Lesson 5.ipynb

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   "source": [
    "## Lesson 5: Building Reproducible Workflows in ArcGIS Pro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### ArcGIS Notebooks\n",
    "Over the course of this workshop, we have been building the foundations of a reproducible ArcGIS Notebook. We've taken efforts to automate our analysis by using Arcpy, standardized our working environment, and then documented and discussed our results in ArcGIS Notebooks. These steps were performed over the duration of the workshop, but they can be summarized and explored in complete detail below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Setup Script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Kangaroo_Island_2019 Mosaic Dataset @ C:\\Users\\col12422\\Documents\\ArcGIS\\Projects\\Arcpy Workshop\\Arcpy Workshop.gdb\\Kangaroo_Island_2019\n",
      "Adding Rasters to Kangaroo_Island_2019 Mosaic Dataset\n",
      "Clipping Kangaroo_Island_2019 Mosaic Dataset to Kangaroo Island Feature Class\n",
      "Successfully Added Kangaroo_Island_2019 to Map\n",
      "\n",
      "Creating Kangaroo_Island_2020 Mosaic Dataset @ C:\\Users\\col12422\\Documents\\ArcGIS\\Projects\\Arcpy Workshop\\Arcpy Workshop.gdb\\Kangaroo_Island_2020\n",
      "Adding Rasters to Kangaroo_Island_2020 Mosaic Dataset\n",
      "Clipping Kangaroo_Island_2020 Mosaic Dataset to Kangaroo Island Feature Class\n",
      "Successfully Added Kangaroo_Island_2020 to Map\n",
      "\n",
      "Setup Complete.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import arcpy\n",
    "arcpy.env.overwriteOutput = True\n",
    "\n",
    "WORKSPACE = arcpy.env.workspace\n",
    "YEARS = ['2019', '2020']\n",
    "RASTER_PREFIX = 'Kangaroo_Island'\n",
    "MASK = 'Kangaroo Island'\n",
    "\n",
    "try:\n",
    "    for year in YEARS:\n",
    "        arcpy.env.addOutputsToMap = False\n",
    "        MOSAIC_DATASET_NAME = '{}_{}'.format(RASTER_PREFIX, year)\n",
    "        MOSAIC_DATASET_PATH = os.path.join(WORKSPACE, MOSAIC_DATASET_NAME)\n",
    "        STAC_CSV = os.path.join(os.path.dirname(arcpy.mp.ArcGISProject(\"CURRENT\").filePath), \"Rasters_{0}.csv\".format(year))\n",
    "        \n",
    "        print(\"Creating {0} Mosaic Dataset @ {1}\".format(MOSAIC_DATASET_NAME, MOSAIC_DATASET_PATH))\n",
    "\n",
    "        arcpy.management.CreateMosaicDataset(\n",
    "            in_workspace=WORKSPACE,\n",
    "            in_mosaicdataset_name=MOSAIC_DATASET_NAME,\n",
    "            coordinate_system='PROJCS[\"WGS_1984_UTM_Zone_53S\",GEOGCS[\"GCS_WGS_1984\",DATUM[\"D_WGS_1984\",SPHEROID[\"WGS_1984\",6378137.0,298.257223563]],PRIMEM[\"Greenwich\",0.0],UNIT[\"Degree\",0.0174532925199433]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"False_Easting\",500000.0],PARAMETER[\"False_Northing\",10000000.0],PARAMETER[\"Central_Meridian\",135.0],PARAMETER[\"Scale_Factor\",0.9996],PARAMETER[\"Latitude_Of_Origin\",0.0],UNIT[\"Meter\",1.0]]',\n",
    "            num_bands=13,\n",
    "            pixel_type=\"\",\n",
    "            product_definition=\"CUSTOM\",\n",
    "            product_band_definitions=\"AOT # #;Coastal # #;Blue # #;Green # #;Red # #;VRE1 # #;VRE2 # #;VRE3 # #;NIR # #;'Water Vapor' # #;SWIR1 # #;SWIR2 # #;VRE4 # #\"\n",
    "        )\n",
    "\n",
    "        print(\"Adding Rasters to {0} Mosaic Dataset\".format(MOSAIC_DATASET_NAME))\n",
    "\n",
    "        arcpy.management.AddRastersToMosaicDataset(\n",
    "            in_mosaic_dataset=MOSAIC_DATASET_NAME,\n",
    "            raster_type=\"Raster Dataset\",\n",
    "            input_path=STAC_CSV,\n",
    "            update_cellsize_ranges=\"UPDATE_CELL_SIZES\",\n",
    "            update_boundary=\"UPDATE_BOUNDARY\",\n",
    "            update_overviews=\"NO_OVERVIEWS\",\n",
    "            maximum_pyramid_levels=None,\n",
    "            maximum_cell_size=0,\n",
    "            minimum_dimension=1500,\n",
    "            spatial_reference=None,\n",
    "            filter=\"\",\n",
    "            sub_folder=None,\n",
    "            duplicate_items_action=\"ALLOW_DUPLICATES\",\n",
    "            build_pyramids=\"BUILD_PYRAMIDS\",\n",
    "            calculate_statistics=\"CALCULATE_STATISTICS\",\n",
    "            build_thumbnails=None,\n",
    "            operation_description=\"\",\n",
    "            force_spatial_reference=None,\n",
    "            estimate_statistics=\"ESTIMATE_STATISTICS\",\n",
    "            aux_inputs=\"QueryPixelFiles NO\",\n",
    "            enable_pixel_cache=\"NO_PIXEL_CACHE\"\n",
    "        )\n",
    "\n",
    "        print(\"Clipping {0} Mosaic Dataset to {1} Feature Class\".format(MOSAIC_DATASET_NAME, MASK))\n",
    "\n",
    "        kangaroo_clip = arcpy.ia.Clip(\n",
    "            in_raster= MOSAIC_DATASET_NAME,\n",
    "            aoi= MASK\n",
    "        )\n",
    "        \n",
    "        arcpy.env.addOutputsToMap = True\n",
    "        arcpy.MakeRasterLayer_management(kangaroo_clip, \"Kangaroo_Island_{0}\".format(year))\n",
    "        \n",
    "        print(\"Successfully Added {0} to Map\".format(MOSAIC_DATASET_NAME))\n",
    "        print()\n",
    "\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    arcpy.env.addOutputsToMap = True\n",
    "    \n",
    "    geodatabase_rasters = arcpy.ListDatasets()\n",
    "    for ds in geodatabase_rasters:\n",
    "        print(\"Deleting...\", ds)\n",
    "        arcpy.management.Delete(os.path.join(arcpy.env.workspace, ds))\n",
    "    print(\"Falling back to Image Services...\")\n",
    "    \n",
    "    arcpy.MakeRasterLayer_management(\"https://civsci.esrigc.com/image/rest/services/Kangaroo_Island_2019_Clip/ImageServer\", \"Kangaroo_Island_2019\")\n",
    "    print(\"Successfully Added Kangaroo_Island_2019 to Map\")\n",
    "    \n",
    "    arcpy.MakeRasterLayer_management(\"https://civsci.esrigc.com/image/rest/services/Kangaroo_Island_2020_Clip/ImageServer\", \"Kangaroo_Island_2020\")\n",
    "    print(\"Successfully Added Kangaroo_Island_2020 to Map\")\n",
    "    \n",
    "kangaroo_2019 = arcpy.Raster(\"Kangaroo_Island_2019\")\n",
    "kangaroo_2020 = arcpy.Raster(\"Kangaroo_Island_2020\")\n",
    "    \n",
    "print(\"Setup Complete.\")"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "numpy.seterr(invalid='ignore')\n",
    "\n",
    "arcpy.env.addOutputsToMap = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def nbr(nir, swir):\n",
    "    print(\"Calculating NBR...\")\n",
    "    nbr_index = numpy.nan_to_num(numpy.divide((nir - swir), (nir + swir)))\n",
    "    return nbr_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def delta_nbr(nbr_base, nbr_post):\n",
    "    print(\"Calculating delta...\")\n",
    "    d_nbr = numpy.subtract(nbr_base, nbr_post)\n",
    "    return d_nbr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def kangaroo_nbr_analysis(raster_base, raster_post):\n",
    "    print(\"Starting Kangaroo Island NBR Analysis...\")\n",
    "    nir_base = arcpy.RasterToNumPyArray(raster_base.getRasterBands(\"NIR\"), nodata_to_value=0).astype(float)\n",
    "    swir_base =  arcpy.RasterToNumPyArray(raster_base.getRasterBands(\"SWIR2\"), nodata_to_value=0).astype(float)\n",
    "    \n",
    "    nir_post = arcpy.RasterToNumPyArray(raster_post.getRasterBands(\"NIR\"), nodata_to_value=0).astype(float)\n",
    "    swir_post = arcpy.RasterToNumPyArray(raster_post.getRasterBands(\"SWIR2\"), nodata_to_value=0).astype(float)\n",
    "    \n",
    "    return delta_nbr(nbr(nir_base, swir_base), nbr(nir_post, swir_post))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Kangaroo Island NBR Analysis...\n",
      "Calculating NBR...\n",
      "Calculating NBR...\n",
      "Calculating delta...\n",
      "Completed.\n"
     ]
    }
   ],
   "source": [
    "kangaroo_delta_nbr = kangaroo_nbr_analysis(kangaroo_2019, kangaroo_2020)\n",
    "\n",
    "lower_left = arcpy.Point(kangaroo_2019.extent.XMin,kangaroo_2019.extent.YMin)\n",
    "cell_size = kangaroo_2019.meanCellWidth\n",
    "Delta_NBR = arcpy.NumPyArrayToRaster(kangaroo_delta_nbr, lower_left, cell_size, value_to_nodata = 0)\n",
    "Delta_NBR.save(\"Kangaroo_Island_NBR\")\n",
    "\n",
    "print(\"Completed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The Normalized Burn Ratio (**NBR**) is an index that uses the differences in the way healthy green vegetation and burnt vegetation reflect light to find burnt area. The equation is defined below:\n",
    "$$NBR =\\dfrac{nir-swir}{nir+swir}$$ <br>\n",
    "We'll leverage the NumPy library to perform matrix artithmetic to calculate **NBR** for Kangaroo Island. <br><br>\n",
    "We can create a function to perform the subtraction, addition, and division of our arrays as seen here:"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "$$ \n",
    "NBR_{2019} =\n",
    "\\frac{\\begin{bmatrix}\n",
    "0.43 & -1.34 \\\\\n",
    "1.33 & 0.59 & \\cdots\n",
    "\\end{bmatrix}\n",
    "-\n",
    "\\begin{bmatrix}\n",
    "-3.21 & 0.12 \\\\\n",
    "1.33 & -4.32 & \\cdots\n",
    "\\end{bmatrix}}{\\begin{bmatrix}\n",
    "0.43 & -1.34 \\\\\n",
    "1.33 & 0.59 & \\cdots\n",
    "\\end{bmatrix}\n",
    "+\n",
    "\\begin{bmatrix}\n",
    "-3.21 & 0.12 \\\\\n",
    "1.33 & -4.32 & \\cdots\n",
    "\\end{bmatrix}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "$$ \n",
    "NBR_{2020} =\n",
    "\\frac{\\begin{bmatrix}\n",
    "3.53 & 0.32 \\\\\n",
    "4.55 & 3.76 & \\cdots\n",
    "\\end{bmatrix}\n",
    "-\n",
    "\\begin{bmatrix}\n",
    "6.31 & 10.99 \\\\\n",
    "44.73 & 8.39 & \\cdots\n",
    "\\end{bmatrix}}{\\begin{bmatrix}\n",
    "3.53 & 0.32 \\\\\n",
    "4.55 & 3.76 & \\cdots\n",
    "\\end{bmatrix}\n",
    "+\n",
    "\\begin{bmatrix}\n",
    "6.31 & 10.99 \\\\\n",
    "44.73 & 8.39 & \\cdots\n",
    "\\end{bmatrix}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def nbr(nir, swir):\n",
    "    nbr_index = numpy.nan_to_num(numpy.divide((nir - swir), (nir + swir)))\n",
    "    return nbr_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "With a function for calculating the **NBR** for our arrays, we now have a normalized way of comparing values from one year to another. We can find the delta from **NBR<sub>2019</sub>**, before the fire, to **NBR<sub>2020</sub>**, after the fire, to get a comparison of the wildfire damage! We'll use NumPy to subtract the **NBR<sub>2019</sub>** array from the **NBR<sub>2020</sub>** array to get our **NBR<sub>&Delta;</sub>** array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "$$ \n",
    "NBR_{\\Delta} = NBR_{2019} - NBR_{2020}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def delta_nbr(nbr_base, nbr_post):\n",
    "    d_nbr = numpy.subtract(nbr_base, nbr_post)\n",
    "    return d_nbr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "With the ```nbr``` and ```delta_nbr``` functions defined, it's now possible to repeatably calculate the **NBR<sub>&Delta;</sub>** for any given arrays. But to make our functions easy to use, let's also write out another function that accepts the base and post-fire rasters for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def kangaroo_fire_analysis(raster_base, raster_post):\n",
    "    nir_base = arcpy.RasterToNumPyArray(raster_base.getRasterBands(\"NIR1\"), nodata_to_value=0).astype(float)\n",
    "    swir_base =  arcpy.RasterToNumPyArray(raster_base.getRasterBands(\"SWIR2\"), nodata_to_value=0).astype(float)\n",
    "    \n",
    "    nir_post = arcpy.RasterToNumPyArray(raster_post.getRasterBands(\"NIR1\"), nodata_to_value=0).astype(float)\n",
    "    swir_post = arcpy.RasterToNumPyArray(raster_post.getRasterBands(\"SWIR2\"), nodata_to_value=0).astype(float)\n",
    "    \n",
    "    return delta_nbr(nbr(nir_base, swir_base), nbr(nir_post, swir_post))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### ArcGIS ModelBuilder\n",
    "ArcGIS ModelBuilder is another way to create reproducible workflows in ArcGIS Pro, without the need of writing any code. It's a visual coding tool used for stringing together geoprocessing tools. And when configured properly with parameters, it can be saved as a custom geoprocessing tool and used in ArcGIS Pro's GUI. We'll build a ModelBuilder that automates the image processing section of our previous workflow:"
   ]
  },
  {
   "attachments": {
    "arcpy_modelbuilder-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![arcpy_modelbuilder-2.png](attachment:arcpy_modelbuilder-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Open ModelBuilder in ArcGIS Pro and replicate the model above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "1. Drag & Drop the Create Mosaic Dataset tool into the ModelBuilder pane. <br>\n",
    "- Right click the tool, then navigate to Create Variable > From Parameter > Output Location\n",
    "    - Double Click the variable and set the location to the arcpy_workshop.gdb file geodatabase\n",
    "    - Right Click the variable and select Parameter\n",
    "- Right click the tool, then navigate to Create Variable > From Parameter > Mosaic Dataset Name\n",
    "    - Double Click the variable and set the name to MB_Mosaic\n",
    "    - Right Click the variable and select Parameter\n",
    "- Right click the tool, then navigate to Create Variable > From Parameter > Coordinate System\n",
    "    - Double Click the variable and set the coordinate system to WGS_1984_UTM_Zone_53S\n",
    "    - Right Click the variable and select Parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "2. Drag & Drop the Add Raster to Mosaic Dataset tool into the ModelBuilder pane. <br>\n",
    "- Right click the tool, then navigate to Create Variable > From Parameter > Input Data\n",
    "    - Double Click the variable and set the input data to the Kangaroo_Island_2019 Image Service located in ArcGIS Online\n",
    "    - Right Click the variable and select Parameter\n",
    "- Drag the Output Mosaic Dataset to the Add Rasters to Mosaic Dataset to connect the output to the input. Set it as the Mosaic Dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "3. Drag & Drop the Clip Raster tool into the ModelBuilder pane. <br>\n",
    "- Right click the tool, then navigate to Create Variable > From Parameter > Output Extent\n",
    "    - Double Click the variable and set the input data to the Kangaroo Island Feature Service located in ArcGIS Online.\n",
    "    - Right Click the variable and select Parameter\n",
    "- Drag the Updated Mosaic Dataset output to the Clip Raster tool to connect the output to the input. Set it as the Input Raster.\n",
    "- Right Click the Output raster variable and select Parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Validate, Run, & Save the tool as ```ModelBuilder_Clip```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now open the tool from the Catalog Pane in ArcGIS Pro by navigating to Toolboxes > arcpy_workshop.atbx > ModelBuilder_Clip. Double click to open the tool as a geoprocessing tool."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Challenge: Raster Function Template\n",
    "Similar to ArcGIS ModelBuilder, Raster Function Templates are a way to create reproducible workflows, but with a focus on raster analysis. With the previous example in ModelBuilder, we automated the process for image processing.To automate the image analysis process, then we would could create a Raster Function Template that calculates our Delta NBR. As a challenge, create a Raster Function Template that calculates NBR for each image, then subtracts the two to find Delta NBR."
   ]
  },
  {
   "attachments": {
    "raster-function.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![raster-function.png](attachment:raster-function.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ArcGISPro",
   "language": "Python",
   "name": "python3"
  },
  "language_info": {
   "file_extension": ".py",
   "name": "python",
   "version": "3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}