ravin_dem_difference.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEM alignment, modification, and subtraction complete.\n"
     ]
    }
   ],
   "source": [
    "from osgeo import gdal, gdalconst\n",
    "import numpy as np\n",
    "\n",
    "def align_dems(base_dem_path, target_dem_path, aligned_dem_path):\n",
    "    # Open base DEM (reference) and get its properties\n",
    "    base_dem = gdal.Open(base_dem_path, gdalconst.GA_ReadOnly)\n",
    "    base_dem_proj = base_dem.GetProjection()\n",
    "    base_dem_geotrans = base_dem.GetGeoTransform()\n",
    "    base_dem_xsize = base_dem.RasterXSize\n",
    "    base_dem_ysize = base_dem.RasterYSize\n",
    "    base_dem_nodata = base_dem.GetRasterBand(1).GetNoDataValue()\n",
    "\n",
    "    # Open the target DEM\n",
    "    target_dem = gdal.Open(target_dem_path, gdalconst.GA_ReadOnly)\n",
    "    target_dem_nodata = target_dem.GetRasterBand(1).GetNoDataValue()\n",
    "\n",
    "    # Create a new aligned DEM file\n",
    "    driver = gdal.GetDriverByName('GTiff')\n",
    "    aligned_dem = driver.Create(aligned_dem_path, base_dem_xsize, base_dem_ysize, 1, target_dem.GetRasterBand(1).DataType)\n",
    "    aligned_dem.SetProjection(base_dem_proj)\n",
    "    aligned_dem.SetGeoTransform(base_dem_geotrans)\n",
    "\n",
    "    # Set NoData value for aligned DEM\n",
    "    nodata_value = target_dem_nodata if target_dem_nodata is not None else base_dem_nodata\n",
    "    aligned_dem.GetRasterBand(1).SetNoDataValue(nodata_value)\n",
    "\n",
    "    # Perform the alignment/resampling\n",
    "    gdal.ReprojectImage(target_dem, aligned_dem, target_dem.GetProjection(), base_dem_proj, gdalconst.GRA_Bilinear)\n",
    "\n",
    "    # Close the datasets\n",
    "    base_dem = None\n",
    "    target_dem = None\n",
    "    aligned_dem = None\n",
    "\n",
    "    # Modify the aligned DEM to set 0 values to NoData\n",
    "    modify_dem(aligned_dem_path, nodata_value)\n",
    "\n",
    "def modify_dem(dem_path, nodata_value):\n",
    "    # Open the aligned DEM\n",
    "    dem = gdal.Open(dem_path, gdalconst.GA_Update)\n",
    "    band = dem.GetRasterBand(1)\n",
    "    data = band.ReadAsArray()\n",
    "\n",
    "    # Set 0 values to NoData\n",
    "    data[data == 0] = nodata_value\n",
    "\n",
    "    # Write the modified array back to the DEM\n",
    "    band.WriteArray(data)\n",
    "\n",
    "    # Close the dataset\n",
    "    dem = None\n",
    "\n",
    "def subtract_dems(base_dem_path, aligned_dem_path, subtracted_dem_path):\n",
    "    # Open the base (reference) DEM\n",
    "    base_dem = gdal.Open(base_dem_path)\n",
    "    base_band = base_dem.GetRasterBand(1)\n",
    "    base_array = base_band.ReadAsArray()\n",
    "\n",
    "    # Open the aligned DEM\n",
    "    aligned_dem = gdal.Open(aligned_dem_path)\n",
    "    aligned_band = aligned_dem.GetRasterBand(1)\n",
    "    aligned_array = aligned_band.ReadAsArray()\n",
    "\n",
    "    # Perform subtraction (aligned DEM minus base DEM)\n",
    "    nodata_value = base_band.GetNoDataValue()\n",
    "    difference = np.where((base_array == nodata_value) | (aligned_array == nodata_value), nodata_value, aligned_array - base_array)\n",
    "\n",
    "    # Create a new raster file for the subtracted DEM\n",
    "    driver = gdal.GetDriverByName('GTiff')\n",
    "    subtracted_dem = driver.Create(subtracted_dem_path, base_dem.RasterXSize, base_dem.RasterYSize, 1, gdal.GDT_Float32)\n",
    "    subtracted_dem.SetProjection(base_dem.GetProjection())\n",
    "    subtracted_dem.SetGeoTransform(base_dem.GetGeoTransform())\n",
    "\n",
    "    # Write the difference array to the new DEM file\n",
    "    subtracted_band = subtracted_dem.GetRasterBand(1)\n",
    "    subtracted_band.SetNoDataValue(nodata_value)\n",
    "    subtracted_band.WriteArray(difference)\n",
    "\n",
    "    # Close the datasets\n",
    "    base_dem = None\n",
    "    aligned_dem = None\n",
    "    subtracted_dem = None\n",
    "\n",
    "# Specify your DEM file paths\n",
    "base_dem_path = r\"D:\\Bylot\\summer_2017\\Blocs\\2017_ravin_DEM.tif\"\n",
    "target_dem_path = r\"D:\\Bylot\\Summer_2022\\Blocs\\ravin_corrige.tif\"\n",
    "aligned_dem_path = r\"D:\\Bylot\\Summer_2022\\Blocs\\ravin_corrige_aligned4.tif\"\n",
    "subtracted_dem_path = r\"D:\\Bylot\\ravin_difference4.tif\"\n",
    "\n",
    "# Align the DEMs\n",
    "align_dems(base_dem_path, target_dem_path, aligned_dem_path)\n",
    "\n",
    "# Subtract the aligned DEM from the base DEM\n",
    "subtract_dems(base_dem_path, aligned_dem_path, subtracted_dem_path)\n",
    "\n",
    "print(\"DEM alignment, modification, and subtraction complete.\")\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-20T21:49:38.221995500Z",
     "start_time": "2023-12-20T21:48:45.728717300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Load the libraries\n",
    "import rasterio as rio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Specify the path to the subtracted DEM file\n",
    "subtracted_dem_path = r\"D:\\Bylot\\ravin_difference4.tif\"\n",
    "\n",
    "# Open the DEM with Rasterio\n",
    "with rio.open(subtracted_dem_path) as src:\n",
    "    elevation_difference = src.read(1)\n",
    "    # Set masked or NoData values to np.nan\n",
    "    nodata_value = src.nodata\n",
    "    elevation_difference[elevation_difference == nodata_value] = np.nan\n",
    "\n",
    "# Flatten the array and remove NaN values for statistics\n",
    "flattened_elevation = elevation_difference.flatten()\n",
    "flattened_elevation = flattened_elevation[~np.isnan(flattened_elevation)]\n",
    "\n",
    "# Compute basic statistics\n",
    "mean_difference = np.mean(flattened_elevation)\n",
    "median_difference = np.median(flattened_elevation)\n",
    "std_deviation = np.std(flattened_elevation)\n",
    "min_difference = np.min(flattened_elevation)\n",
    "max_difference = np.max(flattened_elevation)\n",
    "\n",
    "# Round the statistics to two decimal places\n",
    "mean_difference = round(mean_difference, 2)\n",
    "median_difference = round(median_difference, 2)\n",
    "std_deviation = round(std_deviation, 2)\n",
    "min_difference = round(min_difference, 2)\n",
    "max_difference = round(max_difference, 2)\n",
    "\n",
    "# Define the bin range from -0.6 to 0.6 with a bin size of 0.02\n",
    "bin_range = np.arange(-0.6, 0.62, 0.02)\n",
    "\n",
    "# Separate positive and negative elevation differences\n",
    "positive_elevation = flattened_elevation[flattened_elevation > 0]\n",
    "negative_elevation = flattened_elevation[flattened_elevation < 0]\n",
    "\n",
    "# Create histograms for positive and negative values\n",
    "plt.figure(figsize=(12, 8), dpi=300)\n",
    "plt.hist([positive_elevation, negative_elevation], bins=bin_range, color=['blue', 'red'], edgecolor='black', label=['Positive', 'Negative'])\n",
    "plt.title('Histogram of Elevation Differences (-0.6 to 0.6 with 0.02 bin size)')\n",
    "plt.xlabel('Elevation Difference')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xticks(np.arange(-0.6, 0.62, 0.1))\n",
    "plt.legend()\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-20T21:49:58.393548400Z",
     "start_time": "2023-12-20T21:49:38.224510900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Difference: 0.019999999552965164\n",
      "Median Difference: 0.019999999552965164\n",
      "Standard Deviation: 0.12999999523162842\n",
      "Minimum Difference: -22.049999237060547\n",
      "Maximum Difference: 1.4199999570846558\n"
     ]
    }
   ],
   "source": [
    "# Print computed statistics\n",
    "print(f\"Mean Difference: {mean_difference}\")\n",
    "print(f\"Median Difference: {median_difference}\")\n",
    "print(f\"Standard Deviation: {std_deviation}\")\n",
    "print(f\"Minimum Difference: {min_difference}\")\n",
    "print(f\"Maximum Difference: {max_difference}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-20T21:49:58.397584200Z",
     "start_time": "2023-12-20T21:49:58.394553700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import rasterio\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Load the subtracted DEM\n",
    "subtracted_dem_path = r\"D:\\Bylot\\ravin_difference4.tif\"\n",
    "\n",
    "with rasterio.open(subtracted_dem_path) as dem:\n",
    "    dem_data = dem.read(1)  # Read the first band\n",
    "    nodata = dem.nodata  # Get the NoData value\n",
    "\n",
    "# Mask the NoData values\n",
    "dem_data_masked = np.ma.masked_where(dem_data == nodata, dem_data)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(20, 14))\n",
    "plt.imshow(dem_data_masked, cmap=\"RdYlGn_r\", vmin=-0.6, vmax=0.6)  # Using original \"coolwarm\" colormap\n",
    "cbar = plt.colorbar(label=\"Delta z (m)\")\n",
    "cbar.ax.tick_params(labelsize=10)\n",
    "cbar.set_label(\"Elevation change (m)\", size=14)# Change font size of color bar ticks\n",
    "plt.title(\"Delta Z from 2022-2017 DEM\", fontsize=22)  # Change title font size\n",
    "#plt.xlabel(fontsize=12)\n",
    "#plt.ylabel(fontsize=12)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.savefig(r\"D:\\Bylot\\ravin_dem_difference.jpeg\", dpi=300)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2023-12-20T21:51:56.149412Z"
    }
   }
  }
 ],
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