0.032258 0.032258) *\\n    29) bsi>0.296587 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n   15) mndwi>-0.417357 13 4.6154 0 (0.64706 0.23529 0.058824 0.058824)\\n    30) B9<=0.331500 11 1.8182 0 (0.73333 0.13333 0.066667 0.066667)\\n     60) B4<=0.326950 7 0.0000 0 (0.72727 0.090909 0.090909 0.090909) *\\n     61) B4>0.326950 4 1.5000 0 (0.50000 0.25000 0.12500 0.12500)\\n      122) B8A<=0.367500 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n      123) B8A>0.367500 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n    31) B9>0.331500 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B5<=0.151850 73 39.205 3 (0.051948 0.012987 0.44156 0.49351)\\n  4) B8A<=0.134575 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n  5) B8A>0.134575 41 7.3659 3 (0.088889 0.022222 0.044444 0.84444)\\n   10) B1<=0.0705750 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n    20) B11<=0.146425 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    21) B11>0.146425 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n   11) B1>0.0705750 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n 3) B5>0.151850 60 29.867 1 (0.45313 0.51563 0.015625 0.015625)\\n  6) B2<=0.109650 31 8.3871 1 (0.17143 0.77143 0.028571 0.028571)\\n   12) bsi<=0.179226 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n   13) bsi>0.179226 26 0.0000 1 (0.033333 0.90000 0.033333 0.033333) *\\n  7) B2>0.109650 29 9.5172 0 (0.72727 0.21212 0.030303 0.030303)\\n   14) mndwi<=-0.370698 7 2.8571 1 (0.27273 0.54545 0.090909 0.090909)\\n    28) B3<=0.160700 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n    29) B3>0.160700 5 0.0000 1 (0.11111 0.66667 0.11111 0.11111) *\\n   15) mndwi>-0.370698 22 1.9091 0 (0.84615 0.076923 0.038462 0.038462)\\n    30) slope<=0.154832 19 0.0000 0 (0.86957 0.043478 0.043478 0.043478) *\\n    31) slope>0.154832 3 1.3333 0 (0.42857 0.28571 0.14286 0.14286)\\n     62) slope<=0.191210 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     63) slope>0.191210 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B4<=0.113825 70 34.886 3 (0.013514 0.013514 0.45946 0.51351)\\n  4) B11<=0.0682500 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n  5) B11>0.0682500 39 3.7949 3 (0.023256 0.023256 0.069767 0.88372)\\n   10) B3<=0.0497000 2 0.0000 2 (0.16667 0.16667 0.50000 0.16667) *\\n   11) B3>0.0497000 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n 3) B4>0.113825 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n  6) B12<=0.303350 25 1.9200 0 (0.86207 0.068966 0.034483 0.034483)\\n   12) B1<=0.0583000 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n    24) bsi<=0.178621 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    25) bsi>0.178621 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n   13) B1>0.0583000 23 0.0000 0 (0.88889 0.037037 0.037037 0.037037) *\\n  7) B12>0.303350 38 11.421 1 (0.19048 0.76190 0.023810 0.023810)\\n   14) ndbi<=0.105686 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n   15) ndbi>0.105686 36 8.6111 1 (0.15000 0.80000 0.025000 0.025000)\\n    30) B2<=0.238950 34 5.4706 1 (0.10526 0.84211 0.026316 0.026316)\\n     60) ndvi<=0.0456087 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     61) ndvi>0.0456087 33 3.7576 1 (0.081081 0.86486 0.027027 0.027027)\\n      122) B8A<=0.266450 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      123) B8A>0.266450 32 1.9375 1 (0.055556 0.88889 0.027778 0.027778)\\n       246) B2<=0.0789500 4 1.5000 1 (0.25000 0.50000 0.12500 0.12500)\\n        492) B8<=0.309000 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n        493) B8>0.309000 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n       247) B2>0.0789500 28 0.0000 1 (0.031250 0.90625 0.031250 0.031250) *\\n    31) B2>0.238950 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B8A<=0.0875000 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n 3) B8A>0.0875000 102 69.078 3 (0.30189 0.31132 0.028302 0.35849)\\n  6) B4<=0.0970000 39 3.7949 3 (0.023256 0.023256 0.069767 0.88372)\\n   12) ndbi<=0.247683 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n    24) B5<=0.0763500 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    25) B5>0.0763500 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n   13) ndbi>0.247683 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n  7) B4>0.0970000 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   14) B8A<=0.266450 20 1.9000 0 (0.83333 0.083333 0.041667 0.041667)\\n    28) B9<=0.207700 4 1.5000 0 (0.50000 0.25000 0.12500 0.12500)\\n     56) B9<=0.202500 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n     57) B9>0.202500 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    29) B9>0.207700 16 0.0000 0 (0.85000 0.050000 0.050000 0.050000) *\\n   15) B8A>0.266450 43 17.302 1 (0.27660 0.68085 0.021277 0.021277)\\n    30) B3<=0.241200 34 7.0588 1 (0.13158 0.81579 0.026316 0.026316)\\n     60) B12<=0.297775 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n     61) B12>0.297775 32 3.7500 1 (0.083333 0.86111 0.027778 0.027778)\\n      122) mndwi<=-0.331804 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n       244) bsi<=0.292645 30 0.0000 1 (0.029412 0.91176 0.029412 0.029412) *\\n       245) bsi>0.292645 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      123) mndwi>-0.331804 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    31) B3>0.241200 9 1.7778 0 (0.69231 0.15385 0.076923 0.076923)\\n     62) elev<=0.378000 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     63) elev>0.378000 8 0.0000 0 (0.75000 0.083333 0.083333 0.083333) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B3<=0.0949500 68 33.882 3 (0.013889 0.013889 0.45833 0.51389)\\n  4) B6<=0.104175 30 0.0000 2 (0.029412 0.029412 0.91176 0.029412) *\\n  5) B6>0.104175 38 3.7895 3 (0.023810 0.023810 0.071429 0.88095)\\n   10) ndbi<=-0.237735 19 3.5789 3 (0.043478 0.043478 0.13043 0.78261)\\n    20) B8A<=0.260750 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    21) B8A>0.260750 18 1.8889 3 (0.045455 0.045455 0.090909 0.81818)\\n     42) B2<=0.0298000 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n      84) ndvi<=0.782075 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n      85) ndvi>0.782075 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n     43) B2>0.0298000 16 0.0000 3 (0.050000 0.050000 0.050000 0.85000) *\\n   11) ndbi>-0.237735 19 0.0000 3 (0.043478 0.043478 0.043478 0.86957) *\\n 3) B3>0.0949500 65 34.431 1 (0.46377 0.47826 0.028986 0.028986)\\n  6) B11<=0.383550 27 7.1852 0 (0.77419 0.096774 0.064516 0.064516)\\n   12) B8A<=0.100350 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n    24) slope<=0.100226 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    25) slope>0.100226 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   13) B8A>0.100350 25 3.6800 0 (0.82759 0.10345 0.034483 0.034483)\\n    26) bsi<=0.167446 21 0.0000 0 (0.88000 0.040000 0.040000 0.040000) *\\n    27) bsi>0.167446 4 2.0000 0 (0.37500 0.37500 0.12500 0.12500)\\n     54) B11<=0.244800 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     55) B11>0.244800 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n      110) B7<=0.168125 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      111) B7>0.168125 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n  7) B11>0.383550 38 12.632 1 (0.21429 0.73810 0.023810 0.023810)\\n   14) B2<=0.197400 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n    28) B9<=0.264625 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    29) B9>0.264625 30 0.0000 1 (0.029412 0.91176 0.029412 0.029412) *\\n   15) B2>0.197400 7 0.0000 0 (0.72727 0.090909 0.090909 0.090909) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B11<=0.0682500 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n 3) B11>0.0682500 101 67.782 3 (0.30476 0.31429 0.019048 0.36190)\\n  6) B12<=0.177825 39 3.8462 3 (0.046512 0.023256 0.046512 0.88372)\\n   12) B4<=0.0970000 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n    24) mndwi<=-0.574839 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    25) mndwi>-0.574839 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n   13) B4>0.0970000 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n  7) B12>0.177825 62 30.968 1 (0.46970 0.50000 0.015152 0.015152)\\n   14) mndwi<=-0.378142 34 7.0588 1 (0.13158 0.81579 0.026316 0.026316)\\n    28) bsi<=0.156867 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n    29) bsi>0.156867 32 3.7500 1 (0.083333 0.86111 0.027778 0.027778)\\n     58) B8<=0.184250 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     59) B8>0.184250 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n      118) ndbi<=0.211023 25 0.0000 1 (0.034483 0.89655 0.034483 0.034483) *\\n      119) ndbi>0.211023 6 1.6667 1 (0.20000 0.60000 0.10000 0.10000)\\n       238) B3<=0.129650 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n       239) B3>0.129650 5 0.0000 1 (0.11111 0.66667 0.11111 0.11111) *\\n   15) mndwi>-0.378142 28 3.7143 0 (0.84375 0.093750 0.031250 0.031250)\\n    30) B6<=0.314675 22 0.0000 0 (0.88462 0.038462 0.038462 0.038462) *\\n    31) B6>0.314675 6 2.6667 0 (0.50000 0.30000 0.10000 0.10000)\\n     62) B4<=0.340900 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n     63) B4>0.340900 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B1<=0.0503500 68 33.882 3 (0.013889 0.013889 0.45833 0.51389)\\n  4) B7<=0.0756000 30 0.0000 2 (0.029412 0.029412 0.91176 0.029412) *\\n  5) B7>0.0756000 38 3.7895 3 (0.023810 0.023810 0.071429 0.88095)\\n   10) B2<=0.0326250 7 2.8571 3 (0.090909 0.090909 0.27273 0.54545)\\n    20) B8A<=0.260075 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    21) B8A>0.260075 6 1.6667 3 (0.10000 0.10000 0.20000 0.60000)\\n     42) B7<=0.309400 5 0.0000 3 (0.11111 0.11111 0.11111 0.66667) *\\n     43) B7>0.309400 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) B2>0.0326250 31 0.0000 3 (0.028571 0.028571 0.028571 0.91429) *\\n 3) B1>0.0503500 65 34.431 1 (0.46377 0.47826 0.028986 0.028986)\\n  6) B2<=0.129950 43 20.047 1 (0.27660 0.63830 0.042553 0.042553)\\n   12) B8A<=0.257950 13 4.9231 0 (0.64706 0.17647 0.11765 0.058824)\\n    24) B2<=0.0923500 4 2.5000 1 (0.25000 0.37500 0.25000 0.12500)\\n     48) B8A<=0.179525 2 1.0000 0 (0.33333 0.16667 0.33333 0.16667)\\n      96) B9<=0.106200 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n      97) B9>0.106200 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     49) B8A>0.179525 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n    25) B2>0.0923500 9 0.0000 0 (0.76923 0.076923 0.076923 0.076923) *\\n   13) B8A>0.257950 30 5.5333 1 (0.088235 0.82353 0.029412 0.058824)\\n    26) bsi<=0.136117 2 1.0000 0 (0.33333 0.16667 0.16667 0.33333)\\n     52) ndbi<=-0.101278 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n     53) ndbi>-0.101278 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    27) bsi>0.136117 28 1.9286 1 (0.062500 0.87500 0.031250 0.031250)\\n     54) B11<=0.505100 26 0.0000 1 (0.033333 0.90000 0.033333 0.033333) *\\n     55) B11>0.505100 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n      110) bsi<=0.273495 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n      111) bsi>0.273495 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n  7) B2>0.129950 22 5.1818 0 (0.76923 0.15385 0.038462 0.038462)\\n   14) B9<=0.311050 12 0.0000 0 (0.81250 0.062500 0.062500 0.062500) *\\n   15) B9>0.311050 10 4.2000 0 (0.57143 0.28571 0.071429 0.071429)\\n    30) bsi<=0.193444 8 1.7500 0 (0.66667 0.16667 0.083333 0.083333)\\n     60) B4<=0.265550 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n      120) B8A<=0.332075 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n      121) B8A>0.332075 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     61) B4>0.265550 6 0.0000 0 (0.70000 0.10000 0.10000 0.10000) *\\n    31) bsi>0.193444 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B3<=0.0941000 68 33.882 3 (0.013889 0.013889 0.45833 0.51389)\\n  4) B6<=0.110125 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n  5) B6>0.110125 37 1.9459 3 (0.024390 0.024390 0.048780 0.90244)\\n   10) B5<=0.0763500 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) B5>0.0763500 36 0.0000 3 (0.025000 0.025000 0.025000 0.92500) *\\n 3) B3>0.0941000 65 34.431 1 (0.46377 0.47826 0.028986 0.028986)\\n  6) B6<=0.241450 25 5.5200 0 (0.79310 0.068966 0.068966 0.068966)\\n   12) B6<=0.106950 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n    24) B4<=0.107750 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    25) B4>0.107750 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   13) B6>0.106950 23 1.9130 0 (0.85185 0.074074 0.037037 0.037037)\\n    26) B12<=0.353125 21 0.0000 0 (0.88000 0.040000 0.040000 0.040000) *\\n    27) B12>0.353125 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n     54) slope<=0.0790383 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     55) slope>0.0790383 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n  7) B6>0.241450 40 13.950 1 (0.22727 0.72727 0.022727 0.022727)\\n   14) mndwi<=-0.356394 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n    28) elev<=0.353750 5 1.6000 1 (0.22222 0.55556 0.11111 0.11111)\\n     56) B9<=0.339225 4 0.0000 1 (0.12500 0.62500 0.12500 0.12500) *\\n     57) B9>0.339225 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    29) elev>0.353750 26 0.0000 1 (0.033333 0.90000 0.033333 0.033333) *\\n   15) mndwi>-0.356394 9 1.7778 0 (0.69231 0.15385 0.076923 0.076923)\\n    30) ndvi<=0.0476836 7 0.0000 0 (0.72727 0.090909 0.090909 0.090909) *\\n    31) ndvi>0.0476836 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n     62) bsi<=0.138127 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     63) bsi>0.138127 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B8<=0.0886500 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n 3) B8>0.0886500 101 67.782 3 (0.30476 0.31429 0.019048 0.36190)\\n  6) B3<=0.103050 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n   12) B12<=0.0810500 5 1.6000 3 (0.11111 0.11111 0.22222 0.55556)\\n    24) mndwi<=-0.505418 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    25) mndwi>-0.505418 4 0.0000 3 (0.12500 0.12500 0.12500 0.62500) *\\n   13) B12>0.0810500 33 0.0000 3 (0.027027 0.027027 0.027027 0.91892) *\\n  7) B3>0.103050 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   14) B8<=0.273700 26 3.6923 0 (0.83333 0.10000 0.033333 0.033333)\\n    28) B1<=0.0713000 3 1.3333 0 (0.42857 0.28571 0.14286 0.14286)\\n     56) B5<=0.175575 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n     57) B5>0.175575 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    29) B1>0.0713000 23 1.9130 0 (0.85185 0.074074 0.037037 0.037037)\\n     58) B6<=0.242400 20 0.0000 0 (0.87500 0.041667 0.041667 0.041667) *\\n     59) B6>0.242400 3 1.3333 0 (0.42857 0.28571 0.14286 0.14286)\\n      118) B5<=0.221100 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n      119) B5>0.221100 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n   15) B8>0.273700 37 11.351 1 (0.19512 0.75610 0.024390 0.024390)\\n    30) B11<=0.375300 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n    31) B11>0.375300 32 3.7500 1 (0.083333 0.86111 0.027778 0.027778)\\n     62) bsi<=0.134021 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     63) bsi>0.134021 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n      126) B11<=0.505100 27 0.0000 1 (0.032258 0.90323 0.032258 0.032258) *\\n      127) B11>0.505100 4 1.5000 1 (0.25000 0.50000 0.12500 0.12500)\\n       254) B11<=0.513750 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n       255) B11>0.513750 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B12<=0.162825 69 34.435 3 (0.013699 0.013699 0.46575 0.50685)\\n  4) B7<=0.107950 32 1.9375 2 (0.027778 0.027778 0.88889 0.055556)\\n   8) B2<=0.0563500 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n   9) B2>0.0563500 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) B7>0.107950 37 3.7838 3 (0.024390 0.024390 0.073171 0.87805)\\n   10) B2<=0.0326250 7 2.8571 3 (0.090909 0.090909 0.27273 0.54545)\\n    20) mndwi<=-0.505447 2 0.0000 2 (0.16667 0.16667 0.50000 0.16667) *\\n    21) mndwi>-0.505447 5 0.0000 3 (0.11111 0.11111 0.11111 0.66667) *\\n   11) B2>0.0326250 30 0.0000 3 (0.029412 0.029412 0.029412 0.91176) *\\n 3) B12>0.162825 64 32.969 1 (0.47059 0.48529 0.014706 0.029412)\\n  6) mndwi<=-0.438238 28 5.5000 1 (0.093750 0.81250 0.031250 0.062500)\\n   12) B1<=0.123600 27 3.7778 1 (0.064516 0.83871 0.032258 0.064516)\\n    24) B4<=0.119725 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    25) B4>0.119725 26 1.9231 1 (0.066667 0.86667 0.033333 0.033333)\\n     50) B2<=0.0789500 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n      100) B4<=0.226450 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n      101) B4>0.226450 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     51) B2>0.0789500 23 0.0000 1 (0.037037 0.88889 0.037037 0.037037) *\\n   13) B1>0.123600 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n  7) mndwi>-0.438238 36 11.278 0 (0.75000 0.20000 0.025000 0.025000)\\n   14) B7<=0.286225 20 0.0000 0 (0.87500 0.041667 0.041667 0.041667) *\\n   15) B7>0.286225 16 7.8750 0 (0.50000 0.40000 0.050000 0.050000)\\n    30) B4<=0.348500 11 5.0909 1 (0.33333 0.53333 0.066667 0.066667)\\n     60) elev<=0.428250 9 3.1111 1 (0.23077 0.61538 0.076923 0.076923)\\n      120) B9<=0.298150 3 1.3333 0 (0.42857 0.28571 0.14286 0.14286)\\n       240) B8A<=0.303850 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n       241) B8A>0.303850 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n      121) B9>0.298150 6 0.0000 1 (0.10000 0.70000 0.10000 0.10000) *\\n     61) elev>0.428250 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n    31) B4>0.348500 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B2<=0.0690500 69 34.319 3 (0.013699 0.013699 0.45205 0.52055)\\n  4) B6<=0.110125 32 1.9375 2 (0.027778 0.027778 0.88889 0.055556)\\n   8) B3<=0.101450 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n   9) B3>0.101450 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) B6>0.110125 37 1.9459 3 (0.024390 0.024390 0.048780 0.90244)\\n   10) elev<=0.420250 24 0.0000 3 (0.035714 0.035714 0.035714 0.89286) *\\n   11) elev>0.420250 13 1.8462 3 (0.058824 0.058824 0.11765 0.76471)\\n    22) mndwi<=-0.555806 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    23) mndwi>-0.555806 12 0.0000 3 (0.062500 0.062500 0.062500 0.81250) *\\n 3) B2>0.0690500 64 32.969 1 (0.47059 0.48529 0.029412 0.014706)\\n  6) B8A<=0.270275 23 3.7391 0 (0.81481 0.074074 0.074074 0.037037)\\n   12) B4<=0.113825 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   13) B4>0.113825 22 1.9091 0 (0.84615 0.076923 0.038462 0.038462)\\n    26) B11<=0.337500 20 0.0000 0 (0.87500 0.041667 0.041667 0.041667) *\\n    27) B11>0.337500 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n     54) B2<=0.180600 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     55) B2>0.180600 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n  7) B8A>0.270275 41 15.122 1 (0.24444 0.71111 0.022222 0.022222)\\n   14) ndvi<=0.0453839 7 0.0000 0 (0.72727 0.090909 0.090909 0.090909) *\\n   15) ndvi>0.0453839 34 5.4706 1 (0.10526 0.84211 0.026316 0.026316)\\n    30) bsi<=0.0869242 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n    31) bsi>0.0869242 31 0.0000 1 (0.028571 0.91429 0.028571 0.028571) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B5<=0.147450 71 36.366 3 (0.026667 0.013333 0.45333 0.50667)\\n  4) B8A<=0.103300 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n  5) B8A>0.103300 40 5.6500 3 (0.045455 0.022727 0.068182 0.86364)\\n   10) mndwi<=-0.563474 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) mndwi>-0.563474 39 3.8462 3 (0.046512 0.023256 0.046512 0.88372)\\n    22) B1<=0.0726500 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n     44) B6<=0.189750 3 1.3333 3 (0.14286 0.14286 0.28571 0.42857)\\n      88) slope<=0.0759406 2 0.0000 3 (0.16667 0.16667 0.16667 0.50000) *\\n      89) slope>0.0759406 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n     45) B6>0.189750 35 0.0000 3 (0.025641 0.025641 0.025641 0.92308) *\\n    23) B1>0.0726500 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n 3) B5>0.147450 62 30.968 1 (0.46970 0.50000 0.015152 0.015152)\\n  6) B11<=0.335200 18 0.0000 0 (0.86364 0.045455 0.045455 0.045455) *\\n  7) B11>0.335200 44 17.455 1 (0.27083 0.68750 0.020833 0.020833)\\n   14) B6<=0.341775 35 8.5714 1 (0.15385 0.79487 0.025641 0.025641)\\n    28) B5<=0.326075 34 7.0588 1 (0.13158 0.81579 0.026316 0.026316)\\n     56) ndbi<=0.136031 5 2.4000 0 (0.44444 0.33333 0.11111 0.11111)\\n      112) B6<=0.301125 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n      113) B6>0.301125 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n     57) ndbi>0.136031 29 1.9310 1 (0.060606 0.87879 0.030303 0.030303)\\n      114) B1<=0.116050 28 0.0000 1 (0.031250 0.90625 0.031250 0.031250) *\\n      115) B1>0.116050 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    29) B5>0.326075 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n   15) B6>0.341775 9 3.1111 0 (0.61538 0.23077 0.076923 0.076923)\\n    30) B2<=0.175700 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n    31) B2>0.175700 7 0.0000 0 (0.72727 0.090909 0.090909 0.090909) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B6<=0.110125 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n 3) B6>0.110125 101 67.782 3 (0.30476 0.31429 0.019048 0.36190)\\n  6) B11<=0.337500 60 29.100 3 (0.35938 0.015625 0.031250 0.59375)\\n   12) B12<=0.164050 37 1.9459 3 (0.024390 0.024390 0.048780 0.90244)\\n    24) B2<=0.0298000 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n     48) slope<=0.0313371 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n     49) slope>0.0313371 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    25) B2>0.0298000 35 0.0000 3 (0.025641 0.025641 0.025641 0.92308) *\\n   13) B12>0.164050 23 1.9130 0 (0.85185 0.037037 0.037037 0.074074)\\n    26) B2<=0.0758000 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    27) B2>0.0758000 22 0.0000 0 (0.88462 0.038462 0.038462 0.038462) *\\n  7) B11>0.337500 41 14.049 1 (0.22222 0.73333 0.022222 0.022222)\\n   14) B2<=0.190100 35 7.0857 1 (0.12821 0.82051 0.025641 0.025641)\\n    28) elev<=0.323750 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    29) elev>0.323750 34 5.4706 1 (0.10526 0.84211 0.026316 0.026316)\\n     58) bsi<=0.177114 3 1.3333 0 (0.42857 0.28571 0.14286 0.14286)\\n      116) B4<=0.245850 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n      117) B4>0.245850 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     59) bsi>0.177114 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n      118) mndwi<=-0.585293 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n       236) B12<=0.390875 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n       237) B12>0.390875 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      119) mndwi>-0.585293 29 0.0000 1 (0.030303 0.90909 0.030303 0.030303) *\\n   15) B2>0.190100 6 1.6667 0 (0.60000 0.20000 0.10000 0.10000)\\n    30) ndbi<=0.102868 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n     60) slope<=0.226207 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     61) slope>0.226207 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    31) ndbi>0.102868 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B5<=0.144400 71 37.352 3 (0.040000 0.013333 0.45333 0.49333)\\n  4) B12<=0.0541750 32 1.9375 2 (0.027778 0.027778 0.88889 0.055556)\\n   8) B12<=0.0224750 22 0.0000 2 (0.038462 0.038462 0.88462 0.038462) *\\n   9) B12>0.0224750 10 1.8000 2 (0.071429 0.071429 0.71429 0.14286)\\n    18) B12<=0.0226500 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    19) B12>0.0226500 9 0.0000 2 (0.076923 0.076923 0.76923 0.076923) *\\n  5) B12>0.0541750 39 7.3846 3 (0.069767 0.023256 0.069767 0.83721)\\n   10) bsi<=0.121522 37 3.7838 3 (0.024390 0.024390 0.073171 0.87805)\\n    20) B3<=0.0497000 2 0.0000 2 (0.16667 0.16667 0.50000 0.16667) *\\n    21) B3>0.0497000 35 0.0000 3 (0.025641 0.025641 0.025641 0.92308) *\\n   11) bsi>0.121522 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n 3) B5>0.144400 62 31.903 1 (0.45455 0.50000 0.015152 0.030303)\\n  6) mndwi<=-0.379065 37 10.324 1 (0.14634 0.78049 0.024390 0.048780)\\n   12) ndbi<=0.142436 7 4.0000 0 (0.45455 0.27273 0.090909 0.18182)\\n    24) bsi<=0.183678 5 1.6000 0 (0.55556 0.11111 0.11111 0.22222)\\n     48) B1<=0.0620250 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n     49) B1>0.0620250 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n    25) bsi>0.183678 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n   13) ndbi>0.142436 30 1.9333 1 (0.058824 0.88235 0.029412 0.029412)\\n    26) B11<=0.501850 27 0.0000 1 (0.032258 0.90323 0.032258 0.032258) *\\n    27) B11>0.501850 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n     54) B8<=0.338075 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     55) B8>0.338075 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n  7) mndwi>-0.379065 25 1.9200 0 (0.86207 0.068966 0.034483 0.034483)\\n   14) B2<=0.242200 21 0.0000 0 (0.88000 0.040000 0.040000 0.040000) *\\n   15) B2>0.242200 4 1.5000 0 (0.50000 0.25000 0.12500 0.12500)\\n    30) ndvi<=0.0506296 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n    31) ndvi>0.0506296 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B12<=0.176400 70 34.886 3 (0.013514 0.013514 0.45946 0.51351)\\n  4) B7<=0.107950 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n  5) B7>0.107950 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n   10) B3<=0.0477500 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) B3>0.0477500 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n 3) B12>0.176400 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n  6) B9<=0.275650 19 1.8947 0 (0.82609 0.086957 0.043478 0.043478)\\n   12) B4<=0.168000 6 1.6667 0 (0.60000 0.20000 0.10000 0.10000)\\n    24) mndwi<=-0.424668 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    25) mndwi>-0.424668 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n   13) B4>0.168000 13 0.0000 0 (0.82353 0.058824 0.058824 0.058824) *\\n  7) B9>0.275650 44 18.318 1 (0.29167 0.66667 0.020833 0.020833)\\n   14) bsi<=0.186746 14 4.7143 0 (0.66667 0.22222 0.055556 0.055556)\\n    28) ndvi<=0.0436442 6 0.0000 0 (0.70000 0.10000 0.10000 0.10000) *\\n    29) ndvi>0.0436442 8 3.7500 0 (0.50000 0.33333 0.083333 0.083333)\\n     58) B5<=0.289525 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n     59) B5>0.289525 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n   15) bsi>0.186746 30 3.7333 1 (0.088235 0.85294 0.029412 0.029412)\\n    30) ndbi<=0.241779 29 1.9310 1 (0.060606 0.87879 0.030303 0.030303)\\n     60) bsi<=0.296587 28 0.0000 1 (0.031250 0.90625 0.031250 0.031250) *\\n     61) bsi>0.296587 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    31) ndbi>0.241779 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B7<=0.107725 34 1.9412 2 (0.026316 0.026316 0.89474 0.052632)\\n  4) B3<=0.100850 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n  5) B3>0.100850 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n   10) B7<=0.0759250 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) B7>0.0759250 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n 3) B7>0.107725 99 65.859 3 (0.31068 0.32039 0.0097087 0.35922)\\n  6) B2<=0.0651250 36 0.0000 3 (0.025000 0.025000 0.025000 0.92500) *\\n  7) B2>0.0651250 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   14) ndvi<=0.110388 32 9.7500 0 (0.75000 0.19444 0.027778 0.027778)\\n    28) mndwi<=-0.366170 6 1.6667 1 (0.20000 0.60000 0.10000 0.10000)\\n     56) B7<=0.218675 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     57) B7>0.218675 5 0.0000 1 (0.11111 0.66667 0.11111 0.11111) *\\n    29) mndwi>-0.366170 26 1.9231 0 (0.86667 0.066667 0.033333 0.033333)\\n     58) B1<=0.198600 24 0.0000 0 (0.89286 0.035714 0.035714 0.035714) *\\n     59) B1>0.198600 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n      118) B12<=0.380700 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      119) B12>0.380700 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n   15) ndvi>0.110388 31 8.3871 1 (0.17143 0.77143 0.028571 0.028571)\\n    30) ndbi<=0.104037 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n    31) ndbi>0.104037 28 3.7143 1 (0.093750 0.84375 0.031250 0.031250)\\n     62) mndwi<=-0.335544 27 1.9259 1 (0.064516 0.87097 0.032258 0.032258)\\n      124) bsi<=0.290066 26 0.0000 1 (0.033333 0.90000 0.033333 0.033333) *\\n      125) bsi>0.290066 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     63) mndwi>-0.335544 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) ndvi<=0.389395 96 64.646 1 (0.32000 0.33000 0.33000 0.020000)\\n  4) B8A<=0.102000 33 1.9394 2 (0.027027 0.027027 0.89189 0.054054)\\n   8) B6<=0.0723000 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n   9) B6>0.0723000 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) B8A>0.102000 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   10) B7<=0.257000 19 1.8947 0 (0.82609 0.086957 0.043478 0.043478)\\n    20) slope<=0.331926 18 0.0000 0 (0.86364 0.045455 0.045455 0.045455) *\\n    21) slope>0.331926 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n   11) B7>0.257000 44 18.318 1 (0.29167 0.66667 0.020833 0.020833)\\n    22) ndvi<=0.0382102 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n    23) ndvi>0.0382102 39 12.718 1 (0.20930 0.74419 0.023256 0.023256)\\n     46) B11<=0.375300 8 3.0000 0 (0.58333 0.25000 0.083333 0.083333)\\n      92) B8A<=0.277800 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n       184) B4<=0.210000 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n       185) B4>0.210000 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      93) B8A>0.277800 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n     47) B11>0.375300 31 3.7419 1 (0.085714 0.85714 0.028571 0.028571)\\n      94) B8A<=0.271350 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      95) B8A>0.271350 30 1.9333 1 (0.058824 0.88235 0.029412 0.029412)\\n       190) B6<=0.311650 24 0.0000 1 (0.035714 0.89286 0.035714 0.035714) *\\n       191) B6>0.311650 6 1.6667 1 (0.20000 0.60000 0.10000 0.10000)\\n        382) B7<=0.336100 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n        383) B7>0.336100 5 0.0000 1 (0.11111 0.66667 0.11111 0.11111) *\\n 3) ndvi>0.389395 37 1.9459 3 (0.024390 0.024390 0.048780 0.90244)\\n  6) B11<=0.0882000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n  7) B11>0.0882000 36 0.0000 3 (0.025000 0.025000 0.025000 0.92500) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) ndvi<=0.389395 93 61.978 1 (0.31959 0.34021 0.32990 0.010309)\\n  4) B12<=0.104900 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n  5) B12>0.104900 62 30.968 1 (0.46970 0.50000 0.015152 0.015152)\\n   10) mndwi<=-0.438238 26 1.9231 1 (0.066667 0.86667 0.033333 0.033333)\\n    20) elev<=0.354500 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n     40) B8A<=0.342375 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     41) B8A>0.342375 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    21) elev>0.354500 24 0.0000 1 (0.035714 0.89286 0.035714 0.035714) *\\n   11) mndwi>-0.438238 36 11.278 0 (0.75000 0.20000 0.025000 0.025000)\\n    22) B5<=0.259600 25 1.9200 0 (0.86207 0.068966 0.034483 0.034483)\\n     44) B8A<=0.270275 20 0.0000 0 (0.87500 0.041667 0.041667 0.041667) *\\n     45) B8A>0.270275 5 1.6000 0 (0.55556 0.22222 0.11111 0.11111)\\n      90) ndbi<=0.133469 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n      91) ndbi>0.133469 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    23) B5>0.259600 11 5.4545 1 (0.40000 0.46667 0.066667 0.066667)\\n     46) B7<=0.367650 8 3.0000 1 (0.25000 0.58333 0.083333 0.083333)\\n      92) elev<=0.420250 6 0.0000 1 (0.10000 0.70000 0.10000 0.10000) *\\n      93) elev>0.420250 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n     47) B7>0.367650 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n 3) ndvi>0.389395 40 5.6500 3 (0.045455 0.022727 0.068182 0.86364)\\n  6) mndwi<=-0.574771 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n  7) mndwi>-0.574771 39 3.8462 3 (0.046512 0.023256 0.046512 0.88372)\\n   14) B12<=0.296450 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n    28) B8A<=0.260075 5 1.6000 3 (0.11111 0.11111 0.22222 0.55556)\\n     56) B2<=0.0348500 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n     57) B2>0.0348500 4 0.0000 3 (0.12500 0.12500 0.12500 0.62500) *\\n    29) B8A>0.260075 33 0.0000 3 (0.027027 0.027027 0.027027 0.91892) *\\n   15) B12>0.296450 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) ndvi<=0.389395 95 63.979 1 (0.32323 0.33333 0.32323 0.020202)\\n  4) B5<=0.116575 32 1.9375 2 (0.027778 0.027778 0.88889 0.055556)\\n   8) B6<=0.115475 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n   9) B6>0.115475 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) B5>0.116575 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   10) mndwi<=-0.375605 35 8.5714 1 (0.15385 0.79487 0.025641 0.025641)\\n    20) B11<=0.332750 4 1.5000 0 (0.50000 0.25000 0.12500 0.12500)\\n     40) slope<=0.334210 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n     41) slope>0.334210 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    21) B11>0.332750 31 3.7419 1 (0.085714 0.85714 0.028571 0.028571)\\n     42) B6<=0.219400 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     43) B6>0.219400 30 1.9333 1 (0.058824 0.88235 0.029412 0.029412)\\n      86) B9<=0.344175 27 0.0000 1 (0.032258 0.90323 0.032258 0.032258) *\\n      87) B9>0.344175 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n       174) B8<=0.351475 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n       175) B8>0.351475 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n   11) mndwi>-0.375605 28 3.7143 0 (0.84375 0.093750 0.031250 0.031250)\\n    22) elev<=0.334000 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n     44) bsi<=0.105251 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     45) bsi>0.105251 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    23) elev>0.334000 26 1.9231 0 (0.86667 0.066667 0.033333 0.033333)\\n     46) B7<=0.308300 22 0.0000 0 (0.88462 0.038462 0.038462 0.038462) *\\n     47) B7>0.308300 4 1.5000 0 (0.50000 0.25000 0.12500 0.12500)\\n      94) B1<=0.142925 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n      95) B1>0.142925 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n 3) ndvi>0.389395 38 3.7895 3 (0.023810 0.023810 0.071429 0.88095)\\n  6) B8<=0.133175 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n  7) B8>0.133175 37 1.9459 3 (0.024390 0.024390 0.048780 0.90244)\\n   14) B1<=0.0235500 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n    28) B12<=0.100200 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    29) B12>0.100200 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   15) B1>0.0235500 35 0.0000 3 (0.025641 0.025641 0.025641 0.92308) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B12<=0.177950 72 37.806 3 (0.039474 0.013158 0.44737 0.50000)\\n  4) B8A<=0.0952000 30 0.0000 2 (0.029412 0.029412 0.91176 0.029412) *\\n  5) B8A>0.0952000 42 9.0952 3 (0.065217 0.021739 0.086957 0.82609)\\n   10) slope<=0.0313308 2 1.0000 0 (0.33333 0.16667 0.33333 0.16667)\\n    20) B5<=0.111775 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    21) B5>0.111775 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n   11) slope>0.0313308 40 5.6500 3 (0.045455 0.022727 0.068182 0.86364)\\n    22) B2<=0.0753500 39 3.7949 3 (0.023256 0.023256 0.069767 0.88372)\\n     44) B1<=0.0749250 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n      88) B5<=0.0763500 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n      89) B5>0.0763500 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n     45) B1>0.0749250 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    23) B2>0.0753500 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n 3) B12>0.177950 61 30.426 1 (0.46154 0.50769 0.015385 0.015385)\\n  6) B6<=0.226300 17 0.0000 0 (0.85714 0.047619 0.047619 0.047619) *\\n  7) B6>0.226300 44 17.455 1 (0.27083 0.68750 0.020833 0.020833)\\n   14) bsi<=0.187103 14 5.7143 0 (0.61111 0.27778 0.055556 0.055556)\\n    28) B1<=0.198600 13 4.6154 0 (0.64706 0.23529 0.058824 0.058824)\\n     56) slope<=0.254647 11 1.8182 0 (0.73333 0.13333 0.066667 0.066667)\\n      112) elev<=0.378000 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n       224) slope<=0.115271 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n       225) slope>0.115271 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n      113) elev>0.378000 9 0.0000 0 (0.76923 0.076923 0.076923 0.076923) *\\n     57) slope>0.254647 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n    29) B1>0.198600 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n   15) bsi>0.187103 30 3.7333 1 (0.088235 0.85294 0.029412 0.029412)\\n    30) B8A<=0.269800 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    31) B8A>0.269800 29 1.9310 1 (0.060606 0.87879 0.030303 0.030303)\\n     62) B9<=0.344175 26 0.0000 1 (0.033333 0.90000 0.033333 0.033333) *\\n     63) B9>0.344175 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n      126) B2<=0.0940500 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      127) B2>0.0940500 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B2<=0.0683000 69 34.319 3 (0.013699 0.013699 0.45205 0.52055)\\n  4) B8<=0.0647250 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n  5) B8>0.0647250 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n   10) B3<=0.0525000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) B3>0.0525000 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n 3) B2>0.0683000 64 32.969 1 (0.47059 0.48529 0.029412 0.014706)\\n  6) bsi<=0.174408 30 3.8000 0 (0.85294 0.058824 0.058824 0.029412)\\n   12) ndvi<=-0.208819 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   13) ndvi>-0.208819 29 1.9310 0 (0.87879 0.060606 0.030303 0.030303)\\n    26) B12<=0.366125 24 0.0000 0 (0.89286 0.035714 0.035714 0.035714) *\\n    27) B12>0.366125 5 1.6000 0 (0.55556 0.22222 0.11111 0.11111)\\n     54) B9<=0.318250 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n      108) B9<=0.302850 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      109) B9>0.302850 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     55) B9>0.318250 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n  7) bsi>0.174408 34 5.4706 1 (0.10526 0.84211 0.026316 0.026316)\\n   14) B2<=0.164200 32 1.9375 1 (0.055556 0.88889 0.027778 0.027778)\\n    28) B12<=0.420625 28 0.0000 1 (0.031250 0.90625 0.031250 0.031250) *\\n    29) B12>0.420625 4 1.5000 1 (0.25000 0.50000 0.12500 0.12500)\\n     58) B12<=0.422625 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     59) B12>0.422625 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n   15) B2>0.164200 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B11<=0.0591000 33 1.9394 2 (0.027027 0.027027 0.89189 0.054054)\\n  4) B3<=0.101450 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n  5) B3>0.101450 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n 3) B11>0.0591000 100 67.180 3 (0.30769 0.31731 0.019231 0.35577)\\n  6) B1<=0.0564500 39 7.3333 3 (0.023256 0.093023 0.046512 0.83721)\\n   12) bsi<=0.158360 36 1.9444 3 (0.025000 0.025000 0.050000 0.90000)\\n    24) B2<=0.0297500 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n     48) ndbi<=-0.292095 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n     49) ndbi>-0.292095 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    25) B2>0.0297500 34 0.0000 3 (0.026316 0.026316 0.026316 0.92105) *\\n   13) bsi>0.158360 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n  7) B1>0.0564500 61 31.443 0 (0.49231 0.46154 0.015385 0.030769)\\n   14) B11<=0.366050 27 3.7778 0 (0.83871 0.064516 0.032258 0.064516)\\n    28) mndwi<=-0.452835 2 1.0000 1 (0.16667 0.33333 0.16667 0.33333)\\n     56) slope<=0.406190 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n     57) slope>0.406190 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    29) mndwi>-0.452835 25 0.0000 0 (0.89655 0.034483 0.034483 0.034483) *\\n   15) B11>0.366050 34 9.8824 1 (0.18421 0.76316 0.026316 0.026316)\\n    30) mndwi<=-0.243903 30 3.7333 1 (0.088235 0.85294 0.029412 0.029412)\\n     60) B2<=0.210400 27 0.0000 1 (0.032258 0.90323 0.032258 0.032258) *\\n     61) B2>0.210400 3 1.3333 0 (0.42857 0.28571 0.14286 0.14286)\\n      122) elev<=0.407500 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n      123) elev>0.407500 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n    31) mndwi>-0.243903 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) ndvi<=0.370538 94 62.638 1 (0.31633 0.33673 0.33673 0.010204)\\n  4) B7<=0.111650 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n  5) B7>0.111650 62 30.968 1 (0.46970 0.50000 0.015152 0.015152)\\n   10) bsi<=0.186746 32 7.0000 0 (0.80556 0.13889 0.027778 0.027778)\\n    20) B11<=0.394575 23 0.0000 0 (0.88889 0.037037 0.037037 0.037037) *\\n    21) B11>0.394575 9 4.4444 0 (0.46154 0.38462 0.076923 0.076923)\\n     42) elev<=0.413500 5 1.6000 1 (0.22222 0.55556 0.11111 0.11111)\\n      84) B7<=0.392625 4 0.0000 1 (0.12500 0.62500 0.12500 0.12500) *\\n      85) B7>0.392625 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     43) elev>0.413500 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n   11) bsi>0.186746 30 3.7333 1 (0.088235 0.85294 0.029412 0.029412)\\n    22) B11<=0.339650 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    23) B11>0.339650 29 1.9310 1 (0.060606 0.87879 0.030303 0.030303)\\n     46) B9<=0.341775 28 0.0000 1 (0.031250 0.90625 0.031250 0.031250) *\\n     47) B9>0.341775 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n 3) ndvi>0.370538 39 3.8462 3 (0.046512 0.023256 0.046512 0.88372)\\n  6) B12<=0.296625 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n   12) B4<=0.0371250 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   13) B4>0.0371250 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n  7) B12>0.296625 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B5<=0.147425 71 36.366 3 (0.026667 0.013333 0.45333 0.50667)\\n  4) B8<=0.0647250 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n  5) B8>0.0647250 40 5.6500 3 (0.045455 0.022727 0.068182 0.86364)\\n   10) B1<=0.0726500 39 3.7949 3 (0.023256 0.023256 0.069767 0.88372)\\n    20) B4<=0.0369000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    21) B4>0.0369000 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n     42) B7<=0.0729000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n     43) B7>0.0729000 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n   11) B1>0.0726500 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n 3) B5>0.147425 62 30.968 1 (0.46970 0.50000 0.015152 0.015152)\\n  6) B11<=0.375300 26 3.6923 0 (0.83333 0.10000 0.033333 0.033333)\\n   12) bsi<=0.194861 25 1.9200 0 (0.86207 0.068966 0.034483 0.034483)\\n    24) B6<=0.242400 19 0.0000 0 (0.86957 0.043478 0.043478 0.043478) *\\n    25) B6>0.242400 6 1.6667 0 (0.60000 0.20000 0.10000 0.10000)\\n     50) B8<=0.264500 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     51) B8>0.264500 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n   13) bsi>0.194861 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n  7) B11>0.375300 36 10.000 1 (0.17500 0.77500 0.025000 0.025000)\\n   14) bsi<=0.194802 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n   15) bsi>0.194802 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n    30) B8<=0.262000 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    31) B8>0.262000 30 0.0000 1 (0.029412 0.91176 0.029412 0.029412) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B6<=0.110125 34 1.9412 2 (0.026316 0.026316 0.89474 0.052632)\\n  4) B3<=0.101450 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n  5) B3>0.101450 3 1.3333 2 (0.14286 0.14286 0.42857 0.28571)\\n   10) B9<=0.0185250 2 0.0000 2 (0.16667 0.16667 0.50000 0.16667) *\\n   11) B9>0.0185250 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n 3) B6>0.110125 99 65.859 3 (0.31068 0.32039 0.0097087 0.35922)\\n  6) B4<=0.101125 36 0.0000 3 (0.025000 0.025000 0.025000 0.92500) *\\n  7) B4>0.101125 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   14) mndwi<=-0.346568 35 7.0857 1 (0.12821 0.82051 0.025641 0.025641)\\n    28) bsi<=0.150715 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n    29) bsi>0.150715 33 3.7576 1 (0.081081 0.86486 0.027027 0.027027)\\n     58) B6<=0.176225 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     59) B6>0.176225 32 1.9375 1 (0.055556 0.88889 0.027778 0.027778)\\n      118) B11<=0.504075 29 0.0000 1 (0.030303 0.90909 0.030303 0.030303) *\\n      119) B11>0.504075 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n       238) B8<=0.338075 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n       239) B8>0.338075 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n   15) mndwi>-0.346568 28 1.9286 0 (0.87500 0.062500 0.031250 0.031250)\\n    30) B5<=0.315400 23 0.0000 0 (0.88889 0.037037 0.037037 0.037037) *\\n    31) B5>0.315400 5 1.6000 0 (0.55556 0.22222 0.11111 0.11111)\\n     62) bsi<=0.110110 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     63) bsi>0.110110 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) ndvi<=0.439491 96 64.646 1 (0.32000 0.33000 0.33000 0.020000)\\n  4) mndwi<=-0.0912117 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   8) mndwi<=-0.440430 28 3.7143 1 (0.093750 0.84375 0.031250 0.031250)\\n    16) B1<=0.123600 27 1.9259 1 (0.064516 0.87097 0.032258 0.032258)\\n     32) mndwi<=-0.585293 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n      64) B12<=0.390875 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n      65) B12>0.390875 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     33) mndwi>-0.585293 25 0.0000 1 (0.034483 0.89655 0.034483 0.034483) *\\n    17) B1>0.123600 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n   9) mndwi>-0.440430 35 9.9429 0 (0.76923 0.17949 0.025641 0.025641)\\n    18) B11<=0.382750 24 1.9167 0 (0.85714 0.071429 0.035714 0.035714)\\n     36) slope<=0.385207 23 0.0000 0 (0.88889 0.037037 0.037037 0.037037) *\\n     37) slope>0.385207 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    19) B11>0.382750 11 5.4545 0 (0.46667 0.40000 0.066667 0.066667)\\n     38) B3<=0.289350 7 2.8571 1 (0.27273 0.54545 0.090909 0.090909)\\n      76) ndvi<=0.0456087 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n      77) ndvi>0.0456087 5 0.0000 1 (0.11111 0.66667 0.11111 0.11111) *\\n     39) B3>0.289350 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n  5) mndwi>-0.0912117 33 1.9394 2 (0.027027 0.027027 0.89189 0.054054)\\n   10) B3<=0.101450 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n   11) B3>0.101450 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n    22) bsi<=0.0102411 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    23) bsi>0.0102411 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n 3) ndvi>0.439491 37 1.9459 3 (0.024390 0.024390 0.048780 0.90244)\\n  6) B3<=0.0477500 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n  7) B3>0.0477500 36 0.0000 3 (0.025000 0.025000 0.025000 0.92500) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B6<=0.104175 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n 3) B6>0.104175 101 67.782 3 (0.30476 0.31429 0.019048 0.36190)\\n  6) B3<=0.0960750 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n   12) B3<=0.0525000 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n    24) B7<=0.308250 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    25) B7>0.308250 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   13) B3>0.0525000 36 0.0000 3 (0.025000 0.025000 0.025000 0.92500) *\\n  7) B3>0.0960750 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   14) bsi<=0.188532 31 3.7419 0 (0.85714 0.085714 0.028571 0.028571)\\n    28) B11<=0.441450 24 0.0000 0 (0.89286 0.035714 0.035714 0.035714) *\\n    29) B11>0.441450 7 2.8571 0 (0.54545 0.27273 0.090909 0.090909)\\n     58) B4<=0.348500 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n      116) B8A<=0.341400 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      117) B8A>0.341400 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n     59) B4>0.348500 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n   15) bsi>0.188532 32 3.7500 1 (0.083333 0.86111 0.027778 0.027778)\\n    30) ndvi<=0.0835861 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n    31) ndvi>0.0835861 30 0.0000 1 (0.029412 0.91176 0.029412 0.029412) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B12<=0.168100 71 36.366 3 (0.026667 0.013333 0.45333 0.50667)\\n  4) B8<=0.0923000 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n  5) B8>0.0923000 39 3.8462 3 (0.046512 0.023256 0.046512 0.88372)\\n   10) B3<=0.0486250 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) B3>0.0486250 38 1.9474 3 (0.047619 0.023810 0.023810 0.90476)\\n    22) B4<=0.0952000 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n    23) B4>0.0952000 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n 3) B12>0.168100 62 30.968 1 (0.46970 0.50000 0.015152 0.015152)\\n  6) bsi<=0.177460 26 3.6923 0 (0.83333 0.10000 0.033333 0.033333)\\n   12) B1<=0.197925 25 1.9200 0 (0.86207 0.068966 0.034483 0.034483)\\n    24) slope<=0.407056 24 0.0000 0 (0.89286 0.035714 0.035714 0.035714) *\\n    25) slope>0.407056 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n   13) B1>0.197925 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n  7) bsi>0.177460 36 10.000 1 (0.17500 0.77500 0.025000 0.025000)\\n   14) B2<=0.165700 33 5.4545 1 (0.10811 0.83784 0.027027 0.027027)\\n    28) B8A<=0.269800 3 1.3333 0 (0.42857 0.28571 0.14286 0.14286)\\n     56) B3<=0.118250 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     57) B3>0.118250 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n    29) B8A>0.269800 30 1.9333 1 (0.058824 0.88235 0.029412 0.029412)\\n     58) B3<=0.129650 6 1.6667 1 (0.20000 0.60000 0.10000 0.10000)\\n      116) B3<=0.127200 5 0.0000 1 (0.11111 0.66667 0.11111 0.11111) *\\n      117) B3>0.127200 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     59) B3>0.129650 24 0.0000 1 (0.035714 0.89286 0.035714 0.035714) *\\n   15) B2>0.165700 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) ndvi<=0.371359 97 65.299 2 (0.31683 0.32673 0.33663 0.019802)\\n  4) B6<=0.103675 33 0.0000 2 (0.027027 0.027027 0.91892 0.027027) *\\n  5) B6>0.103675 64 32.969 1 (0.47059 0.48529 0.014706 0.029412)\\n   10) B8<=0.273200 19 3.6842 0 (0.78261 0.086957 0.043478 0.086957)\\n    20) B9<=0.165700 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    21) B9>0.165700 18 1.8889 0 (0.81818 0.090909 0.045455 0.045455)\\n     42) B2<=0.0905500 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     43) B2>0.0905500 17 0.0000 0 (0.85714 0.047619 0.047619 0.047619) *\\n   11) B8>0.273200 45 19.289 1 (0.30612 0.65306 0.020408 0.020408)\\n    22) B1<=0.102450 29 1.9310 1 (0.060606 0.87879 0.030303 0.030303)\\n     44) B11<=0.505100 25 0.0000 1 (0.034483 0.89655 0.034483 0.034483) *\\n     45) B11>0.505100 4 1.5000 1 (0.25000 0.50000 0.12500 0.12500)\\n      90) B11<=0.506275 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      91) B11>0.506275 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n    23) B1>0.102450 16 4.8750 0 (0.70000 0.20000 0.050000 0.050000)\\n     46) B2<=0.190100 4 2.0000 0 (0.37500 0.37500 0.12500 0.12500)\\n      92) B4<=0.234800 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n      93) B4>0.234800 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n     47) B2>0.190100 12 1.8333 0 (0.75000 0.12500 0.062500 0.062500)\\n      94) B1<=0.198600 10 0.0000 0 (0.78571 0.071429 0.071429 0.071429) *\\n      95) B1>0.198600 2 1.0000 0 (0.33333 0.33333 0.16667 0.16667)\\n       190) bsi<=0.101258 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n       191) bsi>0.101258 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n 3) ndvi>0.371359 36 0.0000 3 (0.025000 0.025000 0.025000 0.92500) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) ndvi<=0.386006 98 66.592 2 (0.31373 0.32353 0.33333 0.029412)\\n  4) B8A<=0.120525 34 1.9412 2 (0.026316 0.026316 0.89474 0.052632)\\n   8) mndwi<=0.635511 33 0.0000 2 (0.027027 0.027027 0.91892 0.027027) *\\n   9) mndwi>0.635511 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) B8A>0.120525 64 32.969 1 (0.47059 0.48529 0.014706 0.029412)\\n   10) bsi<=0.187103 31 5.5484 0 (0.82857 0.085714 0.028571 0.057143)\\n    20) B8<=0.119300 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    21) B8>0.119300 30 3.7333 0 (0.85294 0.088235 0.029412 0.029412)\\n     42) slope<=0.385747 29 1.9310 0 (0.87879 0.060606 0.030303 0.030303)\\n      84) B11<=0.467900 25 0.0000 0 (0.89655 0.034483 0.034483 0.034483) *\\n      85) B11>0.467900 4 1.5000 0 (0.50000 0.25000 0.12500 0.12500)\\n       170) B8A<=0.375475 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n       171) B8A>0.375475 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n     43) slope>0.385747 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n   11) bsi>0.187103 33 5.4545 1 (0.10811 0.83784 0.027027 0.027027)\\n    22) B2<=0.165700 32 3.7500 1 (0.083333 0.86111 0.027778 0.027778)\\n     44) mndwi<=-0.366170 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n      88) bsi<=0.296587 30 0.0000 1 (0.029412 0.91176 0.029412 0.029412) *\\n      89) bsi>0.296587 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     45) mndwi>-0.366170 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    23) B2>0.165700 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n 3) ndvi>0.386006 35 0.0000 3 (0.025641 0.025641 0.025641 0.92308) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B2<=0.0676500 69 34.319 3 (0.013699 0.013699 0.45205 0.52055)\\n  4) B11<=0.102900 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n  5) B11>0.102900 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n   10) mndwi<=-0.574839 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) mndwi>-0.574839 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n 3) B2>0.0676500 64 32.969 1 (0.47059 0.48529 0.029412 0.014706)\\n  6) B8A<=0.273625 22 1.9091 0 (0.84615 0.038462 0.076923 0.038462)\\n   12) B7<=0.0976500 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   13) B7>0.0976500 21 0.0000 0 (0.88000 0.040000 0.040000 0.040000) *\\n  7) B8A>0.273625 42 15.238 1 (0.23913 0.71739 0.021739 0.021739)\\n   14) B1<=0.107475 33 3.7576 1 (0.081081 0.86486 0.027027 0.027027)\\n    28) mndwi<=-0.583789 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    29) mndwi>-0.583789 32 1.9375 1 (0.055556 0.88889 0.027778 0.027778)\\n     58) B11<=0.358000 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     59) B11>0.358000 31 0.0000 1 (0.028571 0.91429 0.028571 0.028571) *\\n   15) B1>0.107475 9 1.7778 0 (0.69231 0.15385 0.076923 0.076923)\\n    30) mndwi<=-0.346568 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    31) mndwi>-0.346568 8 0.0000 0 (0.75000 0.083333 0.083333 0.083333) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) ndvi<=0.467782 96 64.646 1 (0.32000 0.33000 0.33000 0.020000)\\n  4) B6<=0.120900 33 1.9394 2 (0.027027 0.027027 0.89189 0.054054)\\n   8) B3<=0.101450 31 0.0000 2 (0.028571 0.028571 0.91429 0.028571) *\\n   9) B3>0.101450 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n    18) B8<=0.0568250 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    19) B8>0.0568250 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) B6>0.120900 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   10) B8<=0.270500 25 5.2800 0 (0.79310 0.13793 0.034483 0.034483)\\n    20) B11<=0.337500 22 0.0000 0 (0.88462 0.038462 0.038462 0.038462) *\\n    21) B11>0.337500 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n   11) B8>0.270500 38 13.737 1 (0.23810 0.71429 0.023810 0.023810)\\n    22) ndvi<=0.0410155 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n    23) ndvi>0.0410155 34 8.5294 1 (0.15789 0.78947 0.026316 0.026316)\\n     46) B11<=0.376100 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n     47) B11>0.376100 30 1.9333 1 (0.058824 0.88235 0.029412 0.029412)\\n      94) B11<=0.501850 26 0.0000 1 (0.033333 0.90000 0.033333 0.033333) *\\n      95) B11>0.501850 4 1.5000 1 (0.25000 0.50000 0.12500 0.12500)\\n       190) B6<=0.321175 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n       191) B6>0.321175 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n 3) ndvi>0.467782 37 1.9459 3 (0.024390 0.024390 0.048780 0.90244)\\n  6) B2<=0.0297500 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n   12) B8<=0.287600 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n   13) B8>0.287600 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n  7) B2>0.0297500 35 0.0000 3 (0.025641 0.025641 0.025641 0.92308) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) ndvi<=0.389395 96 65.271 1 (0.32000 0.33000 0.32000 0.030000)\\n  4) B11<=0.0768750 32 1.9375 2 (0.027778 0.027778 0.88889 0.055556)\\n   8) B6<=0.0726000 30 0.0000 2 (0.029412 0.029412 0.91176 0.029412) *\\n   9) B6>0.0726000 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n    18) elev<=0.390250 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    19) elev>0.390250 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n  5) B11>0.0768750 64 32.969 1 (0.47059 0.48529 0.014706 0.029412)\\n   10) ndbi<=0.136031 25 5.4400 0 (0.79310 0.10345 0.034483 0.068966)\\n    20) B11<=0.452100 22 1.9091 0 (0.84615 0.038462 0.038462 0.076923)\\n     40) B12<=0.120050 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n     41) B12>0.120050 21 0.0000 0 (0.88000 0.040000 0.040000 0.040000) *\\n    21) B11>0.452100 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n     42) B5<=0.371175 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n     43) B5>0.371175 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n   11) ndbi>0.136031 39 13.846 1 (0.23256 0.72093 0.023256 0.023256)\\n    22) ndvi<=0.0904932 9 3.1111 0 (0.61538 0.23077 0.076923 0.076923)\\n     44) slope<=0.155663 7 0.0000 0 (0.72727 0.090909 0.090909 0.090909) *\\n     45) slope>0.155663 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n    23) ndvi>0.0904932 30 3.7333 1 (0.088235 0.85294 0.029412 0.029412)\\n     46) bsi<=0.292645 29 1.9310 1 (0.060606 0.87879 0.030303 0.030303)\\n      92) B9<=0.259100 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      93) B9>0.259100 28 0.0000 1 (0.031250 0.90625 0.031250 0.031250) *\\n     47) bsi>0.292645 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n 3) ndvi>0.389395 37 3.7838 3 (0.024390 0.024390 0.073171 0.87805)\\n  6) B8A<=0.0875000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n  7) B8A>0.0875000 36 1.9444 3 (0.025000 0.025000 0.050000 0.90000)\\n   14) mndwi<=-0.574839 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   15) mndwi>-0.574839 35 0.0000 3 (0.025641 0.025641 0.025641 0.92308) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B4<=0.111100 70 34.886 3 (0.013514 0.013514 0.45946 0.51351)\\n  4) B8A<=0.120525 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n  5) B8A>0.120525 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n   10) B5<=0.0947500 2 1.0000 2 (0.16667 0.16667 0.33333 0.33333)\\n    20) B12<=0.0972000 1 0.0000 3 (0.20000 0.20000 0.20000 0.40000) *\\n    21) B12>0.0972000 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   11) B5>0.0947500 36 0.0000 3 (0.025000 0.025000 0.025000 0.92500) *\\n 3) B4>0.111100 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n  6) B6<=0.241450 23 1.9130 0 (0.85185 0.074074 0.037037 0.037037)\\n   12) mndwi<=-0.502043 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n   13) mndwi>-0.502043 22 0.0000 0 (0.88462 0.038462 0.038462 0.038462) *\\n  7) B6>0.241450 40 13.950 1 (0.22727 0.72727 0.022727 0.022727)\\n   14) B11<=0.393150 6 1.6667 0 (0.60000 0.20000 0.10000 0.10000)\\n    28) B9<=0.268300 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    29) B9>0.268300 5 0.0000 0 (0.66667 0.11111 0.11111 0.11111) *\\n   15) B11>0.393150 34 7.0588 1 (0.13158 0.81579 0.026316 0.026316)\\n    30) B12<=0.485450 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n     60) bsi<=0.106457 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     61) bsi>0.106457 30 0.0000 1 (0.029412 0.91176 0.029412 0.029412) *\\n    31) B12>0.485450 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B5<=0.153025 74 40.568 3 (0.064103 0.012821 0.43590 0.48718)\\n  4) slope<=0.0317381 20 8.1000 2 (0.12500 0.041667 0.66667 0.16667)\\n   8) B9<=0.133250 13 0.0000 2 (0.058824 0.058824 0.82353 0.058824) *\\n   9) B9>0.133250 7 4.5714 3 (0.27273 0.090909 0.27273 0.36364)\\n    18) B5<=0.0984000 2 0.0000 2 (0.16667 0.16667 0.50000 0.16667) *\\n    19) B5>0.0984000 5 2.4000 3 (0.33333 0.11111 0.11111 0.44444)\\n     38) bsi<=0.0231273 3 0.0000 3 (0.14286 0.14286 0.14286 0.57143) *\\n     39) bsi>0.0231273 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n  5) slope>0.0317381 54 26.519 3 (0.051724 0.017241 0.32759 0.60345)\\n   10) B8A<=0.0884500 16 0.0000 2 (0.050000 0.050000 0.85000 0.050000) *\\n   11) B8A>0.0884500 38 7.3684 3 (0.071429 0.023810 0.071429 0.83333)\\n    22) B2<=0.0802000 36 3.7778 3 (0.025000 0.025000 0.075000 0.87500)\\n     44) B1<=0.0749250 35 1.9429 3 (0.025641 0.025641 0.051282 0.89744)\\n      88) B3<=0.0516250 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n      89) B3>0.0516250 34 0.0000 3 (0.026316 0.026316 0.026316 0.92105) *\\n     45) B1>0.0749250 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n    23) B2>0.0802000 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n 3) B5>0.153025 59 29.288 1 (0.44444 0.52381 0.015873 0.015873)\\n  6) ndbi<=0.147859 29 6.8966 0 (0.78788 0.15152 0.030303 0.030303)\\n   12) slope<=0.298050 27 3.7037 0 (0.83871 0.096774 0.032258 0.032258)\\n    24) elev<=0.380250 5 2.4000 0 (0.44444 0.33333 0.11111 0.11111)\\n     48) B4<=0.230000 3 0.0000 0 (0.57143 0.14286 0.14286 0.14286) *\\n     49) B4>0.230000 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n    25) elev>0.380250 22 0.0000 0 (0.88462 0.038462 0.038462 0.038462) *\\n   13) slope>0.298050 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n  7) ndbi>0.147859 30 3.7333 1 (0.088235 0.85294 0.029412 0.029412)\\n   14) mndwi<=-0.250655 29 1.9310 1 (0.060606 0.87879 0.030303 0.030303)\\n    28) B11<=0.505100 23 0.0000 1 (0.037037 0.88889 0.037037 0.037037) *\\n    29) B11>0.505100 6 1.6667 1 (0.20000 0.60000 0.10000 0.10000)\\n     58) bsi<=0.291824 5 0.0000 1 (0.11111 0.66667 0.11111 0.11111) *\\n     59) bsi>0.291824 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n   15) mndwi>-0.250655 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B7<=0.107950 33 0.0000 2 (0.027027 0.027027 0.91892 0.027027) *\\n 3) B7>0.107950 100 66.460 3 (0.30769 0.31731 0.0096154 0.36538)\\n  6) B2<=0.0686000 37 0.0000 3 (0.024390 0.024390 0.024390 0.92683) *\\n  7) B2>0.0686000 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   14) mndwi<=-0.379290 31 1.9355 1 (0.057143 0.88571 0.028571 0.028571)\\n    28) ndvi<=0.0753495 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n    29) ndvi>0.0753495 30 0.0000 1 (0.029412 0.91176 0.029412 0.029412) *\\n   15) mndwi>-0.379290 32 3.7500 0 (0.86111 0.083333 0.027778 0.027778)\\n    30) B9<=0.311250 25 0.0000 0 (0.89655 0.034483 0.034483 0.034483) *\\n    31) B9>0.311250 7 2.8571 0 (0.54545 0.27273 0.090909 0.090909)\\n     62) mndwi<=-0.231013 3 1.3333 1 (0.28571 0.42857 0.14286 0.14286)\\n      124) bsi<=0.191084 2 0.0000 1 (0.16667 0.50000 0.16667 0.16667) *\\n      125) bsi>0.191084 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n     63) mndwi>-0.231013 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n', 'n= 133\\nnode), split, n, loss, yval, (yprob)\\n* denotes terminal node\\n1) root 133 99.594 3 (0.23358 0.24088 0.24818 0.27737)\\n 2) B12<=0.0600250 32 0.0000 2 (0.027778 0.027778 0.91667 0.027778) *\\n 3) B12>0.0600250 101 67.782 3 (0.30476 0.31429 0.019048 0.36190)\\n  6) B4<=0.0970000 38 1.9474 3 (0.023810 0.023810 0.047619 0.90476)\\n   12) B8A<=0.253225 5 1.6000 3 (0.11111 0.11111 0.22222 0.55556)\\n    24) ndvi<=0.666298 4 0.0000 3 (0.12500 0.12500 0.12500 0.62500) *\\n    25) ndvi>0.666298 1 0.0000 2 (0.20000 0.20000 0.40000 0.20000) *\\n   13) B8A>0.253225 33 0.0000 3 (0.027027 0.027027 0.027027 0.91892) *\\n  7) B4>0.0970000 63 31.492 1 (0.47761 0.49254 0.014925 0.014925)\\n   14) bsi<=0.188532 31 5.4194 0 (0.82857 0.11429 0.028571 0.028571)\\n    28) bsi<=0.152340 21 0.0000 0 (0.88000 0.040000 0.040000 0.040000) *\\n    29) bsi>0.152340 10 4.2000 0 (0.57143 0.28571 0.071429 0.071429)\\n     58) B8A<=0.268500 4 0.0000 0 (0.62500 0.12500 0.12500 0.12500) *\\n     59) B8A>0.268500 6 3.0000 0 (0.40000 0.40000 0.10000 0.10000)\\n      118) B3<=0.246700 4 1.5000 1 (0.25000 0.50000 0.12500 0.12500)\\n       236) elev<=0.427250 3 0.0000 1 (0.14286 0.57143 0.14286 0.14286) *\\n       237) elev>0.427250 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n      119) B3>0.246700 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n   15) bsi>0.188532 32 5.4375 1 (0.11111 0.83333 0.027778 0.027778)\\n    30) ndvi<=0.0753495 3 1.3333 0 (0.42857 0.28571 0.14286 0.14286)\\n     60) B9<=0.321500 2 0.0000 0 (0.50000 0.16667 0.16667 0.16667) *\\n     61) B9>0.321500 1 0.0000 1 (0.20000 0.40000 0.20000 0.20000) *\\n    31) ndvi>0.0753495 29 1.9310 1 (0.060606 0.87879 0.030303 0.030303)\\n     62) bsi<=0.292645 28 0.0000 1 (0.031250 0.90625 0.031250 0.031250) *\\n     63) bsi>0.292645 1 0.0000 0 (0.40000 0.20000 0.20000 0.20000) *\\n']}\n"
     ]
    }
   ],
   "source": [
    "# Run .explain() to see what the classifer looks like\n",
    "print(classifier.explain().getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bdf285c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = composite.sampleRegions(**{\n",
    "  \"collection\": validationGcp,\n",
    "  \"properties\": ['landcover'],\n",
    "  \"scale\": 10,\n",
    "  \"tileScale\": 16\n",
    "})\n",
    "\n",
    "# Tune the numberOfTrees parameter.\n",
    "numTreesList = ee.List.sequence(10, 150, 10)\n",
    "\n",
    "def calc_acc(trees):\n",
    "    classifier = ee.Classifier.smileRandomForest(trees)\\\n",
    "        .train(**{\n",
    "            \"features\": training,\n",
    "            \"classProperty\": \"landcover\",\n",
    "            \"inputProperties\": composite.bandNames()\n",
    "    })\n",
    "    \n",
    "    # Here we are classifying a table instead of an image\n",
    "    # ... Classifiers work on both images and tables\n",
    "    return test.classify(classifier).errorMatrix('landcover', 'classification').accuracy()\n",
    "\n",
    "accuracies = numTreesList.map(calc_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ff412000",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = numTreesList.getInfo()\n",
    "y = accuracies.getInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0225249b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y)\n",
    "ax.set_xlabel(\"Number of trees\")\n",
    "ax.set_ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c24716a",
   "metadata": {},
   "source": [
    "## 05. Exporting Classification Results\n",
    "\n",
    "When working with complex classifiers over large regions, you may get a User memory limit exceeded or Computation timed out error in the Code Editor. The reason for this is that there is a fixed time limit and smaller memory allocated for code that is run with the On-Demand Computation mode. \n",
    "\n",
    "For larger computations, you can use **the Batch mode** with the Export functions. Exports run in the background and can run longer than **5-minutes** time allocated to the computation code run from the Code Editor. This allows you to process very large and complex datasets. Here’s an example showing how to export your classification results to Google Drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2582c4c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9550561797752809\n",
      "Generating URL ...\n",
      "Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/thumbnails/94c96ceaf028a15027f25f3fe20dc15e-480846a25813094e21e9d7c5f5d699db:getPixels\n",
      "Please wait ...\n",
      "Data downloaded to /Users/akramzaytar/projects/Learn/GEE/ezmap/notebooks/static/imgs/img.tif\n"
     ]
    }
   ],
   "source": [
    "print(testConfusionMatrix.accuracy().getInfo())\n",
    "\n",
    "# Export the image\n",
    "geemap.ee_export_image(composite.clip(bangalore.geometry()), \n",
    "                       filename=\"static/imgs/img.tif\", \n",
    "                       scale=100, file_per_band=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b5afc58",
   "metadata": {},
   "source": [
    "## 06. Calculating Area\n",
    "\n",
    "Now that we have the results of our classification, we will learn how to calculate the area for pixels in each class. Calculating area for features is done using the `area()` function and for images using the `ee.Image.pixelArea()` function. The `ee.Image.pixelArea()` function creates an image where each pixel’s value is the area of the pixel. \n",
    "\n",
    "We multiply this pixel area image with our image and sum up the area using the `reduceRegion()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11fd5e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "classified = ee.Image(\"users/ujavalgandhi/e2e/bangalore_classified\")\n",
    "bangalore = ee.FeatureCollection(\"users/ujavalgandhi/public/bangalore_boundary\")\n",
    "admin2 = ee.FeatureCollection(\"FAO/GAUL_SIMPLIFIED_500m/2015/level2\")\n",
    "\n",
    "m.addLayer(bangalore, {\"color\": \"blue\"}, \"Bangalore City\")\n",
    "m.addLayer(classified,\n",
    "  {\"min\": 0, \"max\": 3, \"palette\": [\"gray\", \"brown\", \"blue\", \"green\"]},\n",
    "  \"Classified Image 2019\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "441c6d3e",
   "metadata": {},
   "source": [
    "Calling `.geometry()` on a feature collection gives the dissolved geometry of all features in the collection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6a575c08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "713\n"
     ]
    }
   ],
   "source": [
    "# .area() function calculates the area in square meters\n",
    "cityArea = bangalore.geometry().area()\n",
    "\n",
    "# We can cast the result to a `ee.Number()` and calculate the\n",
    "# area in square kilometers\n",
    "cityAreaSqKm = ee.Number(cityArea).divide(1e6).round()\n",
    "print(cityAreaSqKm.getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "69914d10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122\n"
     ]
    }
   ],
   "source": [
    "# Area Calculation for Images\n",
    "vegetation = classified.eq(3)\n",
    "# If the image contains values 0 or 1, we can calculate the\n",
    "# total area using reduceRegion() function\n",
    "\n",
    "# The result of .eq() operation is a binary image with pixels\n",
    "# values of 1 where the condition matched and 0 where it didn't\n",
    "m.addLayer(vegetation, {\"min\": 0, \"max\": 1, palette: ['white', 'green']}, 'Green Cover')\n",
    "\n",
    "# Since our image has only 0 and 1 pixel values, the vegetation\n",
    "# pixels will have values equal to their area\n",
    "areaImage = vegetation.multiply(ee.Image.pixelArea())\n",
    "\n",
    "# Now that each pixel for vegetation class in the image has the value\n",
    "# equal to its area, we can sum up all the values in the region\n",
    "# to get the total green cover.\n",
    "area = areaImage.reduceRegion(**{\n",
    "    \"reducer\": ee.Reducer.sum(),\n",
    "    \"geometry\": bangalore.geometry(),\n",
    "    \"scale\": 10,\n",
    "    \"maxPixels\": 1e10\n",
    "})\n",
    "\n",
    "# The result of the reduceRegion() function is a dictionary with the key\n",
    "# being the band name. We can extract the area number and convert it to\n",
    "# square kilometers\n",
    "vegetationAreaSqKm = ee.Number(area.get('classification')).divide(1e6).round()\n",
    "print(vegetationAreaSqKm.getInfo())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c699de",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Compute and print the percentage green cover of the city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "39bdc705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17%\n"
     ]
    }
   ],
   "source": [
    "vegetation_percent = ee.Number(area.get('classification')).divide(1e6).divide(cityAreaSqKm).multiply(100).round()\n",
    "print(f\"{vegetation_percent.getInfo()}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af89658",
   "metadata": {},
   "source": [
    "## 07. Direct Classification of Change\n",
    "\n",
    "This technique of change detection is also known as **One-pass Classification** or **Direct Multi-date Classification**. \n",
    "\n",
    "Here we: \n",
    "1. Create a single stacked image containing bands from before and after images. \n",
    "2. We train a classifier with training data sampled from the stacked image.\n",
    "3. Apply the classifier on the stacked image to find all change pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "691650b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "bangalore = ee.FeatureCollection('users/ujavalgandhi/public/bangalore_boundary')\n",
    "change = ee.FeatureCollection('users/ujavalgandhi/e2e/bangalore_change_gcps')\n",
    "nochange = ee.FeatureCollection('users/ujavalgandhi/e2e/bangalore_nochange_gcps')\n",
    "s2 = ee.ImageCollection(\"COPERNICUS/S2\")\n",
    "\n",
    "rgbVis = {\n",
    "  \"min\": 0.0,\n",
    "  \"max\": 0.3,\n",
    "  \"bands\": ['B4', 'B3', 'B2'],\n",
    "}\n",
    "\n",
    "# Write a function for Cloud masking\n",
    "def maskS2clouds(img):\n",
    "    qa = img.select('QA60')\n",
    "    cloudBitMask = 1 << 10\n",
    "    cirrusBitMask = 1 << 11\n",
    "    mask = qa.bitwiseAnd(cloudBitMask).eq(0).And(qa.bitwiseAnd(cirrusBitMask).eq(0))\n",
    "    return img.updateMask(mask).divide(10000)\\\n",
    "        .select(\"B.*\")\\\n",
    "        .copyProperties(img, [\"system:time_start\"])\n",
    "\n",
    "filtered = s2\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-02-01'))\\\n",
    "    .filter(ee.Filter.geometry(bangalore))\\\n",
    "    .map(maskS2clouds)\n",
    "  \n",
    "image2019 = filtered.median().clip(bangalore)\n",
    "\n",
    "# Display the input composite.\n",
    "m.addLayer(image2019, rgbVis, \"2019\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "34159915",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.addLayer(change, {}, \"change\")\n",
    "m.addLayer(nochange, {}, \"nochange\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "92c1d5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered = s2\\\n",
    "    .filter(ee.Filter.date('2020-01-01', '2020-02-01'))\\\n",
    "    .filter(ee.Filter.geometry(bangalore))\\\n",
    "    .map(maskS2clouds)\n",
    "\n",
    "image2020 = filtered.median().clip(bangalore)\n",
    "\n",
    "m.addLayer(image2020, rgbVis, \"2020\")\n",
    "\n",
    "stackedImage = image2019.addBands(image2020)\n",
    "\n",
    "# Overlay the point on the image to get training data\n",
    "training = stackedImage.sampleRegions(**{\n",
    "  \"collection\": change.merge(nochange), \n",
    "  \"properties\": ['class'], \n",
    "  \"scale\": 10\n",
    "})\n",
    "\n",
    "# Train a classifier.\n",
    "classifier = ee.Classifier.smileRandomForest(50).train(**{\n",
    "  \"features\": training,  \n",
    "  \"classProperty\": 'class', \n",
    "  \"inputProperties\": stackedImage.bandNames()\n",
    "})\n",
    "\n",
    "# Classify the image.\n",
    "classified = stackedImage.classify(classifier)\n",
    "m.addLayer(classified, {\"min\": 0, \"max\": 1, \"palette\": ['white', 'red']}, 'change') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f6c897",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Add an NDBI band to improve the detection of changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "78d86cc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_NDBI(img):\n",
    "    ndbi = img.normalizedDifference([\"B11\", \"B8\"]).rename(\"ndbi\")\n",
    "    return img.addBands(ndbi)\n",
    "\n",
    "# use addNDBI() function to add the NDBI band to both 2019 and 2020 composite images\n",
    "imgs_2019 = s2\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-02-01'))\\\n",
    "    .filter(ee.Filter.geometry(bangalore))\\\n",
    "    .map(maskS2clouds)\\\n",
    "    .map(add_NDBI)\n",
    "img_2019 = imgs_2019.median().clip(bangalore)\n",
    "\n",
    "imgs_2020 = s2\\\n",
    "    .filter(ee.Filter.date('2020-01-01', '2020-02-01'))\\\n",
    "    .filter(ee.Filter.geometry(bangalore))\\\n",
    "    .map(maskS2clouds)\\\n",
    "    .map(add_NDBI)\n",
    "img_2020 = imgs_2020.median().clip(bangalore)\n",
    "\n",
    "stackedImage = img_2019.addBands(img_2020)\n",
    "\n",
    "training = stackedImage.sampleRegions(**{\n",
    "  \"collection\": change.merge(nochange), \n",
    "  \"properties\": ['class'], \n",
    "  \"scale\": 10\n",
    "})\n",
    "\n",
    "classifier = ee.Classifier.smileRandomForest(50).train(**{\n",
    "  \"features\": training,  \n",
    "  \"classProperty\": 'class', \n",
    "  \"inputProperties\": stackedImage.bandNames()\n",
    "})\n",
    "\n",
    "# Classify the image.\n",
    "classified = stackedImage.classify(classifier)\n",
    "m.addLayer(classified, {\"min\": 0, \"max\": 1, \"palette\": ['white', 'red']}, 'change')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed060428",
   "metadata": {},
   "source": [
    "## 08. Post-classification Comparison\n",
    "\n",
    "When dealing with multi-class images, a useful metric for change detection is to know how many pixels from class X changed to class Y. This can be accomplished using the `ee.Reducer.frequencyHistogram()` reducer as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "038336ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "bangalore = ee.FeatureCollection(\"users/ujavalgandhi/public/bangalore_boundary\")\n",
    "urban = ee.FeatureCollection(\"users/ujavalgandhi/e2e/urban_gcps\")\n",
    "bare = ee.FeatureCollection(\"users/ujavalgandhi/e2e/bare_gcps\")\n",
    "water = ee.FeatureCollection(\"users/ujavalgandhi/e2e/water_gcps\")\n",
    "vegetation = ee.FeatureCollection(\"users/ujavalgandhi/e2e/vegetation_gcps\")\n",
    "s2 = ee.ImageCollection(\"COPERNICUS/S2_SR\")\n",
    " \n",
    "m.centerObject(bangalore)\n",
    "rgbVis = {\n",
    "  \"min\": 0.0,\n",
    "  \"max\": 3000,\n",
    "  \"bands\": ['B4', 'B3', 'B2'] \n",
    "};\n",
    "\n",
    "filtered = s2\\\n",
    "    .filter(ee.Filter.date('2019-01-01', '2019-01-31'))\\\n",
    "    .filter(ee.Filter.geometry(bangalore))\\\n",
    "    .select('B.*')\n",
    "\n",
    "before = filtered.median().clip(bangalore)\n",
    "\n",
    "# Display the input composite.\n",
    "m.addLayer(before, rgbVis, \"before\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a91c4759",
   "metadata": {},
   "outputs": [],
   "source": [
    "training = urban.merge(bare).merge(water).merge(vegetation)\n",
    "\n",
    "# Overlay the point on the image to get training data.\n",
    "training = before.sampleRegions(**{\n",
    "  \"collection\": training, \n",
    "  \"properties\": ['landcover'], \n",
    "  \"scale\": 10\n",
    "})\n",
    "\n",
    "# Train a classifier.\n",
    "classifier = ee.Classifier.smileRandomForest(50).train(**{\n",
    "  \"features\": training,  \n",
    "  \"classProperty\": 'landcover', \n",
    "  \"inputProperties\": before.bandNames()\n",
    "})\n",
    "\n",
    "# Classify the image.\n",
    "beforeClassified = before.classify(classifier)\n",
    "m.addLayer(beforeClassified,\n",
    "           {\"min\": 0, \"max\": 3, \"palette\": ['gray', 'brown', 'blue', 'green']}, \"before_classified\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "0610db2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2020 Jan\n",
    "after = s2\\\n",
    "    .filter(ee.Filter.date('2020-01-01', '2020-01-31'))\\\n",
    "    .filter(ee.Filter.geometry(bangalore))\\\n",
    "    .select('B.*')\\\n",
    "    .median()\\\n",
    "    .clip(bangalore)\n",
    "\n",
    "m.addLayer(after, rgbVis, 'after');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "7c458576",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classify the image.\n",
    "afterClassified = after.classify(classifier)\n",
    "m.addLayer(afterClassified,\n",
    "           {\"min\": 0, \"max\": 3, \"palette\": ['gray', 'brown', 'blue', 'green']}, \"after_classified\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "a0b06078",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reclassify from 0-3 to 1-4\n",
    "beforeClasses = beforeClassified.remap([0, 1, 2, 3], [1, 2, 3, 4])\n",
    "afterClasses = afterClassified.remap([0, 1, 2, 3], [1, 2, 3, 4])\n",
    "\n",
    "# Show all changed areas\n",
    "changed = afterClasses.subtract(beforeClasses).neq(0)\n",
    "m.addLayer(changed, {\"min\": 0, \"max\": 1, palette: ['white', 'red']}, 'Change')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "671d3498",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'101': 4747324.121568627, '102': 170293.53725490184, '103': 13782.603921568627, '104': 201643.80784313733, '201': 188329.3098039216, '202': 418340.15294117655, '203': 1624.4235294117648, '204': 8723.254901960783, '301': 9144.388235294118, '302': 597, '303': 69543.36470588236, '304': 19134.705882352944, '401': 300955.49019607855, '402': 23805.79607843137, '403': 5070.105882352941, '404': 1159799.1411764706}\n"
     ]
    }
   ],
   "source": [
    "# We multiply the before image with 100 and add the after image\n",
    "# The resulting pixel values will be unique and will represent each unique transition\n",
    "# i.e. 102 is urban to bare, 103 urban to water etc.\n",
    "merged = beforeClasses.multiply(100).add(afterClasses).rename('transitions')\n",
    "\n",
    "# Use a frequencyHistogram to get a pixel count per class\n",
    "transitionMatrix = merged.reduceRegion(**{\n",
    "  \"reducer\": ee.Reducer.frequencyHistogram(), \n",
    "  \"geometry\": bangalore,\n",
    "  \"maxPixels\": 1e10,\n",
    "  \"scale\": 10,\n",
    "  \"tileScale\": 16\n",
    "})\n",
    "\n",
    "# This prints number of pixels for each class transition\n",
    "print(transitionMatrix.get('transitions').getInfo())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c2f9b2d",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Show all areas where water became other classes and display the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "bae26d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "water_changed = beforeClasses.eq(3).And(afterClasses.neq(3))\n",
    "m.addLayer(water_changed, {\"min\": 0, \"max\": 1, palette: ['white', 'red']}, 'Water Changed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dfdfcbcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48151880cccb49148812d7d14d5b611b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(), VBox()))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geemap.ee_search()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118d51d5",
   "metadata": {},
   "source": [
    "---"
   ]
  }
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