{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### [WUR GRS-33806 Geoscripting](https://geoscripting-wur.github.io/)\n", "# Week 3 (Python): Raster handling with Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Good morning! Today we will handle some rasters in Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Python modules for raster data handling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The modules used below are:\n", "\n", "* ***`gdal`***: bindings to the GDAL library which is part of the osgeo library\n", "* `numpy` for array calculations.\n", "\n", "These two libraries are the base of all raster processing in Python!\n", "\n", "There are additional libraries that offer wrapper functions for `gdal` and provide additional raster procssesing functionality:\n", "* mapbox [`rasterio`](https://github.com/mapbox/rasterio), see a [NDVI tutorial](http://www.loicdutrieux.com/pyLandsat/NDVI_calc.html)\n", "* [`rios`](http://rioshome.org/), a set of Python modules which makes it easy to write raster processing code in Python\n", "\n", "Many important processing methods are available through additional libraries:\n", "* Python interface for Orfeo toolbox (`otb`), e.g. segmentation\n", "* `rsgislib` e.g. segmentation\n", "* `scikit-image` for image interpretation\n", "* `scikit-learn` for machine learning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the below example we will use an Aster image which you can download from the following [dropbox](https://www.dropbox.com/s/rsc4lzkd3t2adq5/ospy_data5.zip?dl=0). The data is also available [here](http://www.gis.usu.edu/~chrisg/python/2009/lectures/ospy_data5.zip). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading, deriving NDVI, and writing raster data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get started and we will go through the following steps:\n", "\n", "* Open the Aster image (ERDAS *.img format)\n", "* Read in the image data as an array\n", "* Derive the NDVI using array calculations\n", "* Write the resulting file as a *.tif (i.e. GeoTIFF file)\n", "* Close all files" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Information about ./data/ospy_data5/aster.img\n", "Driver: HFA / Erdas Imagine Images (.img)\n", "Size is 5665 x 5033 x 3\n", "\n", "Projection is: PROJCS[\"UTM Zone 12, Northern Hemisphere\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],TOWGS84[0,0,0,-0,-0,-0,0],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"latitude_of_origin\",0],PARAMETER[\"central_meridian\",-111],PARAMETER[\"scale_factor\",0.9996],PARAMETER[\"false_easting\",500000],PARAMETER[\"false_northing\",0],UNIT[\"Meter\",1],AUTHORITY[\"EPSG\",\"32612\"]]\n", "\n", "Information about the location of the image and the pixel size:\n", "Origin = ( 419976.5 , 4662422.5 )\n", "Pixel Size = ( 15.0 , -15.0 )\n" ] } ], "source": [ "# import modules\n", "from osgeo import gdal\n", "from osgeo.gdalconst import GA_ReadOnly, GDT_Float32\n", "import numpy as np\n", "\n", "# open file and print info about the file\n", "# the \"./\" refers to the parent directory of my working directory\n", "\n", "filename = './data/ospy_data5/aster.img'\n", "dataSource = gdal.Open(filename, GA_ReadOnly)\n", "\n", "print(\"\\nInformation about \" + filename)\n", "print(\"Driver: \", dataSource.GetDriver().ShortName,\"/\", \\\n", " dataSource.GetDriver().LongName)\n", "print(\"Size is \",dataSource.RasterXSize,\"x\",dataSource.RasterYSize, \\\n", " 'x',dataSource.RasterCount)\n", "\n", "print('\\nProjection is: ', dataSource.GetProjection())\n", "\n", "print(\"\\nInformation about the location of the image and the pixel size:\")\n", "geotransform = dataSource.GetGeoTransform()\n", "if not geotransform is None:\n", " print('Origin = (',geotransform[0], ',',geotransform[3],')')\n", " print('Pixel Size = (',geotransform[1], ',',geotransform[5],')')\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "NDVI min and max values -99.0 1.0\n", "-1.0\n" ] } ], "source": [ "# Read data into an array\n", "band2Arr = dataSource.GetRasterBand(2).ReadAsArray(0,0,dataSource.RasterXSize, dataSource.RasterYSize)\n", "band3Arr = dataSource.GetRasterBand(3).ReadAsArray(0,0,dataSource.RasterXSize, dataSource.RasterYSize)\n", "print(type(band2Arr))\n", " \n", "\n", "# set the data type\n", "band2Arr=band2Arr.astype(np.float32)\n", "band3Arr=band3Arr.astype(np.float32)\n", "\n", "# Derive the NDVI\n", "mask = np.greater(band3Arr+band2Arr,0)\n", "\n", "# set np.errstate to avoid warning of invalid values (i.e. NaN values) in the divide \n", "with np.errstate(invalid='ignore'):\n", " ndvi = np.choose(mask,(-99,(band3Arr-band2Arr)/(band3Arr+band2Arr)))\n", "print(\"NDVI min and max values\", ndvi.min(), ndvi.max())\n", "# Check the real minimum value\n", "print(ndvi[ndvi>-99].min())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Write the result to disk\n", "driver = gdal.GetDriverByName('GTiff')\n", "outDataSet=driver.Create('./data/ndvi.tif', dataSource.RasterXSize, dataSource.RasterYSize, 1, GDT_Float32)\n", "outBand = outDataSet.GetRasterBand(1)\n", "outBand.WriteArray(ndvi,0,0)\n", "outBand.SetNoDataValue(-99)\n", "\n", "# set the projection and extent information of the dataset\n", "outDataSet.SetProjection(dataSource.GetProjection())\n", "outDataSet.SetGeoTransform(dataSource.GetGeoTransform())\n", "\n", "# Finally let's save it... or like in the OGR example flush it\n", "outBand.FlushCache()\n", "outDataSet.FlushCache()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check the resulting file via *Bash* (all commands in the current document with a **!** in front are run via the system shell, which is *Bash*). Below we call the `gdalinfo` command of the **GDAL** C++ library directly. If you want to learn more about `gdalinfo` go to: http://www.gdal.org/gdalinfo.html" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Driver: GTiff/GeoTIFF\r\n", "Files: ./data/ndvi.tif\r\n", "Size is 5665, 5033\r\n", "Coordinate System is:\r\n", "PROJCS[\"WGS 84 / UTM zone 12N\",\r\n", " GEOGCS[\"WGS 84\",\r\n", " DATUM[\"WGS_1984\",\r\n", " SPHEROID[\"WGS 84\",6378137,298.257223563,\r\n", " AUTHORITY[\"EPSG\",\"7030\"]],\r\n", " AUTHORITY[\"EPSG\",\"6326\"]],\r\n", " PRIMEM[\"Greenwich\",0,\r\n", " AUTHORITY[\"EPSG\",\"8901\"]],\r\n", " UNIT[\"degree\",0.0174532925199433,\r\n", " AUTHORITY[\"EPSG\",\"9122\"]],\r\n", " AUTHORITY[\"EPSG\",\"4326\"]],\r\n", " PROJECTION[\"Transverse_Mercator\"],\r\n", " PARAMETER[\"latitude_of_origin\",0],\r\n", " PARAMETER[\"central_meridian\",-111],\r\n", " PARAMETER[\"scale_factor\",0.9996],\r\n", " PARAMETER[\"false_easting\",500000],\r\n", " PARAMETER[\"false_northing\",0],\r\n", " UNIT[\"metre\",1,\r\n", " AUTHORITY[\"EPSG\",\"9001\"]],\r\n", " AXIS[\"Easting\",EAST],\r\n", " AXIS[\"Northing\",NORTH],\r\n", " AUTHORITY[\"EPSG\",\"32612\"]]\r\n", "Origin = (419976.500000000000000,4662422.500000000000000)\r\n", "Pixel Size = (15.000000000000000,-15.000000000000000)\r\n", "Metadata:\r\n", " AREA_OR_POINT=Area\r\n", "Image Structure Metadata:\r\n", " INTERLEAVE=BAND\r\n", "Corner Coordinates:\r\n", "Upper Left ( 419976.500, 4662422.500) (111d58' 4.52\"W, 42d 6'35.34\"N)\r\n", "Lower Left ( 419976.500, 4586927.500) (111d57'27.92\"W, 41d25'47.74\"N)\r\n", "Upper Right ( 504951.500, 4662422.500) (110d56'24.38\"W, 42d 6'49.98\"N)\r\n", "Lower Right ( 504951.500, 4586927.500) (110d56'26.64\"W, 41d26' 2.04\"N)\r\n", "Center ( 462464.000, 4624675.000) (111d27' 5.89\"W, 41d46'22.91\"N)\r\n", "Band 1 Block=5665x1 Type=Float32, ColorInterp=Gray\r\n", " NoData Value=-99\r\n" ] } ], "source": [ "!gdalinfo ./data/ndvi.tif" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Important here is that the `gdalinfo` contains information about the projection (via `SetProjection`) and also about the corner coordinates (via `SetGeoTransform`)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reproject the NDVI image to Lat/long" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reprojecting a raster image can be done by\n", "\n", "* calling the `gdalwarp` function of the **GDAL C++** library directly\n", "* using `Python` and the `gdal` module\n", "\n", "### Using `gdalwarp`\n", "\n", "The easiest way to reproject a raster file is to use GDAL's [gdalwarp](http://www.gdal.org/gdalwarp.html) tool. As an example, we will reproject the above NDVI image derived earlier into latitude/longitude (WGS84).\n", "\n", "* `-t_srs \"EPSG:4326\"` is the CRS to reproject **to**, i.e. lat/long, known by its [EPSG code](http://spatialreference.org/ref/epsg/4326/). The CRS to reproject **from** is already specified in the source data set, see above.\n", "* `data/ndvi.tif` is the **input** dataset.\n", "* `data/ndvi_ll.ti`f is the **output** dataset (the output format is here automatically set by the extension i.e. GeoTIFF).\n", "\n", "> **Question 1**: What is the CRS of the image before it is reprojected?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating output file that is 6334P x 4215L.\n", "Processing input file ./data/ndvi.tif.\n", "Using internal nodata values (e.g. -99) for image ./data/ndvi.tif.\n", "Copying nodata values from source ./data/ndvi.tif to destination ./data/ndvi_ll.tif.\n", "0...10...20...30...40...50...60...70...80...90...100 - done.\n" ] } ], "source": [ "# via the Shell\n", "!gdalwarp -t_srs \"EPSG:4326\" ./data/ndvi.tif ./data/ndvi_ll.tif" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Driver: GTiff/GeoTIFF\r\n", "Files: ./data/ndvi_ll.tif\r\n", "Size is 6334, 4215\r\n", "Coordinate System is:\r\n", "GEOGCS[\"WGS 84\",\r\n", " DATUM[\"WGS_1984\",\r\n", " SPHEROID[\"WGS 84\",6378137,298.257223563,\r\n", " AUTHORITY[\"EPSG\",\"7030\"]],\r\n", " AUTHORITY[\"EPSG\",\"6326\"]],\r\n", " PRIMEM[\"Greenwich\",0],\r\n", " UNIT[\"degree\",0.0174532925199433],\r\n", " AUTHORITY[\"EPSG\",\"4326\"]]\r\n", "Origin = (-111.967923047226961,42.113899650756700)\r\n", "Pixel Size = (0.000162266608994,-0.000162266608994)\r\n", "Metadata:\r\n", " AREA_OR_POINT=Area\r\n", "Image Structure Metadata:\r\n", " INTERLEAVE=BAND\r\n", "Corner Coordinates:\r\n", "Upper Left (-111.9679230, 42.1138997) (111d58' 4.52\"W, 42d 6'50.04\"N)\r\n", "Lower Left (-111.9679230, 41.4299459) (111d58' 4.52\"W, 41d25'47.81\"N)\r\n", "Upper Right (-110.9401263, 42.1138997) (110d56'24.45\"W, 42d 6'50.04\"N)\r\n", "Lower Right (-110.9401263, 41.4299459) (110d56'24.45\"W, 41d25'47.81\"N)\r\n", "Center (-111.4540247, 41.7719228) (111d27'14.49\"W, 41d46'18.92\"N)\r\n", "Band 1 Block=6334x1 Type=Float32, ColorInterp=Gray\r\n", " NoData Value=-99\r\n" ] } ], "source": [ "# Let's check what the result is\n", "!gdalinfo ./data/ndvi_ll.tif" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To reproject a raster from within Python is not as straight-forward... Have a look at the following links, and use the internet to find a way!\n", "\n", "- The function defined by Prof. P. Lewis (see here [for more information](http://nbviewer.ipython.org/github/profLewis/geogg122/blob/master/Chapter4_GDAL/GDAL_Python_bindings.ipynb)). \n", "- Core GDALDataset Class reference info (see [GeoTransFrom Info](http://www.gdal.org/classGDALDataset.html#a0fe0f81d65d84557b5d71ddc024faa02). For a relevant question and example on GIS Stack Exchange ([see](https://gis.stackexchange.com/questions/24055/raster-data-array-output-flipped-on-x-axis-using-python-gdal)).\n", "- [GIS Stack Exchange](https://gis.stackexchange.com/questions/6669/converting-projected-geotiff-to-wgs84-with-gdal-and-python).\n", "- Or use other raster libraries, e.g. [rasterio](https://github.com/mapbox/rasterio/blob/master/examples/reproject.py)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualise the result\n", "\n", "Let's see the output of the reprojected NDVI image." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Notebook magic to select the plotting method\n", "# Change to inline to plot within this notebook\n", "#%matplotlib inline \n", "%matplotlib inline\n", "from osgeo import gdal\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Open image\n", "dsll = gdal.Open(\"./data/ndvi_ll.tif\")\n", "\n", "# Read raster data\n", "ndvi = dsll.ReadAsArray(0, 0, dsll.RasterXSize, dsll.RasterYSize)\n", "\n", "# Now plot the raster data using gist_earth palette\n", "plt.imshow(ndvi, interpolation='nearest', vmin=0, cmap=plt.cm.gist_earth)\n", "plt.show()\n", "\n", "dsll = None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check in `R`\n", "\n", "We can quickly check what we have done with R (go now to your Rstudio, or R terminal) and try the following code:\n", "\n", "```\n", "library(raster)\n", "a <- brick('./data/ospy_data5/aster.img')\n", "plotRGB(a, 3, 2, 1, stretch='hist')\n", "```\n", "\n", "```\n", "b <- raster('./data/ndvi.tif')\n", "extent(b)\n", "projection(b)\n", "hist(b, 1, maxpixels=500, plot=TRUE)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise lesson 12: NDWI and reprojection in python\n", "\n", "For this scripting challenge I have downloaded a Landsat image with all bands processed to surface reflectance (Level 1T). You can download it from [here](https://www.dropbox.com/s/zb7nrla6fqi1mq4/LC81980242014260-SC20150123044700.tar.gz?dl=0). Unzip the file and you will see that it contains all the invidual bands. \n", "\n", "Now, write a well-structured and documented script where you define ***a function to derive the Normalised difference water index (NDWI)*** and derive it from the landsat image and ***reproject the image to Lat/Long WGS84*** (**hint**: use GDAL from *Bash* in your notebook).\n", "\n", "`NDWI = band 3 - band 5 / band 3 + band 5` ([more info about](http://nsidc.org/data/docs/daac/nsidc0332_smex03_srs_ndvi_ndwi_ok.gd.html) `NDWI`)\n", "\n", "A clean and well structured script is critical here.\n", "\n", "*** Create an Ipython Notebook file (.ipynb) with Jupyter Notebook and put it on GitLab!***\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Additional Resources\n", "- Chris Holden from Boston University on [handling raster data with GDAL](http://www.gis.usu.edu/~chrisg/python/2009/)\n", "- [Blog about Python for GeoSpatial data analysis](http://www.digital-geography.com/python-for-geospatial-data-analysis-part-ii/?subscribe=invalid_email#477)\n", "- https://pcjericks.github.io/py-gdalogr-cookbook/raster_layers.html\n", "- Great basic tutorial: http://www.gdal.org/gdal_tutorial.html\n", "- [GDAL Python info and bindings](http://gdal.org/python/osgeo.gdal_array-module.html#BandReadAsArray)\n", "- yet another example https://borealperspectives.wordpress.com/2014/01/16/data-type-mapping-when-using-pythongdal-to-write-numpy-arrays-to-geotiff/\n", "- Typical problems with GDAL and python: [PythonGotchas](http://trac.osgeo.org/gdal/wiki/PythonGotchas)\n", "* http://www.qgistutorials.com/nl/docs/getting_started_with_pyqgis.html\n", "* [QGIS and Python tutorial](http://www.qgisworkshop.org/html/workshop/python_in_qgis_tutorial2.html)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }